India
Joined August 2017
Happy to share that I’ve been elevated to Fellow of Indian National Academy of Engineering (INAE). Owe it all to my students and collaborators.
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Georgin Jacob retweeted
Students sometimes ask me if it still makes sense, in this accelerating age, to pursue a PhD in AI. Perhaps counterintuitively, I think it's a great time to do so. I wrote up some thoughts on this here: web.mit.edu/phillipi/www/wri…
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Georgin Jacob retweeted
What's left for humans in a world where machine intelligence has so many advantages? I recently got a Tesla, and using full self-driving has been a wake up call to just how many advantages AI has over humans. The few times I disengaged it because I thought it was going into the wrong lane, it turned out that the car was right and I was wrong. I realized that there is no hope of me driving better than a neural net that knows every road, sees in every direction at once, and never gets tired or distracted. Given that AI has certain inherent advantages over human intelligence, what kind of moats will remain for us as humans? It's a big question. One short-term answer is that the world we live in was created for humans, and in some domains, AI has not closed the gap yet. For instance, AI still struggles to use internet user interfaces. While any computer-literate human can navigate a web page with ease, AI is still not great at making accurate clicks and drags because image embeddings are not optimized for such precision. If the internet were designed to be fed into language models instead of rendered as visual interfaces for humans, AI would obviously far exceed humans. But for now, language models still need to be retrofitted to our legacy infrastructure. Robotics is another area where we humans have a home-field advantage. Most tasks in the physical world are designed around fingers and opposable thumbs, which have been pretty hard to build into robots so far. While it is clear that machines can outperform humans in environments optimized for automation, like large-scale manufacturing lines, for now, most of the world is still built for humans. However, these capability gaps are only temporary. There will surely be a day when machines click faster than us and have superhuman general dexterity. What are the real moats that humans will have? Anything involving private knowledge that language models do not have access to feels like a solid moat to me. Romantic matchmaking and high-end real estate are two examples where inventory is often not advertised publicly and matches are made through being in the right circles. Venture capital is another example—although some research and decision making can be automated with AI, much of success hinges on understanding trends ahead of time and connecting the right people, both of which require private knowledge. While machines can and probably will have increasing access to some types of private knowledge, I think there will still be some types of private knowledge that only humans know. I do not see a path for AI to win when critical knowledge is closely guarded in human circles. A second area where humans seem to have a real moat is in entertainment and the arts, which are inherently valued for their human aspects regardless of how well machines can do them. Watching Usain Bolt sprint one-hundred meters is beautiful as an expression of the peak of human ability, even though cars can drive much faster. Watching chess at the amateur or intermediate level is more relatable and satisfying than watching two superhuman AIs play each other. The value of art comes from the creation process, which is why replicas are not as valuable as originals. These types of work feel like they will continue to have markets even as we advance towards superintelligence. More broadly, human presence is a feature that will be, by definition, challenging for AI to automate. For example, a teacher remembering your name or a parent supporting you is valuable even though AI can easily remember your name and probably give better life advice. Someone spending part of a finite life on you counts because their time runs out. As a personal anecdote, I remember the first time I worked with someone who I considered an amazing AI researcher. His advice was solid but what was more important was that I believed I could do great work with him as a collaborator and I raised my own standards. Over the past decades, the development of technology has divided us in some ways, but hopefully AI brings us closer to a world where human presence is reemphasized. Intelligence has been the defining feature of humans and it will be a big change for AI to automate that over the coming decades. In the near term, certain types of intelligence will become very cheap and automate away old jobs, but the moats I described above will not be the only places where humans can hold value. In the same way that computers took away the jobs of secretaries and manual accountants but created far more jobs via the IT industry, I believe there will be much more demand for services created by productive AI-augmented humans, perhaps for services we cannot yet imagine in today’s society. Just seeing how this story plays out will be an adventure in its own right.
