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Manish Gupta, Senior Director at Google DeepMind India, sits down with Aakrit Vaish and Pratyush Choudhury at Mumbai Tech Week for a rare on-record conversation about the frontier AI research happening out of Bangalore. Gupta makes a pointed case against the narrative that India lacks AI research talent: a...

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14,136 次观看 • 2 个月前 •via X (Twitter)

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Had a great conversation with Richard Seroter, Senior Director and Chief Evangelist at Google Cloud, during Google I/O Connect India in Bangalore today. We spoke about how he uses Gemini in his day to day work, how Google is handling the massive compute demand for AI, why defining goals matters more than writing perfect specs, and what's next for Gemini.
10:31

Had a great conversation with Richard Seroter, Senior Director and Chief Evangelist at Google Cloud, during Google I/O Connect India in Bangalore today. We spoke about how he uses Gemini in his day to day work, how Google is handling the massive compute demand for AI, why defining goals matters more than writing perfect specs, and what's next for Gemini.

AshutoshShrivastava

13,866 次观看 • 1 个月前

India has the talent. India has the software muscle. So why didn’t India lead the AI revolution? In this clip, Prannoy Roy asks the tough question and Ruchir Sharma breaks it down with clarity: from low R&D spending to a culture of optimization over innovation. A must-watch conversation on where India stands in the global AI race and what’s holding us back. Full video is live on DeKoder - Presented by 360 ONE and Powered by India Sotheby's International Realty #IndiaAI #AI #RuchirSharma #PrannoyRoy #DeKoder
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India has the talent. India has the software muscle. So why didn’t India lead the AI revolution? In this clip, Prannoy Roy asks the tough question and Ruchir Sharma breaks it down with clarity: from low R&D spending to a culture of optimization over innovation. A must-watch conversation on where India stands in the global AI race and what’s holding us back. Full video is live on DeKoder - Presented by 360 ONE and Powered by India Sotheby's International Realty #IndiaAI #AI #RuchirSharma #PrannoyRoy #DeKoder

DeKoder

19,864 次观看 • 7 个月前

Congratulations to Sarvam (Sarvam) on the first close of their Series B, and a warm welcome to HCLTech (HCLTech) and Bessemer Venture Partners India (Bessemer India), who join us in this journey with the conviction that has defined Sarvam from day one: that India should build and own its AI stack. In under three years, the team has turned that idea into working infrastructure: → Frontier models trained from scratch in India that hold their own against far larger systems → Speech models transcribing over half a million hours of audio a month in real Indian conditions → Voice agents already live, from banking and insurance to government and defence, reaching Indians at population scale| This is what sovereign AI looks like, and the team is just getting started. Here's Vivek on why sovereign AI matters, and why India can't afford to import its intelligence anymore. Once again, congratulations to Vivek (Vivek Raghavan), Pratyush (Pratyush Kumar), and the team. Hemant Mohapatra
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Congratulations to Sarvam (Sarvam) on the first close of their Series B, and a warm welcome to HCLTech (HCLTech) and Bessemer Venture Partners India (Bessemer India), who join us in this journey with the conviction that has defined Sarvam from day one: that India should build and own its AI stack. In under three years, the team has turned that idea into working infrastructure: → Frontier models trained from scratch in India that hold their own against far larger systems → Speech models transcribing over half a million hours of audio a month in real Indian conditions → Voice agents already live, from banking and insurance to government and defence, reaching Indians at population scale| This is what sovereign AI looks like, and the team is just getting started. Here's Vivek on why sovereign AI matters, and why India can't afford to import its intelligence anymore. Once again, congratulations to Vivek (Vivek Raghavan), Pratyush (Pratyush Kumar), and the team. Hemant Mohapatra

Lightspeed India

12,153 次观看 • 2 个月前

I'll remember this week for the rest of my life. Here's my interview with the incredible Sundar Pichai, CEO of Google. We spoke about Gemini, world models, self-improving AI, AI anxiety, and more. Full vid below: 0:00 - Intro 0:40 - Gemini as a World Model 1:30 - Gemini Diffusion 2:33 - Self-improving AI 4:09 - What AI needs to improve 5:06 - Agent Memory 6:46 - AI Form Factors 7:58 - Google Search Future 9:33 - AI Anxiety
11:19

I'll remember this week for the rest of my life. Here's my interview with the incredible Sundar Pichai, CEO of Google. We spoke about Gemini, world models, self-improving AI, AI anxiety, and more. Full vid below: 0:00 - Intro 0:40 - Gemini as a World Model 1:30 - Gemini Diffusion 2:33 - Self-improving AI 4:09 - What AI needs to improve 5:06 - Agent Memory 6:46 - AI Form Factors 7:58 - Google Search Future 9:33 - AI Anxiety

