Amine El Ouassouli
@aelouass.bsky.social
CS / DS / ML / AI (whatever it is called now) Ph.D. Engineer.
'Bluesky has overtaken its flailing rival X in hosting posts related to new academic research, indicating the platform is fast becoming the go-to place for scholars to share their work.'
X’s dominance ‘over’ as Bluesky becomes new hub for research
Data indicates more scholars turning to alternative social media site to post about their work after Elon Musk’s Twitter takeover
timeshighereducation.com
@jfoerst.bsky.social take on how the community sees the ARC Challenge and how we evaluate models and use benchmarks nowadays is 👌. #more_science_less_hype (please). PS: Amazing discussion and good brain food, as usual with MLST.
ImageNet Moment for Reinforcement Learning?
YouTube video by Machine Learning Street Talk
youtube.com
I missed this one when it came out but I can tell that it is one of the most useful piece of research I’ve read in a while. “GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models” arxiv.org/html/2410.05...
GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
arxiv.org
We really need better brain-power allocation. The current algorithm is kind of turning crazy.
Ted Underwood@tedunderwood.com · 2y agoI suspect we’re going to find there is little demand for Operator-like agents that go out on the web and do shopping for you—but huge demand for personal assistants that have read all your email and all your files and can see the 3 tasks you forgot to finish.
The more I read and listen to current debates in the field, the more I’m convinced that we have a model evaluation crisis.
I never understood people going to concerts to spend their time there attending through the tiny screens of their phones.
Is it just me or are we in an Eliza effect pandemic?
Huh, what a year ! Happy new year, everyone ! May it be a better one than 2024 (it’s not that hard, though) Take care of your loved ones.
There is no ultimate benchmark. Having good results on a benchmark means that a model cracked it down. How it cracked it down shows the extent of the progress towards solving the problem=>sometimes cracking a benchmark tells you more that it is not sufficient to measure progress anymore.
Definitely a good book. Not a textbook but a good resource for anyone interested in the field and have some math literacy.
Kevin Mitchell@wiringthebrain.bsky.social · 2y agoWhy Machines Learn - The Elegant Math Behind Modern AI By Anil Ananthaswamy @anilananth.bsky.social - a fantastic, accessible introduction to the math behind AI www.penguinrandomhouse.com/books/677608...
An updated intro to reinforcement learning by Kevin Murphy: arxiv.org/abs/2412.05265! Like their books, it covers a lot and is quite up to date with modern approaches. It also is pretty unique in coverage, I don't think a lot of this is synthesized anywhere else yet
Reinforcement Learning: An Overview
This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based met...
arxiv.org
I think I've got more interesting insights from here's feed in not even a week than i got from the other place's in more than a year.
The unofficial GIF-based pandas library documentation. pandas.DataFrame.rolling
a panda bear is laying down in the grass .
Alt: a panda bear is rolling down in the grass. It's a side-ways roll, hlding some type of object. I give 10/10.
media.tenor.com
I just deactivated my X account. Blsky now has exclusivity on my procrastinating scrolling activity.