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Georgin Jacob retweeted
How well can you describe the feature selectivity of a vision neuron … with words? Interpretability has long borrowed from neuroscience — and maybe it can give back too! 🧵
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Georgin Jacob retweeted
I'm proud we are releasing LAION-fMRI, a densely sampled 7T fMRI dataset of natural images, with very broad stimulus sampling for testing countless hypotheses & deeply exploring brain representations. It is now available at laion-fmri.hebartlab.com/ What does LAION-fMRI offer? 🧵
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Georgin Jacob retweeted
💫 Introducing NeuralSet: a simple, fast, scalable Python package for Neuro-AI 📦 pip install neuralset 📄 kingjr.github.io/files/neura… 🔍 facebookresearch.github.io/n… Supports 🧠 fMRI, EEG, MEG, ECoG, spike… preprocessing 💬 text 🔊 audio ▶️ video 🏞️ image… embeddings 🧵 Details👇
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Georgin Jacob retweeted
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Georgin Jacob retweeted
Please circulate widely! Open positions at the Vision Lab, Centre for Neuroscience, Indian Institute of Science (IISc) Deadline: March 31 2026 (Link in thread below)
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Georgin Jacob retweeted
We are excited to announce Open Day 2026! 📷 Visit our campus on March 7th between 9 am and 5 pm. Explore the exciting research demos, displays, exhibits and experiments! Use the hashtag #IIScOpenDay2026 to share what you see! Details: openday.iisc.ac.in/
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Georgin Jacob retweeted
Our awesome poster trilogy happening Tuesday morning! Don't miss it.
Come check out our lab at SFN this week!
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Georgin Jacob retweeted
Today in Nature Machine Intelligence, Kazuki Irie and I discuss 4 classic challenges for neural nets — systematic generalization, catastrophic forgetting, few-shot learning, and reasoning. We argue there is a unifying fix: the right incentives & practice. rdcu.be/eLRmg
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Georgin Jacob retweeted
Our new study in @NatComputSci, led by Haibao Wang, presents a neural code converter aligning brain activity across individuals & scanners without shared stimuli by minimizing content loss, paving the way for scalable decoding and cross-site data analysis. nature.com/articles/s43588-0…
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Georgin Jacob retweeted
Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions. Work with @lu_zejin @martisamuser and Radoslaw Cichy arxiv.org/abs/2507.03168
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Attention! The American Heart Association has dropped their 2025 update on alcohol and the heart. This is intense. Here is what you need to know in plain language summary! 1/13
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Such a great experience working on this study with @sparuniisc and @PramodRT9! Learned a lot—so glad to see it out in the world.
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Georgin Jacob retweeted
Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.”​ Statements discouraging people from learning to code are harmful! In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment. As coding becomes easier, more people should code, not fewer! Over the past few decades, as programming has moved from assembly language to higher-level languages like C, from desktop to cloud, from raw text editors to IDEs to AI assisted coding where sometimes one barely even looks at the generated code (which some coders recently started to call vibe coding), it is getting easier with each step. I wrote previously that I see tech-savvy people coordinating AI tools to move toward being 10x professionals — individuals who have 10 times the impact of the average person in their field. I am increasingly convinced that the best way for many people to accomplish this is not to be just consumers of AI applications, but to learn enough coding to use AI-assisted coding tools effectively. One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is a great way to do that. When I was working on the course Generative AI for Everyone and needed to generate AI artwork for the background images, I worked with a collaborator who had studied art history and knew the language of art. He prompted Midjourney with terminology based on the historical style, palette, artist inspiration and so on — using the language of art — to get the result he wanted. I didn’t know this language, and my paltry attempts at prompting could not deliver as effective a result. Similarly, scientists, analysts, marketers, recruiters, and people of a wide range of professions who understand the language of software through their knowledge of coding can tell an LLM or an AI-enabled IDE what they want much more precisely, and get much better results. As these tools are continuing to make coding easier, this is the best time yet to learn to code, to learn the language of software, and learn to make computers do exactly what you want them to do. [Original text: deeplearning.ai/the-batch/is… ]
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Georgin Jacob retweeted
The goal of the PhD programme in Brain, Computation, and Data Science is to train students such that they are able to address significant research questions in brain, computation, and machine intelligence. Interested students can apply at admissions.iisc.ac.in
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Inauguration of the Zeiss-IISc lab focused on AI for Eye Care.
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