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How courts are coping with a flood of AI-generated lawsuits

Most days in her chambers, Judge Maritza Braswell, a federal magistrate judge in Colorado, sifts through stacks of documents written by people without a lawyer. Many of them can’t afford to hire a lawyer, and others have cases too weak or too small to interest one. She reads each one carefully, mindful of how daunting it is to walk into the courtroom alone.  Lately, like many judges across the US, she has seen a noticeable uptick in such filings. According to a new study that examined 4.5 million federal civil cases from 2005 to 2026, the share of lawsuits brought by self-represented people increased from 11% in 2022 to 16.8% in 2025. Within those cases, the number of filings made more than doubled from pre-2023 levels.  Judge Braswell puts that jump down to AI.  “I do correlate that to AI in part because I see AI use,” she says. As a tech-savvy judge who uses AI to vet court documents, she’s learned to recognize how large language models write. She can tell from the prose and at times, hallucinated cases and fabricated quotes.  “I’m also actually seeing better-drafted pleadings,” she says.  But while AI appears to be expanding access to justice, it doesn’t seem to be improving people’s chances of winning. Judges are also starting to question what kinds of rights and responsibilities large language models should bear as they step into lawyers’ shoes. For example, they ask whether a chatbot has a duty to provide good advice, as a human lawyer does. And a growing number of lawmakers across the US are starting to grapple with who should pay the price when chatbots dish out bad legal advice.  AI supercharges lawsuits To test whether AI was driving the increase in lawsuits filed by people without a lawyer, the authors of the study, Anand Shah at MIT and Joshua Levy at the University of Southern California, ran 1,600 randomly sampled court documents through Pangram, a commercial AI-text detector. The share flagged as containing AI-generated writing rose from 1% in 2023 to 18% in 2026.  To Judge Braswell, that’s not necessarily a cause for concern. While the surge of AI-assisted filings might be adding to their workloads, she and many other judges find the cases easier to rule on because AI is helping people without legal training better articulate their arguments.  Court documents written by people without lawyers are notoriously hard to decipher. Some arrive as handwritten scrawls bordering on gibberish that judges take a while to decode. However cryptic, judges are required to read them charitably. These days, Judge Braswell has been churning through motions drafted by AI faster than the ones written by the litigants. “I have to be really careful because some of them contain hallucinations and errors, but I can generally understand what they’re arguing better with AI assistance from them than without it,” she says. The clearer filings let Judge Braswell hear them better. “If I understand an argument a little bit better, I’m probably going to be able to help a little bit more,” she says. Online communities are springing up to trade self-help guides on using AI to sue. In December 2024, a viral Reddit post walked immigration applicants through suing the United States Citizenship and Immigration Services over delayed review of their applications: draft a writ of mandamus with Microsoft Copilot, pay a lawyer $150 to polish it, and file in the expedient District of Vermont. Cases filed by people without lawyers in Vermont rose from about 45 a year before 2022 to more than 1,100 in 2024.  Even so, people without lawyers are far more likely to lose their case than people with lawyers, and that’s not changing even with the addition of AI, the study found.  “It turns out that mounting a lawsuit is a complex, multifaceted task. Not all of it is just drafting text,” says Levy.  Chatbot-client privilege Judge William Garfinkel, a federal magistrate judge in Connecticut, has served on the bench for three decades, pondering all sorts of questions about lawyers’ relationship with their clients. Lately, he has been wondering whether people’s conversations with chatbots dispensing legal advice should be privileged, the way their conversations with lawyers are.  “You can make a good argument that … conversations with large language models like Claude or ChatGPT or Grok should deserve some protection,” he says. Courts are starting to grapple with this question. In February, a federal court in Michigan ruled that a self-represented person’s conversations with ChatGPT to prepare her case were work product—legal work that is shielded from the opposing side. The decision came on the same day a federal court in New York held that documents a criminal defendant had generated using Claude were not privileged attorney-client conversations or work product. The court argued that Claude is not an attorney and that a user has no “reasonable expectation of confidentiality in his communication” with it because AI companies can disclose user data to third parties.  In March, Judge Braswell ruled that a self-represented person’s use of a chatbot should stay off limits. “It is true that AI systems like ChatGPT, Claude, Gemini, and others … collect user data for training and other purposes. But … that does not eliminate all expectations of privacy,” she wrote. Courts have since remained split on the issue. Malpractice without a pulse Some judges are also wondering whether a chatbot, like a lawyer, has a duty to provide good legal advice. Judge Allison Goddard, a federal magistrate judge in California, has noticed that people without lawyers often get the wrong advice from ChatGPT when trying to assess the value of their case during settlement negotiations. In one case, a plaintiff who slipped and fell in a store asked for $700,000 from the store, which was wildly more than the case was worth. “Where are you getting the idea that you’re getting $700,000? Did you go to ChatGPT?” Judge Goddard asked. “Well …” the plaintiff mumbled. She then walked the person through the

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The Download: AI-generated lawsuits and virtual power plants for data centers

