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Talked all things AI with Swaroop Mishra, Research Scientist at Google DeepMind 🧠, diving into his work on Gemini and what the shift to AI-assisted coding actually means for software engineers. We covered: - Building Bard & Gemini - Early days of instruction tuning (2021) - The shift from...

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Khushboo Verma

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24,855 просмотров • 1 месяц назад •via X (Twitter)

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After 2️⃣5️⃣ years Google, our Chief Scientist and co-lead of the Gemini foundation model, Jeff Dean has a unique insight into the past, present and future of AI. Listen in to his conversation with Professor Hannah Fry on the latest episode of Google DeepMind: The Podcast ↓
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After 2️⃣5️⃣ years Google, our Chief Scientist and co-lead of the Gemini foundation model, Jeff Dean has a unique insight into the past, present and future of AI. Listen in to his conversation with Professor Hannah Fry on the latest episode of Google DeepMind: The Podcast ↓

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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
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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

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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. 👉
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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. 👉

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“Scientific progress is becoming computable.” DeepMind CEO Demis Hassabis says AI is becoming a scientific infrastructure layer, not just a productivity tool. At Google I/O, he introduced Gemini for Science: a new set of AI systems designed to help researchers read papers, write code, and generate hypotheses faster. The real shift is that research itself is starting to scale like software.
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“Scientific progress is becoming computable.” DeepMind CEO Demis Hassabis says AI is becoming a scientific infrastructure layer, not just a productivity tool. At Google I/O, he introduced Gemini for Science: a new set of AI systems designed to help researchers read papers, write code, and generate hypotheses faster. The real shift is that research itself is starting to scale like software.

Jon Hernandez

25,478 просмотров • 2 месяцев назад

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 team of roughly 75 researchers, a third of them fresh out of college, producing work on par with the best in the world and feeding directly into Gemini. The conversation goes deep on what DeepMind India actually builds, why Gemini is considered the most efficient model on the planet, and what India needs to become a research leader rather than a fast follower. In this conversation, they go deep on: 0:00 Intro: Manish Gupta of DeepMind India at Mumbai Tech Week 1:23 What DeepMind India does, and why it's a "mystery" 1:59 The three roles: languages, efficiency, continual learning 2:19 The Haryanvi demo at Google I/O and the cultural playbook 2:38 Making models efficient: from mobile to servers 3:25 Matryoshka transformers and why nested models win 4:20 Why Gemini is the most efficient model on the planet 5:11 Continual learning: using Gemini to improve Gemini 5:50 Google's India plans: consumers, agents, enterprise, government 7:44 Government officials live-coding with AI Studio and NotebookLM 8:30 The India AI talent debate and how the team is structured 10:25 Why India lacks courage and R&D investment, not talent 11:16 DeepMind's global labs and India's outsized impact 13:15 On-device AI: Gemma 3n and 4n 14:08 The headline: 75 world-class researchers in Bangalore 14:58 25 of the 75 are fresh out of college If you're a founder, builder or researcher thinking about AI, frontier models, or India's place in global AI research, this one's for you. Aakrit Vaish Pratyush Choudhury (PC) Google DeepMind Google India Manish Gupta
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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 team of roughly 75 researchers, a third of them fresh out of college, producing work on par with the best in the world and feeding directly into Gemini. The conversation goes deep on what DeepMind India actually builds, why Gemini is considered the most efficient model on the planet, and what India needs to become a research leader rather than a fast follower. In this conversation, they go deep on: 0:00 Intro: Manish Gupta of DeepMind India at Mumbai Tech Week 1:23 What DeepMind India does, and why it's a "mystery" 1:59 The three roles: languages, efficiency, continual learning 2:19 The Haryanvi demo at Google I/O and the cultural playbook 2:38 Making models efficient: from mobile to servers 3:25 Matryoshka transformers and why nested models win 4:20 Why Gemini is the most efficient model on the planet 5:11 Continual learning: using Gemini to improve Gemini 5:50 Google's India plans: consumers, agents, enterprise, government 7:44 Government officials live-coding with AI Studio and NotebookLM 8:30 The India AI talent debate and how the team is structured 10:25 Why India lacks courage and R&D investment, not talent 11:16 DeepMind's global labs and India's outsized impact 13:15 On-device AI: Gemma 3n and 4n 14:08 The headline: 75 world-class researchers in Bangalore 14:58 25 of the 75 are fresh out of college If you're a founder, builder or researcher thinking about AI, frontier models, or India's place in global AI research, this one's for you. Aakrit Vaish Pratyush Choudhury (PC) Google DeepMind Google India Manish Gupta

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The President of Anthropic said something at Stanford that no other AI CEO will say publicly. She talked about what AI is doing to the people who use it, why she walked out of OpenAI, and what skills will actually matter when AI can do most of what we do today. 1/ What AI safety actually means
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The President of Anthropic said something at Stanford that no other AI CEO will say publicly. She talked about what AI is doing to the people who use it, why she walked out of OpenAI, and what skills will actually matter when AI can do most of what we do today. 1/ What AI safety actually means

Ihtesham Ali

150,207 просмотров • 1 месяц назад

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.
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Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

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.Ramp Labs (the AI unit of Ramp) has been *cooking* with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories
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Walk through of the Google DeepMind Hackathon for Google AI Studio and Gemini in Bangalore, courtesy of ray Cerebral Valley! 🙌💖🇮🇳 Gorgeous, *gorgeous* venue - and so many developers locked in, hard at work and shipping code. Notable differences from the US hackathons I've been to: feels like *everyone* is using Telegram Messenger for their AI-enabled bots (not just WhatsApp).

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From writing the first lines of code for Dropbox on a bus to New York to navigating the era of AI, CEO Drew Houston reflects on building and scaling Dropbox, and why gen AI has “reset the scoreboard” for what’s possible. Hear his conversation with Joubin Mirzadegan on Grit Podcast:

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Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

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.Amjad Masad : Creating software is going to be one of the most exciting areas. The average person will soon be as capable as what a senior software engineer is today, as we shift from doing the work to directing it. Instead of coding line by line, you’ll run multiple agents in parallel, building the frontend, refactoring the database, shipping features, all in the background. On top of that, you’ll have a clean interface to design and interact with AI more creatively. Software creation is moving from execution to orchestration, and soon, it won’t be a specialised skill, it’ll be the baseline.

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