A solely technology-focused approach would definitely fail. I have been speaking with many policy makers, business leaders, and technologies in the last few months, and it's quite worrisome that not many of them really understood this at all. It's time to change this mindset! 2/2
Why AI typically fails in real life (so far)? Many people don't understand that applied AI requires interdisciplinary approach. It only works in real life if it's problem-driven and the solution is co-created from the very beginning by people from multiple disciplines 1/n
A paper co-authored by Matthias C. Caro has been featured on the cover of PRX Quantum, a premier journal for quantum information science and technology research ⚛️
More info: https://t.co/qkizNwx73u
Image credit: APS/YOHOHO
Personal Update: I've moved to the University of Warwick, UK 🇬🇧.
It was a tremendous 5 years in Singapore @NUSComputing, and my move was bittersweet.
But I'm so very excited to be part of Warwick (and UK academia more broadly). Stay tuned for more updates!
We are happy to announce that Dr Sayan Bhattacharya has been awarded a €2million ERC Consolidator grant for a 5-year project entitled "Towards a Dynamic Algorithms Centric Theory of Linear Programming" 👏
More info: https://t.co/s9PJAa8mma
The original RL algorithms, inspired by natural learning, were online and incremental—they were streaming in the sense that they learned from each increment of experience as it happened, then discarded it, never to be processed again. The streaming algorithms were simple and elegant, but the first big successes of RL in deep learning were not with streaming algorithms. Instead, methods such as DQN chopped the stream of experience into individual transitions, then stored and sampled them in arbitrary batches. Subsequent work followed, extended, and refined the batch approach into asynchronous and offline RL, while the streaming approach languished, unable to produce good results in popular deep learning domains.
Until now. Now researchers at the University of Alberta have shown that streaming RL algorithms can work just as well as DQN on Atari and Mujoco tasks (https://t.co/S4D6lSvdxz). How did they do it? Mostly just by getting signal normalization and step-size bounding right for the streaming case—otherwise they use standard streaming algorithms like TD(lambda) and Q(lambda). To me it looks like they were simply the first researchers knowledgeable of streaming RL algorithms to seriously address deep RL without being over-influenced by batch-oriented software and batch-oriented supervised-learning ways of thinking.
The original RL algorithms, inspired by natural learning, were online and incremental—they were streaming in the sense that they learned from each increment of experience as it happened, then discarded it, never to be processed again. The streaming algorithms were simple and elegant, but the first big successes of RL in deep learning were not with streaming algorithms. Instead, methods such as DQN chopped the stream of experience into individual transitions, then stored and sampled them in arbitrary batches. Subsequent work followed, extended, and refined the batch approach into asynchronous and offline RL, while the streaming approach languished, unable to produce good results in popular deep learning domains.
Until now. Now researchers at the University of Alberta have shown that streaming RL algorithms can work just as well as DQN on Atari and Mujoco tasks (https://t.co/S4D6lSvdxz). How did they do it? Mostly just by getting signal normalization and step-size bounding right for the streaming case—otherwise they use standard streaming algorithms like TD(lambda) and Q(lambda). To me it looks like they were simply the first researchers knowledgeable of streaming RL algorithms to seriously address deep RL without being over-influenced by batch-oriented software and batch-oriented supervised-learning ways of thinking.
Due to a high demand for registrations, NeurIPS will be moving towards a randomized lottery system, effective immediately. Authors of accepted conference and workshop papers are still guaranteed registration, but this may change as we release spots to the lottery, so we urge authors to register ASAP.
Read more:
https://t.co/BhCBy7rfhz
As @arxiv is down, first time ever appreciate the fact that I have 40+ Chrome tab open with different arxiv pdfs and don’t have to reload them. Now it’s time to read them (haven’t read at least 75% of them yet tbh)
The flagship conference in knowledge representation and reasoning #KR2024 will be in Hanoi, Vietnam between 2-8Nov 2024. Keynote speakers are Meghyb Bienvenu, @rao2z, Nina Narodytska, @SheilaMcIlraith, and Murray Shanahan. Join us to enjoy both the conf and the cultural heritage.
Warwick DCS has 8 papers accepted to @NeurIPSConf this year. In the last 2 years we have been heavily investing in #FoundationOfAI, building on top of our renowned reputation in theoretical research in CS and AI, and will be hiring more! Join us if you’re into theoretical AI/ML
Researchers from the Department of Computer Science at the University of Warwick have had eight papers accepted for publication at NeurIPS 2024! Congratulations to all involved 👏 Read more: https://t.co/gXG0VLMF5e
@CsabaSzepesvari Yeah, it’s a very unfortunate, myopic and populist reaction. The good news is that I just got my ETA for 5 years. Now I can come and visit you at some point :)
This is insane! My PhD student, who got 2 accepted first-authored papers to #NeurIPS2024, cannot attend the conf because the visa processing time in Canada is 10 months (sic!). What’s happening in Canada? It’s time to look for better accessible places to host Neurips!!
We are seeking PhD candidates in the topic of Multiagent Systems and related areas, with particular emphasis on one or more of: computational social choice, algorithmic game theory, multiagent learning, and social and economic networks. More info: https://t.co/VymGKyYmY7