Who would deny this world's transience, so clear to see?
Who does not know, though long we live, that death will surely be?
Who would deny three measures of cloth (the shroud) are the final legacy?
Who does not know this very grave is our true homeland.
06.05.2025 09:22 β π 0 π 0 π¬ 0 π 0
Rough English translation:
O, people of wisdom, look upon our state,
This is, for certain, human's ultimate fate.
A troubled, ephemeral dominion is this earthly place,
Where every soul mistakes its hardship as some grace and comfort.
06.05.2025 09:22 β π 0 π 0 π¬ 1 π 0
Started my NLP lectures today exploring the fascinating levels of natural language.
This slide features an interesting example: Turkish, using Greek letters (orthography) on a historic tombstone from Istanbul. A poetic and meaningful common lesson transcending barriers...
06.05.2025 08:59 β π 1 π 0 π¬ 1 π 0
We still know very little about complexity theory and the emergent behaviors of self-organizing processes across micro-to-macro systems.
I guess, todayβs highly compartmentalized mainstream scientific tradition hinders progress toward a holistic understanding.
07.04.2025 05:31 β π 0 π 0 π¬ 0 π 0
Why Everything in the Universe Turns More Complex | Quanta Magazine
A new suggestion that complexity increases over time, not just in living organisms but in the nonliving world, promises to rewrite notions of time and evolution.
The universe, in a baffling sense, creates local pockets of complexity (decreasing entropy through biological/physical self-organized structures) while relentlessly advancing toward a state of maximum global entropy.
www.quantamagazine.org/why-everythi...
07.04.2025 05:31 β π 1 π 0 π¬ 1 π 0
Don's main distinction for a CS mentality:
- ability to jump very quickly between levels of abstraction, between a low level and a high level, almost unconsciously
- deal with non-uniform (he means mathematically dis-continuous, discrete IMO) structures
15.03.2025 10:49 β π 0 π 1 π¬ 0 π 0
Don's main distinction for a CS mentality:
- ability to jump very quickly between levels of abstraction, between a low level and a high level, almost unconsciously
- deal with non-uniform (he means mathematically dis-continuous, discrete IMO) structures
15.03.2025 10:49 β π 0 π 1 π¬ 0 π 0
Came across a book (actually a transcript of lectures at @mitofficial.bsky.social) from a CS legend Donald Knuth, the author of The Art of Computer Programming. Not nearly as popular as TAOCP.
Love the line "Computer God talks about God" in the foreword, we'll see where it leads...
09.03.2025 13:11 β π 1 π 0 π¬ 1 π 0
VLDB is a good example with a monthly cycle and accepted papers getting published concurrently in PVLDB each month.
02.03.2025 06:57 β π 3 π 0 π¬ 1 π 0
The origins of aesthetics is really fascinating. Why deem this scene utterly spectacular and even tie it to the "long-tailed mountain lady"? What mechanisms shaped this "taste" and how?
02.03.2025 05:44 β π 0 π 0 π¬ 0 π 0
#Severance isnβt a typical tv show. Itβs a sharp dive into philosophy of mind, probing identity, memory, and mind-body duality with surprising depth. Highly recommend...
27.02.2025 06:02 β π 5 π 0 π¬ 0 π 0
There appears to be a striking correlation between ignorance on a topic and the confidence with which people make bold statements about it.
Can easily use this principle as a de-noising filter...
18.01.2025 08:31 β π 0 π 0 π¬ 0 π 0
YouTube video by Big Think
The illusion of self and the illusion of free will, explained | Annaka Harris
Self (and its counterpart the other) is a very handy abstraction to make the most of our limited processing power.
Illusion of free will is a beneficial yet erroneous causal explanation we created after we observed the self interacting with the other(s) for some time.
m.youtube.com/watch?v=_Ig9...
12.01.2025 18:47 β π 0 π 0 π¬ 0 π 0
People with absolute no theoretic or practical knowledge/experience (not a single call to nvidia-smi) about deep learning seems to easily predict the future of AI.
Their self-proclaimed prophetic confidence -still- amazes me.
01.01.2025 13:43 β π 0 π 0 π¬ 0 π 0
Incentives influence choices and mass choices create the Zeitgest.
Unrestricted social media ecosystem favors the quick and superficial, seducing everyone βfrom professors to everyday individualsβ to contribute to content that appeals to our lower faculties, much like primal instincts.
29.12.2024 11:08 β π 0 π 0 π¬ 0 π 0
Some reflections and insights after 1993 NIPS by Leo Breiman known for developing CART, bagging, and random forests.
Always find less formal writings of the pioneers more insightful.
29.12.2024 08:46 β π 2 π 0 π¬ 0 π 0
Bridging Generative AI and Truth: Ancient Lessons for Modern Tech
When a seasoned lawyer last year filed a brief citing six precedent-setting cases, he trusted the AI chatbot that assisted him.
AGI isn't around the corner and scaling auto-regressive LLMs won't get us AGI.
I argue, while AR-LLMs are great improvements, we need some very important paradigm shifts with lessons from the past.
open.substack.com/pub/beravci/...
22.12.2024 13:37 β π 4 π 0 π¬ 0 π 0
The infamous event of "cultural generalization made by a keynote speaker" shows how bias and miss-generalization are hard problems even for humans (even if a MIT professor). So, we maybe more compassionate with LLMs trained on our data.
14.12.2024 19:37 β π 0 π 0 π¬ 0 π 0
Hallucination, especially somehow grounded ones, is not a bug but rather a feature.
Artists, for centuries, have been the "hallucinators of human society" challenging the common sense, i.e. the common daily patterns in the society.
