Core Insights - Google has introduced a new "universal commerce protocol" aimed at enhancing interactions between digital agents and potential shoppers during AI chat sessions [1][2] - The protocol has been developed in collaboration with major retailers like Shopify, Target, and Walmart, and is endorsed by around 20 significant payment players including Adyen, PayPal, Stripe, Worldpay, Mastercard, and Visa [2] Group 1: Protocol Development - The universal commerce protocol establishes a common language for agents and systems to operate together, facilitating smoother transactions [2] - The protocol will enable shoppers using Google's AI mode to purchase items directly from retailers such as Best Buy, Lowe's, and Walmart, initially through Google Pay [4] Group 2: Future Capabilities - Google plans to work with retailers to expand the protocol globally and add features like discovering related products, applying loyalty rewards, and creating custom shopping experiences [5] - The protocol is designed to streamline operations for various digital agents, allowing them to interact easily without needing unique connections for each agent [6] Group 3: Integration with Existing Standards - Google's AP2 protocol will work alongside the universal commerce protocol to facilitate agentic purchases, enabling agents to make purchases on behalf of users [7]

[music] Today we're talking about RL environments and how to scale them. But the title is a little bit of a red herring. We'll talk a bit about the engineering pieces and like running these with thousands of parallel rollouts and sandboxes on hundreds of GPUs, but I'm mostly going to focus on a different notion of scale. Uh, and what I mean by scaling here is we there's a number of different ways we talk about scaling in the context of AI and research. We know about scaling laws and we talk about how much d ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/2LaIt3GwjaAvi Chawla (@_avichawla):One framework to train, finetune & deploy AI models in a few lines of code!PyTorch Lightning abstracts the boilerplate code of PyTorch and provides 40+ advanced features to do professional AI research at scale by just specifying parameters.100% open-source with 30k+ stars! https://t.co/OG64ublpjQ ...

The expansion, worth tens of billions of dollars, dramatically increases our compute resources as we push the boundaries of AI research and product development.Read more: https://t.co/0Ar2abZwsd ...

RT alphaXiv (@askalphaxiv)Introducing NotebookLM for arXiv papers 🚀Transform dense AI research into an engaging conversationWith context across thousands of related papers, it captures motivations, draws connections to SOTA, and explains key insights like a professor who's read the entire field https://t.co/A37xmW1G4G ...

$THETA is one of those projects that's literally on edge to be breaking out in an uptrend.They recently partnered with Emory University, who will leverage the EdgeCloud software for AI research within the university.Massive steps and I'm sure this will continue to expand. https://t.co/ARqGgIz37A ...

AI Adoption & Strategy - Unifor observes deep AI adoption in specific industries and use cases, despite industry reports suggesting potential failures of AI agents [2] - Unifor focuses on "fit-for-purpose AI" and "business AI," aiming to deliver productivity and automation to businesses, contrasting with the "super intelligence" pursuit of companies like OpenAI and Grock [5] - Unifor's acquisitions of RBI and Autonomate are aimed at accelerating AI innovation, particularly in Agentic AI, to benefit its customers [3] Talent Acquisition & Competition - The AI talent pool is scarce and mission-driven, with researchers choosing between pursuing "super intelligence" or delivering real business applications [7][9] - Unifor attracts AI talent by offering the opportunity to deliver automation solutions to over 2,000 businesses, including Fortune 500 companies, emphasizing real-world impact [7][10] - Unifor competes with larger tech companies for talent by focusing on the mission-driven aspect of AI research, rather than solely on compensation [9][10] Future Outlook & IPO - Unifor aims to increase its customer base from 2,000 to 4,000 and then to 8,000, believing this growth will naturally lead to a potential IPO [14] - Unifor will consider going public when the timing and market conditions are favorable [14] - The company views the opening up of public markets for tech IPOs positively, noting anticipation for mega IPOs from companies like Data Bricks, Canva, and Stripe [13] Measuring AI Success - Unifor suggests that the success of AI in business can be measured by increased productivity, potentially leading to more long weekends for workers [11]

The former Adept CEO said he hopes to be "remembered more as "an AI research innovator" than a "a deal structure innovator." https://t.co/N5ZXrOuQuO ...

Nvidia boss Jensen Huang lauded DeepSeek and China’s other contributions to AI research as he met with political and tech leaders in Beijing https://t.co/JwDBHr3w7j ...