Study at Reykjavik University



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Our international students describe their RU experiences.

Reykjavik University, 2017

Reykjavik University is very closely
connected to the industry of Iceland. This gives our students a unique opportunity in
a very close-knit, advanced, developed industrial society to have access to industry, specialists and
opportunities that very few universities can offer. I really like the place. Everybody is very nice
and is always willing to help you. I selected Iceland mainly because
of Iceland itself, because of the nature. But on the other hand the
school is also very good. It’s a very high
energy environment. We have geothermal and
we also have a lot of water because it rains a lot,
hence hydropower. And there is
also a lot of wind. It’s an opportunity for me to learn a number of
innovative renewable energy technologies and be able to utilize
them when I go back home. In corporate finance we are few people,
small classes and a lot of discussions. A lot of interaction between students and teachers.
So it has a personal feel to it. Here in the computer science department
there is a very advanced course Here in the computer science department
there is a very advanced course for game design and virtual environments.
So that’s the main reason that brought us here. Classes are a lot smaller here and
you have a lot more regular assignments like group work
and presentations. I feel that there is a much more direct
relationship between teacher and student. With much more dialog which
can really help improve your studying. We really sit down with our students
and we tailor the program to each student. Our primary purpose is to educate the
specialists and the leaders of the future. I’m working in the subfield of mathematics
called combinatorics and I work with quite simple
mathematical objects. We’re teaching the computer how to prove
things about these mathematical objects. This is very new. You have study spaces
you have lecture halls and you also have a very nice
library where I study a lot. I think it’s really advanced, a lot of
access to innovative and modern stuff that really makes learning
and studying easier. It’s been really nice. We have met
a lot of different people and we are always doing
something. We have full schedules. We live in the capital and
there is always something to do a lot of cultural events, dancing and places
to meet people and just hang out. At one point it was
a hard decision for me. But when I came here then I knew it was the
right decision. So I’m very happy here. Something that I really liked
was that it was Iceland. So it was perfect, I could study and come
to an amazing place at the same time.

Master's Degree Programme in Biomedical Sciences and Engineering



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Dive in to TUT’s Master’s Degree Programme in Biomedical Sciences and Engineering and see what professor and students say about the studies.

Hello! My name is Jonathan Massera. I am a Professor in Biomaterials and Tissue Engineering and I'm also the Head of the Master's program in Biomedical Sciences and Engineering. A growing and aging society needs modern and innovative medicine and stem-cell technologies. To answer to these challenges in well-being and health care our faculty promotes high quality research that brings new innovations for the benefit of mankind. The industry is in need of talented students with versatile backgrounds to provide expertise to the growing health tech sector. Some continue as Academia researchers. We have high record in attracting public and private funding. if you are an entrepreneur at heart, this program and its environment will give the necessary tools to transform your idea into a product. When applying to the program you can choose from these three majors They are in the core of the expertise of our staff members and the research performed in faculty. Lectures are held by world-recognized scientists with knowledge of the most recent technological and scientific progress. I would like to mention three points: First, the degree program is associated with BioMediTech Institute and the Center of Excellence on Body-On-Chip Second, Tampere is ranked the best place to study in Finland Third, we are an international university with people from over 60 countries How great is that! Once I got to know TUT learning environment and amazing infrastructures and also the supporting staff I knew that this would be one of the best Investments I could ever do for my academic career! It's a very lively University and very lively university life and you really feel like you're studying with the other people and are not just competing with the other students You do things together, you get your voice heard if there's something, so I really appreciate that about TUT!

Dissertation writing (how to) – Lempies



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This video is about thesis writing, how to write a PhD thesis fast, How to write a doctoral thesis fast? Tips for thesis writing

ciao guys and welcome to lamp is as you can say today I'm not in my kitchen because I would like to talk about something completely different from cooking the Thai job for this video is how to write a PhD thesis fast as a PhD student you know how difficult and how long it take to contact those experiments and then at the end of the day you have to compile those thick documents that you have to submit for marking it's just so stressful so turning my thesis writing I've come up with a method that helped me write faster better make the writing process easier and I'm going to share those tips with you today the first section is organizing and planning do not skip this step because is really important before you start writing it's important that you think about how many chapters you are going to write in your thesis and they're pointed in each chapter it's also important that you already start selecting the writing tools the citation tools as well as the statistical tools that you are going to use when you are writing your thesis another important theme which is the thesis writing is different from what you have time before suggests masterpieces and pasta theses because especially at the University of studying they are not due dates so it is important that you set those due dates for your soul nobody's going to give you those days not even your supervisor for instance set up a due date when you want to finish with chapter one once you want to finish with chapter two when you want to submit your pieces so when February or when that time comes you know if you are ahead or behind your schedule so you can start working hard and put in more time another important tip that people use to pretend to forget is before you start writing check if you have met all the university administration requirements that I need it for the pieces to be submitted imagine if you have completed all the writing then just before you submit you discover that oh you have missed a course and that course is gonna take you six months to complete before you submit is going to set you back six months just think about that and make sure that you compile those or your soil those things out before you start writing what I did in my thesis I had a separate word file where I have outlined or where help dealt with organizing and planning and I outlined my thesis structure in a different file and I have done that before I start writing so that one it is time to write I have a clear mind on what and how I'm going to do the writing the next step is quantifying have a method to quantify or to measure your performance when you don't have a method like that you are not sure if you have contributed how much you've contributed or if you have not contributed anything every day my objective was to contribute 500 words every day and what I did is that I had I had an excel file with the two color codes and also with different columns where I have recorded certain information and one of those was the dates and the part of the thesis where I have written or we have wet on that day how many ways have I written in a day and where to continue the next day and then the color codes were like my favorite color and my least favorite color the favorite color I highlighted all the lines or all the days where contributed something always make my objective which is 500 words per day and then the least favorite color was for the days where I have not made my objective maybe I did not write anything or I've written fewer words than what are suppose to you can also make it fun for example are some rewards and Punishment think about it say ok if I finish my if I finish writing chapter 1 by the end of January if I have Flint I'm going to add myself and by myself my favorite book or go to the club with my friends without feeling any Quixote like ok I haven't written my things I haven't finished just have that method it is important that you know you're quantifying and you know how much you're contributing and you can pace at which you are compiling this document I invite you to check the link in the description below there you can download my thesis writing tracking templates for free the next step is actual writing it is important that you separate writing from editing writing is actually when you are composing your text and editing is when you are correcting and modifying your text if you don't separate the two it will be very discouraging when you write a whole page in one day and in the end you TD like 50 to 90% of that because you editing the same day and you feel really frustrated and discouraged so set some days where you are just writing and then after we have done or you have completed writing the chapter 7 chapter move on and say someplace for editing only the thing about writing is also fine and appropriate time that works for you for me it was always I write in the morning because that's when I'm most active so I had thesis writing was the most difficult task during that time I was doing this and I try to make sure that I tackle it in the Pony before I start any other task so that I'm John and I can continue in the day and do other table just without feeling any guilt also have a retry that where for you or that much villager that put you in that action mode start writing some people start adding with the cup of tea a cup of coffee for me is always a cup of tea and some motivational code I had this up my phone that I read every morning it takes about 20 minutes I'm done with that and I can get into my writing writing for me was always every day about two to three hours and that was very appreciated because in the day SS PhD student you still have other daily tasks our to continue that a hackney they do other tasks that I have to contribute to my departments like a scientific seminar other experiments meetings and research proposal writing at the end of every writing day it is important that you indicate where you are going to or what you are going to write next or at which content or at which page you are going to continue writing the next day the next step is keeping the momentum once you have started writing you want to continue until you finish because if you take a long break during your writing this session or during your writing time then you send to you sometimes back again imagine you take a vacation for a month in that one month you didn't do anything about this is writing when you come back you need some time to refresh your mind about the traffic and everything you are writing on the property means that will be just time wasted I had three ways that kept me going that was every single day I made a schedule of way to continue the following day that is really important imagine for instance the next day you want to write about introduction to chapter one what I would do is the day before is think about what do I want to introduce in Chapter one and how many program – I want that chapter to contain and what information I want to provide or what information I would like to provide to my old is in each program that is important because the next day when you start writing your mind is clear and has a straight direction what kind of literature to look for because you are sure of what you want to say to your audience you your writing will be easier and will be way faster another thing that kept me going in my case was I wrote every single day I also wrote during the weekends on Saturdays and Sundays but this worked for me very well because I was working in the morning every day I woke up at 5 or 6 a.m. and I wrote not longer than two hours or not longer than three hours so two to three hours if I woke up at 5:00 in the morning I would wake on till 8:00 and by 8:00 when my family member wake up I've already done with my writing we can continue the day as usual have breakfast together and still enjoy the weekend I didn't miss out on anything and I also wrote when I was on vacation because I had still I would just wake up in the morning and I had already selected tools that did not make me which is that I had also selected two that were flexible that I would have I had them on my personal computer and I used those everywhere I was I wrote when I was on the train who knows on the bus when I was on vacation it was possible another thing that kept me going then I had to make waking up early every day you have it once you get into that then you keep the momentum every single day you add 500 wet it's going to be faster before you know it you'll be done with a thesis writing and get it out of way the last step here is reviewing once you've completed everything you have the phone document from chapter one to the last chapter send it to a reviewer as soon as possible and the Muslim probably the best person to send it to then be your supervisor in that case the person will give you the second opinion as soon as possible so if you need to to major changes you can do them as soon as you can if you you can go ahead and review your document as many times as you want but you have to understand that that is all your opinion do not waste time doing that first CQ supervisors opinion and then he or she is going to tell you if you are in the right direction before you waste a lot of time just deleting and adding things when you're not sure what to ready to and if you need a professional if you're like for English or anything whatever language you're you're writing in this will be also the best time to look for a reviewer so that you can already booked an appointment for sometimes in I Prince put that person to reserve time to work on your document because reviewing a speaking from a personality exam plan it means this document is actually a very large document with about 150 to 200 pages so you need to book for reviewing in advance in summary to make your thesis writing easier and faster organize the thesis outline and have a time plan before you start writing do not skip this step it sound ridiculous that you just want to jump in and start writing but if you're not sure about what you're going to write then take you a whole lot of time and slow down your writing process and bring a lot of and frustration during this process second thing is split your writing into smaller section that are manageable you can just say I want to write and you don't even have an outline you don't set out the chapters you can't digest that much weight you have to speed it down into sections that are manageable and the last step is or the last point is have a clear mind about what you want to communicate to your audience when you have a clear mind about what you want to communicate you also have a clear direction about what kind of literature research article you want to look for and you want to read an ask to your document and that guys will help you write faster and easier and get your PhD as fast as possible tip the given time so guys that was it for today thank you so much for watching I hope you find these tips useful during your thesis writing if you have also some extra tips that I haven't spoken about please leave them in the comment section if you enjoy watching this video please give us a like and subscribe to our channel for more and I wish you all the best with your thesis writing

Amazing Science Toys/Gadgets 1



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NEW VIDEO ➡️ Mr. Mind Blow is bringing you AMAZING SCIENCE Toys/Gadgets! Sit back and relax. Enjoy 10 minutes of …

Lecture 1 | Introduction to Robotics



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Help us caption and translate this video on Amara.org: Lecture by Professor Oussama Khatib for Introduction to Robotics …

Incredible Retrobrighting Discovery 🌞 Deyellow with just Sun? No bleach/dismantling. Read FAQ! 👇



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🙏 Become a patron & more:

Please read FAQ: Since making this free video we’ve seen many happy results. Also, great lightbrighting questions keep popping up. It would seem helpful to address them in one central place. Please watch the full vid before judging the vid. You wouldn’t make a cake using only half the recipe. I went to extraordinary lengths to do a lot of due diligence for this recipe. Thanks!

📖 Sources: Dr James Pickett PhD, plastics scientist for GE for 30 yrs, who worked with me on the vid & literally wrote the book on polymer yellowing. He also co-invented a headlight deyellowing technology used in many modern cars, possibly yours.
✅ Peer review: Dr Richard Blair PhD, polymer yellowing scientist, Florida Space Institute.

☣️ Disclaimer: Try this at your own risk. All plastics react differently. No guarantees. Don’t try it for more than 3 days. Any deyellowing method is not good for plastic as I say in the video. Don’t try this at home, try it at work!

⁉️ FAQ:

1. “I’ve seen someone use this method & their case fell to pieces after 2 years!” Psychologists call it a post hoc ergo propter hoc (false cause) fallacy. The fallacy assumes that since x happened after y, y was DEFINITELY the cause of x. In reality that CAN’T be proven & as we know from these old cases cracking x likely might’ve happened regardless. Explanation: 📺

I addressed this at that forum but for unknown reasons the scientific comments I made were censored/deleted by the moderator whilst toxic/abusive comments remain. (My original comments can be retrieved via

In short, these people who’ve tried it for up to 3 years could be said to have proven it DIDN’T cause cracks: 📷

A 30 yr old case has had 262,800 hrs of household heat, diffused UV, even fluorescent light & nicotine. In the vid I put them in the sun for 3 days = just 24 hrs sun. Will a case with 262,800 hrs of exposure suddenly crack after 24 hrs sun?

The German forum have been trying it for over a year. They HAVEN’T reported cracks.

Dr Pickett’s main paper was published 15 yrs ago. As I say in vid, his advice is STILL that UV causes LESS brittleness than peroxide.

If you’ve seen a case cracked, what is the exact history of that case, from factory plastic injection ingredient ratio correctness, to 30 yr household conditions, to past peroxide use? Would it have been one of the many that cracked that year anyway? The scientists say YES.

2. “Putting it in the sun is worse than using peroxide!” The traditional retrobrighting method is peroxide + UV. Lightbrighting is retrobrighting “MINUS” peroxide. We believe it is unlikely that REMOVING bleach will lead to more damage.

3. “Won’t UV cause brittleness?” In overly high doses yes but the Doctor says LESS than using peroxide, even with the prolonged comparative time. This is because liquid peroxide can leach into invisible fissures in plastic, embrittling from inside. Conversely the science shows that UV is confined to the top 25-50 micrometers ONLY. I also show this in the video.

4. “I’ve seen cases reyellow a year after doing this!” That individual case’s history aside, all deyellowing will eventually undo to a degree. Peroxide is absolutely notorious for reyellowing, requiring repeated, even MORE damaging treatments. See my video for the LED prevention method.

5. “UV will damage the electronics!” A) they’re in the solid shadow of the case. B) air vents are 90° to stop dust falling in, which also SHADES UV. C) we regularly solder these electronics at 650°F for short periods, so backyard heat should be ok for longer periods. D) chips reach 100°F+ in normal use x 30 yrs, & their highly VENTILATED cases are specifically designed to hot air ESCAPE, rather than hotbox inside.

Think about how many awful peroxide results I’ve seen: classic machines streaked, ruined, crumbled, trashed. If you own a yellowed case & you feel there’s nothing to lose by trying a method a yellowing expert says is SAFER, it’s YOUR choice.