14.12.2024 11:17 β π 1 π 1 π¬ 0 π 0
#NeurIPS and other major conferences should consider making presentations, at least important keynotes/highlights, publicly available.
I could easily make an argument with public fundings for research presented. Funding agencies can also support this for more open science.
12.12.2024 06:33 β π 1 π 0 π¬ 0 π 0
Meet Willow, our state-of-the-art quantum chip
Our new quantum chip demonstrates error correction and performance that paves the way to a useful, large-scale quantum computer.
Feels like the beginning of 1900s with huge discoveries each year but this time huge strides in tech.
Exciting to be a witness of the tech revolution ranging from AI to quantum compute...
blog.google/technology/r...
09.12.2024 20:32 β π 1 π 1 π¬ 0 π 0
GPU poor man's home setup ready for a long night...
07.12.2024 20:05 β π 0 π 0 π¬ 0 π 0
Great insights, thx @tiziano.bsky.social. Especially like it because its prospective and interventional experiment.
One question: Any observed/estimated bias because of the "install browser extension" constraint?
29.11.2024 09:05 β π 0 π 0 π¬ 0 π 0
This saga reminds me of the access modifiers I teach in my Java OOP course.
Maybe we need something similar in the generative AI age:
- Public: Content accessible to both AI and humans
- Protected: Human-only consumable public content
Easier said than done with a lot of technicalities though...
28.11.2024 05:27 β π 1 π 0 π¬ 0 π 0
Announcing the NeurIPS 2024 Test of Time Paper AwardsΒ β NeurIPS Blog
Test-of-time awards are the **real impact** metrics akin to revolutionary science in
Kuhnian sense.
Congrats to @ian-goodfellow.bsky.social and Ilya with GANs and Seq2Seq.
blog.neurips.cc/2024/11/27/a...
27.11.2024 17:57 β π 1 π 0 π¬ 0 π 0
Thx Roy, working on multimodal in medicine π
27.11.2024 13:37 β π 1 π 0 π¬ 1 π 0
Another WW2 induced technology
27.11.2024 13:24 β π 1 π 0 π¬ 0 π 0
:) Not an easy task to match 1kw magnetron power with a beamformer in 12cm wavelength (2.4GHz) region in the usual oven size. Maybe with a roomsized oven...
BTW, guessing some EE background Durk?
26.11.2024 17:41 β π 2 π 0 π¬ 1 π 0
Yes, this is from the book but I think it is originally from one of Tenenbaum's previous papers on game engines for learning physics.
26.11.2024 04:25 β π 1 π 0 π¬ 0 π 0
Professor at Cornell Tech. Vice Chair of AI&Eng Research at Weill Cornell Radiology. AI for Medical Imaging. Ex: Princeton, MIT, Harvard. Hobbies: Running, NBA, NFL, Music (Rock!), Books, Broadway, Science, Technology. New here.
Internet pedestrian. Machine learning mercenary. α(γ)α (he/him/his)
https://laurent-dinh.github.io/
Blog: https://sander.ai/
π¦: https://x.com/sedielem
Research Scientist at Google DeepMind (WaveNet, Imagen 3, Veo, ...). I tweet about deep learning (research + software), music, generative models (personal account).
Reverse engineering neural networks at Anthropic. Previously Distill, OpenAI, Google Brain.Personal account.
Associate Professor of Machine Learning, University of Oxford;
OATML Group Leader;
Director of Research at the UK government's AI Safety Institute (formerly UK Taskforce on Frontier AI)
Chief AI Scientist at Databricks. Founding team at MosaicML. MIT/Princeton alum. Lottery ticket enthusiast. Working on data intelligence.
MIT PhD Student - ML for biomolecules - https://hannes-stark.com/
Machine Learning Researcher
https://alexalemi.com
https://blog.alexalemi.com
π§π»ββοΈ scientist at Meta NYC | http://bamos.github.io
Digital Geometer, Associate Professor of Computer Science & Robotics at Carnegie Mellon University. There are four lights.
https://www.cs.cmu.edu/~kmcrane/
Senior Research Scientist at Google DeepMind. I β Optimization β© Machine Learning. Fan of IronMaidenπ€.Here to discuss research π€
ML Professor at Γcole Polytechnique. Python open source developer. Co-creator/maintainer of POT, SKADA. https://remi.flamary.com/
ML + Cells + Proteins. PI @ AITHYRA https://alextong.net
ai. ex ml phd@mit, pytorch@fair, ai&cv@berkeley
Assistant Professor at @EPFL in Computer Science and Life Sciences. PostDoc at @Genentech and @Stanford. PhD at @ETH and @BroadInstitute.
www.aimm.epfl.ch
Associate prof, MIT EECS/CSAIL π»π¬π¦₯π§ποΈββοΈπΌππ»π³οΈβπ he/him/his
machine learning researcher @ Apple machine learning research
Founder & executive & community builder & organizer & researcher
ML Collective (mlcollective.org)
Google DeepMind
rosanneliu.com
I do SciML + open source!
π§ͺ ML+proteins @ http://Cradle.bio
π Neural ODEs: http://arxiv.org/abs/2202.02435
π€ JAX ecosystem: http://github.com/patrick-kidger
π§βπ» Prev. Google, Oxford
π ZΓΌrich, Switzerland
Laplace Junior Chair, Machine Learning
ENS Paris. (prev ETH Zurich, Edinburgh, Oxford..)
Working on mathematical foundations/probabilistic interpretability of ML (what NNs learnπ€·ββοΈ, disentanglementπ€, king-man+woman=queen?πβ¦)