Carrie Ann, I want to thank you for bringing so much enthusiasm to the topic. This is exciting stuff, but it can be shown in a very static and boring way, and you don't do that.
@John PetrovickýI'm sorry if I sounded rude. But you can in fact learn this stuff without college what so ever. It's expensive... So one way to learn by "force" us study is paying education. But now with internet it's possible to do it without the need of school. Best advice I can tell you is to download a curriculum from the web, search your books and not hesitate to learn by yourself, time is the most valuable resource you have... Not a degree, not your capabilities, all comes down to what you do with your time
The problem with self learning is you tend to just learn all the interesting or seemingly important bits and skip the boring. Unfortunately the boring stuff is very necessary to properly understand. That's usually the difference between someone with a formal education and someone without, the formal education makes you a lot more rounded and "solid", knowing a bit about everything and not just what you specifically require for work.
@John Petrovický If it's not an internationally accredited university then you're wasting your time. Either study by yourself or go to a university, don't attend some shitty private course.
@@QuasiELVIS Calculus, linear algebra, multi-variable or vectorial calculus, probability, chemistry, differential equations... Definitely a good appreciation of a book can tell you the same thing, but rather in a kind way suggesting you to find another book before it, so have respect to anything you could learn. Read carefully, like you where going to live forever, you can understand just by transcribing key words of an entire book of 1000 pages in just 3 moths, with experience, probably less... And If we are not geniuses, we can rather save time by doing that or listening and watching a board. I believe my method has been the most fruitful for me, at least not as efficient than reading like any smart fellow. I wish I could drop school but I can´t since I live in a very unforgiving society. If you have resources to make projects on your own. JUST DO IT! And be eager to hear the knowledge that you seek!
The most fascinating part about DeepMind's AlphaZero is that it did not need human-created training data. It only needed to play itself enough times, and by sheer volume, become a master. Humans can't do that, unfortunately.
I agree. Especially if it'd be related to programming and how to do everything in R or something similar. I would love to learn that and make great graphs for /r/dataisbeautiful so that I can bathe in imaginary internet points also called Karma, invented by crooked Hillary who turned my frogs gay with chemicals in the water.
I would really love to see a crash course series on the practical skills that scientists need (IE latex, soldering, writing a research paper, advanced lab skills, etc.)
At 2:30 my eyeballs rolled back in my head and I collapsed, falling backward out of my chair due to total confusion and non understanding of said material. Takes me back to my school days.
As part of a plant anatomy class, many years ago, we did field measurements to determine whether a population of trees in a particular canyon represented two distinct species or showed evidence of interbreeding. I never saw the results of the analysis of that data, but it should have looked very much like some of what's presented in this video.
Longest 10 min in my life , very well explained but i learned more about myself watching this than about AI as i discovered my i cant concentrate and focus very well so i will work on improving that
> Confusion Matrix, which probably should have also been the title of the last two movies in the Matrix Trilogy Yeah, that's what I'm going to call them now. Signed, a die-hard fan of the Matrix Trilogy
i have read numerous articles on this subject and this is one of the very best abcs simple explanations, THANK YOU. but this is from 5 years ago and now with chatgpt out so is there a more recent video i can watch? thanks
It should be noted that Watson won jeopardy not by knowing more correct questions, which it actually didn't, but because of the fact that when it did know something, it could buzz in a matter milliseconds, much faster then any human could. When to and not to buzz and the speed of buzzing in that game is actually as important as knowing the questions.
It's true that Watson didn't get much more questions right compared to Ken Jennings, percentage-wise. It was still a major improvement from previous systems, which wouldn't even be able to beat people who _lost_ on Jeopardy.
A robot walks into a bar. Robot ; " Hey bartender , I'll have a beer please." Bartender: " We don't serve your type in here. Robot: " No, but someday you will."
As good as the video is what I think is even better is if it were broken up into several parts and in between parts were explorable explanations like these: explorabl.es -- e.g. where one can play with the borders for the two-dimension scatter plot and the accuracy score changes as one moves the border around.
I picture the first true strong or general AI will be nested AI: a narrow AI that uses deep learning to decide what narrow AI is best equipped to handle the task. After that, it might be able to learn how different narrow fields are interconnected and give insight towards producing a more flattened decision matrix.