Matthew Berman

98,864 次观看 • 1 年前

Noted economist Ruchir Sharma says 👇 • The entire world today has a monomaniacal focus on AI. • It’s all about which countries will be the winners and losers in AI. • Unfortunately for India, in contrast to what happened during the tech boom of the 1990s and 2000s, foreign investors have concluded that India is a loser in the AI space. • The world is obsessed with building infrastructure for AI, something India sorely lacks. • Countries like South Korea and Taiwan are doing far better, while India is nowhere close. This is the stark reality. Hype and photo-ops won’t suffice. India, which became the flagbearer of the IT and telecom revolution thanks to visionary leadership at the time, cannot afford to let the AI revolution pass us by. But sadly, that is exactly what is happening - and the world is not going to wait.
2:58

Noted economist Ruchir Sharma says 👇 • The entire world today has a monomaniacal focus on AI. • It’s all about which countries will be the winners and losers in AI. • Unfortunately for India, in contrast to what happened during the tech boom of the 1990s and 2000s, foreign investors have concluded that India is a loser in the AI space. • The world is obsessed with building infrastructure for AI, something India sorely lacks. • Countries like South Korea and Taiwan are doing far better, while India is nowhere close. This is the stark reality. Hype and photo-ops won’t suffice. India, which became the flagbearer of the IT and telecom revolution thanks to visionary leadership at the time, cannot afford to let the AI revolution pass us by. But sadly, that is exactly what is happening - and the world is not going to wait.

Supriya Shrinate

47,780 次观看 • 3 个月前

Sonnet 4.5 & the AI Plateau Myth — epic conversation with Sholto Douglas of Anthropic 0:00 - Intro 1:09 - What's Behind The Rapid Pace of AI Releases at Anthropic 2:49 - Opus, Sonnet, and Haiku Model Tiers 4:14 - Sholto's Story: From Australian Fencer to AI Researcher 12:01 - The YouTube Effect: Mastery Through Observation 16:16 - Breaking Into AI Research Without Traditional Academic Signals 18:29 - DeepMind, Gemini, and Building Inference Stacks 23:05 - Why Anthropic? Culture and Mission Differences Amongst AI Research Labs 25:08 - What Is "Taste" in AI Research? 31:46 - This Week's Big Launch: Sonnet 4.5, Best Coding Model in the World 36:40 - From 7 Hours to 30 Hours: The Long-Running AI Agent Breakthrough 38:41 - How AI Agents Self-Correct and Maintain Coherence 43:13 - The Role of Memory in Extended Coding Sessions 47:42 - Pre-Training vs. RL: Textbooks vs. Worked Problems 52:11 - Test-Time Compute & Reinforcement Learning 55:55 - Why RL Finally Started Working on LLMs in 2024 59:38 - The Path to AGI 1:02:05 - Are We Hitting a Plateau in AI? So Many Low Hanging Fruits 1:03:41 - Beyond Coding: GDPVal & Impact Economic Sectors 1:05:47 - Preparing for 10-100x Individual Leverage & The Upcoming Robotics Explosion
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Sonnet 4.5 & the AI Plateau Myth — epic conversation with Sholto Douglas of Anthropic 0:00 - Intro 1:09 - What's Behind The Rapid Pace of AI Releases at Anthropic 2:49 - Opus, Sonnet, and Haiku Model Tiers 4:14 - Sholto's Story: From Australian Fencer to AI Researcher 12:01 - The YouTube Effect: Mastery Through Observation 16:16 - Breaking Into AI Research Without Traditional Academic Signals 18:29 - DeepMind, Gemini, and Building Inference Stacks 23:05 - Why Anthropic? Culture and Mission Differences Amongst AI Research Labs 25:08 - What Is "Taste" in AI Research? 31:46 - This Week's Big Launch: Sonnet 4.5, Best Coding Model in the World 36:40 - From 7 Hours to 30 Hours: The Long-Running AI Agent Breakthrough 38:41 - How AI Agents Self-Correct and Maintain Coherence 43:13 - The Role of Memory in Extended Coding Sessions 47:42 - Pre-Training vs. RL: Textbooks vs. Worked Problems 52:11 - Test-Time Compute & Reinforcement Learning 55:55 - Why RL Finally Started Working on LLMs in 2024 59:38 - The Path to AGI 1:02:05 - Are We Hitting a Plateau in AI? So Many Low Hanging Fruits 1:03:41 - Beyond Coding: GDPVal & Impact Economic Sectors 1:05:47 - Preparing for 10-100x Individual Leverage & The Upcoming Robotics Explosion

