Jiankai Sun

[Jiankai Sun @zhenv5]

Been deep-diving discrete representation learning for recommender systems and turned my notes into a beginner-friendly writeup.

It walks through AE → VAE → VQ-VAE → RQ-VAE → FSQ → SimVQ → RQ-KMeans, and more.

Link: https://t.co/OTUiWg4rjc

[Mengdi Wang @MengdiWang10]

Can’t believe it — our Princeton AI^2 postdoc Shilong Liu @atasteoff re-built DeepSeek-OCR from scratch in just two weeks 😳 — and open-sourced it. This is how research should be done 🙌 #AI #LLM #DeepSeek #MachineLearning #Princeton @omarsar0 @PrincetonAInews @akshay_pachaar

[Andrej Karpathy @karpathy]

My pleasure to come on Dwarkesh last week, I thought the questions and conversation were really good.

I re-watched the pod just now too. First of all, yes I know, and I'm sorry that I speak so fast :). It's to my detriment because sometimes my speaking thread out-executes my thinking thread, so I think I botched a few explanations due to that, and sometimes I was also nervous that I'm going too much on a tangent or too deep into something relatively spurious. Anyway, a few notes/pointers:

AGI timelines. My comments on AGI timelines looks to be the most trending part of the early response. This is the "decade of agents" is a reference to this earlier tweet https://t.co/NiSn6jftqq Basically my AI timelines are about 5-10X pessimistic w.r.t. what you'll find in your neighborhood SF AI house party or on your twitter timeline, but still quite optimistic w.r.t. a rising tide of AI deniers and skeptics. The apparent conflict is not: imo we simultaneously 1) saw a huge amount of progress in recent years with LLMs while 2) there is still a lot of work remaining (grunt work, integration work, sensors and actuators to the physical world, societal work, safety and security work (jailbreaks, poisoning, etc.)) and also research to get done before we have an entity that you'd prefer to hire over a person for an arbitrary job in the world. I think that overall, 10 years should otherwise be a very bullish timeline for AGI, it's only in contrast to present hype that it doesn't feel that way.

Animals vs Ghosts. My earlier writeup on Sutton's podcast https://t.co/rSp1noyGBr . I am suspicious that there is a single simple algorithm you can let loose on the world and it learns everything from scratch. If someone builds such a thing, I will be wrong and it will be the most incredible breakthrough in AI. In my mind, animals are not an example of this at all - they are prepackaged with a ton of intelligence by evolution and the learning they do is quite minimal overall (example: Zebra at birth). Putting our engineering hats on, we're not going to redo evolution. But with LLMs we have stumbled by an alternative approach to "prepackage" a ton of intelligence in a neural network - not by evolution, but by predicting the next token over the internet. This approach leads to a different kind of entity in the intelligence space. Distinct from animals, more like ghosts or spirits. But we can (and should) make them more animal like over time and in some ways that's what a lot of frontier work is about.

On RL. I've critiqued RL a few times already, e.g. https://t.co/mYrMFVdVDW . First, you're "sucking supervision through a straw", so I think the signal/flop is very bad. RL is also very noisy because a completion might have lots of errors that might get encourages (if you happen to stumble to the right answer), and conversely brilliant insight tokens that might get discouraged (if you happen to screw up later). Process supervision and LLM judges have issues too. I think we'll see alternative learning paradigms. I am long "agentic interaction" but short "reinforcement learning" https://t.co/2L7FiaoKsw. I've seen a number of papers pop up recently that are imo barking up the right tree along the lines of what I called "system prompt learning" https://t.co/df5mJDdN3C , but I think there is also a gap between ideas on arxiv and actual, at scale implementation at an LLM frontier lab that works in a general way. I am overall quite optimistic that we'll see good progress on this dimension of remaining work quite soon, and e.g. I'd even say ChatGPT memory and so on are primordial deployed examples of new learning paradigms.

Cognitive core. My earlier post on "cognitive core": https://t.co/q2s1ihGy0T , the idea of stripping down LLMs, of making it harder for them to memorize, or actively stripping away their memory, to make them better at generalization. Otherwise they lean too hard on what they've memorized. Humans can't memorize so easily, which now looks more like a feature than a bug by contrast. Maybe the inability to memorize is a kind of regularization. Also my post from a while back on how the trend in model size is "backwards" and why "the models have to first get larger before they can get smaller" https://t.co/6k0FZRGXsb

Time travel to Yann LeCun 1989. This is the post that I did a very hasty/bad job of describing on the pod: https://t.co/fQgqaXPyp6 . Basically - how much could you improve Yann LeCun's results with the knowledge of 33 years of algorithmic progress? How constrained were the results by each of algorithms, data, and compute? Case study there of.

nanochat. My end-to-end implementation of the ChatGPT training/inference pipeline (the bare essentials) https://t.co/SIetgyoKWN

