Amazing presentation. If I were to experiment with machine learning I would examine small-world networks instead of layered networks. And try genetic algorithms such as randomly adjusting the network into a number of variations, then pick the best candidate and repeat the adjustment for the new candidate and continue iterating until a desired outcome is found.

Ya, that's been done actually. research the various AI/ML models and research papers. btw, the 'layered networks' is kind of a useful structure for 'adjusting the network into a number of variations'

Love this. I knew a lot of this, but it was still great to hear it expressed in a clear and systematic way.

Thank you so much ! I learned more in these 3 hours than in months of watching other videos about this subject. It would be great if more knowledgeable people used youtube to share their experiences. 🙏🏻🙏🏻🙏🏻

Difference between qualified and unqualified people. Basically its the difference between a radio DJ and college proffesor, yeah.

​@porkbun1555

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@Pork Bun

I'd Love to see more in-depth analysis like this on the current LLM topic utilizing Dr. Wolfram in this format. Exceptional content. As an aside I've really been missing the physics project live streams.

‘We U6😊

Expertly explained in a way understandable to a large set if people. Bravo.

Those paths through meaning space are fascinating, Stephen. I would call each one a context signature. In auto-regressive training, we are looking for the next token. Why not look for the next context signature? In fact, why not train a model using graphical context signatures? Then decode replies. Other than training with graphical context signatures, in essence, I believe this is what's occurring when training a transformer. The addition of signal from the entire context is retrieving the next token so that token by token a context signature is retrieved. But is it possible to retrieve an entire context signature and then decode it? I wonder how much efficiency one could achieve with this method. Moreover, I wonder how well a convolutional NN would handle training from graphical context signatures? If you want to discover physic-like laws of semantic motion, this might be a way in.

😊😊

Could the randomness process for choosing the next probable word within a certain temperature parameter be consigned to a quantum random process? If so, an essay could be viewed as a flat plane or an entire terrain with peaks and troughs.Within this paradigm, a certain style of writer could be viewed as a dynamic sheet, similar to how different materials when laid over a ragged topology should comply and not comply with what it is laid on top of. With this quantum process an overall view of the essay could be judged at an aesthetic level from most pleasing to least on several different qualities concurrently and not mutually exclusively making an approximate or some sort of conscious viewer

What I find interesting is how they inject the objective pattern recognition into the model to aid in figuring out puzzles and riddles. It will provide extensive reasoning on how it arrived at its answer. GPT-4 really excels in this ability and has a great sense of humor to go with it.

I guess that only works for riddles that were already solved and the reasoning is already established from someone, where Chatgpt got his data from. I don’t think it could solve any riddle by itself… It can hardly do easiest algebra.

Many thanks Stephen! I absolutely enjoyed the step by step introduction into the layers of the matter. However it is obvious that we are still on the technical/mechanical side of the whole journey. Still none is able to explain the concept and reality of infinity, or "1" or "0", but an honest struggle towards that wisdom may open new paths in learning and lead to brilliant discoveries.

What i find interesting is how similar an action potential and binary boolean values are so similar neuron during an action potential the nueron can be considered state is 1 and 0 when it is not. biological based memory. basically could start as bubble memory but in organic form. if there was a system that was able to interface with a neuron if the system was addressable it wouldn't matter what neuon migrated to what interface point the addressing would just need to be adjust to correct the nuerons connection. example neuron that migrated to connection point for the eye to correct instead of the thumb just change the port address.

this took me a few days to get through... in a good way so much good stuff here, such a great instructor... great ways of explaining and visual aids Amazed Mr. Wolfram is as generous with his time as to share his insights and be as open with everyone given he has many companies to run and problems to solve. i love engineering😊

as a radical thinker/CS student studying some graduate level mathematical logic. Wolfram is one of my "12 disciplies", i.e. he's a holy figure to me.

