AlphaEvolve couples map-elites with LLMs. It's an key step in machine learning, in the vein of DQN for reinforcement learning.
AE brings diversity from the genetic algorithms community to large scale optmized deep learning and RL models.
It is a mandatory step for moving forward. The approach is clean and simple, while generic.
The only caveats is the per optimization problem definition of the map élites dimensions. But surely, this will get tackled somehow over the next few years.
If you don't know about map-elites, go look up Jean-Baptiste Mouret' s work and talks, it's both very interesting and universal.
Hello! Not commenting on content or functionality. Scheduling in AI is a very dense field. An a past researcher in AI decision making, I got confused by the 'Scheduling solved' slogan.
FYI recent AI for scheduling include GNNs and RL applied to NP and P-space problems that plague many industries. A larger scope I believe from vela's (rightful) target, a bit confusing IMO. Good luck with your endeavor, all scheduling problems are beautiful :)
I like the glasses path, well I do wear glasses, but some elements remain unclear to me:
- are prescription glasses available for display ? I guess not ?
- these glasses need to be online, I guess they do so with a phone and bluetooth connection nearby ? So that's the glasses, the band and the phone, oh and the glasses case, seems a lot to carry.
- pedestrian navigation seems to be rolled out per city, so it's not like having gmaps available right out of the box.
Congrats, this solution resembles AlphaEvolve. Text serves as the high-level search space, and genetic mixing (map-elites in AE) merges attemps at lower levels.
Losing the mental map is the number one issue for me. I wonder if there could be a way to keep track of it, even at a high level. Keeping the ability to dig in is crucial.
Hello, very interested in the scrollback! I've used mosh for 10+ years and it still runs my 100+ opened terminals to this day !
Would love to try your alternative
Exactly, for real time applications VTO, simulators,...), i.e. 60+FPS, diffusion can't be used efficiently. The gap is still there afaik. One lead has been to distill DPM into GANs, not sure this works for GANs that are small enough for real time.
I'd be interested in what implementation of D3PM was used (and failed). Diffusion model are more data efficient than their AR LLM counterpart but les compute efficient at training time, so it'd be interesting to know whether with more time.to.converge the diffusion approach does succeed. I guess I'll try :)