9:40 Nowadays, the term *Artificial General Intelligence* (AGI) is more often used than *Strong AI* , which has more philosophical connotations (e.g. mind, consciousness, thinking), and not only depends on what the AI can do. en.wikipedia.org/wiki/Artificial_general_intelligence#Relationship_to_.22strong_AI.22
Holy cannoli did I just have 🤯. I just realized that higher dimensions are not just possible but almost certain. We encounter this every day in math using higher dimensional vectors, in computers and analysis.
Crash Course needs to become Crash Degree. This and the episode on Natural Language Processing, pretty much saved me hours of tedious reading of some O'Reilly book.
And here we tinker with metal, to try to give it a kind of life, and suffer those who would scoff at our efforts. But who's to say that, if intelligence had evolved in some other form in past millennia, the ancestors of these beings would not now scoff at the idea of intelligence residing within meat? ~"The Fallacies of Self-Awareness" P.F. Aki Zeta-5
asdfrozen, I didn't understand how addition, multiplication etc can determine the correct type. I mean the final number could be anything. How can we say the largest number will tell the answer.
Mihir Bindal, Let's pretend for a moment that you've got a single layer with a single output. Input 1 * weight 1 + input 2 * weight 2 + input 3 * weight 3 = output. This is the equation for a 3 dimensional plane in Cartesian space. If the output is positive, we are on one side of the plane. If the output is negative, we are on the other side. The equation becomes more complicated and less linear with multiple layers, but the concept is the same. We're splitting the data using multiplication and addition, like she did graphically in the video. If we have multiple outputs, we have multiple planes splitting the data. One plane splits things into stuff that looks like a lunar moth and stuff that doesn't. Another plate splits things into stuff that looks like an emperor moth and stuff that doesn't. These planes could line up but be oriented opposite directions (like she had graphically in the video, one line to split two dimensional data). You could then make the decision based on if the output was positive or negative. That would work. You can do a binary classifier with just one output. But, in her example, we've got two outputs, so we've got two planes. Ideally, only one output will be positive at a time. The emperor moth output shouldn't be positive with a lunar moth input because a moth can't both be a lunar moth and an emperor moth. But, if both are positive or both are negative, one of your outputs got it wrong. The magnitude can be thought of as the confidence (how far were we from where we split the data?). The output with the larger magnitude is the most confident in its answer, so we pick that one.
SirTripalot , thank you for explaining me the topic. Seems that computer science is slowly adopting concepts of statistics and math to solve complex problems. The day is not far when computer science will include study of all subjects and won't be confined to a basic computers.
how often do most programmers need to optimize their code? When there is a need, how often is computational complexity (as opposed to, say, a memory bottleneck) the limiting factor? If one of my coworkers comes to me with a well-written, well documented, well tested, program that can't run fast enough, profiling it and speeding it up is easy. If one of my coworkers comes to me with untested, disorganized code, I'll be tempted to restart from scratch.. The math in the application domain, understanding what you're doing, is incredibly important. You can't write tests to make sure your code works unless you understand the mathematical model you are working with. Understanding computational complexity so that you can estimate how fast your program will run if you give it more data? I can do it, but I don't think I've needed to professionally for years. That's on the bottom of my list of priorities.
All of machine learning boils down to somewhat complicated math, particularly optimization and statistics. Like, you can understand machine learning without knowing how to program, but you can't understand it without knowing the mathematics. I also wouldn't say that neural networks are just logistic regression--not at all, really. A neural network is a directed, weighted graph, where each node has an activation, and that node's activation is determined by a system of linear equations. The network "learns" by minimizing an error function, often using an optimization method such as gradient descent.
Please make spanish subtitles for this video (and all the course) I understand but a lot students from my university have a lot of trouble learning english and the videos that teachers use to explain "what is AI" are very very bad
Spectacularly well done video on fairly complex topics! I'm new to machine learning in general and I want to learn more. I'll be watching your other videos on related topics next. Thank you!
"A human can only watch 24 hours of youtube per day at maximum"
Me: You underestimate my power.
(starts opening additional tabs)
lol
Me an intellectual: opens additional tabs in multiple browsers on multiple computers and devices. Then launches the UA-cam app on my Apple TV.
@@brycedurham7101 Nice Comment. Thumbs up!
Carrie Ann, I want to thank you for bringing so much enthusiasm to the topic. This is exciting stuff, but it can be shown in a very static and boring way, and you don't do that.
She is such a good teacher , never knew Machine leaening and AI could be this interesting.
