On a task to demistify a cognition. 🤖 ML Engineer 📚 Mathematician  🧠 Cognitive Scientist 🤸 Movement Enthusiast

Joined March 2025
Tom Bukic retweeted
people often take deep learning as synonymous with backprop, but deep networks were originally trained with probabilistic energy-based methods! found this great talk by hinton from 2012 about EBMs, boltzmann machines, and deep belief nets at the start of the deep learning era
Tom Bukic retweeted
Replying to @lucas_montano
That’s true. If you want to use Claude Code with a local LLM model, here is your way Setting Up Claude Code Locally with a Powerful Open-Source Model: A Step-by-Step Guide for Mac… medium.com/@luongnv89/settin…
Tom Bukic retweeted
U prošloj godini, Budisavljević je za svoju tvrtku od Grada Zagreba primila 157.000 eura. Ove godine je na račun Hulahopa uplaćeno 176.500 eura. Ukupno, za privatnu tvrtku glasačice stranke Možemo, Tomaševićeva je vlast u nepune dvije godine uplatila 333.500 eura! Možemo!
Tom Bukic retweeted
Možemosi: Na stranu to što je bio istaknuti intelektualac i dugogodišnji predsjednik Matice hrvatske, Filip Lukas bio je "ustaški simpatizer" Isto Možemosi: Na stranu to što je bio tajnik SANU-a i koautor velikosrpskog Memoranduma, Dejan Medaković bio je povjesničar umjetnosti
Tom Bukic retweeted
This is 100% correct. I'm happy I still got to experience a small part of the old Google.
It's actually quite interesting to see how Google will handle this. If you had made the same statement a few years ago, your colleagues would have legitimately formed a protest with letters and walkouts until the company fired you. The protesters would use extreme hyperboles such as claiming that they fear for their physical safety, they have anxiety going to the office now, or that he can't be trusted as a scientist because science "proves" he's wrong. While society as a whole has learnt to become more accepting of different viewpoints, I find it difficult to believe that Google's debate culture has progressed at the same pace. This wasn't always the case though. Back in the day, Google had a very healthy debate culture. We had a huge number of active email lists, TGIF was a place where you could ask Larry and Sergey hard questions, and even in tough moments, things didn't get leaked. All of this changed between 2016 and 2018 where the very worst patterns of cancel culture were embraced by a small but vocal and politically active group of Googlers. One important instrument in that change was Memegen which turned from one of the happiest places on the internet into a tool for amplifying their voices and frankly disparaging the company and its leaders. All of a sudden, any means of achieving a political goal were fair: leaking TGIF, talking to Gizmodo, live tweeting leadership Q&A. Think of all the things that happened in that timeframe and that got cancelled into oblivion: Project maven, Damore, project dragonfly. The protesters won. Google embraced their viewpoints and worked hard to suppress dissent. The thing that personally hurt me was that we went from a culture of extreme trust and honesty, to one where leaders became unable to answer any questions in a public forum. All we got was corp speak. It was around that time when Larry and Sergey stopped doing TGIF. I have obviously never talked to them but at least in my mind this newly formed political activism was an important factor in their decisions to retract. Long story short: let's see if this culture still exists at Google or if it has changed.
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This is the way if you care about your business mission. 👇💯
Replying to @Yuchenj_UW
No. Google should take the @brian_armstrong route and ban political speech or initiatives within the company or in the name of the company.
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Tom Bukic retweeted
Replying to @DavidSKrueger
You should comitt this to memory and pass it through your culture as oral tradition. Writing is a corruption that is guilty of eroding our capacity for wonder and individual imagination.
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Tipping point moment? MGGA! 🤞🤞
Agreed with many points here, and awaiting how this story will turn out. While I cannot pinpoint to an exact timeline, such as 2016-18, I did witness Google's culture going downhill throughout the years. Not only did Google leaders introduce the cancel culture, but they eventually grew to fear being cancelled. Nobody on the "wrong" side dared to speak up during Gebru's incident, Damore's incident, the CO2 footage incident, and many other incidents, only in the fear of being cancelled. Let's see if they choose to cancel the person who literally saved their Gemini series.
Tom Bukic retweeted
Gee, it would be great if "scientists" hadn't destroyed their credibility by failing to speak out against COVID bullshit and transgender bullshit and climate-catastrophism bullshit, wouldn't it? I'd love to be an advocate for funding basic research. I used to be a strong one.
Ozempic exists because scientists studied Gila monster saliva. The exact kind of basic research this Administration would have cut without hesitation. Breakthroughs like this happen only if we choose to fund science even if the outcome is impossible to see at the start.
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Tom Bukic retweeted
(1) Our team at @GoogleDeepMind has been collaborating with Terence Tao and Javier Gómez-Serrano to use our AI agents (AlphaEvolve, AlphaProof, & Gemini Deep Think) for advancing Maths research. They find that AlphaEvolve can help discover new results across a range of problems.
