Large Language Models: Part 2

[@5133937]

This is one of the best explainer vids on LLMs I’ve seen yet. Not too long, not too short, good pacing, good visualizations. Great work, thanks!

[@TimHulse]

I really enjoyed both of these LLM videos. They are so concise and informative and the pacing is excellent.

[@w花b]

He is an Arab from the Middle East. My dog wants a walk.

[@apc067]

I [or shall I say, my internal GPT?] first misread your comment as "... and the pancake is excellent". 😃

[@kahoku451]

It’s amazing and so funny to me how LLM can produce fully functional python programs and write these poetic Bob Dylan inspired lyrics… but when prompted with “you’re going north. You turn right, then you turn left. Now you’re going …” it said SOUTH ☠️

[@samandoria]

Just like human "brain cramps" or "brain farts". The problem is that the current models aren't self learning or made to analyse their own answers so they don't correct these error before output or can correct the weights in the future when confronted with new data.

[@TimHulse]

It Re-raises old but interesting points about the difference between language and logic. You can make linguistically sensible statements that defy logic. Try the book “Godel, Escher, Bach” if you like this area.

[@webcache1000]

6:02 If you are just just south of the North Pole, turn right and turn left over the North Pole, you would be heading South. I suspect it is getting confused due to the similar riddle where if you head North, turn right and you are now heading South - where are you? It has probably seen this riddle a confusing number of times and weaved that into its weights/response.

[@distrologic2925]

Because it doesn't necessarily understand how directions work, it only knows how "north" and "right and left" has been used in language before, and is only an estimation. People are unlikely to talk about celestial directions and turn right or left, so there was probably not enough similar to your query in its training data that it could draw on and other examples such as "north and south" have taken more weight.

[@swanandjoshi333]

Logic is a whole different thing ig

[@scottlott3794]

Dude, I have been researching Transformers and Attention for weeks. You’re a master mind, I get it now!

[@csmac3144a]

This should be adopted by high schools everywhere. Superb teaching. A bit fast for me (I’m 61), but I can re-watch until I get it.

[@f18a]

Great stuff. Possibly the best intro material to LLMs that I have seen. Thunbs up!

[@roywem]

This is the single best explaination I’ve come across on LLM’s

[@Charlieee1]

I like that you used a recipe prompt to demonstrate what a LLM is good at doing, then actually followed the recipe and proved that it actually worked (and tasted good!).

[@fazlfazl2346]

The good thing with short tutorials is that if they are good, you will save hours of work. If they are bad, no time is wasted. This is a good one. Saved hours of work which I would never do anyway.

[@stuartthomas7441]

So pleased to get a clear and credible glimpse under the hood. Thank you.

[@poklet]

Thank you. Those were very clear explanations of just the right length. Loved that you cooked the pancakes too!

[@ianassarandas]

you actually made the cookies hahaha that's awesome, great video btw!

[@jaunalapa]

You, sir, have talent for teaching. Even with my fairly technical background and watching a lot of videos I was struggling to get my head around LLMs until I watched your videos. Hope you continue what you started it would help a lot of people especially now when we see a lot of people trying to run their own local LLM versions on their PCs.

[@yojimbomk]

Just 16 minutes, it is amazing. These are the best concise videos on the topic including basics in neural networks I have seen. Many thanks for sharing your knowledge!

[@markbordelon1601]

part one and this part two are masterful introductory teaching

[@shoe_Bin]

Soothing voice to learn about transformers and warm room is perfect for recipe for my sleep.🛌

[@aidarfaizrakhmanov1901]

This is a really good intro indeed! I encourage to make more content like this

[@janicem5942]

Brilliant overview for a non-technical person like me... and glad to see you tested the recipe!

[@EGlobalKnowledge]

An excellent video on language models

[@MaleGeminiCat]

These are two great videos that introduced how large language model works in a very comprehensive way.👍👍👍

[@fernwood]

This brings me one step closer to understanding. Thanks.

[@ypetkar]

Very informative and concise videos to understand LLMs their complexities and what i takes to make these models successful.

[@xybnedasdd2930]

I think this is the best, most intuitive and most illustrative video describing LLMs/transformers. Thank you so much!

[@eenvleugjegoeiegames]

Man you NEED more subscribers, the content and video quality is way too good for only 15k!

[@vast634]

You really have a talent to teach things.

[@0815Snickersboy]

This is the best video on Language Models that I seen. Probably the best on the Internet. You should maybe add chatgpt to the title to get more views.

