In their paper Credit-assigned Policy Gradient for Early Stage Retrieval in Two-stage Ranking, and study how to improve early-stage retrieval in two-stage decision processes. We caught up with first author Haruka to find out more. This is a project that she worked on during an internship with the Central Applied Science (CAS) team at Meta. What is the topic of the research in your paper and why is it an interesting area for study? This paper studies how to improve early-stage retrieval in two-stage decision processes.

Want to Get Off Your Phone? "I think that's been the most helpful thing," says the ESPN journalist. "So there you go, Zoomers. There's a reason to have children." I just got it replaced because I cracked the screen with my car. I was resting it on the roof and it slid down as I was opening the trunk, and it was a real crime scene. When you bring a cracked phone to an Apple store, you know that they've seen everything, but you still feel deeply ashamed.

Meta has revealed its long-promised personal AI agent that requires "no technical experience" to set up. Called Muse, Meta says the agent is able to complete a wide range of actions on users' behalf, including online shopping, drafting and sending emails, planning trips and other productivity-focused tasks. CEO Mark Zuckerberg has previously said that he wants Meta to create agents that can work proactively to help people get things done, but in a more accessible format than other platforms like OpenClaw. Muse, which has its own dedicated apps and website, is more capable than the Meta AI assistant because it can handle complex tasks and work independently. "Once a person shares a goal with Muse, it helps them develop a personalized plan and coordinate their time and resources, then advances the work on its own," the company explains in a blog post.

Five years ago, people who hang out in the stranger corners of the web started arguing that the internet was mostly fake . It had been this way since about 2016, they said, when Google introduced its artificial neural network for machine translation. Supposedly, from that point forward, online interactions began to be replaced, until nearly every message and picture had been posted by a bot. Everywhere you looked, websites were "empty and devoid of people," as one scholar of the theory put it. This wasn't true, but the core idea was tethered to a real phenomenon.

I Asked 100 Companies for My Data. California residents have a legal right to access the data that companies collect about them. Actually exercising that right is a burdensome nightmare. I filed a request with McDonald's earlier this month to access all of the personal data the fast food company collected about me, and I received a stunning 515-page report a few days later that detailed my app interactions in granular detail and predicted I would never stop eating there. Under the California Consumer Privacy Act, I have the legal right to request access to information from large companies that collect personal data.

AI privacy controls for ChatGPT, Meta AI and Apple Intelligence now cover memory, ads and personalization. Review privacy settings and data controls to limit what chatbots remember.

What does it take for a robot to hold a conversation with a room, not just a person? That was one of the questions at the heart of my last week (20th-24th July) at the Imperial Robotics Summer School, hosted at Imperial College, London; a week that sharpened my thinking and pushed me to look at robotics problems from angles I don't usually get to in my day-to-day work. The summer school brought together emerging researchers, academics, and industry professionals from across the robotics community for an intensive, hands-on programme. I came away having learned a huge amount, from expert-led lectures on robot kinematics, dynamics, sensing and control, and robot learning, through to specialised sessions on aerial robotics, robot intelligence, surgical robot vision, bio-inspired sensing and control, and personal assistive robotics. One of the standout parts of the week was getting into Imperial's robotics labs themselves, seeing research up close across adaptive and intelligent robotics, aerial robotics, robotic surgery, manipulation and touch, and assistive robotics.

Rhys, right, was sent this photo of himself taken at a Melbourne cafe by a stranger wearing Meta's Ray-Ban glasses Photograph: Supplied Rhys, right, was sent this photo of himself taken at a Melbourne cafe by a stranger wearing Meta's Ray-Ban glasses Photograph: Supplied This man was secretly snapped by someone with smartglasses. The backlash to what some dub'pervert glasses' is growing - but people living with disabilities point out the new tech can also be life-changing Fri 7 Aug 2026 11.00 EDTLast modified on Sat 8 Aug 2026 01.47 EDT One summer day earlier this year, Rhys left a cafe in Melbourne where he had been working remotely, when his phone pinged with a message on the dating app Grindr. The message was a photo of him sitting at a table outside the cafe on his laptop, with his dog seated on the ground next to him. In the foreground, in a man's hand was an iPhone unlocked. The photo had been taken moments before with Meta's Ray-Ban glasses, unbeknown to Rhys.

AI is like ultra-processed food for developing brains. T he human brain has evolved to learn from specific instructors. When a child hears a gentle spoken word or a soothing lullaby from another person, when their cry is responded to, their brain lights up. Nurturing interactions, or what child-development experts call "serve and return" exchanges, help shape and strengthen the 1 million new neural connections taking place in a child's brain every single second. Every time a child cries or serves up a bid for attention and receives a caregiver's response, that back-and-forth helps build the circuits underlying their communication, learning, and emotional well-being.

Imagine you want to teach a robot to push an object on a table. The standard recipe in robot learning is to collect hundreds of expert demonstrations on a real robot, train an imitation learning policy on that data, and then evaluate the policy by running it many times on the same real robot. Both stages (data collection and evaluation) are slow, expensive, and hard to reproduce: hardware breaks, lighting changes, objects drift out of place, and every new task means more hours in the lab. A natural question is whether we can replace some of this real-robot work with a simulator. Classical physics-based simulators are powerful, but building one for a new task means manually modeling geometries, contacts, friction, and deformation, and the resulting simulator often still does not match reality closely enough for policies trained inside it to transfer.