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(one could argue that POS-tagging wasn't made obsolete and useless by LLMs because it was obsolete and useless also pre-LLMs. i wont argue, but its nitpicking)
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Tom Bukic retweeted
Can’t believe it — our Princeton AI^2 postdoc Shilong Liu @atasteoff re-built DeepSeek-OCR from scratch in just two weeks 😳 — and open-sourced it. This is how research should be done 🙌 #AI #LLM #DeepSeek #MachineLearning #Princeton @omarsar0 @PrincetonAInews @akshay_pachaar
Discover DeepOCR: a fully open-source reproduction of DeepSeek-OCR, complete with training & evaluation code! #DeepLearning #OCR
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Tom Bukic retweeted
I see a lot of bad takes on X about PhDs and frontier labs (not just this quoted tweet), so let me chime in. For context, I didn't do a prestigious undergrad, worked a bit in a startup as an applied ML engineer, then did a PhD, and now work in a frontier lab. A PhD isn't following a course from a teacher or magically becoming an overpowered scientist. It's about doing a few things with intense focus for a long time, alone. You may have a great advisor guiding you, but that only affects the learning rate. At the end of the day, it's a lot of alone time with you and your thoughts on a problem few people care about. Good news: if you can find time, you can get a very similar experience! It'll be slower since you may not have as much free time, compute (though a free T4 on Colab is great), or an advisor and teammates (but there are great open communities). Today's distinction between ML research and applied ML is often small. Grinding paper reproductions on Colab and improving them one step at a time is a great way to become a researcher. The real worry is "Can I join a frontier lab without a PhD?" You'll face fierce competition for research scientist jobs, but even top PhD grads do—it's a mix of talent and luck. Re-implementing a paper and posting it on X probably isn't enough now, but publicly trying improvements and sharing interesting results, even as a blog post, can work! I know several frontier lab researchers, some extremely famous on X, who started this way. Personally, I loved my PhD. It was time to learn and explore ideas fully and freely. Do you absolutely need one? No.
HOT TAKE: Reality is, you can't actually work in top-quality ML research labs without a PhD. Top research labs still look for people with PhDs and excellence in maths, stats, PyTorch, neural networks, and CUDA kernels. In India, quality ML research labs are virtually nonexistent. Most good research labs are in the US/UK and China. Implementing papers and working on T4 Colab is cool, but you won't cross the threshold to become a researcher. 99% of ML people belong to the applied side, which has better practical perks: - MNCs or SF startups - You can switch and get promoted every 1.5 years - You can move to product management or CTO - All you need is hands-on experience and not many research papers - Cashflow is best I really respect people who code research papers, but how long will you wait for your breakthrough? In 3 months, research evolves, and you're following it without actually building anything. Stop following blindly! The world's best research labs pick only from top universities, not because you've implemented papers and posted on X! Either go for a PhD outside India or stick to the applied ML side. The job market is saturated and will remain so because we're evolving post-COVID. On the other hand, no startup or research lab thinks about you. You must focus on your growth and money first, then look for impact.
Tom Bukic retweeted
It was great to help with this interactive tutorial on SAEs, what they can be used for, and how they work. Fantastic work by the team!
Mapping LLMs with Sparse Autoencoders pair.withgoogle.com/explorab… An interactive introduction to Sparse Autoencoders and their use cases with Nada Hussein, @shivamravalxai, Jimbo Wilson, Ari Alberich, @NeelNanda5, @iislucas, and @Nithum
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Tom Bukic retweeted
Applying the method significantly reduces verbatim recitation while keeping outputs coherent, without needing a targeted "forget set". The result holds across architectures & modalities (LLM & ViT)! (3/7)
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Tom Bukic retweeted
I'm with Perplexity here. Websites can't dictate how people access them, IMO. It's just "information wants to be free", with a 2025 twist.
⚖️ Amazon has sued Perplexity alleging that Perplexity’s Comet browser runs an AI shopping agent that logs into Amazon with a user’s credentials, places orders, and browses while pretending to be a human, which Amazon says is a prohibited, covert automated access. Amazon’s core claim is that Comet disguises bot traffic as normal clicks, so Amazon’s systems cannot apply its bot rules, audit trails, or safeguards that normally kick in for automation. Amazon also says this automation enters private account areas, touches carts and checkout, and therefore creates security and fraud risk, because any script mistake or prompt misuse could buy the wrong item, ship to the wrong address, or expose sensitive data. Amazon argues the agent breaks site terms and bypasses controls that govern third-party tools, instead of using approved interfaces or clearly identifying itself as an automated agent. Amazon further claims the bot degrades the personalized shopping experience, because recommendations, pricing tests, and ranking are tuned for human behavior, not for rapid scripted requests. Amazon also says it told Perplexity to stop but the agent kept operating, which strengthens Amazon’s position that this is knowing unauthorized access. Perplexity’s defense is that the agent helps users comparison-shop and checkout on their behalf, with credentials stored locally, and that users should be free to pick their assistant even if Amazon dislikes the competitive impact. So the fight is about who controls the logged-in session, whether a browser-based AI can act as the user inside Amazon, and whether it must self-identify as a bot instead of masquerading as a person. --- theguardian .com/technology/2025/nov/05/amazon-perplexity-ai-lawsuit
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Tom Bukic retweeted
You can't know if your ideas are correct until you test them. Failure should be a plausible option
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Tom Bukic retweeted
Replying to @MLStreetTalk
It's closer to the other way around -- I arrived at these ideas by doing experimental deep learning research and trying to make sense of my results. Theorizing requires data. And once you have the theory you must test it -- which is why I made ARC (an experimental test) and immediately launched a Kaggle competition in 2020 to put it to the test. I considered it very plausible that I was mistaken and that ARC could be hacked easily, maybe 15-20% chance
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Tom Bukic retweeted
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She reminds me of thalamus, though.
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