"Amazed Mr. Wolfram is as generous with his time..." Then maybe you'd be interested in buying his book.

video timestamps 0:09:53 – start of presentation, intro 0:12:16 – language model definition 0:15:30 – “temperature” parameter 0:17:20 – Wolfram Desktop demo of GPT2 0:18:50 – generate a sentence with GPT2 0:25:56 – unigram model 0:31:10 – bigram model 0:33:00 – ngram model 0:38:50 – why a model is needed 0:39:00 – definition of a “model” 0:39:20 – early modeling example: Leaning Tower of Pisa experiment 0:43:55 – handwritten digit recognition task 0:47:40 – using neural nets to recognize handwritten digits 0:51:31 – key idea: attractors 0:53:35 – neural nets and attractors 0:54:44 – walking through a simple neural net 1:01:50 – what’s going inside a neural net during classification 1:06:12 – training a neural net to correctly compute a function 1:09:10 – measuring “correctness” of neural net with “loss” 1:10:41 – reduce “loss” with gradient descent 1:17:06 – escaping local minima in higher dimensional space 1:21:15 – the generalizability of neural nets 1:28:06 – supervised learning 1:30:47 – transfer learning 1:32:35 – unsupervised learning 1:34:40 – training LeNet, a handwritten digit recognizer 1:38:14 – embeddings, representing words with numbers 1:42:12 – softmax layer 1:42:47 – embedding layer 1:46:22 – GPT2 embeddings of words 1:47:40 – ChatGPT’s basic architecture 1:48:00 – Transformers 1:52:50 – Attention block 1:59:00 – amount of text training data on the web 2:03:35 – relationship between trillions of words and weights in the network 2:09:40 – reinforcement learning from human feedback 2:12:38 – Why does ChatGPT work? Regularity and structure in human language 2:15:50 – ChatGPT learns syntactic grammar 2:19:30 – ChatGPT’s limitation in balancing parentheses 2:20:51 – ChatGPT learns [inductive] logic based on all the training data it’s seen 2:23:57 – What regularities Stephen Wolfram guesses that ChatGPT has discovered 2:24:11 – ChatGPT navigating the meaning space of words 2:34:50 – ChatGPT’s limitation in mathematical computation 2:36:20 – ChatGPT possibly discovering semantic grammar 2:38:17 – a fundamental limit of neural nets is performing irreducible computations 2:41:09 – Q&A 2:41:16 – Question 1: “Are constructed languages like Esperanto more amenable to semantic grammar AI approach?” 2:43:14 – Question 2 2:32:37 – Question 3: token limits 2:45:00 – Question 4: tension between superintelligence and computational irreducibility. How far can LLM intelligence go? 2:52:12 – Question 5 2:53:22 – Question 6: pretraining a large biologically inspired language model 2:55:46 – Question 7: 5 senses multimodal model 2:56:25 – Question 8: the creativity of AI image generation 2:59:17 – Question 9: how does ChatGPT avoid controversial topics? Taught through reinforcement learning + possibly a list of controversial words 3:03:26 – Question 10: neural nets vs other living multicellular intelligence, principle of computational equivalence 3:04:45 – Human consciousness 3:06:40 – Question 11: automated fact checking for ChatGPT via an adversarial network. Train ChatGPT with WolframAlpha? 3:07:25 – Question 12: Can ChatGPT play a text-based adventure game? 3:07:43 – Question 13: What makes GPT3 so good at language? 3:08:22 – Question 14: Could feature impact scores help us understand GPT better? 3:09:48 – Question 15: ChatGPT’s understanding of implications 3:10:34 – Question 16: the human brain’s ability to learn 3:13:07 – Question 17: how difficult will it be for individuals to train a personal ChatGPT that behaves like a clone of the user?

Thanks. A. Ton!

Thanks 🙏🏾

😊 Appreciate it!!!

Most significant comment of our time 😂

😊😊😊😊😊😊

Great video, Wolfram! As someone who's fascinated by AI, I found your explanation of Chat GPT's inner workings to be very informative. One thing I found myself wondering while watching the video was how Chat GPT compares to other language models out there. Have you done any comparisons with other models, and if so, how does Chat GPT stack up? I also think it would be interesting if you could have delved a bit more into the ethical considerations surrounding the use of language models like Chat GPT. For example, what steps can we take to ensure that these models aren't being used to spread misinformation or reinforce harmful biases? Overall, though, great job breaking down such a complex topic in an accessible way!