Matt Turck

93,678 次观看 • 10 个月前

A LOT happened last week. ICYMI, koray kavukcuoglu (CTO of Google DeepMind and Chief AI Architect of Google) and Logan Kilpatrick discuss Gemini 3, the state of AI, and where we are on the path to AGI. Chapters: 0:00 - Intro 2:00 - Gemini 3 launch reception 4:16 - Continuous progress and innovation 6:47 - Key areas for Gemini improvement 11:45 - Product scaffolding for model improvement 13:56 - Chief AI architect role 17:04 - Engineering mindset and collaboration 18:37 - Future growth areas for Gemini 20:33 - From research to engineering mindset 23:22 - The rise of generative media 27:22 - Nano Banana Pro capabilities 29:31 - Towards unified model checkpoints 36:26 - Organizing for AI success 38:26 - Balancing exploration and scaling 41:40 - DeepMind's collaborative culture 45:21 - Innovating at Google 48:37 - Closing
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A LOT happened last week. ICYMI, koray kavukcuoglu (CTO of Google DeepMind and Chief AI Architect of Google) and Logan Kilpatrick discuss Gemini 3, the state of AI, and where we are on the path to AGI. Chapters: 0:00 - Intro 2:00 - Gemini 3 launch reception 4:16 - Continuous progress and innovation 6:47 - Key areas for Gemini improvement 11:45 - Product scaffolding for model improvement 13:56 - Chief AI architect role 17:04 - Engineering mindset and collaboration 18:37 - Future growth areas for Gemini 20:33 - From research to engineering mindset 23:22 - The rise of generative media 27:22 - Nano Banana Pro capabilities 29:31 - Towards unified model checkpoints 36:26 - Organizing for AI success 38:26 - Balancing exploration and scaling 41:40 - DeepMind's collaborative culture 45:21 - Innovating at Google 48:37 - Closing

Google AI

181,292 次观看 • 9 个月前

We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the cost 🍌⚡️ 1️⃣ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2️⃣ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:
0:27

We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the cost 🍌⚡️ 1️⃣ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2️⃣ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:

Google AI

126,755 次观看 • 1 个月前

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science
49:06

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

Matt Turck

66,936 次观看 • 2 个月前

The "big announcement" just dropped. I just watched Sundar Pichai and Demis Hassabis announce biggest AI infrastructure deal in history. $15 billion to build India's first complete AI hub. Let me break down what Google is building: A massive AI data center in Visakhapatnam (a coastal city in India). Think of it like this: • The compute power of thousands of Google data centers • New underwater internet cables connecting 4 continents • Clean energy plants to power everything • Training programs for 100+ million people All in one place, over 5 years. AI doesn't work without fast internet. Google is laying NEW cables under the ocean: → India to Singapore → India to South Africa → India to Australia → Mumbai to Western Australia Right now, most of the world's internet flows through cables landing in the US, Europe, or China. Google is creating an entirely new route, with India at the center. If you're in Africa, Asia, or South America, your AI tools will get FASTER. Why? Shorter distance = faster data. Instead of your request traveling: Africa → Europe → US → back to Africa It will go: Africa → India → back to Africa that's the infrastructure play everyone's missing. numbers that matter: 💰 $15 billion for the data centers and cables 💰 $30 million to help governments use AI 💰 $30 million for AI research grants 💰 100 million+ people getting free AI training But here's the kicker: Google is plugging AI directly into India's government. • 20 million government workers getting AI tools • Students getting AI tutors for entrance exams • Real-time translation in 70+ languages • Scam detection built into search This isn't "AI for tech companies." This is AI for clerks, teachers, railway staff, police officers, the people who actually run a country. ✅ 20 million+ people used Google's AI detection tool to spot fake images ✅ India is now #3 globally for AI chatbot usage ✅ AI scam detection helping millions avoid fraud daily ✅ 10+ million government workers already on the AI training platform Google is building the pipes that deliver AI to the entire Southern Hemisphere. Different game. Different strategy. If they're right, the next billion AI users won't connect through Silicon Valley. They'll connect through India. 🇮🇳
1:26