To sum up, if you use common sense, don’t put it out for months, follow the advice & caution in the vid & your case doesn’t have an underlying issue, the science says you’ll see nice results. Enjoy your refurbs! ✌️🕹️

┄┄ ЯRECIPE INGREDIENTS: ┄┄
🕹 Research paper:
🕹 Lightbrighting Turntable: 🇺🇸 | 🇬🇧
🕹 Headlight Deyellowing Kit: 🇺🇸 | 🇬🇧

💿 Stream the music:

┄┄ THANKS TO ┄┄
🕹 Dr. Richard Blair, Florida Space Institute
🕹 Dr. James Pickett, a chemist who worked on these problems back in the 1980s
🕹 Superingo:
🕹 The 9-Bit Guy
🕹 Jan Beta
🕹 Gaz Marshall
🕹 Pablo Roldan

┄┄ CONTACT, DONATE, SHOP ┄┄
💌

All music & content ©1988-2019

Hello bit biters! Well, in part one of my investigations We're trying to find a no-mess way to retrobite a computer without dismantling it into tiny pieces And we made some pretty interesting discoveries but no huge successes with that ozone method But oh, how the tables have turned! No.. no, they haven't turned yellow And I just can't wait to share an incredible discovery with you That I'm gonna call Lightbrighting Now, I'm ready to enlighten you.. now So, welcome back to Retrobrighting Recipes! Now as well as continuing to work with Groggle Bob from part one In this video I've also been working with Dr. Richard Blair, PhD From the research faculty in the Florida Space Institute. And Dr. James Pickett, PhD Yeah, it's a paradox Well, he's the chemist who's worked for Plastic Polymer Degradation issues for GE Global Research since the 80's Until recently… talk about retro.. But before I share the mind-blowing discovery with you We should first quickly address some of the other ideas that came from you guys, after the last video Number 1 As to why the 7-Bit Guy got results with ozone and I didn't in identical test It was pointed out that Texas is more humid than California And indeed humidity can help catalyze that reaction In theory, we could add a fine water mister and get better results Or a water misses… equal rights Number 2 We could try Sterile Hydrogen Peroxide Gas Plasma Machine And just put the computer in there And they're pretty cheap Only $5,000 to $8,000, actually So, I've ordered seven of them No, seriously though, I haven't … ..because they would likely cause the same damaging oxidation that broke my Macintosh drive gear Oh, geez! No, make it stop! But it might effectively give us a ready-made air brightening machine Number 3. Someone also pointed out that many hair dryers produce ozone They include ozone generators And they're usually called ionic And they could provide both heat and ozone in one Though still, we're left with the issue of oxidation of the electronics inside Number 4 There was my theory that the $8 Guy's dollar coins Placed in his bag, might have helped catalyze things And it was pointed out by someone that copper is used as a catalyst In some cases for organic synthesis And you know plastic is an organic Carbon containing compound So, an ozone reaction might be catalyzed by the presence of copper coins in the bag with the ozone Hm.. And 5 By far the most common suggestion was to put the ozone generator inside the bag So it would concentrate its own ozone I've tried this for 24 hours, at room temperature, to no avail Maybe with heat and humidity and coins, it would go better But again, it doesn't really solve that problem of oxidizing the delicate electronics But one thing does Stefan Aquila directed me to a thread on a German forum called Forum64.de Now, I had a lot of trouble understanding the discussion My German isn't really so good Even if I do do an okay Jan Beta impersonation (Jan Beta voice) Hi! It's Jan Beta! And Google Translate wasn't really much help either But one thing I did understand, were their pictures A huge thanks to Superingo for sharing them with us here And the method seems simple, you know Almost too simple They just put the computers outside in the Sun And that's it No, seriously. That's it. I couldn't find in the forum or understand a real hypothesis of why this might work And even the 8-bit Governor agreed, it make no sense But I did at least give it a go And one thing I did understand was that they mentioned how bones are not really white in real life Wel.. not alive. Yeah, but if you leave them in the desert They do bleach bright white And then one who spoke patio furniture outside Or left the towel in the sun for a few weeks, we've seen that bleaching effect of the sun Even your hair can go bleach blonde in the sun But in either case, it takes weeks or months Well, anyway, I placed these items outside on what was a very overcast day, here in California The high was freezing 20 degrees Celsius. That's .. double it and add 30.. Yes, 70 Fahrenheit Well, Dr. Richard Blair also put a cloth over half of his Windbest keyboard, on the same day We both tried our experiments And when I brought them in at night and looked at them inside I couldn't believe my ears.. eyes.. sorry But while I was in shock, you know, they showed a genuine lightening In one day! Even that ski-mask yellowing around the Macintosh screen where it had had a Polaroid filter on it Was noticeably less Stark Oh.. Tony Stark Ah.. Arya Stark! Well, the Omnibot was also a little whiter particularly at the bottom where had been facing upwards And the Amiga 2000 keyboard In honor of Commodore who went out of business 25 years ago this week It too looked lighter And you can clearly see where the cloth was on the Windbest keyboard What hadn't changed was that Amiga 500 keyboard But that was an extreme case. But yeah, generally the results were really mind-blowing I also engaged a small army of my Patrons to run their own tests at home over just one day Here are the before and after photos And it's clear to see a definite lightening effect after just a few hours and often on overcast cloudy days Mattsoft also shared with me a one-week test he did By placing a strip of tape over the case And that Commodore 128 came noticeably lighter around the tape And Xavier Bell also tried using a sunbed However, I couldn't see any results And really nothing can match the solar power of the Sun In a moment, I'll take you through our hypothesis of what we think is going on here After all if Sun makes plastics yellow How can Sun make plastics Deyellow, right? Right. But first speaking of that A500 keyboard Let's run some very quick tests Firstly, let's melt some of the keys with a hot air gun to test that theory that heat can also yellow plastics Because we've all heard stories of computers that have yellowed from just being in a dark stuffy attic With no un Casualties of war here And look at that The melted key on the left is noticeably darker than the ones next to it So that reaction I explained in the first video is definitely sped up by heat alone Even devoid of UV So that tells us heat is a factor in yellowing Not just sunlight And sunlight is what we're using to Deyellow here Next, someone asked how deep the yellowing is And that's another good question Dr. Pickett actually suggested we try a headlamp deyellowing polish I've never heard of that before but my fear is it will rub away logos or key lettering But he coinvented the yellowing coating used on many modern car headlights today I think it's worth trying I did find these at my local auto parts store And sure enough, they mentioned the deyellowing Now, I don't really have time to do a full investigation in this video But let me know if you give it a go I'll put a link in the description below to this product and all the recipe ingredients that you need from today's menu Interesting that these things come with UV blocking wipes Which, gave me an idea More on that in a moment I'll also put a link, below by the way, for the Lightbrighting Turntable, as I'm' calling it Perrfect to ensure even distribution of the sun's deyellowing effect See I told you the tables are turned! But let's test how deep the yellowing goes on those donor keys ..to see if the polish is likely to work And don't worry, we'll refurbish this key in a future episode As we can see the yellowing is really superficial In fact, Dr. Pickett says UV damage will usually be confined to the top 35 µM of the surface But lets sand away a rarely light layer Wow! So, It's quite possible that headlight deyellowing polish would work wonders In some cases .. oh, keys.. or cases Alright, so back outside to the mind-blowing experiment And let's look at some before and after photos, after three days Even if one of them was rainy You remember Dr. Blair's cloth test? Well, I asked him to keep trying on successive days And he moved the cloth over an inch each time This gives us a nice scientific depiction of what's going on each day And he moved it over again for day three And now we have a tritone gradient showing the cumulative lightbrighting effect If we just enhanced the contrast, you can really see what's happening to the plastic Now, I'm first to admit that this method is slower than using bleach But as a result is also less destructive And because you don't have to rely on buying bottles or putting things in one table at a time You can put your whole collection out at once Albeit, for several days You know, as these things took 10, 20, 30 years to yellow What's a week or two, really? And even that stubborn A500 keyboard looks a little bit lighter Interestingly, also those number keys that I'd melted and darkened Responded even faster to the lightening And my amstrad PCW was even more interesting You can see the original case color under what used to be an Apple sticker And then the yellowing under the rectangle And the lightbrighting effect around the rest of the case Is nearly back to the original color Here's our hypothesis of what's going on The new computer with it's fresh healthy plastic starts its life Well inside your house, usually out of direct blazing sunlight When it yellows over a long period of time, usually many years, sat on a desk, or under a TV, away from the window This means it's getting three things. It's getting diffused sunlight Specifically UVA rather than direct windowsill sunlight 2. It's getting exposure to often fluorescent lighting Which also has a damaging yellowing effect on plastics 3. It's getting general air oxidation warmth And what we showed with the Melting experiment that warmth is a factor Even low warmth over many years It's our hypothesis that these three factors together caused the yellowing So if you have an old computer that miraculously hasn't yellowed I'd be willing to bet money on one or more of those three variables being less in your particular environment fact In fact, I'll bet some of the 8-Bit Guy's Coins The second differentiating factor is crucial one Is that, after those years the plastic is in a sensitive highly degraded yellow state Is changed from when you first opened the box and got that distinctive ABS aroma And quite ironically, that sensitivity Makes it sensitive to deyellowing And that might explain why the plastic that we had just melted was more sensitive And therefore more sensitive to that quicker deyellowing that we just saw The Sun gave and the Sun has taken away Blessed be the name of the Sun Steve Jobs 1:21 So how does simply popping it in the garden caused deyellowing? Well, in that instance, instead of diffused in direct sunlight inside the house The sensitive computer plastic is getting bright direct sunlight Even through clouds, it still gets UV And that all happens in a concentrated dose over a few hours, not years Even three days is really just 24 hours of sunlight So put really simply Your hair doesn't lighten inside your house even on a sunny day But out on the beach hair will degrade and bleach Bleach Beach Even if you don't have plastic hair like some people Ultraviolet, wave. Yes! It's a glitch in the plastics matrix Neo So, let's recap Diffused sunlight through window glass plus fluorescent lighting and warm air, oxidation Over the course of years Causes slow yellowing to that new plastic Conversely, bright direct sunlight in a concentrated dose makes that degraded and sensitive older plastic Receptive to bleaching Well, that's the incredible discovery And as the why most of us haven't really realized this till now I didn't really think that just hardly anybody thought of trying it because At first glance, it just seems so counterintuitive What isn't counterintuitive? Ordering your PCBs from PCBWay! They offer free worldwide shipping on PCB assembly orders And up to 30% off PCBs right now And as I mentioned, PCB stands for Polymer Carbon Bonds Doesn't it? So what about downsides? Well, Dr. Pickett, he did some research in some similar areas, not quite the same And that research indicates that the yellowing might return But will never reach its original yellow level You can see here the massive drop in yellowness when exposed to sunlight And a more gradual return But never to its full peak And the more you repeat the process, the more the yellowing stays away I'll put a link to that similar research paper in the recipe ingredients below And yes, you'll get similar bleaching results on a windowsill, as long as it's in direct sunlight Though outdoors is probably just easier And more interesting for your pets But the yellowing that might return can be prevented apparently With exposure to blue light Even standard warm white LED light bulbs contain a decent amount of blue light And more and more houses have windows with you know, UV coatings and LED lighting nowadays Anyway, so this re-yellowing effect might become a thing of the past Literally. In any event, if you've Retrobrighted using any method I now recommend changing to LED bulbs in the room where that computer is Once I have the bleaching where I want it I'm also gonna use these UV blocking wipes, similar to the ones we saw earlier, on the plastic Sort of as a sunscreen for computers But for every downside there is the light bright side It doesn't cause brittleness as fast as the traditional retrobrighting method might At least, that's our hypothesis You see hydrogen peroxide is likely to penetrate into any uneven cracks Undetectable cracks that might be in the plastic And Dr. Pickett says that would be very bad for the brittleness. Hmm. So there it is Lightbriting Test it out. Let me know. Let Puppyfractic know too – how you get on in the comments below Feel free to share Google photos links or whatever But remember, as with any technique, there are no guarantees it will work the same for you As it has for us.. due to a multitude of different environmental factors Both in what caused your yellowing and this method of fixing it Alright, now huge thanks as well, to the paradox And to Gargle Bob, Superingo, Gaz Marshall, and all my Patreons for their help with this experiment Thank you too if you become a supporter Subscribe or just do any of those small things that helped me keep making these videos for you What do you think of lightbrighting? And our hypothesis and test results? Is this the new light of your life? Or has it just clouded your day? Comment below and Cheerio!

Lec 2 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008



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Lecture 2: Operators and operands; statements; branching, conditionals, and iteration Instructors: Prof. Eric Grimson, Prof. John Guttag View the complete course …

Getting stuck in the negatives (and how to get unstuck) | Alison Ledgerwood | TEDxUCDavis



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Alison Ledgerwood joined the Department of Psychology at UC Davis in 2008 after completing her PhD in social psychology at New York University. She is …

Mythstory #9 – Aztec Mythology



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The Aztec were a flourishing Mesoamerican culture from the 1300-1521. The capital of the Aztec empire was Tenochtitlan. During the empire, the city was built on a raised island in Lake Texcoco. Modern-day Mexico City was constructed on the ruins of Tenochtitlan. The Spanish colonisation of the Americas reached the mainland during the reign of Montezuma II. In 1521, Hernán Cortés, along with an allied army of other Native Americans, conquered the Aztecs through germ warfare, siege warfare, psychological warfare, and direct combat. Lucky for us, much of their mythology still survives to this day. So let’s begin from the beginning

hello everyone welcome to myths tree the shore we talk about myths from around the globe for today's show we're going to be discussing the central religion of Mesoamerica Aztec mythology the Aztek were a flourishing Mesoamerican culture from 1300 to 15 21 the capital of the Aztec empire was Tenochtitlan during the empire the city was built on a raised island in Lake Texcoco modern-day Mexico City was constructed on the ruins of Tenochtitlan the Spanish colonization of the Americas reached the mainland during the reign of Montezuma ii in 1521 Hernan Cortes along with an allied army of other Native Americans conquered the Aztecs through germ warfare siege warfare psychological warfare and direct combat lucky for us much of their mythology still survives to this day so let's begin from the beginning creation there are two variations of the Aztec creation myth that are very popular in discussions I picked the one that seemed to have the most support if you would like to learn about the other creation myth feel free to check out the big myth site for an overview in the beginning there was only chaos and the god of duality it was both male and female the Lord of duality no meant acutally and the Lady of duality oh mecha waddle this God was good and bad male and female and gave birth to four other gods who each preside over one of the four cardinal directions over the West resides Quetzalcoatl the god of light mercy and wind over the South presides witzy lapaki the God of War over the east presides she pathetic the god of gold farming and springtime and over the North presides tests cat lopaka the god of judgment night deceit sorcery and the earth these gods set out to create the world they created many other gods and the first humans who are Giants one of these gods had to play the part of the Sun and they would end up switching their roles as time went on this creation model is called five sons each son had to be replaced by a new God because the world always came to end Tezcatlipoca was chosen as the first son but either because he had lost the leg or because he was god of night he only managed to become half a son the world continued on in this way for some time but a sibling rivalry grew between Quetzalcoatl and his brother the mighty son who Quetzalcoatl knocked from the sky with a stone club with no son the world was totally black and in his anger Tezcatlipoca commanded his Jaguars to eat all the people the gods created a new group of people to inhabit the earth this time they were normal-sized Quetzalcoatl became the new son and as the years passed the people of the earth grew less and less civilized and stopped showing proper honor to the gods as a result Tezcatlipoca demonstrated his power and authority as a God of sorcery and judgment by turning the animalistic people into monkeys Quetzalcoatl who had loved the flawed people as they were became upset and blew all the monkeys from the face of the earth with a mighty hurricane he then stepped down as the Sun to create a new people tolik the god of rain became the next Sun but Tezcatlipoca seduced and stole his wife Chuckie Quetzal the goddess of sex flowers and corn Pollock then refused to do anything other than wallow in his own grief so a great drought swept the world the people's prayers for rain annoyed the grieving son and he refused to allow it to rain but the people continued to beg him then in a fit of rage he answered their prayers with a great downpour of fire it continued to rain fire until the entire earth had burned away the gods then had to construct a whole new earth from the ashes the next Sun and also Pollux wife was Shashi Whitley qu she was very loving toward the people but Tezcatlipoca was not both the people and sha Chi wheatley ku felt his judgement when he told the water goddess that she was not truly loving and only faked kindness out of selfishness to gain the people's praise sha Chi weakly ku was so crushed by these words that she cried blood for the next 52 years causing a horrific flood that drowned everyone on earth during the fifth son the current one humans would once again walk the earth Quetzalcoatl rescues humanity Quetzalcoatl would not simply stand by as the gods continued to kill people he needed to save them so he asked for the help of his brother Shilla tell the God with the face of a hound she little said I am shuttle the evening star every night I lead the Sun down to machlin the Aztec hell to die I know the way to the land of the dead and will guide us there Quetzalcoatl with his wise old face and brilliant feathers said I am Quetzalcoatl the morning star every morning I lead the Sun back out of Midland to be reborn with the dawn I know the way out of the land of the dead and I will guide us back home to the sweet paradise to mountain the Aztec heaven they retrace the path that the Sun took every night down to the depths of the underworld all the way to the Palace of the Lord of the Dead we must be careful Quetzalcoatl said I know Lord McClintock wittily will not be pleased by our request he is a wily God and may try to trap us Shalako agreed and they cautiously proceeded to the throne of the Lord and Lady of the Dead Quetzalcoatl approached the Lord of mifflin who sat on his throne surrounded by spiders and owls as well as the bones of humans piled up like treasure I've come for the bones the precious bones the Jade bones said Quetzalcoatl can I have them in order to populate the earth MacLeod too quickly replied and how do i benefit from this no I don't think I'll give up my splendid bones if I give them to you I'll never get them back and I'll be poorer for it no you can't have my bones Quetzalcoatl had anticipated this oh no you misunderstand me we don't intend to keep the bones we just want to borrow them the humans would be mortal and would eventually return to you just like how everything else is born and eventually dies even the Sun itself only we the gods live forever you wouldn't really lose anything in the end and in the meantime your fame would grow lady McClintock wattle looked pleased by these words Lord McClintock oddly considered them then spoke hmm an interesting idea all right you can have the bones if continued McClintock Whitley and shelah Tov Rose if you can play my conch shell trumpet and circle my kingdom four times in honor of me he handed Quetzalcoatl a conch shell that did not look like any kind of instrument and they left the chamber she Lovell looked at the trumpet and dismay the conch shell couldn't make a sound he's trying to trick us I've got a plan said Quetzalcoatl and he called the worms and other gnawing insects and ordered them to chew holes into the conch shell then he took the shell and held it up and summoned the bees to climb inside through the holes and buzz loudly the sound echoed through the shadowy realm like a trumpet blast Quetzalcoatl and shelah rule came back into the room and asked for the bones very well then said McClintock wittily you can have them for now but the humans will not be immortal they must die again someday and return to me just as you had said earlier the gods agreed gathered up the bones and left lady McClintock wattle looked terrified our treasure we can't let them carry it off of course we won't I may have said they could have the bones I never said they could leave my kingdom with them and then he ordered some of his servants to dig a pit along the path that the two gods must take to escape and others to chase after them the gods were able to escape but lost some of the bones along the way all the gods got together and sprinkled the bones with their blood restoring them to life and thus humankind was born from the penance of the gods of themselves journey of a princess when the Aztecs settled down on a magical island that had appeared they did not go to war as they usually did they still had to feed their hungry gods but they used their own people instead of captives that's what their God had told them to do after a while the Aztec emperor sent a message to a nearby tribe inviting the Chiefs daughter to visit the Aztec capital and meet his son his invitation was accepted the princess of the nearby tribe arrived at the capital city with many servants and many presents for the royal family she was delighted to meet the emperor's son they had a lovely dinner together by the end of the evening she was more than willing to become his bride a few days later her father arrived in Tenochtitlan the Aztec capital city he expected to discuss what goods his daughter would bring to her marriage with the Emperor's son that's when he heard that his daughter and her many attendants had been sacrificed to feed the many hungry Aztec gods in fact he probably would have been sacrificed himself only fortunately for him he had traveled to the city with many armed guards the Emperor tried to explain that it was an honor to be sacrificed but the chief would have none of it he hurried home to his people and the very next day sent his army to destroy the awful Aztec people as their God had told him to do the Aztecs had taken time to grow in strength before they had contacted this nearby King their young men had become capable warriors they had many weapons and they won easily the Aztecs demanded tribute in the form of jewels food clothing and of course captives to feed their hungry gods that made the Aztecs very happy the Aztecs went on to conquer other tribes in the area and that made them very rich there aren't many Aztec tales that aren't related to creation most of the mythology is just the story of the five sons or expansions on it I was lucky to find some that had nothing to do with it Aztec mythology is very in-depth and contains a very rich story thanks for watching this episode of myths Treon Aztec mythology make sure to LIKE and subscribe and tell us what mythology you want us to cover next in the comments below and if you really love us consider supporting us on patreon we'll see you next time

Forensics Expert Examines 20 Crime Scene Investigations from Film & TV | Technique Critique | WIRED



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In this episode of ‘Technique Critique’, crime scene analyst and investigator Matthew Steiner examines forensics investigations in crime scenes from movies and television to see how accurate they are. Crime scenes are from The Wire, NCIS, Zodiac, The Flash, The Boondock Saints, Heat, Seven, The Other Guys, How to Get Away with Murder, CSI: Miami, The Dark Knight, Dexter, Insomnia, True Detective, Bone Collector, Criminal Minds, Family Guy, Iron Man 3, Minority Report and more.