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How courts are coping with a flood of AI-generated lawsuits Most days in her chambers, Judge Maritza Braswell, a federal magistrate judge in Colorado, sifts through stacks of documents written by people without a lawyer. The number of these filings has more than doubled compared to before 2023. She puts that jump down to AI.  But while AI appears to be expanding access to justice, it doesn’t seem to be improving people’s chances of winning. Judges are starting to question what rights and duties chatbots should have as they stand in for lawyers. Lawmakers, meanwhile, are grappling with who should pay the price when chatbots produce bad legal advice. Read the full story on how AI is reshaping access to the law. —Michelle Kim How virtual power plants could provide energy for data centers Would you take a payment to ramp down your electricity use? Would it change anything if you were doing so to help power a local data center? A new project backed by Google will put those questions to the test. The company has signed a deal to fund a virtual power plant in the largest power grid in the US. The system will group together devices like electric vehicles and smart thermostats, paying customers to adjust their usage when the grid is stretched. The project could free up capacity for Google’s data centers—but there’s a catch: people might not play along. Find out what the future holds for these virtual power plants. —Casey Crownhart This story is from The Spark, our weekly newsletter giving you the inside track on all things climate. Sign up to receive it in your inbox every Wednesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 The EU has proposed new legislation to end its Big Tech dependenceThe laws aim to boost domestic ​cloud, AI and semiconductors. (CNBC)+ US firms would be blocked from critical public tenders. (Reuters $)+ It also wants to make sure non-EU actors cannot disrupt tech services with a “kill switch.” (The Guardian)+ But the proposal needs to be negotiated with EU member states. (Politico $) 2 Intelligence agencies warn Chinese spies are recruiting on LinkedInThe Five Eyes alliance said Beijing is using job platforms for espionage. (BBC)+ The spies are allegedly recruiting government and military staff. (Politico $)+ The Chinese embassy in the UK condemned the accusations. (Bloomberg $)+ Meet the man hunting the spies in your smartphone. (MIT Technology Review) 3 AI CEOs have called for a law protecting against biological weaponsThey warn that synthetic DNA could be used for bioweapons. (Wired $)+ Sam Altman, Dario Amodei, and Demis Hassabis joined the call. (WSJ $)+ No one’s sure if synthetic mirror life will kill us all. (MIT Technology Review) 4 Firms are using Reddit to manipulate ChatGPT and Google AI searchThey’re spamming subreddits to get posts scraped by chatbots. (404 Media)+ What we’ve been getting wrong about AI’s truth crisis. (MIT Technology Review) 5 Meta keeps delaying the launch of its new AI modelThe new Muse Spark ‌AI model API still has no release date. (WSJ $)+ Which is hampering Meta’s plans to monetize its AI investments. (Reuters $) 6 For the first time, a US city has voted to permanently ban data centersMonterey Park, California, voted in favor of the move. (LA Times)+ Should we be moving data centers to space? (MIT Technology Review) 7 China is betting on household chore training to advance roboticsData harvested in homes and factories provides a scaling edge. (Rest of World)+ Gig workers are training humanoids at home. (MIT Technology Review) 8 Sam Altman will urge US lawmakers not to require AI model approvalsHe’s advocating against proposals for new AI rules. (Reuters $)+ His move comes after President Trump signed a new AI order. (Wired $) 9 Quantinuum raised $1.68 billion in an IPO as quantum computing rises Investors flocked to one of the fast-growing sector’s leaders. (Reuters $) 10 Someone finally wants to hire philosophers: Silicon ValleyBig tech hopes they will help build better machines. (The Atlantic $) Quote of the day “Historically, these companies have been very willing to play Russian roulette—and they’re playing another round.” —Connor Leahy, an AI researcher, former hacker and US director of ControlAI, tells the Financial Times why he’s concerned about Anthropic’s relentless race to the top. One More Thing HENRY HORENSTEIN/GETTY What an octopus’s mind can teach us about AI’s ultimate mystery Emily Bender, a linguist at the University of Washington, has developed a thought experiment she calls the octopus test. It involves an octopus learning to copy patterns in human writing and produce squiggles in response. But does the animal actually understand the language or are we merely projecting meaning onto it? Bender’s octopus is a stand-in for AI systems like ChatGPT. The intelligence we see in these machines is also projected on them by us. The same applies to consciousness: we may claim to see it, but it remains unclear whether it is really there. Read the full story on the debate over machines with minds. —Will Douglas Heaven We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + Discover where iconic sound effects actually came from in this fabulous audio history.+ Need a serotonin boost? Then tune into this live puppy cam from Denali National Park.+ Linux lovers can try 570 extinct operating systems at a new virtual museum.+ Beethoven’s “Moonlight Sonata” becomes something entirely different in this lightning-fast bass guitar performance.

