I just asked ChatGPT how many r’s are in blackberry and it said 3. I’m so confused about the range of AI capabilities

This doesn’t look like “normal” technology to me…

Shades of Neo interpreting the green scrolling crawl to understand the world without seeing it...

it’s more likely to be the case that astra can indeed recognize certain parts of a small open-source model. But the improvement based on the edition is probably the information leak by model card of small model which is in astra’s search result.

Through what means exactly? Is Astra just doing byte manipulation? Or can there be an "SQL for neural network weights"?

Throw out whatever timeline you previously had

So is the idea here that this'll make it cheaper to fine-tune models than by using reinforcement learning?

Nah, this is just completely implausible. You're using a closed model so how are you sure that on the back end it's not loading those weights into an NN library and testing them with common tasks?

Or maybe it's been trained on those models or similar ones. There may be an academic paper about common chess models and their problems.

Or maybe it just bruteforced it (with the help of calculator tools).

We know invisible tools exist because they're how ChatGPT does math.

I expected us to reach RSI when the programming skills went above humans, but this seems like it could get us there much faster

At what cost? That's the part I'm curious about.

On one hand, I'm sure that these targeted improvements are reaping huge benefits overall, but those weights are used by multiple processes - I wonder what elements are lost along the way. I almost wonder if a hybrid approach here for training might be invaluable - ie do some standard RL for a 100 steps, do a targeted weight improvement, then do another 100 RL steps to smooth it out / heal the surgical edit.