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Forensics Expert Examines 20 Crime Scene Investigations from Film & TV | Technique Critique | WIRED

look at the blood spatter bang-bang-bang My name is Matthew Steiner Matt Steiner is a senior crime scene analyst and veteran investigator of over 21 years today we're gonna break down the portrayal of forensic science and TV and movies as we're critiquing these clips understand that their goal is to entertain and they definitely do that the wire trajectory analysis first off the gun safety and this was it was comical and not realistic you wouldn't take a loaded weapon and start waving around at a crime scene initially the original crime scene investigator on the scene should've took note of directionality we do that by looking at the Concordia fracturing of glass to see which direction of force is going off oh so we would have known in this case originally that the bullet was coming from outside to the inside of the kitchen so what they do from that point is they try to figure what would the trajectory line be from that bullet hole through her deceased body which way was that bullet going fuckin'-a lo and behold there is the bullet in the door from that point I think we deviate from reality but we wouldn't just start picking at that hole they could be destroying rifling that's on that bolt and that's what really we're going to sent in a lab to do some sort of analysis compared to a suspects gun NCIS personal protective equipment no rule number two is always wear gloves but when there's this much blood reindeer in the truck so in this scene they actually talk about wearing personal protective equipment Rainier yet no one is wearing any sort of garb what happened everyone is wearing just like a jumpsuit or the regular tire and just gloves basically you would never want to do that apparently that was beyond that our crime-scene technician he's taking photographs he then wipes his brow Danny handles Ebbets basically what he's done he's taken DNA that was on that camera on his brow and put it on to our evidence on the scene and then proceeded to have a whole conversation while holding that evidence right by his face you see the counselor that's a pretty good movie again improper procedure for collection of DNA evidence he could have been wearing a tie back suit a mask you may even want to wear goggles or glasses and multiple layers of gloves rule number two is always wear clothes I would strip out the outer layer of gloves put another layer of gloves on top of it and then proceed to collect evidence I think I found that out boss recovering evidence zodiac initial observations is that the vehicle might have been wiped down which you can see sometimes with oblique lighting and a scene you can see sometimes fingerprints so that's a pretty good observation thank you as we proceed the other side of the vehicle there's a shell casing on the driver's side front floor which wasn't marked or photographed as far as I could tell oh well that's good if things got to be documented in situ or as is as found he just picks it up with a pen where is that pen been beforehand you know was it in your hand was it in your ear was it in your nose it's got DNA from who knows where I don't know where this started unfortunately I've seen this in real life if anything if this is like a PSA of what not to do at a crime scene that's one of those big things you wouldn't want to do at a crime scene an idiot tire impression evidence the flash getaway cars and Mustang Shelby gt500 Shelby's have a rear super-wide tire specific to that model 12 inches with an asymmetrical tread I don't think anyone with just by looking at a tire impression to say it came from a specific vehicle sorry we went do some sort of dental stone casting of that impression recover it send to the lab and then there's database which they can search to say what sort of vehicles it could come from there's something else and there's a pen again being used to collect evidence like that gave me that pen which was improper before he died another thing you wouldn't do is if anything is inside of our impression at our scene we would leave it there we would cast it in place we wouldn't sample it because you could be destroying tread we're inside that impression yeah yeah after he samples with the pen fecal excrement animal I guess I don't know how he would know the difference between human and animal feces which I don't think is realistic no loaded weapon examining a body so what are we looking at here doc the worst dress tip I've ever seen this stiff and I found it a beat together for five years so show some respect so it's common for TV and movies to present crime-scene investigation in this fashion where we have all these different people doing a million different things all at once inside of a crime scene that means it's working we want that crime scene clear we wouldn't want anyone in there except the people who are doing the investigation in real life not that we would make fun of someone who is dead show some respect but there is a certain gallows humor that goes along with crime scene investigation and death investigation and it's one of the ways to cope with it is to adopt this type of humor I know crime scene analysis as portrayed in boondocks aids you got any theories time he was the only one done right double tap back of the head independence new hit man wants to leave his mark that's a possibility another possibility is they were placed there with Aitchison camped in the scene they focus too much on the why the crime happened what is this their crime scene analysis usually deals with the who the what the where oh I forgot about that one when the howl of something but never the walk and it makes me feel like river dancing the Y has to do it motive and there's no way that we can prove why with science heat crime-scene analysis this guy here he's got what appears to be a DoubleTap entry wound to the sternum tattooing around the head wound scorched the bone close-range probably executed in this scene we have our I'm assuming he's a crime scene investigator he makes an assumption that a gunshot entry wound was that close range and if you're willing to the sternum close range we can make those determinations when you fire a gun besides the bullet that comes out of the barrel we have burning an unburned gunpowder what difference does it make that burning gunpowder will go a certain distance we then also have unburned gunpowder that goes a further distance to that and then obviously the bullet goes the furthest is bang bang bang so we'll look at the injuries we'll look at whether there is burning and then we'll look for other things like stippling and that is the operations caused by the unburned gunpowder operating the skin what about them not having a weapon itself you really couldn't say exactly but we could take that weapon in the same ammunition bring it to ballistics and do so test firings at different distances to replicate that pattern to see we get the same diameter of gunshot residue and burning rock-and-roll fingerprint evidence this is kind of overkill it's probably because it looked cool the reason they put the alternate light source in there with fluorescent powder it really could have kept doing just regular black powder probably is what we would have done in the field it's like prescribing brain surgery for headaches out there beyond that the techniques aren't that great because they're using too much powder and then you're using compressed air to blow out to friction Ridge detail we would never do that Jodie Foster told me also right here he calls it a swirl pattern there's no such thing as a swirl pattern there's arches loops and whorls so there's some parts of this that are true but a lot of it is fans what is his mummy doing processing a vehicle the other guy we found a lot of stuff bodily fluid and hair samples we determined that a bunch of old homeless dudes had an orgy in the car so what they get right in the scene is that they're processing indoors they bring into a garage and that's what you want to do in real life yeah and then the top it all off some joker comes along I think he knew you guys were cops because this is what I would call a spite also the attire of our crime-scene technicians is correct oh yeah they're all wearing personal protective equipment Tyvek suits gloves masks dirty Mike and the boys which is funny it's a comedy but they got it right I'm a dear vagina the rest of it is just kind of silly and funny determining you know that it was a spaceship or that you know there was a deer vagina what or homeless guys had an orgy inside the car it's just kind of funny but very unrealistic turned my beautiful prints into a nightmare how to get away with murder detecting latent blood they didn't find it so in this clip way too many people inside this crime scene all doing random things at once just like we saw with the loaded weapon cliff some people have gloves on some people don't no one is wearing a tie Vic suit or booties and probably the biggest cardinal sin is that we have the suspect inside the scene how much longer you know that's not reality at all what they start pulling out knives out of a knife block and a swab but they swab one side of the blade not the other side of the blade nor do they swab the handle seems like a foolish move the last step was to do luminol testing and that's a chemical we used inside of crime scenes to look for late in blood everyone want to be cleared out of the room at that point is a possibility that luminol is carcinogenic so you'd be wearing a mask as well as Tyvek suit oh you're being paranoid but you know you just start randomly spraying the chemicals and everyone else is around breathing it that's a no-no and maybe he's just being smart also using some sort of UV light with it or some sort of blue light with it luminol you don't need that you don't need any sort of alternate light source you just make the room dark you spray it in combination with hydrogen peroxide and if there's a reaction it will glow I'm gonna run what's called a luminol test so this is where they actually do get it right later on he's called out for diluting the blood by spraying too much luminol the more luminol you spray the more you dilute the blood real life we'd be very careful on how much of the chemical we're gonna add there's other chemicals that will react in glow or chemiluminescent ooh luminol okay all right I get it ballistic and fingerprint analysis the dark night that's Brooke underneath I'm gonna take ballistics off the shouted bullet no thank you recovery technique is pretty good we would try to cut it out of the wall this is your original scam do it is reengineered there's the thumbprint left when you pushed around in the clip this is completely impossible yeah there's not based on any sort of science and the rifling inside the barrel is gonna create markings on that bullet which would have destroyed a fingerprint that was there anyway let alone the extreme heat that would have burned off most of the fingerprint good luck let's same pattern analysis as portrayed in Dexter the male victim was standing right here and the killer plunged his knife into the shoulder severing the carotid artery and here and assume yep Dexter doing blood stain pattern analysis so over here you have nice clean sprays of blood he gets the description a categorization of the blood same patterns correctly but that's really about it first off those types of patterns you would never do that sort of reconstruction floor you would never do stringing for arterial or gushing or cast-off because there's no way that you could figure out exactly where it came from okay never know as opposed to some sort of impact spatter from a gunshot or for someone that was beaten with a bat so we're looking for a sushi chef looks like just someone just took a bucket of red paint and threw it on one wall and then someone kind of randomly squirted blood on another wall this week a finger painting then cast-off they don't look realistic they're a little more linear than that they sometimes can be curvilinear depending on how you're moving your hand but to say it definitely came from a sharp knife not a sword there's there's not really in a way to say the exact object that it came from yes sushi chef is possible insomnia autopsy clear cause of death was herniation of the brainstem due to into cerebral hemorrhage beaten to death these contusions superficial most of the trauma was to her face in the top of her neck first up what I don't like about the portrayal of this which they do sometimes in these shows is that someone's got to turn on a light not this time yeah we'd want as much light as possible told you it didn't appear that there was an even an autopsy done who's gonna be there was no Y incision on our deceased so to say that the neck injury is superficial how would you even know that without actually looking at it to see how the part of that tissue is damaged or if the hyoid bone was broken or fractured there's no way to know just by doing a visual inspection to see that sort of thing okay buddy gave us nothing at the end you know he's handling the body takes his clothes off and then touch her hair she could be bleeding from the head and now you're taking your bare hand and touching her blood didn't even blink and then maybe later on having a sandwich or whatever so it's kind of ridiculous it's good across the line crime scene assumptions true detective this is gonna happen again this is his vision her body is a paraphilic love map they might not have known her but this idea goes way back with him I get that from one of your books I did I got to thank Woody Harrelson for this he basically says what I'm gonna say anyway you got a chapter in one of those books I'm jumping to conclusions any time we start an investigation with an assumption or we come up with a theory I'm guaranteed an s-curve we always have to be ready to abandon that theory you attach an assumption to a piece of evidence you start to bend the narrative to support it what I like about this clip is that woody houses character kind of stops which I got a problem with Bone Collector collection of evidence we're gonna need those handcuffs immunity well then we can remove them when they get here look in the suitcase there's a small saw I want you to saw her hands off at the wrist line this is not realistic at all we would never destroy the body intentionally like that it's true to recover handcuffs we would somehow get those removes off her hands and she takes a couple seconds to try to remove them they're stuck so the next option is to cut off the victims hands no they're still gonna be blood that's gonna be coming out of the other end of that hand which is gonna completely consume whatever DNA or fingerprints that would be on the handcuffs so not a great option hey why don't you knock it off like I don't need to knock it off forensic anthropology Castle yo this building was set to be demolished that is until the salvage crews stripping the place came across his body buried under concrete no less guy cause of death I tell I do a full exam but he's probably been here since they poured the concrete back in 1978 anything else you can tell us about them he was maybe early thirties and a sharp dresser check out that powder-blue suit in this scene they're able to somehow miraculously perfectly excavate this skeleton fully intact there another concrete no less unlikely what if you were to be breaking up concrete with sledge hammers or jackhammer you would have already damaged it probably crushed a rib cage if not a bunch of the bones I know who the victim is what beyond that to give it a general age estimation I think that is possible he was maybe early thirties your skeleton is going to age in a very predictable manner I'm intrigued so that's what they look for certain growth in and deterioration of bone and a testament to the truly indestructible nature polyester early forensic investigations in cold blood find a shell casings no didn't leave any fingerprints either first off we can note that they're not wearing any sort of personal protective equipment or we wouldn't want to just take powder and just pour it onto a surface and then dust that powder around man would take that powder and put it onto a separate clean surface it's not related to the scene at all do other than that they make an assumption that casings were cleaned up somehow or picked up initial occasion could have been a revolver semi-automatic automatic weapon casings gonna be a ejected out of the side of that weapon but a revolver that casings gonna stay inside so that would be the reason why you might not have a shell casing at the scene probably Criminal Minds crime scene analysis chained Bernie and Vinny a dev were here Jane tried to run Vinny I didn't how do you know she's half under her desk which means she tried to hide an enzyme found it one of the worst things you could do as a crime-scene investigator is to lose your objectivity what if she was just sitting at the desk is that the position she died in maybe she crawled to her desk so these three were stabbed and the rest were shot to death they also make an assumption that we have one unknown subject or killer as opposed to two have you considered two killers yes bloody footprints all seem to come from the same pair of shoes it's possible that someone else didn't step in blood right yes or that they both wore the same pair of shoes we see this a lot you would have to recover those shoe wear impressions and analyzing the left which is a quick visual analysis couldn't give you that information evidence at a crime scene Family Guy and now back to Jake and The Fatman Hey look over here on the carpet that's a cigarette butt this is probably evidence yeah can you bring it over to me I can't move it this is a crime scene so what we see in here is the typical chalk outline where the body was its iconic look can you describe it to me but it's a joke no one does that anymore you're destroying evidence you could be adding chalk on top of DNA that could be important or are the trace evidence that could be important you know what forget it Iron Man 3 the use of virtual reality and crime scene analysis 3,000 degrees Celsius any subjects within 12.5 yards were vaporized instantly no bomb parts found in the three mile radius of the Chinese Theater no sir this is something that could happen maybe not to that extent that we see in Iron Man 3 but there is current research in the use of virtual reality and augmented reality to help assist crime scene investigators I'm happy so if we document our scene with the three-dimensional laser scanner the laser scanner is gonna collect millions and millions of points of data and we'd be able to see anything that's in the view of that scanner that at the end we're one with the three-dimensional model we could then take ourselves virtually through VR technology place ourselves in a scene to make observations and those are the observations that we've used for a reconstruction where as evidence what's the context of this evidence what happened and what order did things happen its pageantry going on here lots Theatres minor report predictive crime analysis time horizon 12 minutes what he's doing now we call scrubbing the image looking for clues as to where the murder is gonna happen beyond that the date of the crime all we have to run out of the images that they produce so in this movie we see our precogs our psychics producing images of crimes that haven't happened yet precogs can see a murder four days out now I don't think that that is ever possible but there is cutting-edge research and technology that does help us identify crimes in progress oh this is good one of those technologies is what's called shot spotter that identifies audio from gunshots and specific to gunshots happening at certain areas and giving it a GPS location it's a park Dutch police are using augmented and virtual reality to go back and look at crime scenes in the past to help in their analysis and reconstruction conclusion with the increased popularity of these types of shows the public's perception has been affected by it both in positive and negative ways on the positive side we have more people coming into the field and more attention is being given to forensic science and a negative side things are always shown absolutes the timeline of analysis is not true and not correct techniques and technology that just not even scientifically valid and does not exist now I understand that the goal of this is strictly for entertainment and it does that