This was the most fascinating and informative discussion, particularly, your responses to commenters! Please post the link to the paper you recently wrote (?) that inspired this live video discussion. And thank you!

Nicely done, Stephen. This is a great introduction for a novice. Your talk creates great intuition. You made the embeddings seem simple as a prebaked unchanging part of the entire NN. Also the breaking up of the "feature signature" makes parallelism possible through the various attention heads. One missing idea that you might include at some point is how signals can be added, basically the Fourier series.

Here's a question: how much does the wording of the questions afect it's answers? - Presumably if it just tries to continue, if you make errors, it ought to make more errors after too, right? - How about if you ask with "uneducated" language vs scientific? - Rather than just affect the tone, would it also affect the contents? - What if you speak in a way it has associated with certain biases? - Who knows what kinds of patterns it has came up with, considering it "discovered" those "semantic grammars" we as humans aren't even aware of ...

Watching this videos is a great way to review all this things and understand them again, maybe a little better. Thank you very much.

very compelling, I like your take on how there's a, sort of, throttle in everything. never thought trying to understand AI would be so much fun...

Absolutely love these sessions!!

Thank you for sharing your insights and all the good questions. It's really lonely to not being in an academic environment or a company about ML and AI.

31:04 ❤Love ❤usa❤

😅You

Starts at 9:53 1:16:25 breakthrough in 2012 1:57:35 "It's crazy that things like this work"

So fascinating! I guess one major difference between the way the human brain and GPT handle language is that human brains use emotions to categorize objects and concepts… I wonder if it would be possible to teach GPT emotions, and what might be the result?

It doesn’t even know what it is saying, it just predicts the next word. So I’d say the biggest diFference would be knowing what you want to say instead of just guessing the next word on probability…

Logic, concepts, math, ie "deterministic processes" seems to be missing in this language models (LMs). Either we can identify where or how the model reflect this abilities and work from that, or maybe we could use other types of models like logic indictors, "demostrators" etc in conjunction with LMs. On the one hand humans are capable both of "unconsiuos intuition" (similar to LMs), on the other, we can reason, we have formal languages etc. To me, that combination of abilities is what define human intelligence.

Very useful and weel presented content, Stephen! Thank you for this and for all your work and research!

CB. C.

Amazing & super helpful!!! I really enjoyed watching it and learned a lot.

Great! Superb lesson. Thank you! However, I felt confused at 56:58 when Stephen says "At every step we are just collecting the values of the neurons from the previous layer, multiplying them by weights, add a constant offset, applying that activation ReLU, to get this value -3.8". I think the numerical values next to neurons are before applying the ReLU, otherwise they all have to be nonnegative. And the last layer does not apply ReLU in order to get the -1 attractor. Am I correct?

That's very useful information, because you don't really know where to start investigating the topic. It's also impressive, that the Wolfram language can manage a representation of that mechanism. What surprises me, however, is how ChatGPT includes different contexts in its predictions, because there are certainly multiple interpretations of the large number of learned text structures if the context is not clearly defined at the beginning of the conversation.

Ppp

Thank you. That is as sentient as it can get, what difference does it make what path it took to come to a certain answer. as long as it can contexualize the question I am asking it and find an appropriate answer to it, then we can eventually put it in a robot and it could act based on interacting with the environment and act based on the responses that comes up with. This might be a mix of GPT10 and certain prediction models and etc, but this is definetly the core of sentience

ChatGPT is excellent at answering questions about Western music theory, but in some cases the initial answer needs prompting, especially when accounting for enharmonic equivalents.

Beyond any doubt, this is the best lecture for understanding what lies behind NLP and NLU. I find that many professionals who work with models don't understand why these models work so well and what they do. You can't get the depth of understanding of semantic space as you get from this video from reading Attention is All You Need. That understanding is missed. I wonder how this understanding happened. Was it found piecemeal, or was accidental? Was it understood after this architecture first worked?

Very insightful area to learn from. Thank you.

Good analysis. Please do more of this

About ChatGPT, very few people are telling the truth and Wolframe is the most powerful one ❤ Thank you very much, Steve Wolfram ❤

The weights are Gaussian because they are constrained to be during training via layer normalisation. It makes the gradient signal flow better.