[@DavidDiaz-zg5sv]

I love that you finished cooking the recipe! Great video :)

[@DrNuyenVanFaulk]

Thanks so much for both of these videos. They are wonderful. I think I understood them a bit more since I’ve done some basic assisted machine learning dev (up to neural networks). If anyone is a bit lost, read up on linear and logistic regression, then onto neural networks.

[@craftycurate]

Really easy to follow, well paced, easy on the ear, and just the right level thanks!

[@TheCaioKyleBraga]

This is my first search for a LLM explanation and very pleased with the video. I am not a mathematician or programmer but I am very interested in learning how LLM works. From my humble perspective I can say we reached a point of no return and this technology is progressing at an exponential rate. With the development of quantum computing, I have no doubt that it will surpass human intelligence in ways we don't understand.

[@sciexp]

Very interesting... Now I understand more about how ChatGPT works...

[@GodofStories]

Absolute best explainer. Where have you been though since GPT-4?? Would love to see you do more content. You have a talent!! I think you might be busy working on LLM's for research or in a company, but would be cool to see more videos!

[@bingolio]

EXCELLENT Vid, PLS DO MORE, on Deep learning , covering the whole workflow of making an LLM, especially, what os LORa, VEctor embeddings, etc I'm sure you'll get huge interest You have a gift for explaining. Thanks!

[@Paul-rs4gd]

Thanks for an original presentation of Large Language Models. It gave me new insight.

[@frank-reneschafer5512]

Great effort - nice video, good and comprehensive explanation.

[@roscatres]

I hope you keep up with these videos, they are seriously great. Already suscribed and I'll check the rest of your channel. Thank you.

[@stefanbiesdorf4637]

Thank you Steve for a personal moment of enlightening by opening the "black box of AI". This is an outstanding educational piece, in particular in combination with the first part.

[@smudgepost]

Very much enjoyed these two videos. More please! Clear and detailed.

[@dv6165]

This was awesome! Thanks

[@padetiit7014]

I loved that you actually cooked that recipe! :-)

[@ravinatarajan4894]

Thanks for the crisp walkthrough of the technology. It is a very good introduction.

[@sreenivasulupala2374]

Excellent explanation. Thank you.

[@somethingness]

Typo alert! Frost's wonderful poem begins as so: "Two roads diverged in a yellow wood". (Not "diverted"!)

[@koho]

Great overview! People need to see this video pair before freaking out that LLMs are actually intelligent.

[@yushpeaceandlove]

That language neural network at 0:50 belongs on a tshirt somewhere

[@KalebPeters99]

Not a second wasted. Just brilliant ❤️

[@saisaske1]

Very good explanation

[@Patapom3]

Very well made!

[@jteichma]

What a great walk through! Thanks so much for sharing.

[@AncientSlugThrower]

Great video, leaving a comment to let you know it was very insightful. Thank you.

[@xflory26x]

Please do more videos on LLMs!!! But also I need to know, how were the pancakes?

[@danjsy]

Great, thanks so much, massively useful.

[@SvenJankowski-x7c]

amazing video! thank you!

[@MindyMcAdams]

Thank you! This is excellent. I love the animations. They are helpful!

[@preethamrangaswamy7371]

Very underrated and underappreciated video.

[@SethWieder]

I would love more of the visual explainers on ML concepts. Subscribing. "Some folks say they're overhyped / But I do think that's true / I think they're just misunderstood / Just like me and you" That generated lyric gave me CHILLS

[@kahoku451]

Sameee

[@KumquatChampion]

This is awesome. Would love to learn more!

[@tvillaluz]

Haha you made the pancake

[@DJWESG1]

Ever think about audio synthesis and wave forms?? And how analogue synthesis utilising wave tables can offer a way to both communicate and comput information.

[@abvll5049]

amazing videos .thanks a lot

[@carrumar]

Great videos! Btw, how did you like your avocado cocoa thing? 😄

[@Sukant98]

amazing videos!! learnt so much

[@rajachan8588]

Awesome. Thank you

[@bloodywolftr]

Pretty nice explanation in both videos.Thanks!

[@speicaldark]

This is really nice! Now I understand why chatGPT tends to make up a lot of stuff with coherent sentences

[@w花b]

What I'm wondering is how are they correcting its errors. For traditional NN, we have heat maps but I'd like to see something similar with transformers at the highest level to see what kind of patterns it noticed. Maybe that's what they use to correct its mistakes

[@gideonk123]

@@w花b ChatGPT used a process called reinforcement learning from human feedback (RLHF): They used an already trained GPT-3 which already at the time. Humans both submitted new sentences as input prompts to ChatGPT being trained, and also ranked the output (responses) of the model. Then the ranking of the responses were then used as reward targets to continue training the model to obtain more desirable responses (measured by how the response rankings had increased).