The "big announcement" just dropped. I just watched Sundar Pichai and Demis Hassabis announce biggest AI infrastructure deal in history. $15 billion to build India's first complete AI hub. Let me break down what Google is building: A massive AI data center in Visakhapatnam (a coastal city in India). Think of it like this: • The compute power of thousands of Google data centers • New underwater internet cables connecting 4 continents • Clean energy plants to power everything • Training programs for 100+ million people All in one place, over 5 years. AI doesn't work without fast internet. Google is laying NEW cables under the ocean: → India to Singapore → India to South Africa → India to Australia → Mumbai to Western Australia Right now, most of the world's internet flows through cables landing in the US, Europe, or China. Google is creating an entirely new route, with India at the center. If you're in Africa, Asia, or South America, your AI tools will get FASTER. Why? Shorter distance = faster data. Instead of your request traveling: Africa → Europe → US → back to Africa It will go: Africa → India → back to Africa that's the infrastructure play everyone's missing. numbers that matter: 💰 $15 billion for the data centers and cables 💰 $30 million to help governments use AI 💰 $30 million for AI research grants 💰 100 million+ people getting free AI training But here's the kicker: Google is plugging AI directly into India's government. • 20 million government workers getting AI tools • Students getting AI tutors for entrance exams • Real-time translation in 70+ languages • Scam detection built into search This isn't "AI for tech companies." This is AI for clerks, teachers, railway staff, police officers, the people who actually run a country. ✅ 20 million+ people used Google's AI detection tool to spot fake images ✅ India is now #3 globally for AI chatbot usage ✅ AI scam detection helping millions avoid fraud daily ✅ 10+ million government workers already on the AI training platform Google is building the pipes that deliver AI to the entire Southern Hemisphere. Different game. Different strategy. If they're right, the next billion AI users won't connect through Silicon Valley. They'll connect through India. 🇮🇳

Shruti

399,771 次观看 • 6 个月前

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing
54:56

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

Matt Turck

51,317 次观看 • 8 个月前

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google
0:44

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google

Latent.Space

529,014 次观看 • 6 个月前

Google just did it again. They just launched "Google Skills" a free platform to turn anyone into an AI expert. 3,000+ courses, labs & certificates from Google Cloud, DeepMind, and Grow with Google all in one place. → Learn AI, data, and coding from real Google engineers → Earn credentials that actually get you hired → Build hands-on projects with Gemini Code Assist → Compete on global leaderboards and share your wins No paywalls. No BS. Just world-class AI education free. This might be the biggest AI upskilling move of the decade. 👉
0:56

Google just did it again. They just launched "Google Skills" a free platform to turn anyone into an AI expert. 3,000+ courses, labs & certificates from Google Cloud, DeepMind, and Grow with Google all in one place. → Learn AI, data, and coding from real Google engineers → Earn credentials that actually get you hired → Build hands-on projects with Gemini Code Assist → Compete on global leaderboards and share your wins No paywalls. No BS. Just world-class AI education free. This might be the biggest AI upskilling move of the decade. 👉

Alex Veremeyenko

41,920 次观看 • 10 个月前

Demis Hassabis (Demis Hassabis) has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads Google DeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's Garry Tan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve, and what the next big scientific breakthroughs might be. 01:48 — What’s Missing Before We Get To AGI? 03:36 — Why Memory Is Still Unsolved 06:14 — How AlphaGo Shaped Gemini 08:06 — Why Smaller Models Are Getting So Powerful 10:46 — The 1000x Engineer 12:40 — Continual Learning and the Future of Agents 13:32 — Why AI Still Fails at Basic Reasoning 15:33 — Are Agents Overhyped or Just Getting Started? 18:31 — Can AI Become Truly Creative? 20:26 — Open Models, Gemma, and Local AI 22:26 — Why Gemini Was Built Multimodal 24:08 — What Happens When Inference Gets Cheap? 25:24 — From AlphaFold to the Virtual Cells 28:24 — AI as the Ultimate Tool for Science 30:43 — Advice for Founders 33:30 — The AlphaFold Breakthrough Pattern 35:20 — Can AI Make Real Scientific Discoveries? 37:59 — What to Build Before AGI Arrives
40:56

Demis Hassabis (Demis Hassabis) has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads Google DeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's Garry Tan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve, and what the next big scientific breakthroughs might be. 01:48 — What’s Missing Before We Get To AGI? 03:36 — Why Memory Is Still Unsolved 06:14 — How AlphaGo Shaped Gemini 08:06 — Why Smaller Models Are Getting So Powerful 10:46 — The 1000x Engineer 12:40 — Continual Learning and the Future of Agents 13:32 — Why AI Still Fails at Basic Reasoning 15:33 — Are Agents Overhyped or Just Getting Started? 18:31 — Can AI Become Truly Creative? 20:26 — Open Models, Gemma, and Local AI 22:26 — Why Gemini Was Built Multimodal 24:08 — What Happens When Inference Gets Cheap? 25:24 — From AlphaFold to the Virtual Cells 28:24 — AI as the Ultimate Tool for Science 30:43 — Advice for Founders 33:30 — The AlphaFold Breakthrough Pattern 35:20 — Can AI Make Real Scientific Discoveries? 37:59 — What to Build Before AGI Arrives