Crazy that this was posted 5 years ago, and now Strong AI is becoming a reality
My whole AI Semester in almost 12 minutes
@John Petrovický only if you are lazy enough, but for being employed without any background projects or experience, it's necessary to have a BD
@John PetrovickýI'm sorry if I sounded rude. But you can in fact learn this stuff without college what so ever. It's expensive... So one way to learn by "force" us study is paying education. But now with internet it's possible to do it without the need of school. Best advice I can tell you is to download a curriculum from the web, search your books and not hesitate to learn by yourself, time is the most valuable resource you have... Not a degree, not your capabilities, all comes down to what you do with your time
The problem with self learning is you tend to just learn all the interesting or seemingly important bits and skip the boring. Unfortunately the boring stuff is very necessary to properly understand.
That's usually the difference between someone with a formal education and someone without, the formal education makes you a lot more rounded and "solid", knowing a bit about everything and not just what you specifically require for work.
@John Petrovický If it's not an internationally accredited university then you're wasting your time.
Either study by yourself or go to a university, don't attend some shitty private course.
@@QuasiELVIS Calculus, linear algebra, multi-variable or vectorial calculus, probability, chemistry, differential equations... Definitely a good appreciation of a book can tell you the same thing, but rather in a kind way suggesting you to find another book before it, so have respect to anything you could learn. Read carefully, like you where going to live forever, you can understand just by transcribing key words of an entire book of 1000 pages in just 3 moths, with experience, probably less... And If we are not geniuses, we can rather save time by doing that or listening and watching a board. I believe my method has been the most fruitful for me, at least not as efficient than reading like any smart fellow. I wish I could drop school but I can´t since I live in a very unforgiving society. If you have resources to make projects on your own. JUST DO IT! And be eager to hear the knowledge that you seek!
Ha! Speed at 1.5x. Now I can watch 36 hours of UA-cam a day.
it's still 24 hours, just missing some frames.
you broke the system lol, GOOD ONE
@@derickrk i see, thanks.
there's a chrome extension that lets you go to at least 10x. we have to outsmart the machines
I've watched the entire series at .75 w/ subtitles, you freak.
The most fascinating part about DeepMind's AlphaZero is that it did not need human-created training data. It only needed to play itself enough times, and by sheer volume, become a master. Humans can't do that, unfortunately.
as someone aiming to double major in entomology and computer science this video is right up my alley! love the example chosen for this episode!
A Crash Course Statistics would be awesome!
I agree. Especially if it'd be related to programming and how to do everything in R or something similar. I would love to learn that and make great graphs for /r/dataisbeautiful so that I can bathe in imaginary internet points also called Karma, invented by crooked Hillary who turned my frogs gay with chemicals in the water.
It's coming 2018! Already confirmed by Hank
I'm already there :D
hey josh mcgee, do you know if the statistics series will also cover lessons on probability?
I'd imagine it would be hard to cover statistics without covering probability
I would really love to see a crash course series on the practical skills that scientists need (IE latex, soldering, writing a research paper, advanced lab skills, etc.)
At 2:30 my eyeballs rolled back in my head and I collapsed, falling backward out of my chair due to total confusion and non understanding of said material. Takes me back to my school days.
As a newcomer to Machine Learning this was a great overview. Thank you!
As part of a plant anatomy class, many years ago, we did field measurements to determine whether a population of trees in a particular canyon represented two distinct species or showed evidence of interbreeding. I never saw the results of the analysis of that data, but it should have looked very much like some of what's presented in this video.
it's amazing how the essence of trial and error can be emphasized in every aspect of life
Way to go Carrie Anne!
Well.. GPT4 exists. So much for poetry and recipes..
Longest 10 min in my life , very well explained but i learned more about myself watching this than about AI as i discovered my i cant concentrate and focus very well so i will work on improving that
I'd love to see a video on how Quantum computing will influence/improve this
If you knowed about ChatGPT at that time, would you said what you said?
I live for this series
Damn Carrie Anne bout to make me act up
Thanks for the intelligently designed graphics that pops at times.. They were so useful .. :)
"So thank you gamers for being so demanding about silky smooth frame rates"
AYE AYE CAPTAIN
Such avant garde content makes UA-cam an avant garde platform.
> Confusion Matrix, which probably should have also been the title of the last two movies in the Matrix Trilogy
Yeah, that's what I'm going to call them now.