[@fenandamilanda2032]

nice work, really good to visualize these things even though I already know this.

[@AMGbot]

Loved this! Thanks for the great video!

[@andresroca9736]

Great the concepts transition. Great illustrations. The best of the best this couple of videos. What about more on other networks like r-cnn and audio nets? 😃

[@g5min]

Thanks! I'm working on one on reinforcement learning now...

[@food4yann]

I love the fact that you really tried that recipe! How was it?

[@dormin1850]

This was super informative and concise, loved it! But my real question is how were those pancakes?

[@bgustinjr]

Thumbs-up for actually making the pancakes. 😂

[@abhaychandrol]

Thanks

[@jorgesoberon6866]

Very clear. Thanks a lot.

[@deand6411]

Thank you. Just the right level for my tiny organic brain.

[@coraltown1]

the Mona Lisa of LLM explanations .. thanks!

[@g5min]

Okay, that's the best comment yet -- thank you :-)

[@christianwestermann4680]

Well done!

[@prathams8685]

Big thanks mate

[@GeorgeJohnsonJackofAllTrades]

Rather than asking chatgpt to translate, I recommend asking the question in the target language. You'll significantly different results - more accurate in the target language. Stated without proof or explanation which should be intuitively obvious after watching these two videos...

[@StratosFair]

Great explanation, glad I stumbled upon it ! Subscribed :)

[@civic.sentinel]

Was the chocolate guacamole pancake any good?

[@chnolte]

Just look how far we have come in only eight months.

[@martinstu8400]

eliminating bias and stereotypes from language models is a lost cause, because it's the same as asking the network to lie.

[@parabolicpanorama]

you didn't understand anything did you

[@ivanocj]

Very good content! Keep going! thanks.

[@triton62674]

Fantastic work, hope those pancakes tasted better than they looked! xD

[@g5min]

they were seriously delicious

[@mineralt]

those pancakes look pretty good

[@efisgpr]

GPT-4: Might I suggest dubbing them "Chocomole Pancakes"? 😮😂

[@CoreDump07]

liked and subscribed

[@mlusalin2379]

It looks like GPT hands you a great chocolate guacamole pancake recipe... now I wanted to try too loool

[@nagualdesign]

Wonderful stuff. 👍 Also, _please can I get some oven and the oven please let us have to do the run and not a big difference in a bit more about people who have been in touch your own house is the best way of a bit more about people..._ *My phone wrote the part in italics.

[@moedemama]

Cool video, thanks

[@richardharris9708]

ChatGPT gets the 37 question right now.

[@DonaldTamMisterDee]

This is super fascinating. I want to learn to grow my own language models from the ground up with languages like c++/rust. I dont care if the language model i develop is inaccurate or not. I just want to understand chatgpt under the hood. How may i get started?

[@spenarkley]

Why dont you ask the language model how to make a language model?

[@infiniteplanes5775]

I have! Really great tips!

[@parabolicpanorama]

ask gpt to stack transformer layers and add some input output layers. add some loss functions to optimize and get a large dataset.

[@thomasforrest1931]

great video bud cheers!

[@KeikosCake]

pancake approved 🥞👍

[@zuqini]

I'm a bit confused by how stacking attention layers works at 4:12. Does the second layer take the first layer's prediction as input? Is the first layer's prediction still "next words" at that point, or is it now some sort of abstract intermediate value? How exactly does that capture higher level reasoning? Would appreciate any clarification!

[@leeamraa]

fun funny fantastic and I am a fan!

[@berbudy]

5:53 lol this gut feeling based measurement to decide to ship this to public is one of the achilles heels of current AI providers cause they need to generate revenue fast.

[@LCTesla]

what bothers me is that (if I am to accept your account of them) these models still seem largely sequence based. in my time in university we focused extensively on parsing the grammar before we even thought about letting AI predict the next word. some grammatical structures are triangular in the sense that they wrap around the structure of the previous iteration and put a word in front of AND behind it. think of constructs like "on the one hand X, on the other". things like that seem impossible to learn efficiently on the basis of sequence alone, since the length of X is variable. grammatical structure also simply eliminates a lot of possibilities when it comes to next word prediction. two words that have the same literalization aren't even the same word grammatically in a lot of cases.

[@altalt7653]

I can't get why we stack them like so. If the first transformer block predicts a word, what second does, third? And why do they still need attention then?

[@f4ls381]

5:55 the german one is correct