Y Combinator

360,550 次观看 • 3 个月前

#NewEpisodeAlert In this episode of NPCI’s Innovators Playground Podcast, Dilip Asbe sits down with Sridhar Vembu, Co-founder and Chief Scientist, Zoho, to unpack what it will take for India to build world class #AI technology that is self-reliant and designed for the next billion users. Here is what the episode dives into: ✅ What it takes to build deep technology from India ✅ Why India needs a different approach compared to West in AI innovation ✅ How rural India can become a global talent hub ✅ How India can create affordable AI for the masses ✅ What the next generation of software will look like 🎧 A must listen for students, engineers, founders, and anyone thinking deeply about India’s technology future in an AI led world. Watch the full video here - Dilip Asbe 🇮🇳 Sridhar Vembu Zoho #NPCI #InnovatorsPlayground #SridharVembu #Zoho #IndiaTech #DeepTech #DigitalIndia
1:44

#NewEpisodeAlert In this episode of NPCI’s Innovators Playground Podcast, Dilip Asbe sits down with Sridhar Vembu, Co-founder and Chief Scientist, Zoho, to unpack what it will take for India to build world class #AI technology that is self-reliant and designed for the next billion users. Here is what the episode dives into: ✅ What it takes to build deep technology from India ✅ Why India needs a different approach compared to West in AI innovation ✅ How rural India can become a global talent hub ✅ How India can create affordable AI for the masses ✅ What the next generation of software will look like 🎧 A must listen for students, engineers, founders, and anyone thinking deeply about India’s technology future in an AI led world. Watch the full video here - Dilip Asbe 🇮🇳 Sridhar Vembu Zoho #NPCI #InnovatorsPlayground #SridharVembu #Zoho #IndiaTech #DeepTech #DigitalIndia

NPCI

154,469 次观看 • 3 个月前

The making of Techno East India Company by USA 🇺🇸 : Why did an entire generation in India go mad for IT and CSE in engineering? Why did we churn out more engineering graduates than the market could ever absorb? Why did our so-called “tech giants” stick to safe outsourcing and services, instead of daring to build world-class products? And most importantly, why did India, despite having the talent, miss the real IT revolution and end up dependent on the West for a decade? What shackled our innovation, and who benefited from keeping India out of the global tech race? To uncover these uncomfortable truths, listen to the eye-opening explanation by Navroop Singh ji on Def Talks with Aadi Achint 🇮🇳.
2:13

The making of Techno East India Company by USA 🇺🇸 : Why did an entire generation in India go mad for IT and CSE in engineering? Why did we churn out more engineering graduates than the market could ever absorb? Why did our so-called “tech giants” stick to safe outsourcing and services, instead of daring to build world-class products? And most importantly, why did India, despite having the talent, miss the real IT revolution and end up dependent on the West for a decade? What shackled our innovation, and who benefited from keeping India out of the global tech race? To uncover these uncomfortable truths, listen to the eye-opening explanation by Navroop Singh ji on Def Talks with Aadi Achint 🇮🇳.

Sarvagraharoopini

143,288 次观看 • 10 个月前

Last year in Parliament, I spoke about India’s AI journey and two clear priorities. First, from Make in India to Make AI in India. Build our own capability, invest in research, and strengthen the infrastructure and compute we need to compete. Second, make advanced generative AI tools accessible and affordable for every Indian, in every language, so that no one is left behind. It is good to see AI back at the centre of national conversation as India hosts key discussions on its future. Now the focus must shift from intent to implementation. If we expand access and build with ambition, India can move from being a technology user to a technology leader. The question is not whether AI will shape the future. The question is whether India will help define it.
2:55

Last year in Parliament, I spoke about India’s AI journey and two clear priorities. First, from Make in India to Make AI in India. Build our own capability, invest in research, and strengthen the infrastructure and compute we need to compete. Second, make advanced generative AI tools accessible and affordable for every Indian, in every language, so that no one is left behind. It is good to see AI back at the centre of national conversation as India hosts key discussions on its future. Now the focus must shift from intent to implementation. If we expand access and build with ambition, India can move from being a technology user to a technology leader. The question is not whether AI will shape the future. The question is whether India will help define it.

Raghav Chadha

112,381 次观看 • 6 个月前

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think
1:16:39

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Matt Turck

33,195 次观看 • 3 个月前