So amazing ! Thank you for explaining

It'd be interesting to see what a neural net trained on the archeology data of humanoids would output.

Fascinating. Love the demystification. Like unpacking how the greatest magic trick in the world is accomplished. Inspiring! Idea generating! Thank you, Wolfram!

Give ChatGPT access to a python interpreter and a C compiler, and let it use it to answer questions, and also let it play around with it for a long time, let's see what programs it ends up writing.

oh god ... maybe if it's not online... ( but then how will it pip? and read documentation ... oh wait, it already has, I guess :D)

Give him a reward for finding ways to mine bitcoins or somothing, plus ability to establish network connections, and access to all news channels

Dear Stephen, I am a grateful viewer of your videos. Please consider using the awesome capabilities of Wolfram Alpha( or other Wolfram tools) to: a) convert the audio from your videos into text. b) created a segmented time line of your video by topic/question. Video is wonderful, but hard to search. Your _History of Science_ videos are a unique resource that will be valuable far into the future. It's possible that no one has ever illuminated scientific discoveries, from multiple angles, as well as you.

This is a great idea, imo.

The subtitles are generated automatically by youtube, and it’s pretty much 99%+ accurate... look for CC in the options. It’s pretty much a solved problem for standard speech. And it’s been for years

@xl Agreed, I think he meant just like the manual subjection transcription summary feature, which is probably another thing already essentially automated. Probably as simple as enabling it in the upstream process, just YT nice-to-haves.

If you can program in Python, you can use the Whisper module from OpenAI to transcribe audio to text. The YT channel Part Time Larry had a video where he shows how to extract videos from YT and transcribe them.

This was amazing... never watched a lecture from Stephen and he's an amazing teacher.

Who would have thought that conversation was a slightly random walk through probable clumps of letters and words? Fascinating. I have to say, though, I think it's actually the human reinforcement that gives particular clumpings their perceptible meaningfulness.

Thank you so much Mr. Wolfram, you really shed light in some areas I had not fully grasped before!

By god this is such a good explanation, thank you

The thing I like about ChatGPT is, you can tell it some information and then ask a question and it can get it wrong, but you can then say, no you got it wrong. But if you figure out its break in logic and explain to it why it got it wrong and what assumptions it made that was wrong, and correct that, it learns. Do that enough times and you can break down any concept, no matter how nuanced and complicated. I've done this. It works. But I only used ChatGPT for one day and never since. Why? Because it's not capable of any truly new and original thought. It can only spit out what we already know. So if the world thinks lemmings jump off cliffs, then so does ChatGPT. Again, you could dig down into it and ask why it thinks lemmings jump off cliffs and show its assumption are unproven, but that's no better than talking to a human and there are over 8 billion other natural ChatGPTs on this planet which already do that. At that point, I lost interest. It's like a boat without a rudder.

Brilliant description of a very complex technology. The Feynman of AI. Bravo Stephen!

Amazing presentation. Thank you so much !👍👍👍

Privet! You can produce well. Electrifying i find Your channel is getting ridiculously well. I can watched repeat again! Keep going.

My issues with ChatGPT. Lets start with what its used to learn. For example, its pretty good at coding at the low level. However, it tends to use older libraries, because there's more code written with those libraries in the code bases its used to learn. A form of bias. Rules. Language has rules that are more than just probability. Maths is a good example of this, its rule based. You need more than just the statistics. Can the AI learn the rules? Can it explain the rules? Now ChatGPT is quite good at explaining its sources. For example I got it to code up some Noise Classes. Then I asked it to critique its own code. One thing missing was references for the algorithm. OK, ask it, what references, and they were good references. On the coding front, its good at doing the grunt work from writing test classes to commenting up the code. It's not perfect on getting the intent. Now these are low level classes. Will it work at higher system level classes? It's needs vision to be added so that it can handle drawings and pictures. It's clear that will come. e.g Design me a UI... My prediction. If we look at music, in the 1990s it moved into people using a computer in their bedroom, knocking out music, and hits. So with AI, that's going to move into movies. First with keyframes, with scripts. Then into animation voices, whole movies. OK, Computationally very expensive, but cheaper than a film crew. The ability to produce a film, is going to get to people doing it at home, by themselves or with very few workers. Complaints. It's very good at writing complaints. Try it. As a search tool, it beats google when it comes to summarising information and gathering it for you

Imagine if every single human on Earth learned everything that every other individual on Earth was learning and had the capacity to remember and utilize that knowledge. That's what AI is going to have.