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MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation

arXiv:2604.08782v3 Announce Type: replace Abstract: Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational turns, yet multi-turn (MT) interactions dominate chat interfaces. The routine approach of appending full chat history to prompts rapidly exhausts context windows, leading to increased latency, higher computational costs, and diminishing returns as conversations extend. We introduce MT-OSC, a One-off Sequential Condensation framework that efficiently and automatically condenses chat history in the background without disrupting the user experience. MT-OSC employs a Condenser Agent that uses a few-shot inference-based Condenser and a lightweight Decider to selectively retain essential information, reducing token counts by up to 72% in 10-turn dialogues. Evaluated across 13 state-of-the-art LLMs and diverse multi-turn benchmarks, MT-OSC consistently narrows the multi-turn performance gap – yielding improved or preserved accuracy across datasets while remaining robust to distractors and irrelevant turns. Our results establish MT-OSC as a scalable solution for multi-turn chats, enabling richer context within constrained input spaces, reducing latency and operational cost, while balancing performance.

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Nous Research Releases Hermes Desktop: A Native Cross-Platform Front End for Hermes Agent v0.15.2 with Streaming Tool Output

Nous Research has released Hermes Desktop in public preview. It is a native application for macOS, Windows, and Linux. It gives the open-source Hermes Agent a graphical interface. Until now, users ran Hermes through a CLI and messaging gateways. The current build is Hermes Agent v0.15.2. Per Nous Research’s documentation, the desktop reuses the same agent core. It shares configuration, API keys, sessions, skills, and memory with the CLI and gateway. The desktop is another surface over one agent, not a fork. What is Hermes Desktop Hermes Agent is an autonomous AI agent. It is not a coding copilot tied to an editor. It runs tasks, calls tools, and keeps state across sessions. An agent here means a model that plans, acts, and observes in a loop. Hermes Desktop is a GUI on top of that same agent core. It needs no terminal to use. The window shows streaming responses and live tool activity. A right-hand pane previews web pages, files, and tool outputs. It also includes a file browser, voice input and output, and a settings UI. Sessions are shared across surfaces. A conversation started in the desktop resumes in the CLI or TUI. The reverse also works, because state is not duplicated. macOS and Windows offer direct installers. Linux installs from the terminal on any distribution. An install script with an –include-desktop flag builds the app against an existing install. The Closed Learning Loop Nous research team describes Hermes as having a closed learning loop. This is what separates it from a simple chat wrapper. After a complex task, the agent writes a reusable skill. Those skills then self-improve during later use. Memory is persistent and agent-curated, with periodic nudges to save knowledge. Cross-session recall uses FTS5 session search with LLM summarization. User modeling runs through Honcho dialectic user modeling. In practice, longer use means more retained context and reuse. Skills follow the agentskills.io open standard. How It Connects, Schedules, and Sandboxes Hermes runs across messaging platforms from one gateway. The desktop lists Telegram, Discord, Slack, WhatsApp, Signal, Email, and CLI. You can start a task on one platform and continue on another. Scheduling uses natural language for reports, backups, and briefings. These run unattended through the gateway on a built-in cron scheduler. Delegation spawns isolated subagents with their own conversations and terminals. A subagent is a separate worker that handles one job. Python RPC scripts collapse multi-step pipelines into zero-context-cost turns. Execution is sandboxed. The desktop lists five backends: local, Docker, SSH, Singularity, and Modal. It applies container hardening and namespace isolation. Namespace isolation limits what a running process can see or touch. Built-in tools include web search, browser automation, vision, image generation, text-to-speech, and multi-model reasoning. Hermes also connects external tools through MCP. MCP is the Model Context Protocol, a standard for tool integration. Nous Portal and the Tool Gateway Hermes works with any provider, so API keys are optional. Nous Portal bundles them under one subscription instead. Portal tiers are Free, Plus, Super, and Ultra. Paid tiers include monthly credits and access to 300+ models. They also include built-in tool use. The Tool Gateway routes several tools through one account. Web search uses Firecrawl and image generation uses FAL. Text-to-speech uses OpenAI and the cloud browser uses Browser Use. The next evolution of Hermes Agent is here! Introducing Hermes Desktop: everything you love about Hermes, now native on your machine. First demoed in Jensen’s GTC keynote, it’s now in public preview. pic.twitter.com/8ND1k8hyaz — Nous Research (@NousResearch) June 2, 2026 Strengths and Questions Strengths: Native installers remove the terminal requirement for most users Streaming output and previews make tool calls easier to inspect Persistent memory and self-improving skills reduce repeated instructions Model-agnostic design avoids lock-in to a single provider The MIT license allows audit, self-hosting, and modification Questions: The product is in public preview, so expect rough edges Autonomous memory and scheduling raise oversight and review questions The Linux desktop still installs through the terminal Broad capability means a steeper learning curve for beginners Key Takeaways Nous Research released Hermes Desktop in public preview, a native macOS, Windows, and Linux app for its open-source Hermes Agent. The GUI shares one agent core, configuration, API keys, sessions, skills, and memory with the CLI and gateway; sessions resume across surfaces. It runs no-terminal with streaming tool output, a side-by-side preview pane, file browser, voice I/O, and a settings UI. Hermes is model-agnostic and MIT-licensed, working with Nous Portal, OpenRouter, OpenAI, or any compatible endpoint. The current build is Hermes Agent v0.15.2, backed by a closed learning loop, MCP tool support, and five sandbox backends. Check out the Project here. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Nous Research Releases Hermes Desktop: A Native Cross-Platform Front End for Hermes Agent v0.15.2 with Streaming Tool Output appeared first on MarkTechPost.

