11–20 of 280 posts
As the article alluded to at the end, my thoughts immediately go to using R1 as a data generator for complex problems, since we have many examples of successful distillation into smaller models on well-defined tasks.
The claim is that this removes the human bottleneck (aka SFT or supervised fine tuning) on domains with a verifiable reward. Critically, this verifiable reward is extremely hard to pin down in nearly all domains besides mathematics and computer science.
By verifiable do they mean it in the complexity theory P/NP sense of the word?
The claim is that this removes the human bottleneck (aka SFT or supervised fine tuning) on domains with a verifiable reward. Critically, this verifiable reward is extremely hard to pin down in nearly all domains besides mathematics and computer science.
By verifiable do they mean it in the complexity theory P/NP sense of the word?
The idea that a lot of compute is moving towards inference has a huge consequence for the current "AI investments". This is bad news for NVDA particularly. The inference focused solutions have better economics than paying NVDA those huge margins (e.g. Grog)
The industry has to find a way to separate itself from Nvidia's GPGPU technology if they want to stop being gouged. The issue is that nobody, not Apple, not AMD, not Intel, has been treating Nvidia's hardware as a serious threat.
The idea that a lot of compute is moving towards inference has a huge consequence for the current "AI investments". This is bad news for NVDA particularly. The inference focused solutions have better economics than paying NVDA those huge margins (e.g. Grog)
We're super proud to support this work. If you're thinking of running deepseek in production, give us a shout!
Mike from Baseten here We're super proud to support this work. If you're thinking of running deepseek in production, give us a shout!
Earlier quoted context omitted.
By verifiable do they mean it in the complexity theory P/NP sense of the word?
As in there's an objective truth that can be determined by a computer. E.g. whether code compiles, whether a unit test passes, whether the answer given to a mathematical question like 3+5 is correct. Many other fields have no objective truth (like art or creative writing), or objective truth requires measurement of the physical world (although if the world can be simulated accurately enough for the problem class at h…
e.g you would need to prove that for all inputs the code produces the correct output which would in turn make the problem way more complex
I predict that the future of LLM's when it comes to coding and software creation is in "custom individually tailored apps". Imagine telling an AI agent what app you want, the requirements and all that and it just builds everything needed from backend to frontend, asks for your input on how things should work, clarifying questions etc. It tests the software by compiling and running it reading errors and failed tests a…
It doesn't need to write tests: it can just use the application and figure out if it works.
Yet, they said when it was announced:
"OpenAI shared they trained the o3 we tested on 75% of the Public Training set. They have not shared more details. We have not yet tested the ARC-untrained model to understand how much of the performance is due to ARC-AGI data."
These two statements are completely opposed. I can't take seriously anything this article says about o3.