Something About the Hindenburg Nobody Noticed



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hey I'm in trowing my outro double-double time because we're gonna talk about the Hindenburg explodes it's copyrighted news more than 30 probably 33 perish when the world's largest airship is totally destroyed and purposed by fire and explosion in that order May the 9th 1936 all the world claims whatever so there it is the Hindenburg supposedly a new technology at the time when it was nothing of the sort there was a remnant of a former civilization now look at New York City the buildings look empty ish to me Charmin ease pide completes its first crossing to America mmm-hmm here come the Nazis woohoo and oh man the guy fell out no that's just the Rope oh all their pee all the pee that they peed on the way over is just pouring out the other than the Nazis then Germans wouldn't do that to the Nazis to make Heil see Kyle to the new arrival blah blah blah painted up silver cameras are there on this special occasion now I'm going to watch that bottom part with great interest so they're reeling it in and sew the fabric on the outside isn't airtight and if you didn't know that see it's got open windows it that's just a skin you're just covering oh they add some more pee the Germans like drink their beer and coffee see those windows all right so here's the deal the skin the fabric of it okay in one year 20 safe crossing sounds like a great technology that you'd want to keep well well well we'll see about that well that's kind of a shaky way to do it looks like they hadn't really thought about long-term use now look at that look how high the ceilings are in there it's four tall folks folks tall it's a tall Volkswagen it's meant for very tall beings catarrian giant type beings perhaps whatever I don't know the giant mistress of the skies arrives in New York City crossing in front of an empty building actually that building they had already tried to burn it and everything that I know that building look at all those buildings they're not full they can barely fill them up today there's steam coming out the top all is serene the calm before the storm oh are they going to show the Statue of Liberty no they don't no they don't dusk the Hindenburg nears its mooring mast and the false flag is ready as the skin has been painted up with thermite they're not going to show you the ignition even though all the cameras are on it suddenly the fatal moment the thermite paint was ignited yes paint it up with with a silver paint made with thermite and yeah filled with hydrogen air mixture and the cameras were ready and rolling yet they didn't catch the ignition because that would have shown how it ignited because even paint it up with thermite they had to actually like fire a flare into it so what caused the blaze well they don't know but it's sure making a big headline in the News rescuers rushed into the mass of twisted white-hot steel you know again what is burning you know they had incendiary chemicals and obviously okay the hydrogen gas burns yeah but it kind of flash burns it it's quick puts out heat sure lot see yeah that much hydrogen gas a whole lot of heat but honestly you know there are multiple bladders on the inside it's just one of these things like the Titanic they they just it was even more than the Titanic because this was meant to just once and for all destroyed the idea of dirigible x' for air travel they didn't want it they did not want it look at that cloak into the clouds like that and that cool is that not cool though but sure they had to copyright it copyright copyright just a quickie there just wanted to show you Giants for the Hindenburg you know had to get rid of it maybe maybe but we know it's fake news you

Blue Bell Is Looking For Woman Filmed Licking Ice Cream & Putting It Back In A Store Freezer | TIME



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Blue Bell Ice Cream is looking for a woman seen in a viral video opening a container of the brand’s Tin Roof flavored ice cream, licking the top and putting it back in a store freezer.
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Blue Bell Is Looking For Woman Filmed Licking Ice Cream & Putting It Back In A Store Freezer | TIME

Lecture 2 | Machine Learning (Stanford)



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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