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NVIDIA Releases Cosmos 3: A Two-Tower Mixture-of-Transformers Foundation Model Unifying Physical Reasoning, World Generation, and Action Generation

NVIDIA AI team have released Cosmos 3. It is a family of omnimodal world models for physical AI. The models combine physical reasoning, world generation, and action generation. All three capabilities live inside one open model. NVIDIA open sourced the checkpoints, training scripts, deployment tools, and datasets. The Cosmos 3 release targets robotics, autonomous vehicles, and warehouse monitoring teams. NVIDIA Cosmos 3 Physical AI systems must understand the world before acting in it. Robots and vehicles need to perceive, predict, and then act. Earlier Cosmos releases split these jobs across separate models. Cosmos 3 unifies them with a Mixture-of-Transformers (MoT) architecture. The architecture is built around two towers. The reasoner tower is a vision-language model (VLM). It interprets images, videos, and text using an autoregressive architecture. It understands motion, object interactions, and other physical context. NVIDIA team describes this tower as the model’s brain. The generator tower produces future observations and action sequences. It uses a diffusion-based process for physics-aware video and actions. These outputs are conditioned on the reasoner tower’s understanding. Information flows one way, from reasoner to generator. The reasoner can run alone. The generator always activates both towers for guided generation. A single model can therefore handle reasoning and generation together. https://developer.nvidia.com/blog/develop-physical-ai-reasoning-world-and-action-models-with-nvidia-cosmos-3 The Model Family NVIDIA team describes three model scales: Edge, Nano, and Super. Each uses the dual-tower Mixture-of-Transformers design. The two towers are initialized from pre-trained Qwen3-VL weights. That roughly doubles the parameter count of the backbone transformer. Cosmos3-Nano is a 16B model built on a dense 8B transformer. It adapts the Qwen3-VL 8B architecture. Nano targets efficient inference on workstation GPUs. It runs on hardware like the NVIDIA RTX PRO 6000. That suits real-time robotics and on-device physical AI. Cosmos3-Super is a 64B model built on a dense 32B transformer. It adapts the Qwen3-VL 32B architecture. Super targets datacenter GPUs, including NVIDIA Hopper and Blackwell. It fits large-scale synthetic data generation and advanced reasoning. This release ships Nano and Super, along with task-specific variants. These include Super Text2Image, Super Image2Video, and Nano-Policy-DROID. How the Unified Design Works Both towers share one transformer architecture and a joint attention operator. They use a 3D multimodal rotary position embedding (mRoPE). mRoPE aligns video, audio, and action tokens on one temporal axis. In Reasoner Mode, tokens pass through causal self-attention. This enables next-token prediction for perception, planning, and reasoning. In Generator Mode, noisy tokens are denoised through full attention. The autoregressive tokens are never updated by the diffusion tokens. The model treats action as a core modality with dedicated action tokens. Supported inputs include text, image, video, and JSON action arrays. Outputs include images, video, synchronized sound, action states, and text. The reasoner follows Qwen3-VL-compatible message conventions for vision inputs. Generation supports 256p, 480p, and 720p resolution tiers. Frame counts range from 5 to 300, defaulting to 189. That equals about 7.9 seconds of video at 24 FPS. Sound is generated as stereo AAC at 48 kHz. Action conditioning spans camera, vehicle, egocentric, single-arm, dual-arm, and humanoid embodiments. Each embodiment uses a fixed action dimension, such as 9D for cameras. The Benchmark Case NVIDIA team evaluated Cosmos 3 across reasoning and generation suites. On reasoning, Super and Nano lead VANTAGE-Bench at their respective tiers. VANTAGE-Bench tests VLMs on real-world fixed-camera footage. It covers warehouses, transportation, and smart spaces. Cosmos 3 also tops the Traffic Anomaly Reasoning (TAR) leaderboard. TAR is the official leaderboard for AI City Challenge 2026 Track 3. On generation, NVIDIA reports open-source state-of-the-art results. Cosmos 3 is the open-source SOTA on R-Bench. It also leads PAI-Bench, Physics-IQ, and RoboLab on public leaderboards. On Artificial Analysis, it leads two open-source leaderboards. These cover text-to-image and image-to-video without audio. NVIDIA team also introduced its Cosmos Human Evaluation framework, called HUE. HUE decomposes each generated video into yes/no fact questions. It scores four dimensions across seven physical AI domains. The dimensions are semantic alignment, physical laws, geometric reasoning, and visual integrity. A VLM pipeline drafts the questions, and human experts refine them. Marktechpost’s Visual Explainer marktechpost@guide ~ /nvidia/cosmos-3 01 / 09 DEVELOPER GUIDE · PHYSICAL AI NVIDIA Cosmos 3 Open omnimodal world models for physical AI. Released May 31, 2026. One model for physical reasoning, world generation, and action generation. Mixture-of-Transformers Open weights OpenMDW-1.1 Use ← → or swipe to navigate 01 · WHAT IT IS A unified model for understanding and generation Cosmos 3 is a family of omnimodal world models for physical AI. Earlier Cosmos releases split jobs across separate models. Cosmos 3 unifies them in a single open model. Physical reasoning over images, video, and text. World generation of physics-aware video and sound. Action generation for robots and autonomous systems. Subsumes VLMs, video generators, world simulators, and world-action models. 02 · ARCHITECTURE Two towers, one transformer REASONER TOWER An autoregressive vision-language model (VLM). It interprets motion, object interactions, and physical context. NVIDIA calls it the model’s brain. GENERATOR TOWER A diffusion-based path for physics-aware video and actions. It is conditioned on the reasoner’s understanding. Information flows one way, reasoner → generator. Both towers share a 3D multimodal RoPE (mRoPE). 03 · MODEL FAMILY Pick a size for your hardware Cosmos3-Nano 16B total (dense 8B, Qwen3-VL 8B). Workstation GPUs like RTX PRO 6000. Real-time robotics. Cosmos3-Super 64B total (dense 32B, Qwen3-VL 32B). Datacenter Hopper and Blackwell GPUs. Large-scale SDG. Cosmos3-Edge 4B total (dense 2B). On-device scale. Planned for a later release. Plus variants: Super-Text2Image, Super-Image2Video, and Nano-Policy-DROID. 04 · MODALITIES Inputs, outputs, and generation settings Inputs: text, image, video, and JSON action arrays. Outputs: image, video, synchronized sound, action states, text. Resolution: 256p, 480p, 720p. Sound: stereo AAC at 48 kHz. Length: 5 to 300 frames; default 189 (about 7.9s at 24 FPS). Embodiments: camera, vehicle, egocentric, single-arm, dual-arm, humanoid. 05 · BENCHMARKS What NVIDIA reports REASONING Nano and Super lead VANTAGE-Bench at their tiers. Cosmos 3 tops TAR, the AI City Challenge 2026 Track 3 leaderboard. GENERATION Open-source SOTA on R-Bench. Leads PAI-Bench, Physics-IQ, and RoboLab. Top open-source on Artificial Analysis text-to-image and image-to-video.

