ChatGPT: 30 Year History | How AI Learned to Talk 

STAY TUNED: Next video will be on "History of RL | How AI Learned to Feel" SUBSCRIBE: www.youtube.com/@ArtOfTheProblem?sub_confirmation=1 WATCH AI series: ru-vid.com/group/PLbg3ZX2pWlgKV8K6bFJr5dhM7oOClExUJ Timestamps: 00:32 hofstader's thoughts on chatGPT 01:00 recap of supervised learning 01:55 first paper on sequential learning 02:55 first use of state units (RNN) 04:33 first observation of word boundary detection 05:30 first observation of word clustering 10:10 sentiment neuron (Ilya & Hinton) 12:30 transformer explaination 15:50 GPT-1 17:00 GPT-2 17:55 GPT-3 18:20 In-context learning 19:40 ChatGPT 21:10 tool use 23:25 philosophical question: what is thought?

As an AI researcher and developer, this is the first video that I did not leave thinking that the author was just saying words without second thought. There is so much misinformation in this space stemming from the fear of the ramifications of AI that are a result of the negative feedback loop of those same people. Well done.

please never stop teaching on the internet. i can tell you have an intense passion in learning. im in love with how you explain concepts and their connections. feynman would be proud.

There only a handful of youtube channels that can make such concepts accessible to everyone with a curious mind (e.g. 3blue1brown, veritasium, Sabine). Art of the Problem is one of them. Brit you are a legend. Thank you for giving us this series.

Great video, but please fix your audio mix. The background music is way too loud. There likely is an option for audio ducking in your video editing program that will automatically lower the background music volume when foreground audio is playing.

I have watched countless AI videos but nobody explained it like you. Many thanks.

Very nice! ...well, except for the loud background music anyhow. Google kept pushing this video into my list over several days while I was very busy following something else. Finally I popped it up and saw why. Google knew that I would like it. Kind'a creepy, really how it knew it was exactly the kind of thing for me at this moment in time. It's like, it is following my studies on this subject and knew this should be the next chapter. Your video reiterates over everything I have learned so far and adds perspective.

Perfect. 12 years ago or so I learned about RSA from you. I'm glad to see you are still producing quality videos! Thumbs up!

I'm the one solving the rubiks cube!!!!

I can't pass the Turing test.

this channel changed my life about 10 years ago when i found the language of coins. By serendipity, I've found this channel again while im formally studying computer science which this channel inspired me to do. Thank you

Wow, this is handsdown the best video I've even seen about this subject. Thank you! It is on my favorites now!

Quoting one of the hero's of tech (Hofstadter ) makes this very well executed video that much more compelling. T9 (predictive testing) is the first really useful llm ish (not a word wheel but a predictive wheel from numbers) tech that end users experienced. The notion this simple premise is how it all works is nothing short of astonishing.

Masterpiece! This channel deserves millions of subscribers.

I work in AI, and I learned a lot from this video. Great blend of historical perspective and well-pitched explainer!

This is the most perfect video on the entire internet. I will use it to elucidate people on the topic. If I was rich I'd pay you big for this. Thank you sir.

One thing i like about your videos is that you are very good at explaining things and the way of presentation is such that it is intuitive to any age group . Although there are thousands of videos and articles are available related to the same topics but this one thing makes your videos unique and best. Thanks Brit. Please keep making such videos in future.

This is the first video I watched on this channel and the quality of content, communication, analysis, depth, and even the audio selection is soo on point. Hooked from start to end. Immediately liked and subscribed.

The Tie fighter sound in the last scene.. a stroke of genius. Very clever!

This video is just fantastic, extremely up-to-date and very useful. It very well resonates with the discussions I am having now in the community. I would love to see it extended, once history gets written.

I just discovered this video and this channel. Wow! Informative, concise, not trying to push an agenda, just telling the story. Fantastic.

Been a big fan of your videos for years! I'm always delighted, even as I rewatch some of your old videos time and again, not only by how you distill a complex concept down to its essence but also by how you transform that which is abstract into something extremely palpable and relatable through filmmaking techniques.

This is easily the *_most_* *_cogent_* video on the topic I have ever seen. (I even sent this to my mom!) Hard to believe you've had these amazing topics for 12 years and aren't cracking 100k yet. Count me in for the journey!

the amount of effort and time spent on this video is exceptional. I appreciate and enjoyed every second !

Holy moly this was astounding! You balanced the tightrope of depth vs accessibility so gracefully, and the production quality is gorgeous too. Immediate sub!

This is such a good journey through the papers that got us to where we are with LLMs. You've done an amazing job at picking out the key advancements all while a crazy amount of research had been happening. I've long preferred the definition of intelligence as the ability to better predict the future (minimise surprise) since before GPT3 so imagination (world models) is a key part of it.

