Загрузка видео...

Не удалось загрузить видео

На главную

From airplane wings to Dyson spheres, Paul Eremenko explains why physical engineering AI has lagged behind coding AI, and how @p_1_ai's approach to synthetic training data could change everything.

sonyatweetybird's profile picture

Sonya Huang 🐥

21,641 subscribers

27,686 просмотров • 1 год назад •via X (Twitter)

Наука и технологииОбразование
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 2

Фото профиля Sonya Huang 🐥
Sonya Huang 🐥1 год назад

Listen here: YouTube: Apple: Spotify:

Фото профиля Coral AI News
Coral AI News2 лет назад

Coral AI is the most powerful AI for documents. See the difference yourself:

Похожие видео

We've hit the data wall- all public internet data has been used to train AI. explains why the future of AI training lies in unlocking user-owned data from walled gardens 🔓
1:34

We've hit the data wall- all public internet data has been used to train AI. explains why the future of AI training lies in unlocking user-owned data from walled gardens 🔓

vana

16,489 просмотров • 1 год назад

Imagine a gaming AI venturing into uncharted territories, discovering new worlds and experiences. This exploratory approach can be leveraged to generate surprisingly effective synthetic data, as our Chief AI Officer Jen D. explains. To watch previous episodes of The Greatest AI Podcast in Crypto:
0:58

Imagine a gaming AI venturing into uncharted territories, discovering new worlds and experiences. This exploratory approach can be leveraged to generate surprisingly effective synthetic data, as our Chief AI Officer Jen D. explains. To watch previous episodes of The Greatest AI Podcast in Crypto:

KIP Protocol

14,629 просмотров • 1 год назад

Meta's Joe Spisak explains how AI models can train themselves by generating images, asking itself questions about them, and choosing the best answers, in order to move beyond human data and human fine-tuning, and teach itself from synthetic data
1:13

Meta's Joe Spisak explains how AI models can train themselves by generating images, asking itself questions about them, and choosing the best answers, in order to move beyond human data and human fine-tuning, and teach itself from synthetic data

Tsarathustra

42,390 просмотров • 1 год назад

The next frontier of AI is physical. 🤖 From simulation to synthetic data, developers are training robots to understand the real world with NVIDIA Cosmos, Isaac simulation frameworks and open-source physics engine, Newton. Watch the video to see how partners like Hon Hai Technology Group (Foxconn), Hexagon, Humanoid, Noble Machines, and Skild AI are building the future. 🎥
0:51

The next frontier of AI is physical. 🤖 From simulation to synthetic data, developers are training robots to understand the real world with NVIDIA Cosmos, Isaac simulation frameworks and open-source physics engine, Newton. Watch the video to see how partners like Hon Hai Technology Group (Foxconn), Hexagon, Humanoid, Noble Machines, and Skild AI are building the future. 🎥

NVIDIA Robotics

21,664 просмотров • 5 месяцев назад

Physical AI is transforming manufacturing, from design to the factory floor. 🦾 Hear how leaders from ABB Robotics, JLR, and Tulip are using AI-powered simulation, synthetic data, and real-time video analytics to unlock new levels of efficiency across the entire product lifecycle. Watch the full video ➡️
1:00

Physical AI is transforming manufacturing, from design to the factory floor. 🦾 Hear how leaders from ABB Robotics, JLR, and Tulip are using AI-powered simulation, synthetic data, and real-time video analytics to unlock new levels of efficiency across the entire product lifecycle. Watch the full video ➡️

NVIDIA Omniverse

45,348 просмотров • 4 месяцев назад

Leaked system prompts from Vercel’s v0 😳 Here's everything you need to know about AI prompt engineering from Vercel's approach with v0. 🧵👇
0:33

Leaked system prompts from Vercel’s v0 😳 Here's everything you need to know about AI prompt engineering from Vercel's approach with v0. 🧵👇

Wes Winder

77,153 просмотров • 1 год назад

Web3 has a critical role to play in changing the AI landscape! At #Consensus2024, GaiaNet Co-founder Shashank Sripada explains how blockchain technology could change how AI works through proper attribution and validation, ensuring proper copyright protection.
1:12

Web3 has a critical role to play in changing the AI landscape! At #Consensus2024, GaiaNet Co-founder Shashank Sripada explains how blockchain technology could change how AI works through proper attribution and validation, ensuring proper copyright protection.