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How virtual power plants could provide energy for data centers

Would you take a payment to ramp down your electricity use? Would it change anything if you were doing so to help power a local data center? Google just signed a new deal to help pay for a virtual power plant (VPP) in the largest power grid in the US. The agreement is with Voltus, a leading VPP and distributed energy resources platform. Voltus will set up the virtual power plant, grouping together devices like electric vehicles and smart thermostats. It’ll pay customers to participate, and the company will dial back power or use the stored energy during times when the grid is stressed. Google will foot the bill for setting it up, and the extra capacity generated by the project will help run its data centers in the region. This is one of the most concrete examples so far of a tech giant using a VPP to help meet energy demand for data centers. But there are still some lingering questions about just how far this sort of program can go, and what the limits are. Last year, it felt as if everyone was talking about data center flexibility. A high-profile study from Duke University found that if data centers agreed to decrease their energy demand for roughly 40 hours per year, a whole bunch of them (about 100 gigawatts’ worth) could come online without making new power plants or transmission equipment necessary. The underlying reason is that our power grid is designed not for our average energy use, but for the absolute maximum: the brutally hot July evening when everyone is blasting their air conditioners, watching Love Island, and microwaving popcorn. If a data center is willing to refrain from pulling so much power during those high-stress times, the grid can happily support it the rest of the year. One lingering question here is about incentives: How would you get data centers to agree to this? After all, they might not have a very flexible load, especially now that AI use is more widespread—training a model can easily be delayed or shifted, but customer demand is more immediate. Giving up computing capacity could mean losing revenue. Regulation is one approach that could work here. One proposal in the US would allow new data centers to come online years sooner if they agree to lower demand when the grid is nearing its max.  And a new Texas law requires large users to switch to backup power or curtail their demand in emergency situations. Another approach is for data center operators to pay for other people to be flexible. Voltus announced a new program in September that allows data centers to finance flexibility on their local grid. The company calls it “Bring your own capacity.” Google is now the first named customer taking advantage of this program. In the new agreement, Voltus will pay people who agree to participate in the virtual power plant. The plant will be part of PJM, the grid that covers much of the US East Coast. The company says it will be able to aggregate up to 100 megawatts of distributed energy resources each year. The plant should be operational in 2027, according to Voltus. This isn’t Google’s first foray into flexibility; the company has agreements with utilities across the US to limit or shift its own energy demand, which can help free up grid capacity. As the company pointed out in a blog post earlier this year, though, there are limits on how flexible a data center can be, and not every facility will be able to ramp down its power demand. “There is no one solution for expanding grid capacity and we’re continuing to explore all options, including the many avenues for load flexibility,” said Michael Terrell, Google’s global head of advanced energy, in an emailed statement in response to written questions. Once again, I’m wondering about incentives here. These companies are asking homes and businesses to be flexible. Will they agree? A recent study in California looked at local people’s willingness to participate in managed electric-vehicle charging. Essentially, the program pays people to give up control of when they charge their EVs. This is another way to help smooth out electricity demand and ease the burden on the grid. The problem? Not many people signed up. With no economic incentive, only 1% of EV owners enrolled in managed charging. At $40 per month (about 15% of their power bill), only 4.6% did. This is a different situation and a different region from the one in which Google is working with Voltus. (It’s worth noting that the companies aren’t sharing how much they plan to pay the participants, which will obviously be a big determinant in participation for this kind of project.)  But this study shows that even with money on the table, people may not always jump at the chance to cede control of their electricity demand. And it certainly feels relevant that about 70% of Americans oppose AI data centers in their area, according to recent Gallup polling.  Being flexible sounds like a great idea in theory, and these financed VPPs could provide an immediate route to meeting energy demand. But as we move from idea to implementation, it’ll be interesting to see whether trial runs work as intended.   This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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The Download: Trump’s new AI order, and smart glasses for warfare