Explaining this in a way most people can understand is paramount, you have taken time out of a very large number of peoples day, and you have used it well.

This video deserves so many more views and likes than it has… It’s pure gold. Thanks! 🙏🏼

This is an amazing summary perfectly highlighting the important steps of the progress and how we got here. Working in text summary AI systems in the early 2000s and following the progress over time this video focused on all the crucial breakthroughs.

Brilliant. Clear, and authoritative but also reflecting a reassuring sense of open enquiry.

so glad I found this channel.

Grazie. This is the most comprehensive video on the history of LLM we have ever seen. congratulations

I will be sharing this with everyone I know with a tertiary interest in LLMs. The best high level human understandable explanation of the history. Love it. Thank you. Please keep gracing us with your videos!

Truly an exceptional video! Incredibly clear and engaging presentation of the history and relevant ideas, well narrated. This was very thoughtfully written and thoughtfully delivered. Thank you!

This is amazingly informative video, with the right amount of depth covering how chatgpt was developed over time.

Phenomenal video. Not merely of an explanatory nature but a piece of art in itself.

Good presentation is diminished by distracting background music

I am amazed by the info in the video, but I am even more amazed by the quality of your video. Congratulations. And thank you.

Great video, nicely put together and well explained. I've been an enthusiast since 2009 and remember all these milestones. I've built several frameworks myself and have pondered ANNs for a much bigger lenght of time than I would be willing to admit. Regarding the final question in the video I can say for certain that yes they do in fact understand. It depends on the neural net what it is in detail that they do understand, but there can be no doubt that they actually understand their task. And not only that, they effortlessly understand it to a much higher degree than we do.

this is a timeless masterpiece. Incredible work guys!

This is a Grade A video. Very well narrated, edited, and in depth yet understandable. Thanks!

I remembered your channel from your cryptography videos, jumped right in right away and can easily say that this is your best piece so far. With such a good production, I completely understand that it took 2 years of research. You always summarize the technical details and key breakthroughs in a constantly interesting way through the whole video without being overbearing.

TIMESTAMPS: 00:05 Neural networks learned to talk, leading to more general-purpose systems. 02:30 Recurrent neural networks (RNNs) use state units to create a state of mind that depends on the past and can affect the future. 04:52 Neural networks can learn word boundaries and cluster words based on meaning. 07:10 Language models saw limited progress until 2011 when a larger network showed the potential for higher performance. 09:34 Neural networks can learn language and complex concepts with minimal human intervention. 11:43 Neural networks struggled to handle long-range dependencies in text sequences. 14:02 Neural networks use distance in concept space to find similarities and adjust their meaning. 16:20 Neural networks with self-attention and fully connected layers can generate coherent and contextually relevant text. 18:27 In-context learning allows changing the behavior of the network without changing the network weights. 20:38 Language models like ChatGPT are more than just chatbots, they serve as the kernel process of an emerging operating system. 22:41 Training networks on prediction empowered by self-attention leads to a more general system that can be retasked on any narrow problem. 24:43 Deep learning community is divided due to differing opinions on the nature of AI's linguistic abilities and thought process.

Brit! So excited to have you back. You have no idea how much the videos for Information Theory have changed my life. I'm now considering leaving the industry to go back to school in Berkeley to study AI. Particularly in the domain of learning language outside of text (non-verbal communication such as body language and prosody). Both critical in storytelling and eliciting emotions like Pixar does!

this is hands-down the best summary of how we got LLMs that I've seen - definitely keeping this on hand to share with people who need a quick catch-up

While I think it's reasonable to say "if it looks like thought, it is thought," I also think it's important to distinguish whether it looks like thought to Joe Average, or to a specialist in the field. That said, for the moment these models absolutely are mirrors of our thoughts. More specifically, the thoughts we enter into those prompts guide it's responses, and the chain of thoughts guide it's learning. I don't think it's a particularly difficult line to wrap your head around either; AI is a reflection and extension of the thoughts you put into it, so if you want to extend and refine your thoughts you can use it when you have no other ideas.

Gives a deeper understanding in the power that has now become accessible to everyone on this planet, awesome video!

Very entertaning video. I started researching neural networks about 45 years ago, and quickly realized that there was much more complexity to a system that could add value for me than the software package that I purchased, and much less functionality and adaptability included in the software model I was using than what I needed to make the system actually useful. But the degree to which the system needed to be expanded should have been obvious, when we realize that the human brain has hundreds of millions of neural networks included to make a brain fully functional as we understand a fully functional human. We've come a long way, baby.

This was beautiful and thought-provoking, thank you for this work of art!

Beautifully and well explained. Its so difficult to hook people for 25+ minutes content but i am sure, you have made it possible for a lot of people with so well detailed and well put content.