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this presentation is delivered by the Stanford center for professional development okay so let's get started with today's material so um welcome back to the second lecture what I want to do today is talk about um linear regression gradient descent and the normal equations um and I should also say lecture notes have been posted online and so you know if some of the math I go over today might go over rather quickly if you want to see every equation written out and work for the details more slowly yourself um go to the course home page and then download the detailed lecture notes that are pretty much described all the mathematical technical content so I'm going to go over today um today I'm also going to delve into a fair amount some amount of linear algebra and so if you would like to see a refresher on linear algebra on this week's discussion section would be taught by the TAS and will be a refresher on linear algebra so so some of the linear algebra talked about today so seems to be going by bit quickly or if you just want to see some of the things I'm claiming today without proof if you want to just see some of those things retail in detail um can come to this week's discussion section um so actually one start by showing you a fun video um remember at the last lecture the initial lecture I talked about supervised learning and supervised learning was this machine learning problem where I said um we're going to tell the algorithm what the quotes right answer is for um you know ever ever for a number of examples and then we want the algorithm to replicate more of the same so the example I had that the first lecture was the problem of predicting housing prices where you may have a training set and we tell the algorithm what quotes right housing price was for every house in the training set anyone the algorithm to learn the relationship between you know sizes of houses in the prices and essentially produce more of the quote right answer so let me play for you a video now below the big-screen news so in favor video now um there was from Dean Pomerleau on some work he did at Carnegie Mellon on applying supervised learning to get a car to drive itself um this is this work on a vehicle known as Alvin was done about this it was done to about fifteen years ago um and it was uh I think was very elegant example of the sorts of things you can get supervised learning algorithms to do um on the video you hear Dean Tom Lewis voice mentioned an algorithm called neural networks will say a little bit about that later but um the the essential learning algorithm for this is something called gradient descent which which I actually talk about later in today's lecture let's watch on the video Alvin is a system of artificial neural networks that learns to steer by watching a person drive Alvin is designed to control the nav lab to a modified army Humvee equipped with sensors computers and actuators for autonomous navigation experiments the initial step in configuring Alvin is training a network to steer during training a person drives the vehicle while Alvin watches once every two seconds Alvin digitizes a video image of the road ahead and records the person's steering direction this training image is reduced in resolution to 30 by 32 pixels and provided as input to Alvin's three-layered network so two comments right one is um this is supervised learning because is learning from a human driver in which a human driver shows that you know we're on dis segment of the road I would steer at this angle of it when the segment of RO is D at this angle and so the human provides a number of quotes correct steering directions to the car and then it's the job of the car to try to learn to produce more of these you know quote correct steering directions that keeps the car following the road um on the monitor display up here I'm going to tell you a little bit about what this display means so on the upper left where where the mouse pointer is moving on this horizontal line actually shows a human steering direction and this you know white bar or this white area here shows that the human shows the steering direction chosen by the human driver by moving the steering wheel so the human is steering a little bit to the left here indicated by you know the position of this white region um this second line here where my mouse is pointing the second line here is on the outputs of the learning algorithm and where the learning algorithms things currently things is a very steering direction and right now what you're seeing is the learning algorithm just at the very beginning of training and so that she has no idea where to steer and so it's out put this little white smear over the entire range of steering directions and as the algorithm collects more examples and learns over time you see it you know you see it start to more confidently choose the steering direction so let's keep watching the video using the back propagation learning algorithm Alvin is trained to output the same steering direction as the human driver for that image initially the network steering response is random after about two minutes of training the network learns to accurately imitate the steering reactions of the human driver this same training procedure is repeated for other road types after the networks have been trained the operator pushes the run switch and Alvin begins driving 12 times per second Alvin digitizes an image and feeds it to its neural networks each Network running in parallel produces a steering direction and a measure of its confidence in its response the steering direction from the most confident network in this case the network train for the one lane road is used to control the vehicle suddenly an intersection appears ahead of the vehicle as the vehicle approaches the intersection the confidence of the one-lane network decreases as it crosses the intersection and the two-lane road ahead comes into view the confidence of the two-lane Network Rises when it's confidence Rises the two-lane network is selected to steer safely guiding the vehicle into its lane on the two-lane road all right so who thought driving could be that dramatic right I switch back to the to the trophies um I should say um this work was done about 15 years ago and autonomous driving has come a long ways so many of you were heard of the DARPA Grand Challenge where one my colleague Sebastian Thrun that the winning team the winning team to drive a car across the desert by itself so Alvin was I think absolutely amazing work for his time but you know states of autonomous driving has also come a long way since then um but so what she just saw was an example um again of supervised learning and in particular it was an example of what they call the regression problem because the vehicle is trying to predict a continuous value variable so if a continuous value steering directions we call these so called the called a regression problem um and what I want to do today is talk about to our first supervised learning algorithm and it will also be two regression tasks um so for the running example I'm going to use um throughout today's lecture she's going to return to the example trying to predict housing prices um so here's actually a data set arm collected by our TA Dan Ramage on housing prices in a Portland Oregon um so so here's a data set of a number of houses of sort of different sizes and here are their asking prices in thousands of dollars this 22,000 and so um you can take this data and plot it square feet this price and so you may get the data set like that and the question is given the data set like this so given a trait what we call a training set like this how do you learn to predict the relationship between the size of house and the price of a house um so I'm actually come back and modify this chart a little bit more later but um we're going to introduce some notation which I'll be using actually throughout the rest of this course um first piece of notation is on I'm going to let the lowercase alphabet M denote the number of training examples that just means a number of roles or the number of examples houses and prices would happen you know in this particular data set we have we actually happen 247 training examples I'm old I wrote down only fine um so throughout this quarter um I'm going to use the alphabet M to denote the number of training examples um I'm going to use what the lowercase um alphabet X to denote all the input variables which are which I often also call the features and so in this case X would denote the size of a house that we're looking at um I'm going to use Y to denote the whole output variable which which is sometimes also called the target target variable and so um one pair X comma Y is what comprises one training example in other words one row on the table I draw just now would be what I call one training example and and the I've training example in other words the I've row in that table I'm going to write as um X comma Y I okay and so um for the in this notation I'm going to use this superscript I is not exponentiation so this is not X ^ iy ^ I in this notation the superscript I in parentheses is just 7 index into the I fro of of my list of training examples so um then supervised learning this is how so this is this is the what we're going to do it is we give the training set um and we're going to feed our training set comprising our M training examples of 47 training examples into learning algorithm okay and our algorithm then has to output a function that slip by tradition for historical reasons um is usually denoted lowercase alphabet H and is called a hypothesis don't worry too much about whether the term hypothesis is a deep meaning is a more term this use of historical reasons and the hypothesis job is to take as input you know if there's some new host in whose price want to estimate what the hypothesis does is it takes us input on a new living area in square feet say and output the estimated price of this house so the hypothesis H maps from n plus x2 outputs y um so in order to design the learning algorithm the first thing we have to decide is how we want to represent the hypothesis right and just for the purposes of this lecture for the purposes of a first learning algorithm I'm going to use a linear representation for the hypothesis so I'm going to represent my hypothesis as H of X equals theta 0 plus theta 1 X where X here is an input feature and so that's the size of the house we're considering um and more generally you can come back to this um more generally for many classification for many regression problems we may have more than one input feature so for example if instead of just knowing the size of the houses if we know also the number of bedrooms in these houses on v2 let's say then so if our if our training set also has a second feature of the number of bedrooms in the house then um you may let's say x1 denote the size and square feet on let X subscript to denote the number of bedrooms and then um I would write the hypothesis H of X as theta Rho plus theta 1 x1 plus theta 2 x2 okay and sometimes when so when I want to take the hypothesis H and when I want to make us dependence on the theta is explicit I'll sometimes write this as a true subscript theta of X and so this is the price that my hypothesis predicts a house with features x cost so given a given the new house of features X with a certain size and so the number of bedrooms this is going to be the price that my hypothesis predicts this house is going to cause um lastly one last one ones with one final piece of notation on simple conciseness um just to write this a bit more compactly I'm I'm going to take the convention of defining X 0 to be equal to 1 and so I can now write H of X to be equal to sum from I equals 1 to 2 of theta I I'm sorry 0 to 2 theta I X I and if you think of Thetas and XS as vectors and this is just say they travel is X um and and the very final piece of notation is um I'm also going to let lowercase alphabet n define let lowercase n be the number of features in my learning problem and so this actually becomes a sum oh I'm just a sum from I equals 0 to n where in this example if you have two features and would be equal to two okay all right I realize that was a fair amount of notation um and as I proceed through the rest of lecture today or in future weeks as well if you know if someday you're looking at me write a symbol and you're wondering gee what was that simple lowercase n again or what was that lowercase X again or whatever please raise your hand and also this is fair mountain notation we'll probably I'll get used to it um you know in a few days and we've standardized notation and make a lot of our descriptions of loading office much easier ok put again if if you see me write some symbol and you don't quite remember what it means chances are there are others in this class of forgotten too so please raise your hand and awesome if you're ever wondering what some symbol means um what questions you have about any of this it can be anything uh let's see what else and again o T da doo da 0 0 1 yes right so yeah so well duh this was not an expert the theatres on all the theatre eyes are called the parameters um the Thetas are called the parameters of our learning algorithm and theta 0 theta 1 theta 2 are just real numbers and then is a job of a learning algorithm to use the training set to choose or to learn appropriate parameters theta ok there other questions yeah um Oh transpose right Korea right so just come on we're right here theta 2 and theta transpose X in the product whatever function our hypothesis function or we have in higher orders or theta all great questions um the answers the questions of what is this a typical hypothesis or tan theta be a be a function of other variables and so on and the answer is sort of yes um for now just just for this first um you know learning algorithm will talk about using a linear hypothesis cause um a little bit actually later this quarter we'll talk about much more complicated hypothesis classes um and why she talked about higher-order functions as well a little bit later today okay so um so for the learning problem then um how do we choose the parameters theta so that our hypothesis H will make accurate predictions about housing X's right so you know one reasonable thing to do seems to be that what we have a training set so and just on the training set our hypothesis will you know make some prediction predictions of the housing prices of the of the prices of the houses in the training set so one thing we do is just try to make um the predictions of a learning algorithm accurate on the training set that leads right so given some features eggs and some correct prices why we might want to make let's say the squared difference between the prediction of the algorithm and the actual price small um so to choose the parameters theta plus I want to minimize over the parameters theta of the sort of squared error between the predicted price in the actual price um and so going to folders in we have M training examples so when sum from I equals 1 through m of my M training examples the price predicted on the I polls in my training set are – the you know actual target variable – the actual price on the I train example um and by convention instead of minimizing this sum of squares differences I'm just going to put a 1/2 there which which will simplify some um some of the math we do later ok and so let me go ahead and define J of theta to be equal to just the step 1 home sum from I equals 1 through m on the number of training examples of the value predicted by my hypothesis – the actual value and so what we'll do let's say is minimize as a function of the parameters of theta this quantity J of theta um I say – delta T they've taken the linear algebra classes or maybe those basic statistics sources some of you may have seen things like these before um in the scenic route you know least squares regression ordering of these squares um many of you will not have seen this before I think some of you may have seen it before but either way regardless of what they've seen it before let's keep going and but we just don't see they have seen it before I should say eventually will actually show that this algorithm is a special case of a much broader class of algorithms but let's keep going or we'll get there eventually um so so I'm going to talk about a couple of different algorithms for performing that minimization over theta of J of theta first I'm gonna talk about is a search algorithm where the basic idea is we will start with some um value of my parameter vector theta um maybe maybe initialize my parameter vector theta to be the vector of all zeros um and excuse me let's break that I write also write zero of an arrow on top to denote the vector of all zeros and then um you know when we keep changing my parameter vector theta to reduce um J of theta a little bit until we hopefully end up at the minimum with respect to theta of J of theta okay so um touch the laptop display these and load a big screen so let me go ahead and show you an animation of this first algorithm for minimizing J of theta which is an algorithm called gradient descent so um here's the idea you see on the display a plot arm and the axes so the the horizontal axis are theta zero and theta one is usually minimize J of theta which is represented by the by the height of this plot so the surface represents a function J of theta and the axis of this function or the inputs is function or the parameters theta 0 and theta 1 written down here below so here's the Umbrian descent algorithm we're going to choose some initial point it could be no vector of all zeros or some randomly chosen points let's say we start from that point denoted by the by the crop idea by the star but across um and now one should imagine that um this display actually shows a 3d landscape mentions of you know all in the holy park or something and this is the 3d shape of like a hill in some park and um so imagine they're actually standing physically at the position of that star of that cross and imagine they're going stand on that hill right and look all 360 360 degrees around you and also if i were to take a small step what would allow me to go down hill dimille's it's imagine that this is physically a hill and you're standing there you look around and also if i take a small step what is the direction of steepest descent that would take me down hill as quickly as possible so the gradient descent algorithm does exactly that gonna you know take a small step in this direction of steepest descent or in the direction of the gradient it turns out to be and then you take a small step you end up in the new point um showing up there and then we keep going you know the new point on this whole and again you're going to look around you look all the agencies agree look all 360 degrees around you and ask what is the direction that would take me downhill you know as quickly as possible you want to go downhill as quickly as possible because we want to find a minimum of j data so you do that again you can take another set okay and you sort of keep going on until you end up at a local minimum of this function J of theta um one property of gradient descent is that um where you end up in this case we ended up at this point on the lower left hand corner of this plot um but you know let's let's try running great in the center game from different position also that was where I started grading descent just now let's rerun grading descent but using a slightly different initial starting point so the point slightly to the further to the up and further to the right so it turns out if you run gradient descent from that point then if you take a steepest descent direction again that's the first step and if you keep going um turns out that with a slightly different initial starting point you can actually end up at a completely different local Optima okay so this is a property of grading descent we'll come back to in a second but so be aware that gradient descent can sometimes depend on where you initialize your parameters theta0 and theta1 but um i should switch back to the trapeze um let's go ahead and work out the math of the grand descent algorithm then we'll come back and revisit this issue of local optima so here's the gradient descent algorithm um we're going to repeatedly take a step instead of this direction of steepest descent and it turns out that you can write down as follows which is we're going to update the parameters theta as um theta I minus the partial derivative respect to theta I J of theta okay so this is how we're going to update the I've your parameter theta I how we're going to update theta I on each iteration or very interesting um just a point of notes a notation I use this colon equals notation to denote um so setting a variable on the left hand side to be equal to the variable on the right hand side so if I write a colon equals B then what I'm saying is this is part of a computer program on this part of an algorithm where we take the value of beale the value on the right hand side and use that to overwrite the value on the left hand side um in contrast if I write a equals B then this is an assertion of a true but this is I'm claiming that the value of a is equal to the value of B okay and so whereas this is a computer operation where we overwrite the value of a if I write a equals B then I'm self ascertain to the values of a and B are equal um so let's see this algorithm sort of makes sense um um well actually let's just move on let's go ahead and take this algorithm and apply it to our problem and to work out gradient descent um let's take green descent and just apply to our problem um and this being the you know first somewhat mathematical lecture I'm going to step through derivations much more slowly and carefully than I will later in this course or losses you know work for the steps of these in in much more detailed and then I will later in the school term let's actually work out what this green December was um so and I'll do this just for the case of if we have only one training example okay so in this case we need to work out what the partial derivative with respect to the parameter theta is of J of theta o if we have only one training example then J of theta is going to be one half if subscript theta of X minus y squared right so if you have only one training example comprising one pair X comma Y then this is what J of theta is going to be um and so taking derivatives um you know you have 1/2 something squared so the two comes down your two times 100 M 60 script theta of X 9 um and then by the chamber of derivatives um we also need to do multiply this by the derivative of what's inside the square right arm 2 2 and 1/2 cancel so this usage times that Steve is your X 0 plus the dawn stay the red ok and if you look inside this sum excuse me we're taking the partial derivative of this sum with respect to the parameter theta I but all the terms in this sum except for one do not depend on theta arrive at the own of in the sum the only term that depends on theta I will be some term here of theta I X I and so we take the partial derivative respect to theta I X I um take the partial derivative respect to theta I of this term theta right X I and so you get x excited okay and so this gives us our learning rule eight of later I gets updated as theta I minus alpha times how's that okay um and this Greek alphabet alpha here is a parameter of the algorithm called the learning rate and this parameter alpha controls how how large a step you take those of you you're standing on the hill you've decided on what direction to take a step in and so this parameter alpha controls how aggressively use how large a step you take in this direction of steepest descent okay um in serve you well if alpha and this is a parameter the algorithm that's often set by hand um maybe choose alpha to be too small then your steepest descent algorithms a very tiny steps and take a long time to converge if alpha is too large then the steepest ascent may may actually end up overshooting the the minimum of your if you're taking too aggressive the step um okay yeah oh say that again these are their final vertical mixing somewhere um Sarah you a one-half missing edge oh goody cool I do I do make lots of errors and maps is good too any questions about this okay so so let me just sum well wrap this up properly into an algorithm so over there I derived the algorithm but if you have just one training example um more generally for M training examples gradient descent becomes the following um we're going to repeat until convergence um the following step theta I guess updated us later I and I'm just writing out you know the appropriate equation for M examples rather than one example um theta I guess of the SAR I minus alpha times something like this one to M okay and I won't so bother to show it but you can go home and so verify for yourself that this summation here this is indeed the partial derivative with respect to theta I of J of theta where when you if you use the original definition of J of theta for when you have M training examples okay um so in research I switch back to laptop display I'm going to show you what this looks like when you run the algorithm um so it turns out that um for the specific problem of linear regression or ordinary least-squares which is what we're doing today um the function J of theta actually does not look like this nasty one does showing you just now with multiple local optima in particular it turns out for ordinary least-squares the function J of theta is is just a quadratic function and so we'll always have a nice bell-shaped nice bow shape like what you see up here and I only have one global minimum with no other local optimum so when you're in very descent here here actually the contours of a function J so the contours of a bow shape function like that are going to be ellipses and if you run gradient descent on andhe's album here's what you might get so let's see I initialize the parameters you know so let's say randomly at the position of that cross over there right that cross on the on the upper right and so after one iteration of gradient descent as you change in the space of parameters so that's that the result of one step of Granger sent two steps resets four steps five steps and so on and it you know converges reasonably rapidly to the global minimum of this function J of theta okay um and this is a property of these squares of ordering these squares regression with with the linear hypothesis calls it that the function J of theta has no local Optima this question I see yeah okay um so turns out that um yes it turns out this was done with I just did this with a fixed value of alpha um and one of the properties of green descent is that as the approach to local minimum it actually takes smaller and smaller steps so they will converge and the reason is the update is due your update theta by subtracting for you know alpha times the gradient and so as you approach a local minimum the gradient also goes to zero right so and as you approach local minimum at the local minimum the gradient is zero and as you approach the local minimum the gradient also gets smaller and smaller and so grand descent will automatically take smaller and smaller steps as you approach a local as you approach the local minimum so this um and here's the same plot of and so yeah here's here's a plot so the housing prices data so here let's you initialize the parameters to the vector of all zeros and so this blue line at the bottom shows the hypothesis with the parameters at initialization right so initially theta zero and theta one above zero and so your hypothesis predicts that you know all prices all all prices are equal to zero after one iteration of gradient descent that's the blue line you get after two iterations three four five and after a few more durations um excuse me it converges and you've now found the least squares fit to the data okay um cool let's switch back to the twelve point um other questions about this yeah sit in that we run each sample give all the sample cases ones update available he probably doesn't run it again with the new values yes Ram and converge means that the value will remain same of the two diagram doesn't remain roughly the same yeah so yeah so another question how do you test the convergence right and there are different ways of testing for convergence one is you can look at two different iterations and see if theta has changed a lot and if it hasn't changed much within two iterations you may say is sort of more or less converged something that's done maybe slightly more often is look at the value of J of theta and if J of theta so if the optimization the quantity you're trying to minimize is not changing much anymore then you might be inclined to believe is converge so these are sort of standard heuristics or standard rules of thumb that are often used to decide if gradient descent is conversions all directions 51 and choose add additional Louis gain so every one one one feature educator to the curve about yeah the math I mean I understand where that comes in when you use with your left we go this way or that way fussy okay engine turns out that um so question is that you know how is grading descent looking 360 around J choosing the direction of C business and also oh so it actually turns out I'm not sure I understood the second part it turns out that if you are if you stand on the hill and if you are turns out that we compute the gradient of a function we compute the derivative of function then it just turns out that that is indeed the direction of steepest descent um but there's no point out you would never want to go in the opposite direction because the opposite direction would actually be this direction of steepest ascent right um so it turns out maybe I hope maybe maybe maybe tears and talk bit more about this on on the dissection of this interest um since I'm going to take the derivative of a function the derivative of a function so turns out give you the direction of steepest descent um and so you don't explicitly you know local 306 degrees around you you sort of just compute the derivative and that turns out to be the direction of steepest descent now maybe I maybe I tease this has been asked minute easing talk a bit more about this on Friday um okay let's see um so let me go ahead and give this algorithm on a specific name so this out room here is actually called on batch gradient descent and the term batch isn't a great term but the term batch refers to the fact that on every step of Granderson you're going to look at the entire training set you're going to you know perform a sum over your M training examples um since oh so Bactrian descent often works very well so if I use it very often um and it turns out that sometimes if you have a really really large training set so imagine that instead of having 47 houses from Portland Oregon the training set if you had say the u.s. sends this database of something with us synthesized databases you can often have you know hundreds of thousands of millions of training examples um so if M is you know a few million then if you're running batch gradient descent then this means that to perform every step of gradient descent you need to perform a sum from J equals 1 to a million which is that's there's sort of a lot of training examples for your computer programs have to look at before you can even take you know one step downhill on the function J of theta so it turns out that when you are when you have very large training sets um just let me write down an alternative algorithm that's called on stochastic reinvestment sometimes also called incremental gradient descent but the algorithm is as follows again will repeat until convergence and will iterate for J equals 1 to M um and we'll perform one of these stuff gradients and updates using just the Jade training example okay and as usual this is that really you perform you update all the parameters data rocks you perform this update you know for all values of I rights meaning for I indexes in the parameter vectors you just perform this update all all of your parameters simultaneously um and the advantage of this algorithm is that um in order to in order to start learning in order to start modifying the parameters um you only need to look at your first training examples use it look your first training example and perform an update using you know the derivative of the error with respect to just your first training example and then you look at the second link training example and perform another update and use of keep adapting parameters much much more quickly without needing to take a scan over your entire near us sensors database before you can even start adapting your parameters um so let's see for large datasets on stochastic gradient descent is often much faster and what happens is the constant variant descent is that it won't actually converge to the global minimum exactly but on one that these are the contours are your function then as you run circles degree in the sense you sort of tend to wander around and you may actually end up going uphill occasionally but your parameters will sort of tend to wonder to the region close to the global minimum but sort of keep wandering around a little bit you and then often that's just fine to have a parameter you know that wanders a little around a little bit the global minimum and so the and in practice this often works much faster than bacteria in descent especially if you have a large training set I'm going to clean the copper balls why do that why don't you take a look at the equations and after I'm done cleaning the balls out also a question okay so what questions you have about is gradient descent is it true that are you just sort of like rearranging the order that you that you do the computation like so do you just use the first training sample and update all of the theta eyes and then step and then upgrade with the second finding example and a bit on the theta eyes and then step and is that why you get sort of this really yeah let's see right so so I'm just look at my first training example and then I'm going to take a step and then I'm going to UM perform the second gradient descent updates using my new parameter vector that has already been modified using my first training example and then I keep going that make sense yeah updatable the theta eyes are only using one Chinese on one training example ask for them back to the dam critical um let's see it's definitely recall um I believe this theory that solar supports that as well knows ego yeah this theory that supports that the the how clean the statement of the theorem is I don't remember okay cool so what I've done so far I've talked about an iterative algorithm um for performing this minimization in terms of J of theta um and it turns out that there's another way for this specific problem of these squares regression of ordinary least-squares turns out there's another way to perform this minimization of J of theta that allows you to you know solve for the parameters theta in closed form without needing to run in iterative algorithm um and I know some of you may have seen some of what I'm about to do before in like an undergraduate linear algebra course and the way is typically done so requires you know messy orthogonal projections or taking lots of derivatives and writing lots of algebra what I'd like to do is show you a way to derive you know the closed form solution of theta in just a few lines of algebra um but to do that I'll need to introduce a new notation for matrix derivatives um it turns out that the notation about to UM define here just in my own personal work has turned out to be one of the most useful things that you know I actually use all the time to have a notation how to take derivatives with respect to matrices so that you can solve for the minimum of J of theta with like a few lines of algebra rather than writing our pages and pages of matrixes into verses so then we're go ahead and define this new notation first and then and then we'll go ahead and work on the minimization um given a function J um and J is a function of a vector parameters data right I'm going to define the derivative of the gradient of J with respect to three later as self a vector okay and so this is going to be you know an N plus 1 dimensional vector Rho theta as an n plus 1 dimensional vector with indices ranging from 0 to n and so I'm going to find this derivative to be equal to that okay and so on we can actually rewrite the gradient descent algorithm as follows because this is a batch gradient descent and in rewrite gradient descent as updating the parameter vector theta notice there is no subscript I now updating parameter vector theta as the previous parameter minus alpha times the gradient okay and so in this equation all of these quantities you know theta and this gradient vector all of these are n plus 1 dimensional vectors um oh I see it right as using balls out of order wasn't I so more generally um if you have a function f on that maps from the space of matrices a loops excuse me um then maps from say the space of M by n matrices to the space of real numbers so if you have a function you know F of a where a is an M by n matrix so this function in maps from matrices to real numbers the function that takes this input in matrix let me define the derivative with respect to F of the matrix a right now just taking the gradient of F with respect to its input which is which is a matrix I'm going to define this itself to be a matrix okay so the derivative of F with respect to a is itself a matrix and matrix contains all the partial derivatives of F we respect to the elements of a um one more definition is um if a is a square matrix so if a is an N by n matrix number of rows equals number of columns let me define the trace of a to be equal to the sum of a diagonal elements so this is your sum over I of a I I um for those of you that haven't seen this live operator notation before you can think of trace of a as you know the trace operator applied to the square matrix a but it's more commonly written without the parentheses so I usually write the trace of a like this this is this just means the sum of diagonal elements so um here are some facts about the trace operator and about derivatives and notice I'm going to write these without proof you can also teach to prove some of them at the description section um or you can actually go home and so verify the proofs of all of these yourself turns out that um given two matrices a and B the trace of the matrix a times B is equal to the trace of B a okay I'm not going to prove this but you should be able to go home and prove this yourself from without too much difficulty um and similarly the trace of a product of three matrices so if you can take the matrix at the end and you know cyclically permute it to the front since trace of a times B times C just to the trace of C a B so take the matrix C at the back and move it to the front and this is also equal to the trace of BCA we take the matrix B and move it to the front okay um also suppose you have a function f of a which is defined as a trace of a B ok so this is right the trace is a real number so the trace of a B is of a function that takes us in for the matrix a and outputs a real number so then the derivative with respect to the matrix a of this function of Trey's a B um is going to be B transpose this is just another fact that you can prove by yourself by going back and referring to the definitions of traces and matrix derivative I'm not going to prove it it's real work though I lost Lee a couple of easy ones um the trace of a is equal to the trace of a transpose because the case is just the sum of diagonal elements and so if you transpose the matrix the diagonal elements don't change and if no case a is a real number then you know the trace of a real number is just itself so think of a real number as a one by one matrix so the trace of a one by one matrix is just whatever you know whatever that real number is um and lastly this is somewhat tricky one um the derivative with respect to the matrix a of the trace of a be a transpose C is equal to C a B plus C transpose a B transpose and and I won't prove that either this is just algebra and work about yourself okay and so the um I guess key equations the key facts I'm going to use a game about traces and matrix derivatives of all these five ten minutes okay so armed with these things I'm going to UM figure out let's let's try to come up a quick derivation for how to minimize J of theta in as a function of theta in closed form and without needing to use an iterative algorithm to work this out let me define the matrix X this is called the design matrix um to be a matrix containing all the inputs from my training set so you know x1 was was was the vector of inputs of the vector of features my first training example so I'm going to set x1 to be the first row of of this matrix X set my second training examples inputs to be the second row and so on and have M training examples and so that's going to be my arm design matrix X okay just define this matrix capital X as follows and so now let me take this matrix actually multiplied by my parameter vector theta this is stair vation will just take two or three steps so x times theta remember how matrix vector multiplication skills right you take this vector and you multiply by each of the rows of the matrix so X times theta is going to be just you know x1 transpose theta dot down to X M transpose theta and this is of course just two predictions of your hypothesis on each of your M training examples let me also define the Y vector to be the vector of all the target values y1 through yn in my training set so Y vector is an M dimensional vector so X theta minus y containing the map from the previous board is going to be like that right and now X theta minus y this is a vector this is an M dimensional vector if I have M training examples and so I'm actually going to take this vector and take us inner product works with with itself ok so recall that um you know if Z is a vector then Z transpose Z is just some of my Z I squared right that's how you take the inner product of a vector with worth it with itself so I'm going to take this vector at state 2 – y and take the inner product of this vector with itself and so that gives me some from I equals 1 to M F H of X on minus y squared ok since it's just the sum of you know the sum of squares of the elements of this vector and for the 1/2 there then this is this is our previous definition of J of theta ok vise oh yeah I know I feel a long notation at you today so M is the number of training examples and um the number of training examples runs from 1 through m and then is the feature vector that runs from 0 through n that make sense so um so this is this is a sum from 1 through m this um it sort of theta transpose X that's equal to sum from J equals 0 to n of theta I theta J extreme sense okay so so the feature vectors that in that index from 0 through n where X 0 is equal to 1 whereas the training examples actually index from 1 through end so let me clean a few more boards in and take a look take another look at Disney make sure it all makes sense okay okay yeah oh yes thank you SWAT over that whoosh man yes thank you mister feet great eye training example anything else cool so we're actually nearly done with this derivation um would like to minimize J of theta with respect to theta and we've written you know J of theta fairly compactly using this matrix vector notation so in order to minimize J of theta of respect to theta what we're going to do is take the derivative with respect to theta of J of theta and set this to zero and solve for theta okay so we have derivative with respect to theta of that is equal to UM as you mention there will be some steps here that I'm just going to do fairly quickly without proof so is it really true that the derivative of half of that is half of the derivative and I really exchange you know the derivative and then one half it turns out the answer is yes but later on you should go home and look for the lecture notes and make sure that you know you understand and believe why every step is correct I'm going to do things relatively quickly here and you can work for every step yourself more slowly by referring to lecture notes okay so um that's equal to I'm going to expand out this quadratic function so this is going to be okay um and this is just taking a quadratic function and expanding it out by multiplying yard roots and again work for this step day to yourself if you're not quite sure how I did that um so this thing this vector vector product right x2 you know this quantity here this is just J of theta and so it's just a real number and the trace of a real number is just it so done oh thanks that good right um so this this quantity in parentheses this is J of theta and it's just a real number and so the traits of a real number is just the same roll number in second so I'll take a trace operator without changing anything um and this is equal to 1/2 derivative with respect to theta of the trace of armed by the cyclic permutation property of tracer you can take the state or at the end and move it to the front so this is going to be trace of theta times theta transpose X transpose X minus derivative respect to theta of the trace of and again I'm going to take that and bring it to the UM oh sorry you know what I'm actually this thing here is also a real number and the transpose of a real number is just itself right so and take the transpose a real number without changing anything so let me go ahead and just take the transpose of this so there's a real number transpose itself is just the same real number so this is minus the trace of taking the transpose of that gives 1 transpose X theta then minus theta ok and this last quantity Y transpose Y it doesn't actually depend on theta so when I take the derivative of this last term with respect to theta is zero so just drop that term um and I'll see um well the derivative respect to theta of the trace of you know theta theta transpose on X transpose X I'm going to use um I'm going to use one of the facts I wrote down earlier without proof and I'm going to let this be a remote instead of identity matrix there so this is a be a transpose C and using a rule that written down previously that fine lecture notes um I guess I saw one of the balls bit but you have previously um this is just equal to X transpose X theta um so this is C a B which is suggest the identity matrix which we're going to ignore plus X transpose X theta where this is now C transpose a you know again times the identity which you can ignore times B transpose okay and the matrix X transpose X is symmetric so C transpose is equal to C um similarly the derivative respect to theta of the trace of Y transpose theta X um you know this is the arm derivative respect to a matrix a of the trace of B a and this is just X transpose Y this is just B transpose Phi by again but one of the rules that I wrote down earlier and so if you plug this back in we find therefore that the derivative wow this was really bad so we plug this back into our formula for the derivative okay you find that the derivative with respect to theta of J of theta is equal to you know one half x controls etc plus X transpose X theta minus X transpose Y minus X transpose 1 which is just X transpose X theta minus X is equal to 1 okay so we set this as 0 and we get that um which is called the normal equations and um we can now solve this equation for theta in closed form as X transpose X theta inverse times X transpose Y and so this gives us a way for solving for the least squares fit to the parameters in closed form without needing to use an iterative our library innocent okay and using this matrix vector notation I think it I know it so far few I think we did this whole thing in about ten minutes which we couldn't have if it was writing our reams of algebra okay some of you look a little bit dazed but guys this is our first learning algorithm aren't you excited this any quick questions about this before we close to today inverse of X okay don't care what you've arrived you wasn't that just respect us what had what invited pseudo interest in rivers um yeah I it turns out that in cases if x transpose x is non-invertible then you use the pseudo inverse to minimize to solve this but um intensive x transpose x is not invertible that usually means your features were dependent usually means you did something like repeat the same feature twice in your training set um so this is not invertible it turns out the minimum is obtained by the pseudo inverse and so the universe if you don't know what i just said don't worry about it usually won't be a problem yes don't take that off my yeah like we're running over let's closer today and that there are other questions I'll take them offline okay guys

