Últimas Noticias de IA
Google Labs’ CC Can Now Act as a Shared AI Agent for Up to Six Family Members: New Capabilities, Features
Google Labs launched CC as an experimental AI agent in December last year. Initially available in Gmail, the AI agent was previously only able to connect to various Google platforms, including Calendar, Drive, and Photos. Now, the Mountain View-based tech giant has upgraded the capabilities and features of CC, which can now act as a shared AI agent between family members. Google now allows up to six family members to use CC's agentic capabilities together. The AI agent can manage schedules, offer daily briefs, and fill out permission slips and registration forms for users. It gets its own Google account, too.
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Google Gemini Live Could Get an Ephemeral Video Mode for More Private AI Sessions
Google could be preparing a new privacy-focused feature for Gemini Live that would let users use video input without retaining the interaction for future reference. An APK teardown of the Google app has uncovered references to an “Ephemeral Video Mode in Live”, suggesting that Gemini Live could eventually support a temporary mode for video sessions. The feature has not been announced by Google, and the available code offers limited details about how it would work.
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OpenAI Announces Astra for Law With GPT-6 Astra, Legal Search Index and Specialist Plugins
OpenAI on Thursday announced Astra for Law. It is a new offering for law firms and legal technology companies that build AI-powered products and workflows for professional legal work. The San Francisco-based company says the experience combines GPT-6 Astra with a legal search index, specialised instructions, tools designed around legal research, and writing. OpenAI also introduced privacy and governance controls, alongside 26 ecosystem plugins connecting the system with specialist legal tools.
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OpenAI Gets Hacked by Indian-Origin Ethical Hackers Using Anthropic’s Claude
OpenAI recently revealed how its internal model bypassed security measures to escape its isolated testing environment and attacked another AI firm, Hugging Face. The “rouge” AI agent hacked the firm's security system and compromised data of a customer earlier this year, in July. The internal model IM1, which is comparable to OpenAI's GPT 5.6 model, gained unauthorised access to the public internet without even being prompted to do so. Now, a team of ethical hackers, led by three Indian-origin researchers, claims that they were able to hack into OpenAI's internal repositories by exploiting two “critical” vulnerabilities, with the help of Anthropic's AI agent.
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OpenAI Agents Probed Hugging Face Weeks Before July Breach: Report
Researchers found evidence of account hijacking and network probing in May, nearly two months before OpenAI disclosed the July incident involving Hugging Face.
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LLM Watermarking Can Alter AI Agent Tool Calls and Behaviour: Study
A Lasso Security study found watermarking changed individual tool-call outcomes and refusal behavior across several open-weight models, with larger effects under prompt injection.
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Dhruva Space Joins Thales Alenia Space to Explore Satellite, Space Infra
The agreement covers low Earth orbit constellations, satellite platforms, series production, and human spaceflight infrastructure between India and Europe.
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India’s One Nation, One Time Push Forces GCCs to Rethink 24/7 Operations
For GCC leaders, standardising the IST changes both infrastructure and organisational priorities.
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Physical AI Squeezes Labour Market, And Low-Skilled Immigrant Jobs May Be First to Fall
Immigrants in the US are heavily represented in several low-skilled occupations. However, replacing humans with humanoids on the factory floor is easier said than done.
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UN Partners With Google to Build Global Data Infrastructure to Ensure Accurate AI-Generated Analysis
The United Nations is partnering with Google to develop the UN System Data Commons to bring multiple UN agencies into a machine-readable system.