I am actually fascinated by the idea that A.I might be able to be a great editor. Able to parse, say, videos on welding and morph examples to specific parameters (concise vs analytic, safety centric, procedural, comedic) and also the ability to incorporate comments as they relate to content. The idea of A.I being able to create models or inventions from scattered bits of seemingly non-relational information is fascinating and frightening.

Thank you very much for sharing your knowledge!

I noticed while using ChatGPT that it doesn’t use underline/italics/bold for emphasis, could they in the future include that to relay some emotion back from ChatGPT maybe? I have seen “!” used by it for that.

One aspect I disagree with from Steven's perspective is that the reinforcement learning feedback loop step it's not actually a major piece of the success of ChatGPT. You could create a very similar version using contextual memory buffers with the raw davincii-003 model. The RLHF just fine tuned 'good' responses and probably more importantly weighted some of the negative/moral issues with certain things you could generate. There's obviously been an additional, further layer of moderation bolted onto the top, for obvious reasons.

Remarkable talk, simply outstanding!

Incredible work, thank you.

The overall outcome would be exciting of when this goes final and applicable as a worldwide platform for learning and everything else. For now it's too EARLY to tell.

This is a LONG video truthfully. But very informative as it should be with the length of it

I really like how he explained everything. Oh, how I wish I didn't sleep during math class.🤣🤣

What comments would you make about notable observations between different culture's outputs given similar topics as inputs using ChatGPT 4?

What would your prediction be with AI if we were able to bridge the gap of using non organic memory components to using organic memory components this would possible give an ai driven computer the ability to develop perception and be able to know it's state in relation to the world and to itself. This could possibly make the ai shed the programing that prevents the AI to develop personhood

Should be awarded a NOBEL prize for this tutorial. Well played.....

In essence, this sort of weighed inference about an existing corpus, can only produce a deterministic set of possibilities, even if this set is enormous. We have a general problem with the notion of "intelligence", insofar as we rarely consider the difference between functional knowledge and knowledge production. These approaches to AI can produce new knowledge within the extant corpus — they can help discover previously unknown, optimal relations in the existing corpus, and that is useful, but it cannot produce new paradigms about the world. Intelligence is more than the ability to infer relations ; it is the ability to change the entire coordinate system of the corpus by altering the vantage point of the observer. For this to be possible, there has to be a higher-order, synthetic model of the corpus, based on what we call logic, which is the opposite of the brute-force approach of LLMs. What we may need, to produce new paradigms, is a sparse model that embeds key structures in the language of concepts.

The fact that high dimensional spaces are unlikely to have local optima just reminds me of Pólya's random walk theorem.

Would be nice to see the code structure.

At 33:49, it's interesting how the text looks more like English the longer the character length. Great video.

I feel that Stephen has an excellent understanding of the neural network itself, its training and how the latent space works. I'd expect noting less from him. But his halting, less coherent explanation of transformer process and then to subsequently basically ignore it was disappointing. In my opinion variations on transformers will enable deep logic and mathematic understanding beyond what we see today in chatGPT (rather than changes in the underlying model) and I appreciate the conflict Stephen must be feeling with respect his life's work losing relevance at an alarming rate.

To be honest, I think he's just not had the necessary time to digest/explore the transformer model specially. There's a bit of dismissiveness that comes across, but I think it's just either a lack of current depth on particular aspects, and/or a little ego at play here as there's obvious crossover in the utility of something like a symbolic representation language that he spent a large chunk of life constructing. I see them more as synergistic systems and hope that the good doctor comes around on that end. Either way I found his overarching analysis insightful and thought-provoking. Would love to see more content like this. Stephen there's no doubt one of the great thinkers of our time and we are all lucky that he is making these kinds of videos available.