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FULL DAY UNIVERSITY STUDY WITH ME | MAKING LECTURE NOTES, WRITING ESSAYS & MY DISSERTATION



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hey guys and welcome to today's study with me so this is a full day of me powering through work at University and you're just watching me set up my desk first thing in the morning to get ready to crack on with some wack so I was going to start with making some notes from a lecture that we'd had the previous day so I was just getting up pens I was gonna use so all my four different colors my ruler I got up the PowerPoint slides on my macbook and obviously I was making notes on plain paper because that is just how we were all and yeah we just cracked on essentially so I have made a whole video dedicated to how I make notes I've made a couple actually but I was just going through the notes I made in the lecture and then filling in any gaps that I'd lacked the pens that I'm using are zebra the black one is a zebra pen and the colored ones are paper me and they are actually really good i don't use bic pans anymore since i have started to make notes on plain paper i've just loved sticking to my color coding scheme so I write predominantly in black and then I use green red and blue to just highlight key information or the most important information or separate out something else and so yeah I essentially got all those notes done pretty quickly because I'd done the majority of it in the lecture and then I decided it was about time to open the curtains because the Sun had definitely arisen by then okay so before I got onto something else I decided that I would do a quick five-minute meditation and I used the app called simple habit to help me the app is really easy to use it's available on iOS Android and online on the web and I picked a meditation called enhance your workflow so you guys know that I'm always going on about productivity but I've never really put meditation into practice but with simple habit I was just forced to sit down take a break and clear my mind as a really busy person I am really gonna start using this app to see if it makes a difference and even with the first meditation I did I really felt more productive afterwards so it's a paid full subscription but using the link below you can get a free 7-day trial and I really highly recommend you just try it because getting the Apple for she to just take these small five-minute breaks and I really think it's something we should be doing more of so I made my breakfast afterwards because I was getting very hungry at this point and then I also had a hot lemon because it was quite cold this morning winter is definitely coming and I had just updated my to-do list as you saw even though I'd made it the previous day whilst I was eating my breakfast I quickly did some emails that came in but I wasn't too overburdened today which was quite nice and so I can quickly move on to my dissertation so as I mentioned in one of my previous videos I've done quite a lot of the reading for my dissertation so it's just about writing it now and I like to write sections at a time so I focus on one and then I'll move on to doing something completely different for example today I got one section done and then I had a lecture at 10 a.m. so I packed my bag and yes it is incredibly muddy that is not the design of my bag it's because I don't have a mud guard but I had to pack my bag because I was going to my lecture at 10 a.m. this morning and I was also gonna do some other bits and bobs after my lecture so just run some errands but anyway in the lecture I was making my notes as usual as you can see I'm using plain paper as I mentioned at the start and this is how I kind of make and lectures if I can't sometimes they will go incredibly fast and I'm forced to make notes on the PowerPoint slides that they often give us but today I was able to make my own notes from scratch and then that would mean that I can fill them in later which is why I prefer these these are my old ballet shoes they have holes in both feet and they stink so bad and these which I got today actually went before the make so dancer I normally go for block these they were cheaper and they feel like slippers or might be there for me so well I don't know why I've never gone for them and they had the elastic already sewn on I don't have time to sew elastics right now yeah like they just hug your feet really nice right when you press that so [Applause] I really don't think I have any common sense sometimes but anyway I had been to the shops after my lecture I did a full on out eShop which was a bit spontaneous but I'm glad I brought my big rucksack with me and I also got ballet shoes as I showed you and then I essentially got to work on this essay that I was writing so I had pretty much completed it but I needed to read through it add some extra stuff and just make sure it was ready for kind of handing him for the supervisor to look at this was for my CJD module and it was a topic that required me to really focus on what I was writing because sometimes it's quite confusing when I am I hired a plan it's just my back especially the bubble when I don't have a jumper on especially it's super annoying and I just can't do my hair we just wrap it around we like try it in some of this thing then the plot just hangs well the plot doesn't touch my back it's like the best thing even though I look like an idiot but it's the life no one's ever gonna really see you when you're walking I'm just such a strange person after I'd done my essay I decided that it was a good time to take a break and have lunch and then afterwards I moved on to writing another section of my dissertation so working on my back again and at this point in the day it usually runs out of battery but anyway so my decision is only six thousand words and I am trying to write as concisely as possible but I am realizing that I'm already over the word limit by quite a lot so yeah I am still writing things and I want to include a lot more but I'm just gonna have to be really concise in the way I write things not waffle at all because there is no room for waffling in this dissertation and my topic is quite broad even though I have already narrowed it down slightly because we are able to modify our title we were even able to make up our own side to us I decided not to do that I've just modified my slightly so far but it might even need further tweaking and yes I talk to myself all the time okay so I'm guessing it's not just me but when I am writing things like writing an essay or writing my dissertation as I was doing then I just have to talk to myself I have to see if it makes sense and that's the way I can do it but yeah and the evening I had a hat rehearsal so this was in late afternoon actually I think from four til five or something like that so I had to cycle out of City Center and go to my tap rehearsal which was good and it was getting quite cold as I've said so I've wacked out my scarf recently and whenever I go out on my bike I have to really make sure that I wrap up warm and put on gloves because my hands are starting to dry out really quickly and get really sore so yeah that's my life in the winter unfortunately when I got back I still had time before I was gonna make dinner to do some work and I was just going to finish this section of my dissertation that I didn't finish before I went to the tap rehearsal so as before I was just on my Mac Burke I had my checklist next to me which has a color code which I've mentioned in a previous video so I'm using different colored text to designate different sections for my dissertation and this is actually making it so much easier when I'm writing it I didn't really know whether this was gonna help but it actually is turning out to be quite useful so I do recommend it and if you would like me to talk further about it then definitely like this video and I can do that very soon as you can see I had finished my dinner and I was just doing a tiny bit more of my dissertation before I had a shower and then decided it was time to go to bed so I just essentially reverse what I did in the morning I just put everything away on my huge desk which I can honestly love I looked at my to-do list didn't have to update it and then I just had some almond milk and went to bed thank you for watching this video as usual guys I hope you enjoyed it and I'll see T see you

Word Of The Day: SEDULOUS | Merriam-Webster Word Of The Day | TIME



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Sedulous (adj) : painstaking or diligent.
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UNI VLOG | WRITING MY SCIENCE DISSERTATION & CHIQUELLE CLOTHING TRY ON HAUL



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I have way too many to do this right now I need to condense it all until one to do next I need my to do this if I have two functions what we need to do we just need to do it as if that's good now what happened today hello people why do I look shiny anyway hey guys good morning people wherever you are I don't know where you are but I hope you're gay I am gonna head off to my 9 here make sure this morning I talked to you guys for too long I will be pushing it but I always am kind of pushing it and then I have a lecture an evening at 5 no the only set thing to have this day other than dance really late in the evening but yeah I've got a lot to do today I'm gonna be trying to fake somewhat dissertation and I'm gonna talk to you guys a bit about my dissertation give you a little update and talk to you about that because we have made progress on that front we're not this morning doing you know the usual geo work or any stuff like that I had my breakfast now I just need to pack my bag Nina's carnie 8:45 if I leave it too late I'm gonna have to like grab my bike and go the other day I broke my whole punch the lecture notes was slow of that I'm also really ambitious when it comes to hole punching things it's the same with blending things and my smoothie makeup I blend a lot of frozen stuff and I'm quite over ambitious the same goes with hole punches so I tried to shove it in you know just rammed it in and I was like yeah I'll be fine but no we've ended up with a broken hole punch it now it just doesn't work I don't understand how it breaks it but I've done it multiple times before it's not the first time I've broken a hole punch let's say that but I think that the fact that I break the whole bunch just goes to show but the lecture notes it's just you know too dense speaks for itself so yeah I haven't been able to hold punch anything recently that's why my paper is everywhere and I can't deal with lose paper it has to be organized in a folder almost it is also super super cold in my room today and so I'm wearing this a really big jumper there's something wrong with my window I don't know what has happened to it but it just won't close I'm just wearing my black skinny jeans which are pretty cropped here and then this really nice oversized Coon jumper I haven't actually locked myself out since I put this on my door so it's a good side it's working good morning river car I hate this Junction there are so many bikes ok many cyclists everywhere I feel so English you get to get hit by cyclists I can which is not the first time I've been hit by cyclists I feel it's that broad rushing to the my knee and my shoelace is coming undone and I starts coming down that's all I can think of right now it's so insignificant but when I'm walking along I'm like my socks are falling down and it's really annoying me I'd like to just have an overload of diagrams and it wasn't as if they were simple diagrams of a very complex diagrams with so many names that aren't proteins on that's like what's going on whizzing through these slides there is no paper everywhere I need to get whole bunch today it's almost silly I've got my hot lemon do not spill on my back I'm going to do some dissertation reading this morning and then this afternoon evening I want to go over lecture kind of notes do an essay so now my two major plans for the day and then do all of a sudden between let's crack on with some dissertation reading this Sun is super annoying you know I'm gonna have to close the Carter pink light in case you're wondering I still need to change my bomb shaped pictures okay I don't really like it still but well but we haven't changed it yet I can't believe I've not actually done this before on that computers you can have obviously the full screen window which I love but I didn't know how you get the to full screen windows next to each other I've recently discovered how you do and it's life changing okay so I'm getting really hungry and I'm gonna have lunch but I thought I would update you guys a bit about my dissertation so I'm doing it ascension I'm writing a science dissertation it's an extended piece of where I think I suppose I don't really know how you define dissertation you can either do your dissertation in your major subject or your minor subject so for me because my major subject is pathology and I don't start my minor subject until next time I've just decided to do it in pathology and I'm totally fine with that I was planning to do that anyway so we were given titles and they were split into the different modules that you choose to do in pathology so because I do cancer as one of my modules so cancer in genetic disease and immunology is my second module I was looking at the titles in those two sections the deadline titles is the 9th of November so it has not gone past yet people are still deciding that titles they're so choosing titles we've got quite a lot of time and a lot of flexibility which is nice because you're doing your dissertation it's quite a big thing you want to find a topic that you're interested in I suppose and you want to make sure that it's right for you so it took me quite a long time to decide I thought that I'd see one straightaway and be like oh yes I want to do that one but it wasn't actually a case and I am awful at decision I am so indecisive I must be the world's worst because I overthink things and when I think I've come to a decision I'm like oh no I should do this and I can maybe do this I'm lying there on my bed and I'm trying to sleep and I'm thinking about these things it's just ridiculous but anyway I finally come to a decision let's say on Sunday evening or something a couple of days ago I was facetiming Emily just before I was going to bed so I was lying in bed literally like this Emily please help me this is literally what I look like on FaceTime the title has made no sense to have I didn't mean anything to her but often I think it helps if you can talk to someone about things just to tell them how you're feeling and on Sunday when I'll speak to Emily I chosen my title and I was meeting my supervisor on Monday so I essentially made a decision and I'd already done some reading for it however I was overthinking things on Sunday I was stressed I was like I need your help I don't know what to do I don't know if change my title and I was super worried kind of semi stressing but I now feel so much better that's the good news I stuck with my decision because I'd done a lot of reading for it anyway it made no sense for me to suddenly change but I was just in that mode of overthinking things and I met with my supervisor on Monday as I just said at 6,000 worth I was originally gonna do it on cancer and stuff but my title is actually an overlap between cancer and immunology so that's really cool I really like this field I am really interested in it I don't know why I was suddenly doubting all this on Sunday I just do this and I get really stressed once again to it things will start clicking into place hungry I'm such an idiot sometimes well all the time who we all know that but I said this morning my rooms really cold and I thought the window broken but it turns out the window was not broken at all I emailed maintenance being like I tried to leave my window open all night there's a draft of my room I'm really cold so any help would be greatly appreciated so they came and he looked at it and they were like oh it's just to do with this thing here so I was like oh no wonder panic over the window is fine I just wanted to talk a bit about this jumper because it is new and I will send some stuff from this company called Shekau it's one of Sweden's biggest online fashion stores or something they do ship to the UK and stuff comes really quickly they sent me some of that clothes I know what you're thinking right now you're like oh you see you have your own brand as Nana which is true and I'm all about ethical fashion and trying to move away from fast fashion so I am very wary and cautious when I work with companies I do try to do some research before I work them and find out if there's something that I want to support or not if the campaign is a good message behind it if you go get a benefit from it in any way I asked shakal and I was like what do you do for the environment is your stuff sustainable stuff like this in a nutshell let me get up what they said they say that shakal works in a sustainable way they were actually awarded for that work being sustainable and 2017 they said it says that they work and produce mostly in Turkey ninety percent of the production therefore strict in regulations the employee environment commitments of Manas is Matt's on European level they work with fair trade only it produces on demand it doesn't have mass productions overstock and too much inventory so on their website I couldn't really find this information that's why I emailed so I'd say that they don't really talk about that much they're not promoting themselves as an ethical brand which is unlike myself and my sister's brand Nana we want it to be an ethical inspired apparel clothing line who knows but I think it was nice because they did give me these answers and it was there was a lot of transparency like its bordering on a fast fashion store but I thought that that sounded good to me I suppose it sounds like one of the better fashion companies out there so there were four things and all the links to all the items will be below you can also get 50% off using holy 15 five days after I post the video this jumper which I obviously love it is one size but I really like that so yeah it's just like a cream kind of ribbed knitted jumper can't go wrong with the cream knitted jumper I was down also sent this jumper which I will show you when I put it on you'll see what it's like it's got a high neck it's kind of a brownie orange color which I honestly love iris themselves so shorts some kind of posh looking black shorts so you can see that's a label ruffled paper bag kind of waste thinking that's what you call it with a little tie you might be able to wear it with tights long black Porsche kind of trousers I already hung up they're really kind of meaty trousers I don't think though these are gonna fit me very well to be honest with you but they're like black long posh trousers I thought they looked quite cool we show you this jumper boo oh I love this I don't normally wear things with like a high neck as I said it's like brown it's again one size so it's pretty oversized I have these pockets here in front as well you can kind of see the color a bit better in this light high kind of neck pockets very nice these shoes are actually super super nice and I just put this top on so that you can kind of just see the shorts so they have a bow which you can tie and then it's a zipper and then they just like fit really nice and snug on my waist they're really soft as well and I really like the lightness of these shorts are really nice and light and just floaty tights would actually probably get really nice with these maybe would like some boots um heeled boots it's so sad that about these shadows my hair is a mess with a jumper the length is way too long I will show you the mirror but so hard to find closer fee sometimes they think this is the look they were going for they're supposed to be high-waisted they're just way too long for me yeah thank you to shakal it's not about the trousers but the other three things were good so I've just been working on the essay that I wanted to get done today to do this is going send me okay anyway okay this is what we'd want Brian oh yes student discount Oh ronzo so much stationary this is lecture like a stationary heaven so many pens so many pencils but I can't find hole punches right now I really want this one looks so sturdy I could hole punch anything with that look 40 pounds or a whole bunch they're quite expensive as well so I'm not gonna get those I'm not gonna get this one cuz that just looks like it's gonna break with one bit of paper so I think I'm gonna go for this one this one looks good to me he says apparently has a ten sheet capacity it's getting so dark so early so no i pushing Muhammad down take it off I came out of that lecture before my notes were so neat at the start and then the slides are just bombarded with so much information and so many more diagrams now I'm ready for dance it got changed when I go back straightaway and I'm gonna go to dance it's a buyer house for today so wow that light is bright I kind of semi forgot it was ballet so I just turned out with my hair in a plot that was only rehearsal we're in the long room for no reason I mean it's really long say hi to the blog everyone oh hey Lucy hey when I was cycling up the hill to get here I saw people on scooters is that one listening to me scooters that lets you were going up so quickly this hill the dodgy hill ice I actually started up it really quickly let's put these truck keys on learn to the awesome Atlanta I sure know how different it made in a different situation we've not just come on balance so we're kind of a bit like tired right right yeah we look fabulous in our tracks you bottom classical dancer live here is my bed she can clean detail enjoyed this video you guys like if you did I'll go Martina them so they taught us so strangely with Martina then if you want like morning and evening routines updated give this video a thumbs up and yes I all speak to you in all of you if you're new around here you can subscribe and thank you very soon my friends

