s3 does vectors now vector databases are officially dead
@jxmnop
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Why Self-Play AI Training Hasn’t Scaled Since AlphaZero
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pretty crazy that DeepMind got self-play working w AlphaZero in *2017* yet basically no one has been able to make it work since
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LLMs reduce mediocrity cost but excellence remains expensive
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today’s LLMs have reduced the cost of mediocrity to next-to-nothing unfortunately, the cost of greatness remains high as it’s ever been
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Where Did Validation Sets Go in Modern ML?
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when i first learned Machine Learning, our professor ingrained into us how every ML problem starts by splitting data into train, test, and validation these days there is just train and test. in many cases there is just train and more train where’d all the validation sets go?
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Decoding AI Model Weights and Activations to Plaintext
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weights || activations one day we will be able to perfectly decode either one back to plaintext
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The Challenge of Finding Right Abstractions in AI
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i mean, sure, but coming up with the *right* abstractions is extremely hard
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AI Libraries Finally Balance Hackability With Performance
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nice trend over the last year is that folks in AI have finally produced a few libraries with the right abstractions finally our code can be both hackable and fast, not just one or the other. this never used to happen vLLM, sglang, verl..
this is the dawn of Good Software in AI -
How Frontier AI Models Are Built: The Iterative Process
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How frontier AI models are built 1. identify task model cannot solve
2. create task eval
3. collect new *eval-specific* training data
4. train model
5. model can do the task now
6. if not (AGI achieved): goto step 1 GLUE, MMLU, MATH, AIME, HLE, GPQA, ARC a tale as old as time -
Handling Research Errors: Retractions and Code Fixes
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i guess it depends on the nature of the issue. like how would that happen? if the conclusion of the paper changed, in the worst case, i guess pursue some kind of formal retraction more likely it'd just be a bug in the code in which case i'd rerun experiments then update arxiv