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. 5 key points in Trump’s new AI order Less than two weeks after scrapping an executive order on AI, President Donald Trump signed a new one on Tuesday. Promising to promote innovation and security, the policy represents a turning point in the White House’s AI governance—but is likely to attract criticism from both opponents and supporters of stricter regulation. Here are five key points from the order: 1. It’s created a voluntary review system: tech companies will be asked to share frontier models with the government for review 30 days before they plan to release them.2. There’s no mandatory licensing: the government will not require permits before software can be deployed.3. It establishes a dedicated AI cybersecurity clearinghouse: the new hub will coordinate security checks with the private sector.4. It’s a watered-down version of the order Trump shelved last month: the earlier version requested models 90 days before their release.5. But it’s still a move towards stronger AI oversight: the policy marks a clear departure from the White House’s previous hands-off approach. Plus: here’s why a previous Trump administration’s AI policy was a distraction and how AI is already making online crimes easier.  MIT Technology Review Narrated: inside Anduril and Meta’s quest to make smart glasses for warfare The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it’s prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands. Quay Barnett, who leads the effort at Anduril following a career in the Army’s Special Operations Command, aims to optimize “the human as a weapons system.” His vision is cyborg-inspired: drones and soldiers will see together, share information seamlessly, and make decisions as one. —James O’Donnell This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 President Trump has signed an AI order that expands model oversightThe long-awaited executive order aims to mitigate security threats. (NYT $)+It asks companies to submit models voluntarily for tests before release. (NPR)+ It’s a slimmed-down version of the order Trump shelved in May. (WSJ $)+ And marks a strategic shift in his AI strategy. (Reuters $)+ A war over AI regulation is coming to the US. (MIT Technology Review) 2 SpaceX plans to raise $75 billion in IPO at $135 per shareThe company intends to sell 555.6 million shares. (Reuters $)+ The fixed price breaks from the traditional IPO process. (Bloomberg $)+ Morningstar says the valuation should be nearly 50% lower. (BI) 3 Meta has scaled back plans to track workers’ clicks and keystrokes to train AIAll staff can pause it for 30 minutes, with some fully exempt.(The Information $)+ The changes follow a fierce backlash to the tracking plans. (Reuters $)+ AI is supercharging surveillance. (MIT Technology Review) 4 Microsoft wants to ‘make users addicted’ to its new AI assistantAccording tointernal documents for the “Scout” tool. (404 Media)+ Microsoft launched the assistant on Tuesday. (TechCrunch)  5 Mathematicians fear that AI threatens their fieldA new declaration raises concerns about AI’s trustworthiness. (Ars Technica)+ It arrives a week after OpenAI said it solved a famous math problem. (WSJ $)+ A startup wants to change how mathematicians do math. (MIT Technology Review) 6 Scientists have found a way to supercharge computer worms with AIThe worm could target any known flaw in the world’s computers. (NYT $)+ AI supercharging scams. (MIT Technology Review) 7 Google must let UK publishers opt out of AI search featuresOnline publishers can choose not to appear in the AI Overviews. (BBC)+ Google is now testing features for sites to exit AI search. (Reuters $) 8 America’s data center build-out is falling way behind schedule60% of those planned for completion in 2027 aren’t yet under construction. (WSJ $)+ Nobody wants a data center in their backyard. (MIT Technology Review) 9 EVs are getting cheaper worldwide—except in the USThe US is short on supportive policies and affordable Chinese EVs. (Rest of World) 10 The European Parliament is ditching Google for… QuantThe French search engine is the new default on in-house computers. (Politico)+ The switch comes amid a broader push to wean the EU off US tech. (FT $) Quote of the day “SpaceX’s valuation could be richer than a plate of dauphinoise potatoes.” —Dan Coatsworth, head of markets at AJ Bell, tells CNBC that SpaceX’s IPO price looks overloaded with expectations. One More Thing Marseille’s battle against the surveillance state Heading toward Marseille’s central train station, Eda Nano points out what looks like a streetlamp on the Rue des Abeilles. But this sleek piece of urban furniture is not a lamp. It’s a video camera, with a 360-degree view of the narrow street. Nano, a 39-year-old developer, wants to make Marseille residents more aware that they’re being watched. She’s part of a growing group of activists resisting the rise of policing cameras in their hometown. Find out how the rebellious port city of Marseille is fighting the surveillance state. —Fleur Macdonald We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + These aerial photos of solar farms transform renewable energy into abstract art.+ Open a window over Earth’s water with this hypnotic 4K atmospheric film made from satellite imagery.+ Spend three relaxing hours with David Attenborough narrating this collection of extraordinary wildlife moments.+ Radiohead sounds beautiful on traditional Japanese instruments in this koto performance of “Weird Fishes/Arpeggi”.