10 Strange Phenomena Science Can't Explain



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10 Strange Phenomena Science Can’t Explain
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Society has always been in a bare knuckle fight with Mother Nature. Going toe to toe with some of her more interesting jabs and punches. Testing ourselves, and oftentimes losing, to hurricanes, tornadoes, earthquakes, Tsunamis and meteor strikes. We’ve become accustomed to these attacks… But… every so often, nature has a trick up her sleeve; an Ace that comes out of nowhere and really floors us.

One two strikes that although natural have no rational cause. Today we are going to list 10 strange unexplained phenomena that stomp science.

#phenomena #science #ocean

society has always been in a bare-knuckle fight with mother nature going toe-to-toe with some of her more interesting jabs and punches testing ourselves and oftentimes losing to hurricanes tornadoes earthquakes tsunamis and meteor strikes we've become accustomed to these attacks but every so often nature has a trick up her sleeve an eighth that comes out of nowhere and really floors us one two strikes that although natural have no rational cause today we are going to list ten strange unexplained phenomena that stops science Guatemala sinkhole sinkholes are a natural occurrence of our geological mantle they can be man-made or in most cases brought on by the plain and organic process known as karst a chemical dissolution of carbonate rocks in general they may vary in sizes from three feet up to 1,800 feet in diameter and depth they are found throughout the world as a blight on Florida's real estate due to the state's honeycomb swampland or as tourist attractions in Mexico's Mayan Riviera called cenotes over the last years these chasms have been swelling in numbers in 2017 Florida saw a spike of over 32 sinkholes a stark contrast to the eleven reported in 2016 but as a rule these shallow holes are rather ubiquitous in the grand scheme of things well all but one the Guatemala City sinkhole in 2010 Guatemala City zona Dolls was devastated with a cataclysmic disaster that ended up claiming according to la hora newspaper over 15 lives not to mention an extra 300 more that had to be relocated the sinkhole of over 65 feet across and 300 feet deep ended up swallowing whole a three-story factory to this day the exact cause of that particular sinkhole is uninit ba theories are a dime a dozen from the torrential rains of Tropical Storm Agatha to sewer pipe leakage and even an inner crust volcanic eruption fairy circles Namibia in the arid grasslands of the Namib Desert in coastal southern Africa lies a land of over 2,000 kilometres fraught with mystic and scientific irregularities like the welwitschia plant a living fossil found only in that landscape one of this region's biggest and eeriest mysteries are the fabled fairy circles over a million perfect round circular patches between 2 meters to 15 meters in diameter arranged in a geometric pattern across 2,500 kilometres of land these disks of bare soil pock mark the region in an intricate array that almost seems man-made due to its precision adding to the mystery says geologist Prem raft is the fact that no one knows for certain the cause of these otherworldly formations nonetheless there really are no shortage of theories some plausible and others far-fetched hypotheses like radioactive soil sand termites plant toxins or as local Bushmen are prone to believe they are the footprints of the gods the door to hell derweze Turkmenistan first identified by Soviet engineers in 1971 the door to hell is a collapsed underground gas crater dead set in the middle of the Karakum desert about 160 miles north of ashgabat the capital of Turkmenistan little is known about the gates of hell and what scraps remain in the official records are a bit sketchy in 1971 a cavern flourished out of nowhere right where the doors now stand according to geologists a Nottoli Bush mac'n it was originally thought to be a substantial oil field the oil rig collapsed into the crater and a cloud of toxic methane gas started to spread over the countryside the engineers had no other choice but to intentionally set it to fire to prevent a full-blown environmental disaster since that day the 226 foot wide 98 foot deep pothole has been scorching the landscape frozen methane bubbles Canada they had been described by the press as ice encapsulated jellyfishes frozen over flying saucers and even ancient deep frozen pizza yet in the essence words seem to fail the otherworldly attraction of Canada's frozen methane bubbles methane bubbles are generally found in the higher northern latitude lakes in Alberta Canada the stunning and potentially fatal sites greenhouse gas not only warms the planet but is incredibly flammable is still an unsolved mystery to science the gas bubbles either formed by a natural gas cavern under the region or bacteria that eats biological material and excrete methane or as UFO nuts like to propagate by space aliens or as ancient settlers believed by in fish spirits and goblins what cannot be denied is the spectacular sight these enigmatic orbs decorate the mundanity with Pink Lake Hillier Australia in Western Australia there was a unique visual treat that is nothing short of God giving instagramers the holy photo opportunity nestled in lush emerald foliage and surrounded by the dense and deep sapphire blue of the ocean Australia hides a phenomenon that will shock even the most jaded onlooker a series of bubblegum pink lakes one of the most known Lake Hillier flies on the edge of the middle island in the rachet archipelago encompassed by a thinner ring of sand that adds Beauty to its mystique what is more surprising than its pepto-bismol shade geologist James Garrick says is that nobody seems to be able to definitively explain its distinctive color so position abounds as to their nature green algae with high levels of beta-carotene a microorganism known as halo akia a high concentration of pink brine prawn but the chromatic splendor still manages to elude any real explanation Taos hum everyone has felt the occasional ringing in their ears a buzzing chirping nor humming that nags at the ear canal like a nasty little worm the reasons for that slight shriek are many-fold something as innocuous as a mechanical pitch timber or two above the normal auditory spectrum or the onset of tinnitus in general this ringing can be easily explained by medical science all but one in the town of towns north central New Mexico a region baked by its liberal artist community there is an auditory disturbance that defies logic and rational thinking a hum that can be heard by over 2% of the settled population a hum that three to four tourists visiting the area constantly experienced during their trip a hum that is collectively heard the Taos hum was first reported in early 1990s and since then it has been investigated by local researchers visiting medical physicians and even the universe of New Mexico it is arguably one of the region's most endearing stories but and here's where the tale deepens and grows legs this is not an isolated event the hum which is in a state of continual manifestation in Taos has been experienced throughout the world at different times in Auckland New Zealand in Bristol UK in the Zog Islands near Detroit and in dozens of other latitudes there is no real explanation for its cause there is no definitive answer for what sets it off for that matter there really isn't a scientific response as to what it is all we really know for certain is that the disturbance occurs and that it is not make-believe Tabby's star let's grab a spacecraft for this entry leave our atmosphere and zoom out of the Milky Way in order to grasp one of the most baffling astronomical mysteries out there kick 8 4 6 – 8 5 – nicknamed tabby star after its Discoverer Tabitha Boyajian is one of the 150,000 stars that have been documented by the Kepler space telescope so while the fuzz tabby star is unique because of its light fluctuation most stars usually have a small light dip that indicates that a planet is passing in front of them explains Jason Wright an astronomer at Penn State University Tabby's strange because it has a dramatic and erratic inconstancy up to 20% at a time massive compared to other celestial bodies causes and explanations for this odd lumen activity are wide and all over the place from the logical large clusters of planets passing at a time to the questionable an alien civilization using massive machines orbiting the star to procure energy the Great Attractor we all know the generally accepted model of how the universe came to be the Big Bang Theory quick recap a supergiant explosion some 14 billion years ago all matter hurtling outward leading to an expanding universe most scientists view this theory as the baseline and norm for the origin of the cosmos yet here is where things get a bit iffy the theory doesn't take into account the anomaly known as the Great Attractor in 1970 astrophysics began observing and investigating a force about 150 to 250 million light years away from Planet Earth a force that is actually pulling the Milky Way and multiple other galaxies towards it that cosmic pole essentially disproves the humdrum and prevailing theory that reality exploded and swelled from a distinctive point we didn't explode we were dragged although in 2016 a group of international scientists was able to finally take a gander at this area using the cirrus Parkes radio telescope they couldn't really come up with an explanation for what is pulling galaxies towards that spot they did nonetheless discover over eight hundred eighty three galaxies clustered in the area hauled in by this magnetic dragnet bloody skies on April 2016 the residents of Chowchilla El Salvador were shocked to discover that they were staring down the barrel of that shotgun known as the apocalypse in a moment plucked straight out of the book of Revelation a deep crimson light blanketed the atmosphere for over two minutes plunging the town into a blood-soaked Twilight to this day there hasn't really been a scientific explanation for this phenomenon the international community was stumped as to the cause the blood red sky phenomenon hasn't been witnessed before or after that fateful day there has been talk of a meteor shower or a meteor crash but there is no reliable evidence to back that assumption the prevailing theory backed by the Christian evangelical community is that the red flash is nothing short than a tangible sign of the coming biblical end-of-days maelstroms the last entry before we climb out of this kaleidoscopic rabbit hole and waddle back to our by-the-numbers existence maelstroms although not named as such were first described in Homer's epic The Odyssey Odysseus on his journey to Ithaca had to face the dreaded sea monster known as Charybdis the creature lying underneath the ocean opened its rounded jaws and swallowed ships whole years later right where Homer placed this mythical being on the Straits of Messina a whirlpool and international shipping hazard was discovered proving that the wise Greek knew his stuff maelstroms from the Dutch Malin to world and Strom Stream are immensely powerful deep-sea whirlpools that form when opposing currents meet what ultimately gives these sucking beasts their charm navel cath and Anthony's seat explains is the fact that they can spring into existence out of nowhere we have some charted on maps but others simply blindside you and can actually form a moment's notice under your vessel these ancient watery death traps are one of the most prevalent and nasty surprises Poseidon's kingdom has brewed for those intrepid explorers willing to try to conquer its vast surface well let's dust ourselves off and think happy thoughts maybe someday we will get clear and definitive answers to most of these natural riddles but until then we can swathe ourselves in their glowing mysteries an almost supernatural glow content in the knowledge that there is still some magic in this overly practical and pragmatic world we hope you enjoyed the video have you found something that science can't explain let us know in the comments and if you liked the video then share it with a friend and don't forget to click the subscribe button and turn on notifications so you'll be the first to know when a new video comes out thanks for watching and we'll see you next time

12 People Killed In Virginia Beach Mass Shooting, Police Say | TIME



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Police say 12 people have been killed and four others injured in a mass shooting at a municipal building in Virginia Beach.
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12 People Killed In Virginia Beach Mass Shooting, Police Say | TIME

we heard we were watching the news in there and they said for fatality oh I heard shooting we heard shooting but we didn't think it was that close that close like in proximity of the building so I just thank God that they were able to alert us in time because if it had been 10 minutes more we all would have been outside so that's what I'm grateful for today

Western Science Professors Read Mean Reviews



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you are really boring your voice is monotonous and your jokes are really dry nobody cares about who won a frigging Nobel Prize because of you and your Oracle course my lifelong dream of becoming a doctor is dead other than being a little too well dressed to my taste Christmas awesome I only own one shirt all the questions are tricks so not an accurate way to test walks around with his hands waving around when he talks I am NOT even in this course and he annoys me someone came to class on that day this is terrible professor tried to make jokes that no one thought was funny some people just left to humor you unprofessional don't appreciate you being on your phone and updating everyone about your kid no one cares you're not funny and we're here to learn not for you to test an obviously failed attempt at a career in stand-up this one this one confused me he teaches like a guy who skips late day needs to do a better job at communicating things love the enthusiasm can teach the concepts better with an illogical approach an illogical and I have to do better job communicating things I used to like by not anymore I could have cured cancer but because of happy we'll never know solidified my major in chemistry i I just prevented the cure of okay change your last name to bath so it's I need a bath really I didn't know I smelled apparently I do you are the Beyonce of war Co the students very perplexed how could one lab be three hours long and worth two percent of our mark one a three hour exam is worth 40% it makes no sense just like Happy's lectures

Pre-Socratics: A Painless Introduction by Luke Muehlhauser



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This is a short book about pre-Socratic philosophers for people who care more about the central questions of philosophy themselves — What exists? How should we live? How can we know? — than they do about the historical matter of pre-Socratic thought. But current research in philosophy often refers to the work of pre-Socratic philosophers, so it is worth knowing a bit about what they thought. My book explains the bare essentials about pre-Socratic philosophy you must understand to do philosophy today.

This book does not assume you know much about philosophy. It does not discuss every aspect or interpretation of a philosopher’s work. It will only tell you what you need to know to engage with philosophy today.

My main sources are the histories of philosophy by Kenny, Russell, and Copleston (of whom you will hear echoes below), along with translations of the original works.
Before Socrates

If one wishes to take an ecstatic view of “the Greek miracle,” it is supplied by Bertrand Russell in A History of Western Philosophy:

What [the Greeks] achieved in art and literature is familiar to everybody, but what they did in the purely intellectual realm is even more exceptional. They invented mathematics [other cultures had rules of thumb, but Greece invented deduction from axioms] and science and philosophy; they first wrote history as opposed to mere annals; they speculated freely about the nature of the world and the ends of life, without being bound in the fetters of any inherited orthodoxy. What occurred was so astonishing that, until very recent times, men were content to gape and talk mystically about the Greek genius.

This, then, is our subject: the birth of philosophy in Ancient Greece.

The three fathers of Western Philosophy are Socrates, Plato, and Aristotle. They are so important that all philosophers before them are lumped under one heading: the “pre-Socratics.”
The Milesian School

In the ancient world, philosophy and science blurred together. Thinkers had to “philosophize” about astronomy and medicine more often than they could conduct rigorous tests in those fields. This was for much the same reason we must now philosophize a great deal about the mind until the tools of neuroscience improve.

Thus, the first person we call a “philosopher” is Thales (624-546 B.C.) of Miletus, who was really a physicist and astronomer, except that he had no scientific method or scientific instruments, so he had to philosophize his way to conclusions about the physical world.

Many people at the time assumed that earthquakes and many other events were the acts of the gods, but Thales was one of the first people in recorded history to seek natural explanations instead. He thought the earth floated on water, and earthquakes resulted when waves rocked the earth.

After seeing some moisture turn into air, slime, and earth, he came to believe there was an original substance from which all else is formed — an arche — and that this arche was water. Many others followed him in this approach, proposing other basic elements such as air. If it turns out that string theorists are right and everything is made of subatomic vibrating strings, then Thales’ basic idea is correct, though he guessed the arche incorrectly.

Thales was also skilled at geometry, and is reported to have measured the height of the pyramids by the lengths of their shadows, and to have measured the distances of ships at sea with sticks at two different points on land.

He is famous for predicting a solar eclipse in 585 B.C., though he probably borrowed this skill from the Babylonians, who had been predicting eclipses for hundreds of years.

What historians think is unique about Thales is the universality of his approach. Thales sought universal, rational, natural explanations for the world instead of mythological ones. That is why we call him the first philosopher.

Anaximander (610-546 B.C.) was even more insistent to explain everything in terms of physical forces, and may also have been the first philosopher to write down his ideas, and the first to conduct a scientific experiment.

He believed the arche was an infinite, indefinite mass (apeiron) from which everything came, much like the primordial Chaos of Greek mythology. This was perhaps an improvement on Thales’ view that water was the original element, for water cannot account for the diversity of nature. For example, water is only wet and never dry. So the arche must be more basic than water, fire, earth, or air.

