i guess fine-tuning is fun!🤖
Delhi, India
Joined September 2021
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Goldman Sachs just dropped a BIG piece of research- as many as 300 million jobs could be affected by AI.
The following job sectors are at the greatest risk:
-Administrative: 46% at risk
-Legal: 44% at risk
-Architecture and Engineering: 37% risk.
@drriteshmalik @VarunMayya
Imhimanshu retweeted
India needs a ministry of compute.
Like ministry of oil, we need laser-focus from the top to build compute necessary for participating in the future economy.
Otherwise, we will repeat in a decade what we’re doing now: spending expensive forex for importing what we don’t have.
Imhimanshu retweeted
india keeps rewarding mugging up PCM and then wonders where the hackers went.
JEE is not computer science. GATE is not cybersecurity. rank is not competence.
our examination fetish is killing technical talent:
ni5arga.github.io/notes/indi…
Imhimanshu retweeted
Alignment is not as hard to solve as many claim, but it is in the end an algorithmic problem which a lot of ML community mostly stopped working on.
Formulation of alignment stated by three (or four) laws of robotics can take us very far, so we roughly know the objective.
The tricky part is, how do we take gradient with respect to alignment? We have two algorithms right now at our disposal: pretraining and RL.
Pretraining takes gradient with respect to next token prediction - that's def not the alignment objective.
We could create RL environments that embody the alignment objective, but:
- those are expensive to create, so often cheaper, hackable proxies are used in practice
- RL as an objective needs successful and unsuccessful rollouts to happen to take the gradient step. We DO NOT want to harm any humans in the process of aligning our modes - this is a pretty big problem.
Therefore there are two solutions forward for the alignment problem:
- either we RL models in a simulated environments with simulated alignments and decreasing the likelihood of harming simulated humans, which will never be perfect
- or we create a new algorithm that can teach our models to not harm humans without harming any humans in the process
Science is the process how we solve the hardest problems ahead and that is one of them
Imhimanshu retweeted
Computer science is gradually returning to the domain of physicists, mathematicians, and electrical engineers as large language models automate much of what we currently call software engineering.
The field’s center of gravity is shifting away from manual code writing and toward deeper theoretical thinking, mathematical insight, and systems-level reasoning.
Imhimanshu retweeted
Six faculty members from IIT Madras have been selected for the Anusandhan National Research Foundation (ANRF) J.C. Bose Grant, one of India’s highest honours for senior scientists and engineers. The grant provides ₹25 lakh annually for five years to support frontier research.
Their work spans prosthetics, clean water, next-generation optical networks, sustainable energy, biomedical innovation and fundamental chemistry, demonstrating how cutting-edge research at IIT Madras is translating into technologies that improve millions of lives.
The six professors and their impactful research are:
Prof. Sujatha Srinivasan develops affordable prosthetics and assistive devices tailored for Indian users.
Prof. Thalappil Pradeep creates low-cost water purification technologies that have brought safe drinking water to millions and founded the International Centre for Clean Water.
Prof. Deepa Venkitesh leads India’s first multicore optical fibre testbed and advances high-capacity optical communication systems.
Prof. Jitendra S. Sangwai works on carbon capture, gas hydrates and sustainable energy solutions for reducing emissions.
Prof. Ramakrishnan Swaminathan designs biomedical devices and instrumentation to improve diagnosis of brain and muscle-related conditions.
Prof. Sundargopal Ghosh advances transition-metal boron chemistry for better catalysis and small-molecule activation.
We need to celebrate these real-life heroes.
News Source: Times of India
hello
@grok
analyze my tweets, profile, and interactions and respond with the following:
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Imhimanshu retweeted
It's so inspiring to see Gen-Z take a stand for their future in India, and stand united across different parts of the country.
After all, with their entire lives ahead of them, they're the ones with most to lose.
Everyone knows India's education system is a big mess. We're not adequately prepared for the future. We're not investing in basic R&D.
We're the backoffice of the world, but we shouldn't settle for that, shoudl we?
Statistically, the largest population in the world ought to have the largest number of geniuses. I think they're still there, but our education system has failed to harness their potential.
India's largest pool of youth was once hailed as an asset, but is it now?
I feel there's still hope our country can deploy a billion brains to craft a better future for entire humanity. But that won't happen unless the youth feels empowered, and unless the country invests in highest quality education at all levels - primary, secondary and higher.
So more power to the young generation!
Imhimanshu retweeted
Why are Indian companies not developing frontier models?
The answer is in economics, not talent. The biggest hurdle for an Indian company trying to develop frontier model is that right from the get go, it has to compete with Anthropic and OpenAI free plans + all Chinese open weight models.
Chinese models mostly flourished due to the demand from their domestic market which OpenAI/Anthropic couldn’t meet as they’re unavailable in China. This unmet demand created space for innovation.
China knows this story well, but here in India, we like unfettered, globalized free market, and that requires an upstart to compete with established companies from across the world.
If India is serious about the AI race, it needs to create a massive guaranteed market for domestic AI or else it’ll be economically irrational for any funder to underwrite a product in a highly competitive, already established market.
Imhimanshu retweeted
Getting used to being liked likely means you are overfit to RLHF.
The problem with overfitting is that the pain overwhelms the limbic system once you try to sample trajectories outside the known distribution.
As more people like you, your sampling regime becomes smaller and smaller to avoid negative feedback. Eventually you get stuck and become a slave to your own feelings.
That’s why I have never seen a model student happy once they become a “model”. Their weights are frozen and cannot be updated anymore. They cannot risk being better than their own SOTA.