Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Want Indian DeepTech? Start with Physics, not funding! Prof. Rajesh Gopakumar argues that India cannot build frontier technology without first building the deep science underneath it. That means more than throwing money at startups. We need more scientists, stronger institutions and people curious enough to discover things that haven't...

75,871 görüntüleme • 1 ay önce •via X (Twitter)

15 Yorum

Aravind profil fotoğrafı
Aravind1 ay önce

I will tell a better idea. India can build deep tech without anything except good IT teams. If you don't understand ask China.

The Rocket Media profil fotoğrafı
The Rocket Media1 ay önce

The legend has entered the chat

Vishwaguru profil fotoğrafı
Vishwaguru1 ay önce

Promote MSc education over BTech .

Spontaneous Polarisation profil fotoğrafı
Spontaneous Polarisation1 ay önce

Show me results first then I will give you money. But sir I need money to show you results.

The Rocket Media profil fotoğrafı
The Rocket Media1 ay önce

Sadly that's the vicious cycle that is hard to escape!

Kant immanuel profil fotoğrafı
Kant immanuel1 ay önce

true. Make research cool here.

Ashwani Dev profil fotoğrafı
Ashwani Dev1 ay önce

Completely agree. Deep tech takes long lead time. Invest in fundamental research in quantum, aerospace, mineral research, energy, food based medications, medical research, research on civil and logistics infrastructure (just construction will not do).

Vedic Knowledge profil fotoğrafı
Vedic Knowledge23 gün önce

What if atoms are not objects but symbols of meaning? What if the universe is like a book and not like a box? What if the weirdness of quantum mechanics can be explained by postulating meaning as a fundamental feature of the universe?

Pawan Kumar profil fotoğrafı
Pawan Kumar1 ay önce

@jhasushant

Malolan Cadambi profil fotoğrafı
Malolan Cadambi1 ay önce

Get your plumbing correct first. Learn to build sewerages before you build software!

AsgFoodBytes profil fotoğrafı
AsgFoodBytes1 ay önce

Who studied extra cs to get into IT sector hire them.Start engaging professors and students from all backgrounds from colleges itself.Once people have knowledge that other departments have enough jobs they might do wonders

शोधक profil fotoğrafı
शोधक29 gün önce

People have no clue about the reality. Read Central Educational Institutions (Reservation in Teachers’ Cadre) Act, 2019. Everything has been sacrificed at the altar of social justice! Top institutions are under huge pressure to recruit reservation faculty.

AsgFoodBytes profil fotoğrafı
AsgFoodBytes1 ay önce

Why only talk this or that all thinks are connected u get ece demand in private sector people will have job security they will definitely choose it like it.We must focus on is mutual one trigers other.We already have thousands of iitians,nitians from ece

hatebuster profil fotoğrafı
hatebuster1 ay önce

This is an indictment of poor research infrastructure and lack of academic focus on cutting edge science education. The system and culture favors copy and apply model. BJP is more interested in propping up special interests like Adani and Ambani and lining politicians pockets.

dwasf profil fotoğrafı
dwasf1 ay önce

US universities are well funded and get huge endowments. In China state universities are well funded. Till they were funded they produced anemic output. No army moves without food. Indians won't either.

Benzer Videolar

Today, I’m proud to share something we’ve been building for months - Artham, India’s first Small Language Model (SLM) built exclusively for Indian Capital Markets. We unveiled it at AWS re:Invent 2025 in Las Vegas, marking a defining milestone for India, AI, and Raise on the world stage. 🚀 This isn’t just a product launch. It’s a statement that India no longer has to rely on global AI models that were never designed to understand the depth, nuance, and realities of our markets. We can and we will build for ourselves. Artham is shaped by the realities of Indian markets — SEBI’s rulebook, NSE & BSE dynamics, corporate filings, trading cycles, investor sentiment, and the everyday language of Indian finance. Every layer is grounded in local context, built to serve Indian participants, not global assumptions. This isn’t a “global model adapted for India.” Built in India. Hosted in India. Built for India. 🇮🇳 This is a model born from India’s capital markets. At Raise Financial Services, we’ve always believed that true financial empowerment cannot come from imported solutions. It must be created on-ground, in-context, with an Indian-first mindset. Artham is our step towards making Indian finance more intelligent, more relevant, and more accessible for retail investors, traders, institutions, and the next generation of market participants. Behind this launch is a team that stayed obsessed with one thing: relevance. Relevance to the Indian investor. Relevance to the Indian market cycle. Relevance to India’s financial future. This isn’t a concept or a test model. Artham is already live in production for millions of users: • fuzz AI (AI research) • ScanX (markets insights) • Dhan - Made for Trade (trading and investing) Proud of our team. Proud of our mission. Proud to build for India. 🇮🇳 Anirudha Basak Amazon Web Services AWS Cloud India #AWSreInvent2025

shraddha

94,433 görüntüleme • 10 ay önce

Terence Tao, UCLA mathematics professor and Fields Medallist, on why AI may be succeeding at science while scientists get worse at it: Tao starts by naming the tension directly: "There is this paradox that on the one hand AIs are becoming more powerful and more capable and making fewer mistakes, and they are ostensibly achieving a lot of the goals that we think scientists are trying to do. They're running experiments. They're analyzing data. They're writing papers." Experiments run, data analyzed, papers written — the exact outputs any university or funding body would cite as proof that science is working. Tao's concern is what those outputs stop telling you: "It may be that it comes at the cost of the AI picks up some skill but no human scientist gets any better at doing the science." The skill still accumulates, just in the wrong place. The system gets more capable while the people operating it stand still, because the work that used to build a researcher is now the work being handed off. And the cost shows up in the one thing scientists are supposed to be able to do: "No human can communicate exactly what just happened and why. This scientific discovery is interesting, why this proof is new and what features it has and how it connects." That final clause is the sharp end of it. Tao is describing a result nobody can place — a proof that arrives with no one able to say what makes it new, how it's built, or how it connects to the rest of the field. Understanding is a separate achievement from getting the answer, and it can quietly disappear while the answers keep arriving on schedule. Which is why he thinks the target itself needs re-examining: "We may have to sort of redesign our conception of what science is and what we actually want out of science. What exactly is science for and what are we trying to do? And is there a danger that we are optimizing the wrong thing when we are pointing our AI tools at science?"

Big Brain AI

42,551 görüntüleme • 20 gün önce