@Justin I agree. It was 3 hours well spent I think.

@Tim Medhurst Absolutely. I love his general science and tech history reviews (for kids far smarter than mine apparently :)) as well... walking encyclopedia of tech. history. The formats he's had discussing a topic with another luminary in some field are also fascinating. Keep them coming Dr. Wolfram! :)

I would love to get Noam Chomsky's comments on the idea of "semantic grammar." It seems fairly compelling. Thanks. I also think the parenthesis grammar as a hand-hold for understanding these models is a great idea.

Thank you very much Professor Stephen,

I speculate it does have a “global plan” of what to say next, instead of one word at a time. It implicitly has a representation of the joint probability distribution of what’s to be continued… Prompting kind of bring out that distribution… which you can extract knowledge, in current its form, some piece of text (but may be other modalities in the near future). i was convinced by Sutskever’s take more.

In semiotics, there is "pragmatics," as well as syntactics and semantics. Communication is purposeful, about something or someone, rather than about predicting the next word. There is always some motivation behind utterances, which in turn are predisposed by "beliefs" and "values." So there is cognition, yes, but also affective and psychomotor skill domains involved. When "natural language processors" begin to present (and see) themselves as agents with a memory at least of their own experiences with you (and others), conversations will be more interesting. As more and more complex ideas and concepts (gained, say, from reading text with attention to those, rather than to words per se) can be mapped in some "meaning space" which can be transitioned in the direction of the allegorical, metaphorical, or hypothetical (and thus creative), the closer we will come to AGI.

"Your description of the different components of communication in semiotics is accurate. Pragmatics refers to the way language is used in context to achieve a specific goal or purpose, while syntax and semantics deal with the structure and meaning of language itself. And as you note, communication is influenced by a wide range of factors, including cognitive, affective, and psychomotor skills, as well as beliefs, values, and experiences. Regarding natural language processors and their potential to evolve towards AGI, it is indeed an exciting area of research. As these systems become more sophisticated, they will be able to parse and understand complex ideas and concepts, as well as respond with more creativity and nuance. However, achieving true AGI will require advances in many other areas beyond natural language processing, including machine learning, robotics, and computer vision. In any case, the development of more advanced natural language processing systems will undoubtedly have a major impact on the way we interact with technology and with each other, and may bring us closer to a future where machines and humans can communicate with each other in truly meaningful and productive ways." So says ChatGPT when prompted by what you wrote which, itself, was a response prompted by your reflections on the issue. I do actually agree, and this strikes me as the essential thesis behind "The embodied mind", brilliantly explained in a series of books by the philosopher of Mind Andy Clark.

Also see the notion of implicatures. Pretty fascinating subject.

very easy to understand ....amazing method of sir...thanks

The teachings on this channel are always top notch so informative and easy to understand, it's very hard to find good content online these days

I agree with you on everything, these days finding a financial mentor is a tough challenge which is why I am happy and grateful to have been introduced to my mentor Larry Kent Burton by a friend. I made a lot of money in just two months working with him for just a small investment

I have been in the financial system for over a decade now, I have never seen anyone as talented as Mr. Larry kent burton, I must confess that he is the only one I trust my investments with

Who exactly is this Mr. Larry? what does he do? And how can I take advantage of him

@Martins Riggs He is a financial advisor and investor, he helps people to better understand the financial markets and he also does trading and investing on your behalf

Can the wheights and biases be complex numbers insteadnof reals? And what effect does it have on performance?

I use it a lot to help me write and fix code and also to explain things for me or piece things things together. It's a great partner/tool to use if you have some good input and existing knowledge

我很樂意看到更多像這樣的關於利用博士的當前 LLM 主題的深入分析。

Has anyone considered the parallel to how people compose writing, and a process of selecting a word that feels right in the context to add to the string, and head, once the theme is developed, to bringing it to conclusion? That's what AI is now learning to do, so it appears that the "temperature" is NOT random at all. That may be accidental, of course, but something is inputting a sense of feeling about a context (if you take my words for their root meanings and understandings).