Gaia 🌱

27,692 просмотров • 2 лет назад

Long Live Outputmaxxing: AI compute grids, Anthropic’s coding takeoff, data center backlash, & frontier systems AMP PBC founder Anjney Midha explains why 95% GPU utilization was considered an outage at Google, why the AI race is no longer just about buying more GPUs, how AMP is trying to make FLOPs flow like megawatts, why data center backlash could become one of AI’s biggest bottlenecks, how Anthropic cracked coding through culture and preparation, why DeepMind research hoarding creates a market failure, and why the next frontier may belong to teams that can “output max” across compute, capital, culture, and science.
0:25

Long Live Outputmaxxing: AI compute grids, Anthropic’s coding takeoff, data center backlash, & frontier systems AMP PBC founder Anjney Midha explains why 95% GPU utilization was considered an outage at Google, why the AI race is no longer just about buying more GPUs, how AMP is trying to make FLOPs flow like megawatts, why data center backlash could become one of AI’s biggest bottlenecks, how Anthropic cracked coding through culture and preparation, why DeepMind research hoarding creates a market failure, and why the next frontier may belong to teams that can “output max” across compute, capital, culture, and science.

Latent.Space

30,879 просмотров • 2 месяцев назад

“Say a robot drives into a wall. Why did it do that?” Nikolaus West explains why robotics teams need clearer visibility into sensor data, and how Rerun is helping push physical AI from impressive demos to real reliability. Link to the full episode with Lukas Biewald in comments.
1:00

“Say a robot drives into a wall. Why did it do that?” Nikolaus West explains why robotics teams need clearer visibility into sensor data, and how Rerun is helping push physical AI from impressive demos to real reliability. Link to the full episode with Lukas Biewald in comments.

Weights & Biases

196,608 просмотров • 8 месяцев назад

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.
1:10

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

_victorad

18,903 просмотров • 10 дней назад

Joelle built the AI lab at Meta She worked and learnt from Zuck She is one of the leading minds in AI What everyone thinks they know about AI but get wrong 👇 - Why Scaling Laws Will Continue - Why You Can’t Buy Success in AI with Talent Acquisitions - Future of Synthetic Data & What It Means for Models - Why AI Coding is Akin to Image Generation in 2015 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:16 How Meta Shaped How I Think About AI Research 02:22 Challenges in Reinforcement Learning 08:33 Is It Possible To Be Capital Efficient in AI 13:47 AI in Enterprise: Efficiency and Adoption 21:51 Security Concerns with AI Agents 28:06 Can Zuck Win By Buying The Superstars of AI 32:11 The Rising Cost of Data 36:38 Synthetic Data and Model Degradation 38:42 Why AI Coding is Akin to Image Generation in 2015 51:16 If Joelle Was a VC Where Would She Invest? 51:50 Quick-Fire Round: Lessons from Zuck, Biggest Mindset Shift My 5 takeaways with Joelle Pineau 👇
59:02

Joelle built the AI lab at Meta She worked and learnt from Zuck She is one of the leading minds in AI What everyone thinks they know about AI but get wrong 👇 - Why Scaling Laws Will Continue - Why You Can’t Buy Success in AI with Talent Acquisitions - Future of Synthetic Data & What It Means for Models - Why AI Coding is Akin to Image Generation in 2015 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:16 How Meta Shaped How I Think About AI Research 02:22 Challenges in Reinforcement Learning 08:33 Is It Possible To Be Capital Efficient in AI 13:47 AI in Enterprise: Efficiency and Adoption 21:51 Security Concerns with AI Agents 28:06 Can Zuck Win By Buying The Superstars of AI 32:11 The Rising Cost of Data 36:38 Synthetic Data and Model Degradation 38:42 Why AI Coding is Akin to Image Generation in 2015 51:16 If Joelle Was a VC Where Would She Invest? 51:50 Quick-Fire Round: Lessons from Zuck, Biggest Mindset Shift My 5 takeaways with Joelle Pineau 👇

Harry Stebbings

19,398 просмотров • 10 месяцев назад

Truly personalized AI could change your life for better but it needs your most intimate data to be effective Are you ready to share everything?
1:24

Truly personalized AI could change your life for better but it needs your most intimate data to be effective Are you ready to share everything?

Nillion

33,877 просмотров • 1 год назад

AI is moving into the physical world. Salil Raje, AMD SVP and GM, Adaptive and Embedded Computing Group, explains why AMD is well positioned to power the next era of physical AI.
0:52

AI is moving into the physical world. Salil Raje, AMD SVP and GM, Adaptive and Embedded Computing Group, explains why AMD is well positioned to power the next era of physical AI.

