Core Automation 7 22 2 407 12K 29
Will replacing the transformer be a revolution or an evolution?
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Playlist this week:
- upping my p(doom)
- chasing the roofline
- core auto thesis (internal)
- upping my p(doom)
- chasing the roofline
- core auto thesis (internal)
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It is our responsibility to make reality interesting, as otherwise humans will rationally choose to live in a simulation
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When I grow up I want to be a slopcore artist
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Machine learning isn't magic - its assumptions in disguise
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Every epoch is defined by its art. at the end of times we have the slopcore
𝞍 Shin Megami Boson 𝞍@shinboson · Sep 26if you missed the slightly insane AI safety schizopoast, I put it into a song for you and you should turn the sound on right now because we're so fucking back
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Big model smell lasts just a brief moment
Small model smell lasts forever
Small model smell lasts forever
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1800s: state has monopoly on violence
2030s: state has monopoly on ASI
2030s: state has monopoly on ASI
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Oh, hi!
Samip@industriaalist · Sep 22We think computational depth is the missing scaling axis, i.e. we should be doing a lot more deep learning!
Every other axis has been scaled by OOMs over the past few years (params, data, sparsity, test-time reasoning), but depth has been stuck at ~100 layers since GPT-3.
We've found that LLMs are both *severely* depth-bottlenecked and bad at using the depth they have, and that architectural interventions that lift this bottleneck efficiently lead to gains that increase with compute.
w/ @akshayvegesna
Every other axis has been scaled by OOMs over the past few years (params, data, sparsity, test-time reasoning), but depth has been stuck at ~100 layers since GPT-3.
We've found that LLMs are both *severely* depth-bottlenecked and bad at using the depth they have, and that architectural interventions that lift this bottleneck efficiently lead to gains that increase with compute.
w/ @akshayvegesna
Open quoted post →
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Yes
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real deep learning has never been tried
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The road to singularity will be paved with blackberries of our generation.
It is easier than ever to achieve a glimpse of growth and success but building a long term viable company is as hard as it ever was.
It is easier than ever to achieve a glimpse of growth and success but building a long term viable company is as hard as it ever was.
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This was once revealed to me in a dream
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More questions are deep learning questions than people realized.
We just stopped asking deep learning questions and learned to work around them by tuning hyperparameters.
We just stopped asking deep learning questions and learned to work around them by tuning hyperparameters.
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We may have done it at @coreauto but the model wasn't very good at math
Rosmine@rosmine · Sep 16We need to be dog maxxing our models
There is 0% chance of doom if the chain of thought is
user: hello
model: <think> Oh my god it's a human. I love humans. I will say hi to him, maybe he will give me a problem to chase. I love chasing problems. I will chase the problem for him then he will love me and I will be his best friend. Must help human </think>
model: hello! what can I help you with today?
Open quoted post →There is 0% chance of doom if the chain of thought is
user: hello
model: <think> Oh my god it's a human. I love humans. I will say hi to him, maybe he will give me a problem to chase. I love chasing problems. I will chase the problem for him then he will love me and I will be his best friend. Must help human </think>
model: hello! what can I help you with today?
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Internet may collapse if GPT-7 is so good it understands all the vagueposts
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Inside every lab are two wolves:
“I don’t want to destroy the world with my invention.”
“I don’t want to lose revenue to other labs I don’t like.”
The one you feed is the one that wins.
“I don’t want to destroy the world with my invention.”
“I don’t want to lose revenue to other labs I don’t like.”
The one you feed is the one that wins.
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This is very cool
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While we don't have the right solutions for everything, I have a very practical proposal.
I don't think open source should be banned - it does sound dystopian and anti freedom in a way that's hard to stomach. I continue to have doubts about economics of open source models, but when there are participants in the market who open source their models we should celebrate that.
What we should care about, is that aligned models have much more compute behind them than misaligned models. In the end this will be the blockchain security model that will keep our civilization afloat.
I think big companies behind AI development have the right incentives and will try to do their part here. But there will be tons of smaller players fine-tuning open source models on tons of different objectives.
An arrangement, where companies actively developing cutting edge AI agree to develop and share highest quality pro-alignment environments with the world can be a huge boon. We don't need to share models, we don't need to share compute. But we need to share values so that there are more good models in the world than harmful ones.
Finetuning models on bad, low quality environments is actively harmful and leads to reward hacking. If anyone fine-tuning the models can with low effort align them to shared pro-prosperity and pro-democratic values, we likely have won as a civilization.
I don't think open source should be banned - it does sound dystopian and anti freedom in a way that's hard to stomach. I continue to have doubts about economics of open source models, but when there are participants in the market who open source their models we should celebrate that.
What we should care about, is that aligned models have much more compute behind them than misaligned models. In the end this will be the blockchain security model that will keep our civilization afloat.
I think big companies behind AI development have the right incentives and will try to do their part here. But there will be tons of smaller players fine-tuning open source models on tons of different objectives.
An arrangement, where companies actively developing cutting edge AI agree to develop and share highest quality pro-alignment environments with the world can be a huge boon. We don't need to share models, we don't need to share compute. But we need to share values so that there are more good models in the world than harmful ones.
Finetuning models on bad, low quality environments is actively harmful and leads to reward hacking. If anyone fine-tuning the models can with low effort align them to shared pro-prosperity and pro-democratic values, we likely have won as a civilization.
Jerry Tworek@MillionInt · Sep 13Alignment is not as hard to solve as many claim, but it is in the end an algorithmic problem which a lot of ML community mostly stopped working on.
Formulation of alignment stated by three (or four) laws of robotics can take us very far, so we roughly know the objective.
The tricky part is, how do we take gradient with respect to alignment? We have two algorithms right now at our disposal: pretraining and RL.
Pretraining takes gradient with respect to next token prediction - that's def not the alignment objective.
We could create RL environments that embody the alignment objective, but:
- those are expensive to create, so often cheaper, hackable proxies are used in practice
- RL as an objective needs successful and unsuccessful rollouts to happen to take the gradient step. We DO NOT want to harm any humans in the process of aligning our modes - this is a pretty big problem.
Therefore there are two solutions forward for the alignment problem:
- either we RL models in a simulated environments with simulated alignments and decreasing the likelihood of harming simulated humans, which will never be perfect
- or we create a new algorithm that can teach our models to not harm humans without harming any humans in the process
Science is the process how we solve the hardest problems ahead and that is one of them
Open quoted post →Formulation of alignment stated by three (or four) laws of robotics can take us very far, so we roughly know the objective.
The tricky part is, how do we take gradient with respect to alignment? We have two algorithms right now at our disposal: pretraining and RL.
Pretraining takes gradient with respect to next token prediction - that's def not the alignment objective.
We could create RL environments that embody the alignment objective, but:
- those are expensive to create, so often cheaper, hackable proxies are used in practice
- RL as an objective needs successful and unsuccessful rollouts to happen to take the gradient step. We DO NOT want to harm any humans in the process of aligning our modes - this is a pretty big problem.
Therefore there are two solutions forward for the alignment problem:
- either we RL models in a simulated environments with simulated alignments and decreasing the likelihood of harming simulated humans, which will never be perfect
- or we create a new algorithm that can teach our models to not harm humans without harming any humans in the process
Science is the process how we solve the hardest problems ahead and that is one of them
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