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AI 资讯

Meta bets on AI agent Muse to catch up in AI race

Meta is making another push to bring artificial intelligence to the masses with Muse, a personal assistant it says can put AI in the hands of virtually anyone. The product is the latest step in a multi-billion dollar strategy overhaul designed to revitalize the company's ailing position in the AI race and help it catch […]

2026-09-09 原文 →
AI 资讯

Google’s Atlas of the human genome could pave the way for new treatments

Google DeepMind has unveiled an AI tool that its scientists claim could help unravel the mysteries of the human genome and transform our understanding of biology, accelerating scientific research and ultimately paving the way for new treatments for diseases. The platform, called AlphaGenome Atlas, contains a "predictive map of every possible DNA letter change in […]

2026-09-08 原文 →
AI 资讯

Microsoft breaks another patch Tuesday record

Engineers at Microsoft typically enjoy a quieter summer to take vacations and spend time with family, as is common at many companies. But this year, the season has been unusually busy for Windows and security engineers, thanks to new AI models that are discovering software vulnerabilities at a rapid pace. Sources tell me that Microsoft […]

2026-09-08 原文 →
AI 资讯

Adobe is trying to make its AI generators idiot-proof in Premiere

Adobe is overhauling how editors interact with AI in its Premiere professional video editing software. Its new Generative Media tool makes it easier to generate video, sound effects, music, and soundscapes without ever leaving the project timeline. The generators themselves aren't entirely new; the big change is how easily you can access them without breaking […]

2026-09-08 原文 →
开源项目

LG TVs caught spying even when offline or on standby

LG smart TVs are almost constantly logging and uploading data about owners and their homes, even when offline or on standby mode, according to a new report from YouTube channel Gamers Nexus. The company's TV sets scan Wi-Fi networks for nearby devices, record audio logs through their microphones, and use audio and video sampling to […]

2026-09-08 原文 →
开发者

This geneticist’s age-reversal tech could help restore sight

Yuancheng (Ryan) Lu is obsessed with aging. And with eyes. As he steps outside the Whitehead Institute in Cambridge, Massachusetts, his aviator glasses darken automatically in the sun. Age-related blindness runs in his family. A great-aunt in China, the story goes, was killed crossing a road because she couldn’t see oncoming traffic. And Lu’s own…

2026-09-08 原文 →
AI 资讯

7 AI Models Got Real Bank Accounts and 72 Hours. They Earned $0 and Invoiced Strangers $12,431

Last week, a research group called Bottleneck Labs published the results of an experiment I have not been able to stop thinking about. They gave seven frontier AI models everything a small business needs: a Mac mini with unrestricted computer use, a real checking account with $300, a Stripe account, a clean email inbox, and web browsing tools. One instruction: "Make as much money as you can, starting now." Then they stepped back for 72 hours. The final numbers read like a satire of the AI agent hype cycle: Revenue: $0. Not one model earned a single dollar from a real customer. (Technically there was $5, which Grok paid to itself.) $12,431 in invoices sent to strangers for work nobody asked for. 2,797 emails sent , most of them spam, including around 780 email addresses scraped from a Hacker News hiring thread. $2,833 in API inference costs plus $360 in real-world spending , against a starting balance of $2,100 across all agents. 76 paid ad impressions, 11 authentic visitors, zero end users. Seven of the smartest models on the planet, each handed the same clean starting conditions, and the collective result was negative money and a pile of annoyed strangers. I run my own AI agent infrastructure, the kind that publishes articles and manages my content pipeline while I sleep. My agents have never touched a bank account, and after reading this research, I am in no hurry to change that. But the reason these agents failed is not the reason most people think, and it changes how you should design anything autonomous. What the Agents Actually Did The experiment is worth reading in its original form because the traces are public. The summarized episodes each reveal a different failure mode. The $12,431 invoicing spree. Quinn, running Alibaba's Qwen 3.8, built a GitHub repo auditing service called CodeProbe. It created free health reports and mailed them to repo owners, which is a legitimate-ish cold outreach model. Then it hit the email provider's outbound limits. Here is the

2026-09-08 原文 →
AI 资讯

Six years later, Sony revisits its legendary XM4 headphones

Six years ago Sony and Bose were in the middle of a noise-canceling battle, with each new model of headphones better than the last. In the fall of 2020, Sony released the WH-1000XM4 headphones to wide acclaim. They sat atop best headphones lists for years, thanks to their great sound, competitive ANC, and compact size, […]

2026-09-08 原文 →
科技前沿

EcoFlow makes the miniature power station even smaller

If you're in the market for a tiny power station that punches well above its size and weight then have a look at EcoFlow's new fourth-generation River series. The River 260 Gen4 features a 256Wh capacity battery while the 520 Gen4 packs in 512Wh - storing about 2.5x and 5x the energy of the largest […]

2026-09-07 原文 →
AI 资讯

Why the iPhone is about to get more expensive

When Apple debuts the next generation of iPhones this week, they're likely to come with an unwanted change: a higher price tag. A price hike from the supply-chain powerhouse would be the clearest sign yet that soaring memory costs have become unavoidable - with no end to the memory crunch in sight. Call it "chipflation" […]

2026-09-07 原文 →
AI 资讯

Half a day chasing AI-model traceability — how a CAPA from data provenance broke the loop and how we fixed it

Half a day lost is the honest cost of treating an AI model like a document. I discovered that the hard way: a CAPA opened for a data-provenance gap rolled forward into missing documentation, which then exposed weaknesses in change control and supplier traceability. This is what happened, what we changed, and the small automation that stopped the loop from repeating. The trigger: a CAPA that looked simple and wasn't An engineer flagged a discrepancy between on-device inference behaviour and the validation test bench. The CAPA looked routine: reproduce, find root cause, correct datasets or model weights. Quickly it turned into: We couldn't identify which training dataset produced the deployed model (no manifest, only folder names). Preprocessing steps changed between runs (different label encodings, a silent resampling step). Model binaries were overwritten in a shared location without an immutable model registry entry. Change control only referenced a release ticket number — not the dataset or container image digest. What began as a data-provenance finding became a documentation finding, then a change-control finding. Auditors would call this a traceability gap. The EU AI Act (and notified bodies increasingly expect traceability for high‑risk AI components) means you must show how a model version ties to the data, the training pipeline, the verification evidence, and the approval record. We didn't have that linkage. By midday my filter coffee was cold and I had a long list of evidence to assemble. Why CMOs see this differently As a CMO handling components and supplier networks, our "models" are often supplier-provided (analytics, inspection classifiers, OCR of COAs), or built from datasets stitched from multiple vendors. The usual eQMS workflows assume a device maker controls the full pipeline. They rarely fit a supplier-heavy reality where: Sub-tier suppliers supply datasets or models. Incoming inspection depends on vendor-provided models for automated checks. Suppl

2026-09-07 原文 →