The Download: Trump’s new AI order, and smart glasses for warfare Read Post »

AI, Committee, News, Uncategorized

How small businesses can leverage AI

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here. From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to handle these tasks, but small businesses don’t always have this luxury. That’s where AI comes in. Today’s AI models do a decent job at these tasks. The trick for small businesses is to understand where AI is good enough and where it’s not. One place where a “good enough” AI can already be quite valuable to small business owners is in providing secretarial skills and handling basic administrative matters. Let’s take a look at how one private tutor is using it to improve his recordkeeping and free up his time. Case study Sam Finnegan-Dehn works in fundraising for a charity, but he moonlights as a math and philosophy tutor for university students from his home in London. Through this part-time business, he can leverage his degrees in philosophy and share his love of the subject with clients. But meeting with students is only a fraction of the work it takes to be a good tutor. He also plans lessons and finds fresh reading materials, creates assignments, sends invoices, and keeps up with new research—all on top of his regular job. Given these demands, Finnegan-Dehn doesn’t have as much time as he’d like to grow his tutoring roster. So he’s turned to AI for some help in managing the day-to-day aspects of his business. He says AI has taken on a secretarial role across all of his digital notebooks, where he jots down reminders about his clients’ progress and new readings to keep himself up-to-date. He describes using AI as kind of like having a second memory that helps him connect ideas he’s written down in various places. While he has experimented with different tools like Claude and ChatGPT, he’s now landed on Notion AI because it integrates better with his tutoring notes, which live across his notebook tabs in the Notion app. Finnegan-Dehn doesn’t use AI to create teaching materials, but he does let Notion AI record meetings with his clients (after getting their consent), and then uses its automated summaries to refine his teaching strategy. For example, if he notices from the AI’s summary that it seems like a certain technique was not helping a student, he may change how he approaches the subject next time. Beyond this, Notion AI also helps him with goal-setting, drafting lesson notes, invoicing, and generating and syncing social media posts. For goal-setting, for example, Finnegan-Dehn says he understands his long-term goals for his business but not always the concrete steps to build to them. He uses AI to help fill in these gaps. He starts by writing down a “North Star” goal—say, to have a certain number of clients by the end of the year. Next, he asks his AI to generate the steps that he needs to take to get there, given the profile he has built up in the app. Then, he can reflect on the results and choose which tasks to tackle first. The tool Notion has been a big player in note-taking software for many years. Its AI add-on, released in late 2023, now has tools that enable it to interact with many other online productivity platforms. There’s an email client, calendar integrations, and a newly released agent. And while this level of access has raised privacy concerns, it can also make for a pretty powerful virtual assistant. Many of the tasks targeted by Notion AI are less creative and more rote: syncing information across documents or searching through old scribbles, for example. This makes the tool especially appealing to small business owners, who have limited bandwidth, particularly for menial work. Other companies are developing tools targeted at specific industries. For example, Grandma’s Quilt Shop in Yuma, Arizona, uses Rain, which has a software suite tailored to craft companies, to generate inventory descriptions and pricing for its stock of fabric designs. The owners claim this AI tool cuts the time it takes to list items by 60 to 80%. There are drawbacks, though, as Finnegan-Dehn described some of Notion AI’s idiosyncrasies as “clunky” at times. And the AI add-on for Notion costs $20 per month. As with all new tools, small business owners should carefully assess how the potential gains and headaches measure up against the cost of just doing the job themselves. User tips Consider these points when thinking about whether AI might be able to help you run a business, or make any part of your work life just a little bit easier.  Look before you leap. Since LLMs feed on the data you input to answer your queries or complete tasks, you want to give them information in a way that’s convenient for you and for the model. For many of these notebook AI services, this means, for example, using their platform for notetaking so you don’t have to input or upload notes later. Because of this, it’s a good idea to weigh your options carefully before committing to an AI-powered ecosystem. Work to your strengths. Think about what skills you lack in-house, and see if AI can either help with training or take these tasks on for you. Just be aware: AI hallucinates and makes mistakes, so think about where accuracy is needed and keep humans in charge there. AI isn’t always the best tool. It’s okay to use something off the shelf when that’s the better choice. It’s going to be safer, for example, to use existing payment processing platforms like Shopify or Square than to vibe-code one using AI. Consider using local models for any sensitive information. Our reporting has covered the risks that online AI models have in leaking sensitive data, and there have been many reports about how AI companies collect your data when you

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AI, Committee, News, Uncategorized