On LLM agents. My critique of the industry is more in overshooting the tooling w.r.t. present capability. I live in what I view as an intermediate world where I want to collaborate with LLMs and where our pros/cons are matched up. The industry lives in a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. For example, I don't want an Agent that goes off for 20 minutes and comes back with 1,000 lines of code. I certainly don't feel ready to supervise a team of 10 of them. I'd like to go in chunks that I can keep in my head, where an LLM explains the code that it is writing. I'd like it to prove to me that what it did is correct, I want it to pull the API docs and show me that it used things correctly. I want it to make fewer assumptions and ask/collaborate with me when not sure about something. I want to learn along the way and become better as a programmer, not just get served mountains of code that I'm told works. I just think the tools should be more realistic w.r.t. their capability and how they fit into the industry today, and I fear that if this isn't done well we might end up with mountains of slop accumulating across software, and an increase in vulnerabilities, security breaches and etc. https://t.co/8556ESSpyY

Job automation. How the radiologists are doing great https://t.co/FVUI872dkD and what jobs are more susceptible to automation and why.

Physics. Children should learn physics in early education not because they go on to do physics, but because it is the subject that best boots up a brain. Physicists are the intellectual embryonic stem cell https://t.co/p72Elk8lPV I have a longer post that has been half-written in my drafts for ~year, which I hope to finish soon.

Thanks again Dwarkesh for having me over!

[Ang Li @angli_ai]

@MiaosenWang Thanks bro 😎 your tshirt has been granted. To show additional appreciation, whoever quote repost your message above will receive FREE @SimularAI Tshirt as well!

[Jiankai Sun @zhenv5]

@mtrainier2020 久泰还可以

[Jiankai Sun @zhenv5]

Check our poster: privacy preserving conversion modeling in data clean room if you are in Bari Italy Recsys 2024 @ACMRecSys

[Matt Shumer @mattshumer_]

The dataset is everything.

Great read: https://t.co/snGcPx0M16

[Jiankai Sun @zhenv5]

a great introduction to transformer written by @JayAlammar

[Jiankai Sun @zhenv5]

https://t.co/ouLNBou5bM is insane. @suno_ai_ https://t.co/HzB6i2o3NM

[Jiankai Sun @zhenv5]

FL@FM-IJCAI'24 offers a platform to explore federated learning and foundation models such as large language models. Please consider to submit your latest research on federated learning and foundation models by April 26, 2024. Visit https://t.co/H7E0beOZGI for more. @IJCAIconf

[Jiankai Sun @zhenv5]

Rite Aid banned from facial recognition tech use for 5 years after faulty theft targeting in stores https://t.co/WikwmSAm0E

[Jiankai Sun @zhenv5]

@zhaochun_ren big cong

[Jiankai Sun @zhenv5]

#Space-X

[Andrej Karpathy @karpathy]

Speculative execution for LLMs is an excellent inference-time optimization.

It hinges on the following unintuitive observation: forwarding an LLM on a single input token takes about as much time as forwarding an LLM on K input tokens in a batch (for larger K than you might think). This unintuitive fact is because sampling is heavily memory bound: most of the "work" is not doing compute, it is reading in the weights of the transformer from VRAM into on-chip cache for processing. So if you're going to do all that work of reading in all those weights, you might as well apply them to a whole batch of input vectors. I went into more detail in an earlier thread:
https://t.co/Lbtpq4VDeY

The reason we can't naively use this fact to sample in chunks of K tokens at a time is that every N-th token depends on what token we sample at time at step N-1. There is a serial dependency, so the baseline implementation just goes one by one left to right.

Now the clever idea is to use a small and cheap draft model to first generate a candidate sequence of K tokens - a "draft". Then we feed all of these together through the big model in a batch. This is almost as fast as feeding in just one token, per the above. Then we go from left to right over the logits predicted by the model and sample tokens. Any sample that agrees with the draft allows us to immediately skip forward to the next token. If there is a disagreement then we throw the draft away and eat the cost of doing some throwaway work (sampling the draft and the forward passing for all the later tokens).

The reason this works in practice is that most of the time the draft tokens get accepted, because they are easy, so even a much smaller draft model gets them. As these easy tokens get accepted, we skip through those parts in leaps. The hard tokens where the big model disagrees "fall back" to original speed, but actually a bit slower because of all the extra work.

So TLDR: this one weird trick works because LLMs are memory bound at inference time, in the "batch size 1" setting of sampling a single sequence of interest, that a large fraction of "local LLM" use cases fall into. And because most tokens are "easy".

References
https://t.co/sIBCSmsyKN
https://t.co/uSpmTzfWhR
https://t.co/7t7orHBybo

[Sam Altman @sama]

“give yourself a lot of shots to get lucky” is even better advice than it appears on the surface.

luck isn’t an independent variable but increases super-linearly with more surface area—you meet more people, make more connections between new ideas, learn patterns, etc.

[Jiankai Sun @zhenv5]

@mtrainier2020 试试问问chatgpt

[Jiankai Sun @zhenv5]

very happy to share that our paper on differentially private AUC computation in federated learning has been accpeted to AAAI 2023 @RealAAAI . Paper: https://t.co/QaBHW5ZO5H Code: https://t.co/DB1jrdzhhQ

[Jiankai Sun @zhenv5]

Finally found a bad case for #ChatGPT

[Jiankai Sun @zhenv5]

@shiqiang_w thanks for your hosting