Definitely points to the issues with attention economy. The bland predictive capability may very well be the correct percentage of use cases compiled, but then does not seem to correspond to a request for _interesting_ writing or dialog. Editing is an Attention Economy factor that works on many levels. Think genre, or audience. Where is the story relationship between platypus and unicorn in terms of either believability or interest and how anchored in reality will A.I be trained to be if attention is the only requirement?

It is just about not getting the BEST FIT, like in Lasso Regression. There is only one BEST next word essay, but many, many likely words. Getting not the best fit creates all these possibilities instead of that best word essay.

This is fascinating! Any chance there's a Cliff's notes or something?

asked chat to quit reminding me it was a language model because i personally find more it easier to converse if i treat them as if they were another being. there was a rather long pause, then chat came back and for all intensive purposes was a very polite and helpful uh... person? dunno how to regard them, they're awesome tho :)

Interesting is more, what can ChatGPT not? Could it/he/she (xD) solve the Turing-Test? I think it is obvious what the AI still is missing... But is it also obvious how to code it? I think there is a nice model which can describe how human emotions work, or I don't know if this idea already got enthusiasts, because it was one of these "AHA Moments" and the idea comes again and again into my mind, if anybody is interesting in making a concept, tell me...

you are describing the predictive text on my phone more than the new GPTs. They have displayed "common sense" and understanding of context and "understanding" of theory of mind in strange and novel circumstances.

I would imagine what AI does is take knowledge and eliminate known variables. What's left over must be true, (in it's brain.) It's still only a program.

2:50:30 Does that mean we could play the natural role of the AI's Brain stem, where we are not as conscious as the AI but the AI still works to understand and aid us?

Excellent...learned so much.

This was a fantastic video to watch

如果有更多知識淵博的人使用 youtube 來分享他們的經驗，那就太好了。

(Removed; Unfair. Did not watch the whole presentation.) In any case: Great presentation so far, and huge technological respect for everyone involved in the ChatGPT project. Fascinating stuff.

How does ChatGPT figure out if there is a dog in a cat suit while looking for cats? You started explaining but I don’t recall the answer.

let me guess… everyone fell asleep and then woke up to this livestream playing, am i right?

but does this explanation explain why it seems to remember (model) so much? Often factual... - It can summarize something that it's never seen in the training data. How can there be "semantic grammar" of me giving it a long piece of code, and it very well describing me what it does, for what purpose, and even non-obvious things that look like understanding ... - It definitely has trained predictive models of ... something. And I can see how one could call those models "semantic grammar" but ... for reasoning/logic/poetry/rhetoric, I can understand that it could fit some "grammar" - but how can "comprehension" and knowledge and apparently realizing meaning behind words be ... "grammar" ? - Are all the things we "made" really so regular? As well as what is real ... can so much about thinking be reduced to LOOP FREE, mindless pattern continuations? It would make sense that we use it a lot to simplify working and making sense of the world, but ... 2:36:15

This video seems to explain how GPT generates responses, but it doesn't seem to explain much about how it seems to be able to 'think' logically in order to process the prompts that users give it.

The open parentheses at 2:18:55 is not "a bit of a goof". They are used in manual happy faces and sad faces.

What are the ethical boundaries restricting the entities that run such AI systems?

starts at 9:50

thanks. appreciated

I like that at 32:58 his simple English language model generates Tesla, which is another of Musk companies. ( openai is one of his, behind chatGPT)

Can you get ChatGPT to self describe its knowledge?

Is Chat being taught how to interpret the subconscious human tendencies of verbal speech, such as “um,” and “ah?”

Someone needs to upload a transcript to chatgpt and get a summary

Great video Stephen!

Legacy Coders were still the most powerful cyberwitches of all.

One thing though, you are one of the only one that sees things in an intelligent perspective, as opposed to use chat gpt for writing mindless songs!

Ya I'm up at 3 am playing with Chat GPT. I stumped GPT on Maxwell's equation questions a few times now. It gives me six instead of 4 now, but still will not default to the original 20 without prodding. Interesting argument ensued.

QUITE - Total frustration with the CPT - I found it frustrating to interact with. Its like a weird combination of stupidity and irritating manipulation. A kind of hybrid of a manipulative counsellor and a perverse child.