Suhas Shrinivasan
180 posts

Suhas Shrinivasan
@suhas_shrinivas



Hi 👋, PhD Chemist here:- Anyone else agree that literally everything is made of chemicals?


Talent and academics will leave the US. The EU (Member States) should initiate (tech) talent visas that can be expedited swiftly



The #ELLISPhD application portal is now open! Apply to top #AI labs & supervisors in Europe with a single application, and choose from different areas & tracks. The call for applications: ellis.eu/news/ellis-phd… Deadline: 15 November 2024 #PhD #PhDProgram #MachineLearning #ML

Geoff and John are a truly inspired choice for the Nobel Prize in Physics. Not only because they have done groundbreaking work for machine learning research, but also since this choice reflects an understanding that machine learning methods are changing how science is done (1/2)


BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”


A surprising number of people have asked me versions of "Is the Physics Nobel Prize today really for Physics?" What counts as a field is surprisingly complicated. As a rough and incomplete classification, a field can be: 1. Based on exploration of and development of a set of agreed-upon deep principles. The Maxwell-Lorentz equations, quantum field theory, general relativity, and quantum computing are each (separately!) examples of fields in this sense 2. Based on exploration of some (more or less) agreed-upon set of questions. The search for a basic fundamental theory and quantum information are both examples of fields having this flavour. The questions tend to shift over time, sometimes substantially, and such fields sometimes fission or fusion or change substantially as a result 3. A group of people investigating a common domain of application. Examples of fields with this flavour are atomic physics, optical physics, and condensed matter physics. This can be viewed as a case of 2. It's striking how much internal variation there can be - understanding (say) the fractional quantum Hall effect is very different than understanding spin glasses, yet both are part of condensed matter physics. In some sense the question behind condensed matter unifies many different fields of type 1 4. A philosophical, organizational, and political treaty among fields of types 1-3. That's what "Physics" is, in the sense of the prize. It's interesting that antenna design is currently not regarded as part of Physics (it's more EE), while quantum computing to some extent is part of Physics. That's partly a contingent choice: it could have been different if history had just been a tiny bit different. However, to some extent it also represents some general philosophy of "what Physics is about". Ideas like quantum error-correction and topological quantum computers required deep fundamental insights into physics. My guess - it's just a guess - is that, over time, quantum computing will become more detached from Physics, as it becomes more and more commercial, and more and more engineering Today's Physics prize falls outside the usual type 4 philosophical, organizational, and political treaty of "what physics is". People at Caltech used to tell me that John Hopfield had "left physics", and that's why he'd gone to Princeton (from Caltech). But then, a lot of physicists in the 1990s didn't think quantum computing was part of physics. I'm sure some still don't. So: it really is somewhat contingent My own point of view: there is just one nature. I'm delighted when people have and share deep insights into nature, and I don't care so much what we label it. I'm especially delighted by the incredible progress in the past few decades in developing the design sciences. That is: understanding the fundamental principles underlying the incredible systems latent in nature, and which we humans are gradually learning to build. John Hopfield and Geoff Hinton have made enormous contributions to understanding what possibilities lie latent in nature. It so happens that their work falls largely outside the usual Nobel classification, but I am happy to celebrate them for their remarkable contributions, and physics seems as apt an area as any Congratulations to them both!

Did you know that an artificial neural network is designed to mimic the brain? Inspired by biological neurons in the brain, artificial neural networks are large collections of “neurons”, or nodes, connected by “synapses”, or weighted couplings, which are trained to perform certain tasks. An artificial neural network processes information using its entire network structure. The inspiration initially came from the desire to understand how the brain works. Learn more about this year’s physics prize awarded for work on artificial neural networks: bit.ly/3Bi9H8u #NobelPrize

I-7 (Mon 16:30): @SchulzAuguste kicks off the @mackelab poster marathon, introducing LDNS, a diffusion-based latent variable model to generate diverse neural spiking data flexibly conditioned on external variables. Fun project co-led with @_Jaivardhan_: arxiv.org/abs/2407.08751