AMD

34,165 просмотров • 7 дней назад

BCG’s Vlad Lukic outlines the critical steps CEOs must take to make their data strategy AI-ready and explains why CEOs need to be at the center of data conversations to unlock AI success.
2:08

BCG’s Vlad Lukic outlines the critical steps CEOs must take to make their data strategy AI-ready and explains why CEOs need to be at the center of data conversations to unlock AI success.

Boston Consulting Group

74,108 просмотров • 11 месяцев назад

AI performance in dealerships is starting to expose a bigger issue. Brian Abrams, VP of Data and AI at CDK Global, explains why poor data hygiene is limiting results.
0:55

AI performance in dealerships is starting to expose a bigger issue. Brian Abrams, VP of Data and AI at CDK Global, explains why poor data hygiene is limiting results.

Car Dealership Guy

17,819 просмотров • 3 месяцев назад

🚨NEW EPISODE: AI Is Changing How We Work with Crypto Data🚨 In today's episode, we're joined by Kofi to discuss how AI agents are fundamentally changing crypto data analysis, research, and engineering workflows. We discuss: - The evolution from manual coding to AI-assisted development - How MCPs unlock onchain data exploration - Using Herd MCP to analyze smart contracts - AI agents for protocol research and data engineering - Context windows, planning, and agent workflows - Cursor vs OpenCode vs Claude Code (Anthropic) - Managing multiple agents simultaneously - Model selection, costs, and productivity tradeoffs - Building internal AI knowledge bases - Agent orchestration and autonomous workflows - Training agents to replicate human expertise - The future of AI-powered crypto analytics - And much more—enjoy! — Timestamps: (00:00) Introduction (01:24) AI replaces frontends (03:24) Herd MCP demo (05:40) Exploring contracts with AI (08:06) Exploration, planning, execution (11:38) Investigating onchain activity (17:10) Managing agent workflows (20:20) Multi-agent productivity (23:01) Model selection tradeoffs (29:11) Trusting AI outputs (31:28) Missing links today (34:37) Agent orchestration future (38:02) Training agents from work (38:20) Outro
38:58

🚨NEW EPISODE: AI Is Changing How We Work with Crypto Data🚨 In today's episode, we're joined by Kofi to discuss how AI agents are fundamentally changing crypto data analysis, research, and engineering workflows. We discuss: - The evolution from manual coding to AI-assisted development - How MCPs unlock onchain data exploration - Using Herd MCP to analyze smart contracts - AI agents for protocol research and data engineering - Context windows, planning, and agent workflows - Cursor vs OpenCode vs Claude Code (Anthropic) - Managing multiple agents simultaneously - Model selection, costs, and productivity tradeoffs - Building internal AI knowledge bases - Agent orchestration and autonomous workflows - Training agents to replicate human expertise - The future of AI-powered crypto analytics - And much more—enjoy! — Timestamps: (00:00) Introduction (01:24) AI replaces frontends (03:24) Herd MCP demo (05:40) Exploring contracts with AI (08:06) Exploration, planning, execution (11:38) Investigating onchain activity (17:10) Managing agent workflows (20:20) Multi-agent productivity (23:01) Model selection tradeoffs (29:11) Trusting AI outputs (31:28) Missing links today (34:37) Agent orchestration future (38:02) Training agents from work (38:20) Outro

Indexed Podcast

10,562 просмотров • 2 месяцев назад

💡Instead of moving data to AI, why not bring AI to your data? Feifei Li, SVP and President of International Business at Alibaba Cloud Intelligence Group, explains how embedding LLMs into databases, and processing and managing multimodal data with platforms, is transforming how enterprises build intelligent apps. It’s a smarter, faster, and more scalable way to unlock real-time insights.
0:52

💡Instead of moving data to AI, why not bring AI to your data? Feifei Li, SVP and President of International Business at Alibaba Cloud Intelligence Group, explains how embedding LLMs into databases, and processing and managing multimodal data with platforms, is transforming how enterprises build intelligent apps. It’s a smarter, faster, and more scalable way to unlock real-time insights.

Alibaba Group

84,685 просмотров • 8 месяцев назад

you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)
0:36

you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)

Tyler

20,018 просмотров • 1 год назад

💡Why Vana? “Ultimately, AI models are only as good as their training data. So if you want to build the best AI, you need the best data.” shares the vision behind Vana’s mission, centered on DataDAOs that aggregate specific datasets, reward data owners for their contributions and level up AI models with high-quality data🏆
0:56

💡Why Vana? “Ultimately, AI models are only as good as their training data. So if you want to build the best AI, you need the best data.” shares the vision behind Vana’s mission, centered on DataDAOs that aggregate specific datasets, reward data owners for their contributions and level up AI models with high-quality data🏆

vana

108,377 просмотров • 1 год назад