Few fields reward quick pivoting as much as AI, or vice versa punish the very thing a phd is usually meant to be: stick with one research direction for 5 years no matter what, go really deep, becoming a niche expert

for your research to be relevant in AI, you might wanna pivot every 1-2 years

Saying o3 is just a “more principled search technique” is quite reductive. The o series of models don’t require “explicit search” strategies in the form of tree search, wrapped in loops etc. Instead, RL is used to train the model to “learn to search” using long CoT chains.

Come check out our neurips poster today! We will be at West Ballroom #7101 from 4:30pm - 7:30pm.

Website: github.com/gfnorg/diffu...

If you're at NeurIPS, RLC is hosting an RL event from 8 till late at The Pearl on Dec. 11th. Join us, meet all the RL researchers, and spread the word!

www.newsweek.com/united-healt...

I have anecdotal evidence from a friend who works at a client company for a popular insurance firm. They are using shitty “AI models” which are basically just CatBoost to mass process claims. They know the models are shit, but that’s also the point. Truly sickening.

This app is an interesting social experiment. Assuming Bluesky doesn’t just fizzle out, will hostile social relations as in Twitter resurface here too? If hostilities do return, will it be because conservatives come to this app, or will it be new political tensions within left leaning communities?

As AI researchers, we shouldn’t demonize people outside our space who have a passionate distaste for AI. You have to understand that most of the pro-AI sentiment people see online comes from absolutely vile “AI-bros”, especially on twitter. We just need to distinguish ourselves as academics.

Anyone has thoughts about which generative models are also the best for representation learning features for downstream tasks?

My guess is GANs are a dark horse and the latents carry important abstract features. But we haven’t explored this much since they are hard to train.

Two predictions:
1) RNNs will be back for long sequence modeling (the latent bottleneck is long term memory), but attention will be used for local short term context
2) Hierarchical VAEs will be back (with trainable encoders, not like diffusion models)

The new account feed here is so much nicer than “The everything app” 🙂
Hope the hype doesn’t fizzle out turning this app into another “Threads”