Anaximander thought the earth we observe is the flat top of a cylinder, floating still in a vast void. He took note of fossils and proposed that animals had originally come from the sea, and humans had come from those animals. But he had no concept of natural selection.

Anaximenes (585-528 B.C.) continued to seek natural, unifying explanations. He proposed the arche was air

this is the audiobook recording of pre-socratics a painless introduction by Luke mal Hauser read by Luke mal Hauser you can also get the Kindle version of this book for easy reading on a Kindle PC Mac iPhone iPad Android phone or blackberry by going to amazon.com and searching for pre-socratics a painless introduction pre-socratics a painless introduction by Luke mal Hauser a painless introduction this is a short book about pre-socratic philosophers for people who care more about the central questions of philosophy themselves what exists how should we live how can we know than they do about the historical matter of pre-socratic thought but current research and philosophy often refers to the work of pre-socratic philosophers so it is worth knowing a bit about what they thought my book explains the bare essentials about pre-socratic philosophy you must understand to do philosophy today this book does not assume you know much about philosophy it does not discuss every aspect or interpretation of a philosophers work it will only tell you what you need to know to engage with the philosophy today my main sources are the histories of philosophy by Kenny Russell and coppleson of whom you will hear echoes below along with translations of the original works before Socrates if one wishes to take an ecstatic view of the Greek miracle it is supplied by Bertrand Russell in a history of Western philosophy quote what the Greeks achieved in art and literature is familiar to everybody but what they did in the purely intellectual realm is even more exceptional they invented mathematics other cultures had rules of thumb but Greece invented deduction from axioms and science and philosophy they wrote history as opposed to mere annals they speculated freely about the nature of the world and the ends of life without being bound in the fetters of any inherited orthodoxy what occurred was so astonishing that until very recent times men were content to gape and talk mystically about the Greek genius end quote this then is our subject the birth of philosophy in ancient Greece the three fathers of Western philosophy are Socrates Plato and Aristotle they are so important that all philosophers before them are lumped under one heading the pre-socratics the my lesion school in the ancient world philosophy and science blurred together thinkers had to philosophize about astronomy and medicine more often than they could conduct rigorous tests in those fields this was for much the same reason we must now philosophize a great deal about the mind until the tools of neuroscience improve thus the first person we call a philosopher is Daly's 624 – 546 BC of Meletis who was really a physicist and astronomer except that he had no scientific method or scientific instruments so he had to philosophize his way to conclusions about the physical world many people at the time assumed that earthquakes and many other events were the acts of the gods but they Lee's was one of the first people in recorded history to seek natural explanations instead he thought the earth floated on water and earthquakes resulted when waves rocked the earth after seeing some moisture turned into air slime and earth he came to believe there was an original substance from which all else is formed and RK and that this RK was water many others followed him in this approach proposing other basic elements such as air if it turns out that string theorists are right and everything is made of subatomic vibrating strings then Thea's basic idea is correct though he guessed the RK incorrectly Daley's was also skilled at geometry and is reported to have measured the height of the pyramids by the lengths of their shadows and to have measured the distances of ships at sea with sticks at two different points on land he is famous for predicting a solar eclipse in 585 BC though he probably borrowed this skill from the Babylonians who had been predicting eclipses for hundreds of years what historians think is unique about theories is the universality of his approach bailey's sought Universal rational natural explanations for the world instead of mythological ones that is why we call him the first philosopher Anaximander 610 to 546 BC was even more insistent to explain everything in terms of physical forces and may also have been the first philosopher to write down his ideas and the first to conduct a scientific experiment he believed the RK was an infinite indefinite mass appear on from which everything came much like the primordial chaos of Greek mythology this was perhaps an improvement on Thalys view that water was the original element for water cannot account for the diversity of nature for example water is only wet and never dry so the RK must be more basic than water fire earth or air Anaximander thought the earth we observe is the flat top of a cylinder floating still in a vast void he took note of fossils and proposed that animals had originally come from the sea and humans had come from those animals but he had no concept of natural selection an examinees 585 to 528 bc continued to seek natural unifying explanations he proposed the RK was air and that things varied only in their density fire was diffused air while water was condensed air and earth was air condensed further still perhaps he rejected Anaximander 's Apeiron proposal because the notion of an indefinite unlimited substance is unintelligible to us and is really no better an explanation than an origins myth involving gods in chaos as described by the poet Hesiod eighth century BC an examinees may have proposed air as the ark a because it is intelligible and observable and seems like it could be in everything including rocks and trees and people Xenophon 'is born 5 70 BC followed the my lesion school and believed all things were made of earth and water he may be best known as a critic of polytheism writing quote mortals deem that gods are begotten as they are the Ethiopians make their gods black and snub-nosed the Thracian say theirs have blue eyes and red hair end quote following several arguments he concluded that God is one eternal non anthropomorphic being for Xenophon ease God is not much of a person but rather the RK he has no parts and need not physically contact the world but quote remote and effortless with his mind alone he governs all there is end quote like the god of medieval perfect being theology whereas the Egyptians in Hebrews head at different times proclaimed monotheism by divine revelation zennith eni's was the first to arrive at an abstract monotheism by argument he was the first natural theologian the my legion philosophers were wrong about everything but they asked the right questions and for the first time sought natural explanations for the world by theory ins pythagoras late 6th century BC was born on the Greek island of Samos he travelled widely and then settled in southern Italy where he founded a society of disciples after his death magical powers were attributed to him and a religion formed among its commands were to not eat beans to not step over crossbars and to not look in mirrors near a light the pythagorean's often attributed their own views and innovations to their founder so it is easier to say what the pythagorean's believed than to say anything about pythagoras himself the pythagorean's then said that all is number by which they meant that number was in everything they had discovered the mathematical nature of music and harmony and the numbers inherent to many shapes of course they are best known today for the Pythagorean theorem about right-angled triangles their most important influence was on Plato and through Plato on all of Western philosophy Pythagorean had a mystical reverence for the perfection of abstract mathematical thinking and deemed it a firm foundation for philosophy this confidence lay at the heart of Plato's philosophy as did the Pythagorean notion of a perfect eternal world reveal to our minds but not our senses this emphasis on mathematical reasoning developed further as deduction from self-evident axioms to non obvious conclusions by Euclid or in 300 BC and others came to dominate much of Western philosophy and theology through Plato Augustine Aquinas Descartes Spinoza Conte and Newton Plato also borrowed from the pythagorean's an emphasis on the soul and its thoughtful care and perhaps even its tripartite nature the pythagorean's also defended the immortality of the soul though they believed that after physical death the soul did not travel to an alternate world but returned to the present one in a different body and not necessarily a human one pythagoras himself said he could remember fighting as a hero centuries earlier at the siege of Troy Heraclitus and Parmenides Heraclitus 5:35 to 475 bc of ephesus thought the world was unified by a kind of harmony that resulted from strife between opposites health and illness good and evil day and night the world he thought was dominated by a cosmic justice that prevented one opposite from overcoming the other he was the most pithy and quotable of the pre-socratics among his one-liners are donkeys prefer straw to gold and man's character is his fate and swine wash in the mud and barnyard fouls in the dust Heraclitus was enamored with his own prose and wrote as a prophet proclaiming the word of Heraclitus Heraclitus seems to have recognized the problem faced by the Malaysian school if the RK is unmoving and eternal then how do we explain the leap from unmoving being to the dynamic becoming we see all around us Heraclitus solution was to remove being altogether he said that everything is always changing quote you cannot step into the same river twice for new waters are ever flowing in upon you end quote Parmenides 510 2 4 40 see of Alea offered the opposite solution he rejected becoming altogether in favor of motionless being he thought that nothing ever changes our senses give us nothing but illusion and everything is really the one a kind of perfect sphere that cannot be divided everything that exists has always existed and will always exist this doctrine is similar to the block universe theory of modern physics according to which time does not flow but instead the past and present and future all exist but in different directions like backward and forward arguing against this theory Karl Popper exclaimed to Einstein quote you are Parmenides more important than Parmenides metaphysical claim itself is that he gave an argument for it he appears to have argued something like this when you think and speak you think and speak about something but you can think and speak about something at one time as well as another so whatever you can think and speak of must exist at all times so there can be no change for change consists of things beginning to be or ceasing to be this argument is obviously flawed for we often use words to speak of things that do not exist unicorns or things from the past Shakespeare or the potential future interstellar spacecraft but notice that Parmenides gave an argument from the way we use thought and language to a conclusion about the external world he may have been the first to do so and this method has since been used by most of the prominent meta physicians in history though many today doubt its usefulness because he initiated the purely rational method of inquiry about reality and therefore opened the debate between rationalism and empiricism that would later dominate so much of the history of philosophy and because he was the first epistemologists in that he clearly distinguished belief from knowledge Parmenides is often named as the most important philosopher before Socrates Heraclitus and Parmenides mapped the battlefield for centuries philosophical struggle it was of central importance to Democritus Plato Aristotle and others to reconcile being and becoming Zeno Zeno 490 to 430 BC of Alea not to be confused with Zeno acid' iam the founder of stoicism is today best known for his paradoxes which have challenged infuriated and inspired some of philosophies greatest minds down to the present day his arguments may be the earliest examples of reductio ad absurdum a form of argument in which one tries to disprove a proposition by showing that it logically leads to absurdity Zeno used several reductio ad absurdum arguments in defense of Parmenides doctrine that all is one and that change is impossible of xenos nine surviving paradoxes two are of most interest they are one Achilles and the tortoise and two the flying arrow the paradox of Achilles and the tortoise goes like this Achilles and the tortoise are in a footrace and Achilles gives the tortoise a head start of say a hundred meters both start running at a constant speed with Achilles running faster than the tortoise after some time Achilles will have run 100 meters and caught up with the tortoises starting point and in the meantime the tortoise will have progressed some shorter distance say 10 meters it then takes Achilles some time to cross those 10 meters by which time the tortoise will have moved a bit further head and so on so whenever Achilles reaches the point where the tortoise was most recently he still has further to go and so Achilles can never catch up with the tortoise and yet experience tells us that Achilles can easily pass the tortoise hence the paradox the flying arrow paradox arises from divisions of time rather than divisions of space Zeno notes that for motion to occur an object such as a flying arrow must change positions in any given instant of time for the arrow to be moving it must either move to where it is or move to where it is not but it cannot move to where it is not because we're only considering a single instant of time and it cannot move to where it is because it is already there thus at any given instant of time the arrow is not moving therefore the arrow cannot move at any instant of time meaning it cannot move at all there have been many proposed solutions to these paradoxes Thomas Aquinas born 1225 and Peter Linz born 1975 argued against the arrow paradox by claiming that time is not composed of instants in 1958 hans reichenbach argued that given general relativity according to which time and space are not separate entities the paradox might dissolve in 1987 jean-paul van bend again offered a solution by denying xenos assumption that between any two given points in space or time there is always another point but as with Parmenides the influence of Zeno is not so much with his arguments as with their novel form moreover Zeno may have been the first person to practice the dialectic made famous by Socrates that practice of two or more people exchanging arguments and counter-arguments ending in a refutation of one view or perhaps a synthesis of both views empedocles empedocles 490 to 430 BC is perhaps best known for two scientific discoveries involving buckets first he noticed that if you push an upside-down bucket under water the water does not rush in to fill the bucket thus he discovered that air is its own separate substance second he noticed that if you swing a bucket of water around on a rope above your head the water does not fall out of the bucket thus he discovered centrifugal force and pet Cleese thought that the original elements were earth fire air and water which when combined in different ways result in everything we see but there must be active forces that cause these elements to be combined in various ways and these forces are love and strife despite their names empedocles thought of these as physical forces love attracted elements together to form objects and strife pushed them apart and decayed objects this cycle preceded by chance and physical necessity rather than by cosmic purpose he also defended a fantastical version of evolution by natural selection his theory as paraphrase by Bertrand Russell was that quote originally countless tribes of mortal creatures were scattered abroad endowed with all manner of forms a wonder to behold there were heads without necks arms without shoulders eyes without foreheads solitary limbs seeking for union these things joined together as each might chance there were shambling creatures with countless hands creatures with faces and breasts looking in different directions creatures with the bodies of oxen and the faces of men and others with the faces of oxen and the bodies of men there were hermaphrodites combining the nature's of men and women but sterile in the end only certain forms survived end quote Aristotle derided empedocles for replacing teleology with chants and the world followed Aristotle for 2,000 years but impetus had the last laugh when Darwin commended him for quote shadowing forth the principle of natural selection end quote an exact receives a giris 500 to 428 BC brought philosophy to Athens the city which later produced Socrates and Plato he prefigured modern big bang theory he held that the universe was originally infinitely dense and small this primeval pebble rotated throwing off air and ether that later formed stars and planets and everything else this expansion and separation of things is not complete and will continue forever thus everything contains at least a tiny bit of every element but we call it by the element that predominates so fire contains some stone but it appears to us as fire because it is mostly fire the exception is mind noose which exists only in living things and is the cause of all motion Aristotle complained to the an exact arrest tried to offer a natural explanation for everything except that whenever he couldn't explain something he put mind into the gap an exact wrist a mind of the gaps just as many theologians proposed a God of the gaps but Plato was attracted to annex a grocer's idea of mind an exact ERISA ventually banished from Athens perhaps because he said the Sun was a fireball rather than a God Democritus Democritus born 460 BC prefigured the findings of modern science most completely he believed that everything is made of atoms that are physically indivisible that there is empty space void between atoms that atoms are always in motion that atoms are indestructible and that there are many kinds of atoms according to him atoms form different substances based on their shape iron holds together firmly because it's atoms have hooks water flows because it's atoms are smooth and slippery salt has a sharp taste because it's atoms are pointy and so on all atoms interact mechanically and thus the whole world is a machine with no need for gods or a prime mover or a final cause of the universe Democritus was a strict determinist he did not believe in chance but rather thought that everything proceeded due to natural laws even thought and the soul were made of atoms and governed by natural law he also believed in multiple worlds some without Sun or moon some with larger Sun and Moon some without animals or plants or moisture all this resulted from the random motion and collision of tiny atoms which joined each other according to their shape democritus's epistemology is unclear for he used sense data to construct his theory of atoms and yet he rejected the senses as but sources of illusion and proclaimed atoms and the void as the only true reality we could know so perhaps Democritus should have been a sceptic about knowledge as was his student metrodorus who wrote quote none of us knows anything not even whether we know or do not know not even what knowing and not knowing our end quote Democritus was also the first philosopher to offer a systematic morality happiness was to be found in a life of cheerfulness and quiet contentment moderation is good but asceticism is not the trick is to choose the right times for fasting and feasting in placing happiness at the center of ethics Democritus set the agenda for many Greek ethical systems to come but he did not mention that other ground of Greek ethics virtue final thoughts now that we have discussed the pre-socratic philosophers of ancient Greece we might ask what about the ancient philosophers of other cultures did philosophy really have only one berth in all the world in Greece certainly other ancient cultures had philosophies in that they held assumptions about what existed what we should do and how we can no educated men developed and discuss these assumptions and sometimes wrote down their innovations pre Socratic philosophies greatest competitor is ancient Indian philosophy another wellspring of mathematics science dialectic argumentation and materialism that is a subject for another book but it is clear that as a matter of history the marvels of Western science and philosophy that have so profoundly transformed the modern world descended from the work of ancient Greeks not ancient Indians the birth of philosophy is shrouded in the mists of the ancient past what little is preserved of these early thinkers works is preserved mostly in quotations from their opponents which can hardly give us an accurate view of their positions moreover we may never know who really invented method X or who first defended Theory Y all we can say is that so and so is the earliest person known to have used method X or defended Theory Y but however inaccurate and incomplete our picture of pre Socratic philosophy may be it seems that some extraordinary progress was made in ancient Greece here were the first and most elaborate attempts to explain the world in a unified and mechanical here were the origins of geometry as deduction from self-evident axioms to non obvious conclusions here were invented new methods of argument and scientific discovery moreover the pre-socratics set the stage for the revolutions to come in Socrates and Plato you you you

Christianity and Science to Understand Others: by the World of True Science



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This is Just a view into the World of True Science. True Science is the Studies, Research and Understanding of The Great Creator and The Great Creator’s Creation. Truth and reality is all that truly exist. Other than truth and reality, there is just the concept of an absence.

Dr Mark Lehner – Confirmation Bias & Scientific Method on the Giza Plateau



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‘Mark Lehner is an American archaeologist with more than 30 years of experience excavating in Egypt. He was born in North Dakota in 1950. His approach, as director of Ancient Egypt Research Associates (AERA), is to conduct interdisciplinary archaeological investigation.[1] Every excavated object is examined by specialists to create an overall picture of an archaeological site—from the buildings down to the pollen spores. His international team currently runs the Giza Plateau Mapping Project, excavating and mapping the ancient city of the builders of the Giza pyramid complex, which dates to the fourth dynasty of Egypt. He discovered that Pyramid G1-a, one of the subsidiary pyramids of the Great Pyramid, belonged to Hetepheres I; it was originally thought to belong to Queen Meritites I.’

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How to understand BLACKHOLES using a cricket ball? | EXPLAINED | Dumb science tamil



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