Signed,
a die-hard fan of the Matrix Trilogy
You'll be able to classify Matrix 4!
Excellent video for Machine Learning! Kudos! Thank you for sharing!
i have read numerous articles on this subject and this is one of the very best abcs simple explanations, THANK YOU.
but this is from 5 years ago and now with chatgpt out so is there a more recent video i can watch? thanks
It should be noted that Watson won jeopardy not by knowing more correct questions, which it actually didn't, but because of the fact that when it did know something, it could buzz in a matter milliseconds, much faster then any human could. When to and not to buzz and the speed of buzzing in that game is actually as important as knowing the questions.
It's true that Watson didn't get much more questions right compared to Ken Jennings, percentage-wise. It was still a major improvement from previous systems, which wouldn't even be able to beat people who _lost_ on Jeopardy.
Are you sure thats correct ? Im pretty sure Ken Jennings conceded in the final jeopardy stage
@@Robs_fan I got a good chuckle out of that, totally thought it was gonna be a very scientific "Yes" when i read the first part.
A robot walks into a bar.
Robot ; " Hey bartender , I'll have a beer please."
Bartender: " We don't serve your type in here.
Robot: " No, but someday you will."
Great brief introduction of so many related concepts. Thanks!
I can see the decision space going into complex numbers bring it into a fourth dimension of data.
Thank you Carrie so much .. you're such A Hero .
As good as the video is what I think is even better is if it were broken up into several parts and in between parts were explorable explanations like these: explorabl.es -- e.g. where one can play with the borders for the two-dimension scatter plot and the accuracy score changes as one moves the border around.
Did this video foreshadow a Crash Course: Statistics?
It's coming 2018! Already confirmed by Hank
josh mcgee
Yay!
WHY NOT NOW? I NEED CRASH COURSE STATISTICS NOW.
No problem! I always love my silky smooth frame rates!
I learned so much in 12 mins
thank you so much!
I picture the first true strong or general AI will be nested AI: a narrow AI that uses deep learning to decide what narrow AI is best equipped to handle the task. After that, it might be able to learn how different narrow fields are interconnected and give insight towards producing a more flattened decision matrix.
Crash course mathematics would be amazing!!
9:40 Nowadays, the term *Artificial General Intelligence* (AGI) is more often used than *Strong AI* , which has more philosophical connotations (e.g. mind, consciousness, thinking), and not only depends on what the AI can do.
en.wikipedia.org/wiki/Artificial_general_intelligence#Relationship_to_.22strong_AI.22
GPT-5: "Hold my Beer"
Thank you !!!!!
You guys are the best ever..... The way you teach the most complicated tasks are just like we could have invented ourselves..... You are awesome
I love this crash course series!
Amazing video! Thanks Carrie Anne, your explanations are so incredibly clear!!
Holy cannoli did I just have 🤯. I just realized that higher dimensions are not just possible but almost certain. We encounter this every day in math using higher dimensional vectors, in computers and analysis.
good explanation. Thank you
Thank you, for thanking gamers for demanding higher frame rates. I knew we would fit in somewhere :)
This was an amazing video! Thank you CrashCourse!
Crash course statistics confirmed!
Haha, The Confusion Matrix, ZING!
Super good! Thanks alot!
@8:51 you're welcome
her cup(heart) is full of words
I wonder what cup size she is. She is showing a big more bust this time. ♥
God damn. Video really made me marvel at what we and our creations can do.
I like how you explain in this videos.. do you have any courses online?
Nice hair. Thumbs up!
Crash Course needs to become Crash Degree. This and the episode on Natural Language Processing, pretty much saved me hours of tedious reading of some O'Reilly book.
yes, favourite subject!
This was GOOD!
best video ever!
Thanks!
Super Neat!!! Loved it!
And here we tinker with metal, to try to give it a kind of life, and suffer those who would scoff at our efforts. But who's to say that, if intelligence had evolved in some other form in past millennia, the ancestors of these beings would not now scoff at the idea of intelligence residing within meat?
~"The Fallacies of Self-Awareness" P.F. Aki Zeta-5
Wow, thank you so much, this video was super informative!
Fast talking and super educational! Thank You!
This is great, after watching this i coded my own auto-sucker with orange peels!
She is very very good! Enjoy listening so much ^_^ very easy and amusing tone.
Good quick video to understand basic concepts..
Great video! Right now is the chatGPT era, are you thinking about doing a follow up?
