Sourav (@srvmshr) header
Sourav (@srvmshr) profile picture

ML University of Tokyo. Prev: Microsoft Research RF, @virginia_tech. Personal opinions. Coasting life with @jnchrltte. AI Babysitting currently.

Tokyo-to, Japan
Joined April 2013
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Really cool work @randall_balestr 👍🏻
Several recent papers find that you can ~align image and text embeddings with just a rotation. @randall_balestr and @ylecun released a cool demo of this here: huggingface.co/spaces/galila… I tried it on a photo of my cat Moxie. I have to say, it is completely accurate!
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Sourav retweeted
I saw the recent softmax bug which was found by Gemini, and tried to reproduce it with a property-based test in Hypothesis. Surprisingly, even the proposed fix fails this test. So, I wrote a more robust softmax which does indeed pass this test! arxiv.org/abs/2610.36427
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Haven't shared this ever. I was terrible at arithmetic in grade school. My father until grad school, wasn't convinced anything technical was cut for me. He has gone to even discouraging me from studying CS/Math even after I got through INMO. He tried to convince me for BIO/ECON
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It was an uncomfortable topic at home. Part of the reason why he declined to sponsor me for Masters at Columbia or EPFL, when I had admits. I went on a full ride to @virginia_tech PhD against their will (they'd have wanted me to do MBA or civil services)
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Often these stereotypes start at home. Even educated parents fall for the trope going by some flawed evaluation metrics (class tests, tuition ranks etc) Math is about loving structure of world. This ain't a rat race. And if your definition of it is one, this field isn't for you.
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Today we're also releasing Mirror! Mirror is a coding agent that we've been using internally for a while now. It has first-class support for Beam, and you can easily switch to other models on the backend 💻 GitHub: github.com/reflection-oss/mi… 📖 Docs: developers.reflection.ai/mir… 🔑 API key: platform.reflection.ai/
We've released the weights for Beam!! 🤗 huggingface.co/reflection 📄 Model card: reflection.ai/beam-model-car… 🧪 Try Beam in our playground & get an API key: platform.reflection.ai/playg… 📚 AND we take open model safety seriously and have posted our safety report: reflection.ai/beam-safety-re…
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Sourav retweeted
Argon has this amazing natural understanding of the world. It can do so many things from first principles. Here's a pelican riding a bike, using fourier epicycles. Video made by Argon. Time-synced Lyria 3.5 music prompted by Argon.
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Replying to @AndrewCurran_
Also we don't know how paracetamol works, we don't know why people sleep, or how gravity works. We very much don't know *why* cancer drugs work. I think this is a mathematician misunderstanding how empirical sciences work.
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That time of year when I have to update this slide again.
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Not only has the US never televised public executions in its history, but not even the Nazi defendants sentenced to death at the Nuremberg trials had their executions televised (it was deemed violative of the human rights that post-WW2 institutions were trying to re-establish). The most amazing part of this is that Nidal Hasan is paraplegic and close to 60. So this disgusting spectacle will involve rolling him out in a wheelchair, then live-streaming his body as it's pumped full of bullets. When I and others pointed out last night that public executions are carried out only by the countries we're told are the most primitive and savage (Afghanistan, North Korea), MAGA enthusisasts replied that we were too weak. Pummeling an old guy in a wheelchair with bullets -- as a reality TV show -- seems like an odd and dubious defintion of "strength," let alone an expression of the western values they endlessly claim they and they alone are trying to salvage.
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October in The City
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Introducing a new generative model, Empirical Variational Autoencoder (EVA): We can revive VAEs for sequential generation only with an extra single linear layer that predicts the next prior without VQVAEs or diffusion models. Project page: mapooon.github.io/EVAPage/
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Sourav retweeted
wake up babe the freaking pope got schmidhuber'd
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I don't understand why people take that trio seriously anymore. They have been nothing but burning goodwill and bridges everywhere.
Timnit Gebru doubles down on the "stochastic parrots" framing, saying you "cannot expect LLMs to be factual." As evidence to support this, she cites errors in... Google AI Overviews. We need to start a GoFundMe to pay for these people to have access to Opus 5.5 and Astra.
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Fair point 💯
Let's be honest. This is the only definition ever offered for "stochastic parrots" in the paper. In other words, no definition at all. I've seen no disavowal by any of the authors of this awful rhetorical device that has done more harm to the understanding of LLMs by academics. I still see this cited by people as "evidence" that LLMs "don't really think" and therefore could not be used for "research". This was a bad paper written full of bad claims even for its time. I must admit I fell for it then but Google were right to not want to be associated with it.
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Sourav retweeted
So the point of the stochastic parrot argument is that LLMs have zero understanding of language or anything else, they are just regurgitating training data. This hypothesis has been thoroughly disproven by LLMs solving millennium problems our smartest mathematicians have failed.
Lol I’m really not going to answering these people sputtering on about “stochastic parrots” at this point. Why does the accurate description of LLMs trigger you so much?
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re "LLMs vs stochastic parrots" debate, including "but current LLMs are not just LMs anymore" and "but Emily, Timnit etc still make these claims": why do you care so much what a couple of irrelevant scholars say? take any topic, you will find an idiot scholar making bad claims
Replying to @MelMitchell1
Bender's FAQ here mostly leaves me thinking that current models are still very limited for the basic reasons she'd outlined in the paper. She specifically says the framing is "extremely relevant" to current models.
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I believe there was a set of IPAM lectures from 2012 which were incredible as well!
I always believed in free education. I made a decision to never monetise my popular UBC and Oxford lectures. I’ve lived my life by working to bring knowledge to everyone on Earth: the pioneering lectures, the startups past and new, and my AI research. This desire came as the result of witnessing and experiencing a lot of suffering in my childhood. This course is part of my legacy and I’m very proud of it, and thankful to Oxford, the TAs and my students then.
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