Meta has officially introduced Facebook AI Mode for US users, transforming the standard search bar into a conversational tool that answers questions by mining public Group discussions, Reels, and Marketplace data. While the update aims to increase platform engagement and support Meta’s expanding subscription tiers, it faces scrutiny regarding data privacy and the accuracy of crowd-sourced AI summaries. — Read More

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Someone interrupted a livestreamed, employee-only presentation at Meta earlier this week with an expletive-filled outburst about “being the company’s bitch,” according to a recording heard by WIRED. The individual then asked the people leading the call to write to a specific Meta AI executive and “tell him that he’s a piece of shit.”

… The incident, which took place on a call open to thousands of employees, reflects growing frustration inside the company’s Applied AI team, which was formed in March to support the work of AI researchers at Meta Superintelligence Labs.  — Read More

Ayear ago it wasn’t clear how AI was going to work out for Alphabet — GOOGL $365.95 (-1.35%) — , which missed out on the first-mover advantage held by OpenAI’s ChatGPT.

The fear was that AI competition would eat into traffic for Google’s all-important Search business. And that incorporating AI answers into its own searches could cannibalize revenue, since customers would be less likely to pay for their blue-linked pride of place if people got all their answers up top. Those fears have not materialized. — Read More

Today we are announcing a family of seven new models developed in-house at Microsoft AI. Beyond these models, we’re building a superintelligence lab – a system and an approach we believe will define the next phase of AI.

This is an extraordinary time in technology. The compute used to train frontier models has increased by a factor of one trillion. Now we expect another thousand-fold increase over the next three years, which in turn means more advanced capabilities, and the continued rollout of ever more effective AI.

This epic compute ramp will change the nature of work, business and daily life. We all have to prepare for this reality. Our job at MAI is to help you do this – to push the frontier, and to build a hill-climbing machine to keep you at the frontier. — Read More

Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system’s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. While general purpose, we focus the validation in three biomedical applications: drug repurposing, novel target discovery ¹, and explaining mechanisms of anti-microbial resistance ². Specifically, Co-Scientist helped identify new drug repurposing candidates and synergistic combination therapies for acute myeloid leukemia, which were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI empowered scientists. — Read More

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Jassy was once Jeff Bezos’ deputy and the head of Amazon’s cloud computing arm. Five years into his tenure as CEO, he’s killing projects, cutting staff, pleasing Wall Street and steering the everything store through its greatest challenge yet.

… This July will mark five years since Andy Jassy took over the chief executive officer role from Amazon’s founder. At the corporate offices in Seattle, the workforce has grown accustomed to his brand of rigorous oversight and ongoing exhortations to act as if they were at Jeff Bezos’ startup, not a $2.9 trillion behemoth. He recently placed a series of staggeringly expensive bets on artificial intelligence, audacious even by the standards of Silicon Valley’s ongoing trillion-dollar AI bacchanalia. In February he agreed to invest as much as $50 billion in OpenAI in a deal that commits the rising startup to relying in part on Amazon’s data centers and custom-designed microchips. Then in April he expanded a similar partnership with its archrival, Anthropic—a $13 billion investment, with an option for an additional $20 billion. To Jassy’s critics, that spending was the price of Amazon’s late jump into the current AI wave. He wasn’t bluffing, though: Jassy spooked investors by vowing to spend $200 billion this year on big-ticket items including warehouse robots, a far-out effort to launch satellites into space, and in particular more AI data centers, AI chips and networking equipment. “I don’t think the world has ever seen a technology get this much adoption and grow this quickly, at least in my lifetime,” Jassy tells Bloomberg Businessweek. — Read More

Knowledge workers at Meta routinely contend with workflow fragmentation, where critical information — including meeting notes, tasks, key decisions, and code context — is siloed across disparate platforms. Each new AI conversation starts cold: the same explanations, the same links, the same ten minutes of context-setting before any real work begins.

So we tested a simple hypothesis: what if an AI agent had persistent, structured access to everything a person is working on, and carried that context across every interaction? Not a chatbot that answers questions, but a working partner that tracks projects, reads meeting notes, surfaces connections, and builds on prior conversations.
<brthat ai="" second="" brain="" experiment,="" born="" in="" the="" analytics="" org,="" has="" since="" been="" adopted="" by="" over="" 60,000="" people="" across="" meta:="" engineers,="" pms,="" designers,="" legal,="" finance,="" communications,="" and="" sales.="" this="" post="" covers="" how="" it="" was="" built,="" grew,="" what="" we="" learned.="" –="" Read More

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Google is bringing conversational AI search to YouTube, marking the company’s latest push to infuse its products with AI-powered discovery tools. The feature, dubbed “Ask YouTube,” started rolling out to YouTube Premium subscribers in the US today as an experimental test. It transforms the platform’s search bar into a chatbot-style interface that pulls results from longform videos, Shorts, and text summaries – essentially giving YouTube its own version of Google’s AI Mode for search. — Read More

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The race to secure electricity for AI models has reached new heights: Meta has signed an agreement with the startup Overview Energy that could see a thousand satellites beam infrared light to solar farms that power data centers at night.

In 2024, Meta’s data centers used more than 18,000 gigawatt-hours of electricity — roughly enough to power more than 1.7 million American homes for a year — and its need for compute power is only increasing. The company has committed to building 30 gigawatts of renewable power sources, with a focus on industrial-scale solar power plants.

Typically, data centers turning to solar power must either invest in battery storage or rely on other generation sources to operate at night.

Overview Energy, a four-year-old, Ashburn, Virginia, outfit that emerged from stealth in December, has a different solution: The company is developing spacecraft that collect plentiful solar power in space. It then plans to convert that energy to near-infrared light and beam it at sufficiently large solar farms — on the order of hundreds of megawatts — which can convert that light to electricity. — Read More

Meta released an AI model on Wednesday called Muse Spark, which marks its “first step” toward an “overhaul of [its] AI efforts.”

Muse Spark is the inaugural model to come out of Meta Superintelligence Labs, which was created last year because CEO Mark Zuckerberg was reportedly unhappy with the progress of Meta and its Llama models and how they lagged behind OpenAI’s ChatGPT and Anthropic’s Claude. Meta recruited former Scale AI co-founder and CEO Alexandr Wang to lead Meta Superintelligence Labs and invested $14.3 billion in the data labeling company for a 49% stake.

Now, it’s time for Zuckerberg to see if his reconfigured AI team can woo users. — Read More