Hey do you have book , i love your series
you the best ! cant believe how u fitted all that into a 11 min talk !
UA-cams machine learning is getting better each year
Love this series
Neural networks have deep roots in statistics. The basic idea is that you're taking logistic regression and applying that to create many features.
asdfrozen, I didn't understand how addition, multiplication etc can determine the correct type. I mean the final number could be anything. How can we say the largest number will tell the answer.
Mihir Bindal, Let's pretend for a moment that you've got a single layer with a single output. Input 1 * weight 1 + input 2 * weight 2 + input 3 * weight 3 = output. This is the equation for a 3 dimensional plane in Cartesian space. If the output is positive, we are on one side of the plane. If the output is negative, we are on the other side. The equation becomes more complicated and less linear with multiple layers, but the concept is the same. We're splitting the data using multiplication and addition, like she did graphically in the video.
If we have multiple outputs, we have multiple planes splitting the data. One plane splits things into stuff that looks like a lunar moth and stuff that doesn't. Another plate splits things into stuff that looks like an emperor moth and stuff that doesn't. These planes could line up but be oriented opposite directions (like she had graphically in the video, one line to split two dimensional data). You could then make the decision based on if the output was positive or negative. That would work. You can do a binary classifier with just one output. But, in her example, we've got two outputs, so we've got two planes.
Ideally, only one output will be positive at a time. The emperor moth output shouldn't be positive with a lunar moth input because a moth can't both be a lunar moth and an emperor moth. But, if both are positive or both are negative, one of your outputs got it wrong. The magnitude can be thought of as the confidence (how far were we from where we split the data?). The output with the larger magnitude is the most confident in its answer, so we pick that one.
SirTripalot , thank you for explaining me the topic. Seems that computer science is slowly adopting concepts of statistics and math to solve complex problems. The day is not far when computer science will include study of all subjects and won't be confined to a basic computers.
how often do most programmers need to optimize their code? When there is a need, how often is computational complexity (as opposed to, say, a memory bottleneck) the limiting factor? If one of my coworkers comes to me with a well-written, well documented, well tested, program that can't run fast enough, profiling it and speeding it up is easy. If one of my coworkers comes to me with untested, disorganized code, I'll be tempted to restart from scratch.. The math in the application domain, understanding what you're doing, is incredibly important. You can't write tests to make sure your code works unless you understand the mathematical model you are working with. Understanding computational complexity so that you can estimate how fast your program will run if you give it more data? I can do it, but I don't think I've needed to professionally for years. That's on the bottom of my list of priorities.
All of machine learning boils down to somewhat complicated math, particularly optimization and statistics. Like, you can understand machine learning without knowing how to program, but you can't understand it without knowing the mathematics.
I also wouldn't say that neural networks are just logistic regression--not at all, really. A neural network is a directed, weighted graph, where each node has an activation, and that node's activation is determined by a system of linear equations. The network "learns" by minimizing an error function, often using an optimization method such as gradient descent.
That was very concise and extremely informative. If only all content on UA-cam was this good!
This lecture contains a lot of information it is best presentations
What a timing! Concept of capsules in neural nets was just published..
I just glimpsed an understanding of the purpose for multidimensional geometry.
Thanks crashcourse.
Very Informative
Thanks for such information
Extraordinary!!!!!!
Thank you for expanding your catalogue of video topics. Really enjoy this one after American government.
Thank you very much for a clear intro.
Great intro, thank you!
god this acutally makes sense! finally an explanation of neural networks i can understand
7:39 did you mean to say "iterations"?
I guess strong AI has become a reality with ChatGPT.
Very well made video!
so freakin awesome
I love strong AI...🙌🙌
Please make spanish subtitles for this video (and all the course) I understand but a lot students from my university have a lot of trouble learning english and the videos that teachers use to explain "what is AI" are very very bad
This is my seal. I have watched the entire video, understood it, and I can explain it in my own words, thus I have gained knowledge. This is my seal.
The wave is stop from motion in the backgrund?!
She's freaking awesome!
Where's the AI that defeated many of the best Dota Players: Open AI?
I love machine learning
Is our entomologist one Emily Graslie?!
I noticed that, too
Me: watches youtube rewind
*Ha, i have watched a year of youtube*
Spectacularly well done video on fairly complex topics! I'm new to machine learning in general and I want to learn more. I'll be watching your other videos on related topics next. Thank you!
Great video. Thanks!