Alibaba’s Qwen Team Launches Qwen3.7-Plus, Adding Vision, Deep Reasoning, Tool Invocation, and Autonomous Iteration on the Bailian Platform

Alibaba’s Qwen team has released Qwen3.7-Plus. The model is now available through Alibaba Cloud’s Bailian platform. Bailian is the console international users access as Model Studio. It offers API services to external developers. The release follows Alibaba’s May unveiling of the Qwen3.7 generation. Qwen3.7-Plus Qwen3.7-Plus is a multimodal large language model. The model understands images and video, alongside written prompts. Its sibling, Qwen3.7-Max, is text-only. This is visual understanding, not generation. The model reads images and video; it does not create them. Alibaba’s image and video generation work sits in separate model families. Alibaba team describes the release as a step in multimodal hybrid agent technology. An agent is a model that plans and acts across steps. Building on image and video understanding, Qwen3.7-Plus adds five abilities. These are deep reasoning, self-programming, tool invocation, verification and testing, and autonomous iteration. Self-programming means the model writes and revises its own code. Tool invocation means it calls external functions or APIs. Verification and testing means it runs outputs and checks results. Autonomous iteration means it loops until the task is done. Together, they describe a model built to act, not just answer. The Vision Case Qwen3.7-Plus is the multimodal half of the 3.7 family. Its preview already posted measurable vision results. In Vision Arena, Qwen3.7-Plus-Preview ranked #16 overall. That placed Alibaba as the #5 lab in vision. The model rank and the lab rank are separate figures. Vision Arena is a neutral leaderboard run by LM Arena. Users vote on image-understanding answers in blind matchups. The #16 result sits behind the top US labs, but inside the field. For image-heavy work, this is the signal that matters. Think OCR at scale, chart reading, or video-frame analysis. The text-only Max sibling anchors the generation’s reasoning. Max scored 56.6 on the Artificial Analysis Intelligence Index. That was the highest placement for a Chinese model at release. https://qwen.ai/blog?id=qwen3.7-plus The Agentic Loop The clear shift in Qwen3.7 is its agentic focus. Alibaba team is positioning the models for long-running tasks. Bailian, the host platform, adds two relevant pieces. The first is an Agentic RL (reinforcement learning) mechanism. The platform uses real-world execution feedback to refine model accuracy over time. The second is a set of built-in safety guardrails. These keep autonomous tools inside preset operational limits. That detail matters when an agent runs commands or edits files. Marktechpost’s Visual Explainer AI Models · Field Guide 1 / 7 Alibaba Qwen · June 2, 2026 Qwen3.7-PlusAlibaba’s multimodal agent model, now on Bailian A multimodal large language model with image and video understanding, deep reasoning, and agentic features. Available via API on Alibaba Cloud’s Bailian platform, accessed internationally as Model Studio. Use the arrows or swipe to explore → 01 · What it is A multimodal large language model Multimodal — it reads images and video, alongside text input. Visual understanding, not generation — it reads media, it does not create it. The multimodal sibling to the text-only Qwen3.7-Max. Alibaba describes it as multimodal hybrid agent technology. 02 · Capabilities Five abilities beyond seeing Deep reasoning — works through problems step by step. Self-programming — writes and revises its own code. Tool invocation — calls external functions or APIs. Verification and testing — runs outputs and checks results. Autonomous iteration — loops until the task is done. 03 · Vision benchmarks Where it stands on vision The preview ranked #16 overall in Vision Arena (LM Arena). That placed Alibaba as the #5 lab in vision. Model rank and lab rank are separate figures. Relevant for OCR, chart reading, and video-frame analysis. For reference, the text-only Max sibling scored 56.6 on the Artificial Analysis Intelligence Index, the highest Chinese model at release. 04 · The agentic loop Built for long-running tasks Bailian adds an Agentic RL (reinforcement learning) mechanism. It uses real-world execution feedback to refine accuracy. Built-in safety guardrails keep autonomous tools within limits. That matters when an agent runs commands or edits files. 05 · Confirmed vs unconfirmed What we know today Confirmed Image and video understanding Agentic feature set Bailian API access Proprietary, API-only Not yet published Public price sheet Context window size Output token limits Open weights 06 · Why it matters The practical read A vision-capable agent backend through one API. Suits workloads mixing images, video, and tool use. A leaderboard rank shows promise, not a guarantee. Validate accuracy on your own data before committing. ‹ › Marktechpost AI research, news, and developer signal for engineers and data scientists. Read more at marktechpost.com. Key Takeaways Alibaba released Qwen3.7-Plus, a multimodal model now available via API on its Bailian platform (Model Studio). It understands images and video as input — understanding, not generation — and adds agentic features. Capabilities include deep reasoning, self-programming, tool invocation, verification and testing, and autonomous iteration. Its preview ranked #16 in Vision Arena, making Alibaba the #5 lab in vision. Check out the Technical details. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Alibaba’s Qwen Team Launches Qwen3.7-Plus, Adding Vision, Deep Reasoning, Tool Invocation, and Autonomous Iteration on the Bailian Platform appeared first on MarkTechPost.

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