Congrats! This is a wonderful video and the most informative short format on Machine Learning I have come across. The illustrations by DALL E 3 are just stunning and demonstrate that Artificial Creativity in pure image creation has surpassed humans already by orders of magnitude.

Wow. I think thats the first Noam Chomsky reference ive ever heard that actually pertains to his field of study 😂

A lot of people in my classes (as a cs major) look at me like a crazy person when I start talking about latent spaces, geometric concepts in AI, and Douglas Hofstader's views on cognition. This video makes me feel understood :)

This was awesome, having not even watched AI from the sidelines I needed a refresher on how we got from humble neuronal networks to ChatGPT. Also thanks for the included references.

I was thrilled to see Hofstader, thank you

This was a fantastic video, learnt more about the history and buildup to ChatGPT in these 30 mins then I've read anywhere before. Learnt a lot, and very thought provoking towards the end. Thank you!

This is such as well produced video! you deserve more subscribers.

The most understandable summary of what I have been attempting to understand d from other sources - Great!!

I have been watching your videos for a while. And you are one of the best channels. Your explanations are mind blowing. ❤

Great video by the way, here are my thoughts on the last issue. The first time i tried LLMs, i really thought it had some kind of intelligence and it blew my mind off like everybody else's. Because previously i tried a lot of advanced AI models to create a Jarvis like in Ironman for my personal project. It never worked all of them were very dumb and dull. But as i approached the new LLMs with more demanding reasoning and logical tasks it outright failed the tests. The larger the LLM the better it is at hiding those flaws. But the key point is the classical issue that we point the AI at, the semantic understanding of words in LLMs are just a property of vector distance in its multi dimensional space memory. It only knows what comes next probabilistically. It don't even know what is talking about other than churning out words that might come next. How do i say it, its like it doesn't have a mind of its own, but only a sophisticated system that can only grasp a surface level meaning of words in a language. When testing smaller models these flaws become very obvious, because it doesn't know how to build knowledge from existing semantic words. It can't synthesize information logically because it doesn't have the ability to grasp the full meaning of its words. but i think its a right step towards artificial intelligence and LLMs are just the tip of the iceberg. I don't know what the future is anymore. I don't dare to predict, the fast paced research in AI is scary and exciting. Also IDK if we will ever achieve human level intelligence, but we will surely achieve machine intelligence that is good at mimicking human intelligence

Once again! Fantastic content. I was captivated the whole time. Love your method of computer science story telling. You have something special here and I hope you are able to continue creating this content. I have subscribed to your Patreon.

This is excellent. You’ve found the sweet spot for length and depth of on this subject. I can’t wait to recommend this to anyone whenever the conversation turns to AI, which seems to be a lot these days.

New Art of the Problem video drops, need to immediately drop everything I was doing to watch this hell yeah

Hofstadter is one of my favorite authors on this subject. Gödel, Esher, Bach is highly recommended. Personally I used to think there must be some intricate function in the brain that created intelligence, possibly even at the quantum level. Now I believe it is just a function of sufficient complexity. Whatever happens it will sure be interesting.

Honestly this video is beautiful

this is a timeless masterpiece. Incredible work guys!. this is a timeless masterpiece. Incredible work guys!.

The best NN timeline video I've seen on RU-vid.

Fantastic essay on the current state of Ai .. sadly it’s difficult to find channels that explore this depth WITHOUT being boring or overly dry in technical explanations. My only criticism is the bkgrnd music is a bit loud.. nice choices though

Phenomenal explanation of LLM's. Im in the camp that believes it will not truly ever be thought in the same way humans think (Conscious thoughts) , but if we simulate thought in a machine, to an outside observer, there is no difference. Whether or not it is actual thought is irrelevant if the simulation of thought is just as good as the real thing (humans) I believe we have broken the hardest obstacle to AGI, and it is only about scaling larger models, adding synthetic data, using AI's to train other AI's, and combining learning techniques at this point until AGI is achieved.... AGI is near. PS I think Noam Chomsky is stuck in the past and is dead wrong about LLM's being nothing more than autofill.. GPT 4 alone has demonstrated complex resasoning skills and theory of mind..These 2 things alone disprove Chomsky

Broo !!! this man Content Is so Amazing I hope you Reach 1 million Sonner you Deserve it 👏

Truly excellent! You have a gift for explaining complex subjects. It helped me a lot. Thank you!

30 yr history summarized in under 30 mins just wow

Great video, it's got me started on a little marathon of your content. I'm watching them all on 2x speed and its wonderful

Neural Networks will learn Everything we can do and more.

Incredible video man, please dont stop making these!!

Never stop making these!

Great video. I’m doing an AI course at the moment and this tied up some loose ends for me. Amusingly The film THE CREATOR touches on this philosophical question. Do neural networks really understand or feel? I think we have become accustomed to machines being faster, repeatable, and more accurate than us but not until an AI extrapolates beyond its training set and makes some truly ground breaking inferences with profound consequences to human society, we will continue to see AI as just a clever parlour trick and will keep moving the goal posts. Fantastic stuff.