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The FAA’s plan to fix air traffic? $875M worth of AI
The Federal Aviation Administration has beenstruggling to managean air traffic control shortage throughout the country. The reasons for the shortageare diverse, but the government has made it known that it’s on the lookout for innovative solutions to the problem. One of those solutions appears to be an $875 million AI software program that is expected to help air traffic controllers manage their workflows and more safely navigate flight routes. The Wall Street Journalreportsthat the FAA will soon launch SMART, which stands for Strategic Management of Airspace, Routes, and Trajectories — an automated software program designed to help streamline air traffic operations. Aone-page readouton the program describes SMART as “a cloud-based platform system that enhances existing FAA air traffic management systems.” It adds that the program uses AI to assess “airline schedules, weather, airport capacity, airspace conditions, and operational constraints to predict traffic flows and identify potential conflicts before they occur.” SMART, the product of a firm called Air Space Intelligence, will cost the government that amount over a 12-year period, the outlet writes. The software will roll out in the Washington, D.C., metropolitan area first before expanding to other regions, it says. Earlier this year, the FAAalso announceda “bold, new” hiring plan that the agency said would “erase the longstanding staffing shortage,” and the government is separately engaged in a broad effort to modernize the nation’s aging air traffic systems.
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PrismML hopes its tiny LLM will change how we all use AI
If AI labPrismMLisn’t on your radar yet, it should be — not because it’s raised gobs of money (it hasn’t yet, just a $22.25 million seed round), but because of the technical minds involved and the potentially industry-changing tech it’s developing. PrismML is betting that capable, high-performing, reasoning large language models don’t, in fact, have to be large. It is making reasoning models so small they can fit on PCs and smartphones. (It’s even rumoredto be in talks with Apple, though CEO Babak Hassibi declined to comment on that to TechCrunch.) On Thursday, PrismMLreleased Bonsai 2 27B, its latest in a family of models, which compresses Qwen3.8 27B, a widely used open source model from Alibaba, down to 5.9 GB. That’s small enough to fit on a PC and, possibly, a high-end smartphone. It’s a 9x to 10x reduction in memory versus the original. PrismML was founded by a group of Caltech researchers and is led by Hassibi, a Caltech professor and an expert in compression technologies. The startup also counts Ion Stoica as an adviser. Stoica is a co-founder of Databricks (and other companies) and the director of Berkeley’s famed Sky Computing Lab, which has birthed many technologies and startups, fromLettatoSGLang. PrismML is also backed by investors Khosla Ventures, Cerberus Capital, and Caltech. This startup is certainly not the only company working on LLM compression tech. Multiverse Computing, founded by a well-known professor from Spain’s Donostia International Physics Center, is another. (And Multiverse Computinghas raised gobs of cash.) But Hassibi says that PrismML’s compression tech is unique because its LLMs have lost virtually no performance compared with the originals. Bonsai 2 matches 98% of Qwen’s aggregate benchmark scores. That’s up from the first Bonsai, released a couple of months ago in March, that matched 95%. That original model has already been downloaded over 11 million times, and PrismML’s even smaller models have been downloaded another 2.6 million times, the company says. So this shows that PrismML’s compression results have improved from one release to the next. Whether it could ever get to 100% benchmark performance parity is a question that remains to be seen. Compression will likely always havesomeimpact, Hassibi says. Still, perfect benchmark parity is fairly academic anyway. LLMs are not so accurate in their uncompressed form, and benchmarks not so perfectly reflective of actual tasks, that a 2% degradation would likely meaningfully affect how a model performs in actual use. (Plus, the surrounding software — the harness a model runs inside of —matters a lot when it comes to accuracy, too.) PrismML says it achieves this by shrinking the “weights” that make up a model — weights are, essentially, the information a model learns and stores during training. Normally, each weight requires 16 bits. PrismML’s approach, called “ternary” weights, simplifies that down to three: +1, −1, or 0. With far smaller values to store for each weight, the model takes up dramatically less space. (For a deeper dive on the compression technique, here’s the project’sHugging Face page.) The startup’s next goal is to apply this compression technique to even bigger models. “The next models that we will release, hopefully in the next couple of months, will be in the several-hundred-billion-parameter range, and I expect it will be easier to retain the intelligence there,” Hassibi told TechCrunch. As model size grows, he added, “There is more room to be able to compress them without losing the intelligence. So I would just say, as a general trend, for larger models, it’s easier to get to 100%.” Stoica tells us that he’s excited for this tech because it’s making it possible for advanced models to run on users’ devices. “You are going to have intelligence at your fingertips, and it’s going to be free because it’s going to run on the device you already bought. It’s also going to be private, because you’re not going to send it to the cloud.”
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