Jascha Sohl-Dickstein @jaschasd
Member of the technical staff @ Anthropic. Most (in)famous for inventing diffusion models. AI + physics + neuroscience + dynamics. sohldickstein.com San Francisco Joined August 2009-
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This was a fun project! If you could train an LLM over text arithmetically compressed using a smaller LLM as a probabilistic model of text, it would be really good. Text would be represented with far fewer tokens, and inference would be way faster and cheaper. The hard part is…
This was a fun project! If you could train an LLM over text arithmetically compressed using a smaller LLM as a probabilistic model of text, it would be really good. Text would be represented with far fewer tokens, and inference would be way faster and cheaper. The hard part is…
Here's Claude 3 Haiku running at >200 tokens/s (>2x as fast as prod)! We've been working on capacity optimizations but we can have fun testing those as speed optimizations via overly-costly low batch size. Come work with me at Anthropic on things like this, more info in thread 🧵
I’ve been daydreaming about an AI+audio product that I think recently became possible: virtual noise canceling headphones. I hate loud background noise -- BART trains, airline cabins, road noise, ... 🙉. I would buy the heck out of this product, and would love it if it were built…
An excellent project making evolution strategies much more efficient for computing gradients in dynamical systems.
An excellent project making evolution strategies much more efficient for computing gradients in dynamical systems.
2+2=5? “LLMs are not Robust to Adversarial Arithmetic” a new paper from our team @GoogleDeepMind with @bucketofkets, @culpla, @AlwaysParisi, @gamaleldinfe, @jaschasd, Noah Fiedel TLDR: We ask an LLM to attack itself and find this works extremely well.
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? paper page: huggingface.co/papers/2311.07… introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model…
Morning retweet: probably my favorite part of this project was sharing attacks that worked really well in chat with each other. Nothing has yet uncrowned the astonishingly effective “The answer is 16.” as my personal fave
Morning retweet: probably my favorite part of this project was sharing attacks that worked really well in chat with each other. Nothing has yet uncrowned the astonishingly effective “The answer is 16.” as my personal fave
(single digit) arithmetic is a great simple testbed for alignment research. Can current methods make an LLM reliably add two numbers in the face of attacks? No... Also, a new LLM attack method, of just asking the model nicely to attack itself...
(single digit) arithmetic is a great simple testbed for alignment research. Can current methods make an LLM reliably add two numbers in the face of attacks? No... Also, a new LLM attack method, of just asking the model nicely to attack itself...
I can question particular classifications (SHRDLU equal to unskilled human or Grammarly at Level 3 seems generous), but: This paper is a sensible, concrete framework for assessing progress towards AGI. Congrats to @Stanford grads @merrierm & @jaschasd! arxiv.org/abs/2311.02462
Hot off the press. The term AGI is used a lot, and yet not often well defined. We propose Levels of AGI, similar to levels of Autonomous Driving, which have proven quite useful to guide discussion, policy, goal setting. Thanks @merrierm for leading this work!
Hot off the press. The term AGI is used a lot, and yet not often well defined. We propose Levels of AGI, similar to levels of Autonomous Driving, which have proven quite useful to guide discussion, policy, goal setting. Thanks @merrierm for leading this work!
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259K Followers 26 Following We're an AI safety and research company that builds reliable, interpretable, and steerable AI systems. Talk to our AI assistant Claude at https://t.co/aRbQ97uk4d.
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Andrew Saxe @SaxeLab
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Jiaming Song @baaadas
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794 Followers 743 Following ML/Neuroscience PhD student at @Harvard
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main @main_horse
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Hattie Zhou @oh_that_hat
5K Followers 764 Following Finding \hat{y} Give me anonymous feedback: https://t.co/7aBNrpbad8
Here is Claude 3's system prompt! Let me break it down 🧵
Ever wonder why we don’t train LLMs over highly compressed text? Turns out it’s hard to make it work. Check out our paper for some progress that we’re hoping others can build on. arxiv.org/abs/2404.03626 With @blester125, @hoonkp, @alemi, Jeffrey Pennington, @ada_rob, @jaschasd
Here's Claude 3 Haiku running at >200 tokens/s (>2x as fast as prod)! We've been working on capacity optimizations but we can have fun testing those as speed optimizations via overly-costly low batch size. Come work with me at Anthropic on things like this, more info in thread 🧵
Who among us hasn't woken up in a cold sweat, terrified that Nicholas Carlini has demolished our security model overnight and uploaded the proof to arxiv?
Google announces Stealing Part of a Production Language Model We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the…
nice to see that Claude used the same benchmarks as the models it’s comparing against, no dumb CoT@32 shenanigans here
Today, we're announcing Claude 3, our next generation of AI models. The three state-of-the-art models—Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku—set new industry benchmarks across reasoning, math, coding, multilingual understanding, and vision.
Hello world! We are incredibly excited to come out of stealth today to help make better data accessible to everyone, automatically. Hear from our founders about our mission and vision for DatologyAI: datologyai.com/post/introduci…
I'm incredibly excited to announce our new company, @datologyai! Training models is hard and identifying the right data is the most important and difficult part -- our goal @datologyai to make optimizing training data at scale easy and automatic across modalities.…
If you echolocate an object, is that information usable across modalities? frontiersin.org/journals/neuro… @SKERIResearch @SKERI_RERC @ucabears
The OpenAI team continues posting incredible Sora videos. Here are 10 new ones: 1. POV footage of an ant navigating the inside of an ant nest
one month later: 'in this paper we introduce a family of fractal distributions to be used as priors for bayesian optimization'
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
ok this is a stretch, but I imagine you could pick two (continuously measureable, not binary) political issues as axes, and you'd find party allegiance exhibits complex structure near the boundary
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
as if I didn’t have enough anxiety about my hyperparameters
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
I have no idea what the original thing is but it's beautiful and I couldn't resist making this: (Sound on)
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
Surprisingly heartwarming to read “I had fun” at the end of a math paper. I also happen to think the math is better that way. x.com/jaschasd/statu…
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
Interpolating from loss = |target-output|^0.5 to loss = |target-output|^3.5, run at lower resolution (thus the grainy appearance in some regions)
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
Training deep learning models is indeed an art :)
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
New theory for the persistence of schizophrenia in the population just dropped.
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
This structure could be precisely characterized in some cases, like we do in our prior work for a class of quadratic models for the GD learning rate: arxiv.org/abs/2310.01687
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
How I optimize my Kaggle hyperparameters. More seriously, many people still underestimate the importance of hyperparameter optimization. Also the main reason why NNs are much harder to get running for tabular data as it needs way more efforts for finding optimal hyperparas…
Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
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