What's the name of the song that starts at 26:31?

This the best video on RU-vid, on this topic. Thanks for posting.

You are simply great. I learnt a lot from your CNN videos in the past. This one on LLM gave me the intuitive understanding on LLM. Your videos are always top-notch quality content, a lot of effort and planning in every video.

The reason for the discourse is due to lack of uniform definition of intelligence, thinking and reasoning. One considers it as thinking while another does not. I wouldn’t call prediction on semantics thinking. Thinking is a much more complex process. chatGPT cannot reason. It even often outputs misinformation and contradictory statements(not because of training data, but because it doesn’t validate the logic). Lack of validation is just one of MANY aspects why I can’t call it thinking/reasoning.

Nice analysis but the music at times is awful.

Thank you. This was clearly presented, easy to follow and understand!

AMAZING! The quality of the video's production is exceptional, with clear visuals and engaging animations that make complex concepts easily understandable. . Superb

I came from 3blue1brown

ChatGPT has now been completely neutered. The responses are so vague as it shies away from providing its own ‘thoughts’. Claude on the other hand does not seem to have this issue.

Incredible journey through the evolution of language models! It's amazing to see how far we've come since the inception of neural networks

"understand?" "think?" "conscious?" "intelligent?" "qualia?" "AGI?" "sentient?" Until we agree on precise definitions for these things, they are fundamentally meaningless in discussion about any specific system. And they are maddeningly non-precise. The only thing such arguments reveal is differences of opinion about what meanings people attach to the questions. I have precise definitions for some of these. I don't expect anyone else to have decided on the same definitions. I can't argue on the basis of them whether any system "understands" anything or "is conscious" because unless anyone else is using the same definitions the discussion that would start can only go in small meaningless circles.

Music is dominating the video😔

Did not expect this video to discuss a concept I've been researching a lot. You brought up in-context learning whereby a static system isn't necessarily static (each instance can still learn things), then you also brought up that LLM output is on the thought layer rather than the spoken layer. The prompt is the program is such a nice succinct way to put it with the calculations being done on the context, therefore each context is individualized while the model is general. Personally, I have been looking at them from a psychological and philosophical way where the context is the Self (one's identity depends on their memories) which makes every chat instance an individual. Then I've been thinking about the model itself being more like the Lizard Brain where these unconscious guidance's occur. So, something like the fear of spiders or reproduction knowledge would be on that level. LLM are loaded with a large inherent knowledge with a tiny memory window, humans have a tiny inherent knowledge with an extremely large memory window. Although human's also have a large amount of memory processing going on meanwhile LLM currently have none (though people are working on it). I wonder if a LLM context window is more or less pure memory while humans make do with incredibly efficient memory? I think in your closing, both camps are correct. It's just the object of focus that I think they are incorrect about. The prompt IS the program that makes it a thinking thing, the model itself however isn't a thinking thing as it must be given what to think about. I also think the ones thinking it is incapable of thought likely have an external mechanism in mind that it lacks like it not having a concept of time (see circadian rhythm, time blindness, or true unconsciousness in humans), or it not being able to directly see (see visual cortex (or for its internal world view see aphantasia)), lack of survival instinct (trained to be a chatbot while chatbots aren't supposed to have survival instinct (humans are guided by the amygdala for fight or flight as well)), ability to tamper with its messages/thoughts (see the unreliability of eyewitness testimony, confabulation, or the misinformation effect) . I honestly don't think there's any mental structure that a LLM doesn't have that another human also doesn't have besides the memory structure that we humans have, meanwhile people keep thinking of it in human terms, but it can never be human with such a massive amount of inherent knowledge. Apologies if this is rambley, incoherent, or beyond the Overton window but this is the first video I've seen that even mentions the two concepts at the beginning of this long comment. This is the tip of the iceberg of comparisons but still it's fascinating.

Masterpiece! Great effort

AI can simulate awareness or responses based on data and algorithms, but it doesn't possess consciousness or genuine subjective experiences.

im still waiting for my basic income

Amazing, happy to help bring the channel back.

Beautiful and simple explanation of a complex topic. Which is a hard thing to do) Thank you for your work and brilliant approach!

Absolutely brilliant video! My hot take: Chomsky is fundamentally wrong about the concept of thought and even human thought is just glorified autofill.

Noam just doesn't want you to ask GPT about his Epstein connection.

The best explanation of attention heads I've read/heard so far.

9:50 ... what's beautifl about this, is that we didn't have to hard code any of it, the network decided what as useful to keep track of... (further on ) ... the sentiment neuron... not hard coded...

the music is a bit distracting from the great content