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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.

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”.

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.

The global health care sector is under increasing strain.  Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse. The World Health Organization has warned that current shortfalls will increase to 11 million workers by 2030.  In their urgent hunt for a solution, many health-care providers are now pinning their hopes on agentic AI, with more than two-thirds (68%) having already adopted AI agents into their workforce, according to KPMG.  The technology is being deployed to automate complex back-office processes, collaborate with medical teams, and even triage patients, all in a bid to reduce the cognitive load on clinicians and improve quality of care for patients as the supply of human health-care workers dwindles. A different type of digitalization  Until now, the benefits of digitalization within health care have been limited.  Many staff have blamed slow or outdated technology for adding to the administrative burden rather than alleviating it. For example, U.S. patient data was migrated to electronic health records (EHRs) in the early 2000s, but this data remains fragmented and reliant on manual inputs.  New telehealth services and digital care tools, like remote monitors, have had similar shortcomings, says Ashis Barad, MD, chief digital and technology officer at Hospital for Special Surgery (HSS), an academic medical center in New York that focuses on musculoskeletal health. Both technologies have helped improve access to health care by removing geographical barriers, he says, but they’ve failed to replicate the quality of in-person care or win trust from patients.  Agentic AI is different from these existing technologies, he insists.  Rather than relying on manual inputs or defaulting to human workers for any case that sits slightly outside a rigid framework, AI agents can handle nuanced, complex scenarios. They can make autonomous decisions, retrieve information from expert clinical sources, and iterate over time, freeing clinicians to focus on higher-level patient care. As Dr. Barad puts it: “Agentic AI takes your workflow and collapses it, augments it, supercharges it, and makes it more performant.”  At HSS, AI agents have already been deployed in multiple areas. They handle complex backend processes, such as insurance claims that previously took several weeks to complete and involved both HSS staff and a third-party contractor to handle the volume. Now, says Dr. Barad, AI agents complete 1,100 claims per month. They’ve reduced the appeals stage from 45 minutes to five and improved the success rate of those appeals from 65% to 100% in the nine months since implementation. HSS now handles all claims in-house.  Building on that success, HSS is now deploying AI agents in non-clinical patient-facing settings with an AI scheduling and triage service, as part of a collaboration with enterprise agentic AI developer Ema Unlimited. The service is accessible 24/7 via web, text, or phone. It uses conversational AI to ask patients clarifying questions about their condition and then books appointments with the most appropriate clinician, factoring in location, insurance coverage, and physician availability. “It completes the whole loop,” says Dr. Barad. The AI agent is trained on “all of our context, all of our rules, and all of our knowledge base,” he adds, providing patients with streamlined access to highly specialist knowledge from world-leading surgeons. Given the high-stakes decisions delegated to AI agents, the triage service has built-in safeguards—sensitive, complex, or uncertain scenarios are escalated to human specialists. Every decision made by the AI agent is auditable and human staff can step in at any point. Patient data is kept secure and the system is trained on all HSS protocols, policies, and care pathways. By keeping humans in the loop, Ema says its technology strikes the balance between efficient automation, patient-first safety, and human-informed decision making.  As the technology becomes more prolific, it will be incumbent on providers to ensure they have these sorts of guardrails embedded into systems, says Dr. Barad. At HSS all decisions around the technology are filtered through an AI subcommittee that Dr. Barad co-chairs alongside a senior nursing executive. AI agents that may touch on patient care will be scrutinized with far more rigor than, say, backend processes, he explains. AI agents prompt systems-level change For example, Dr. Barad has plans to create a dedicated AI lab at the HSS main campus in New York City—a move that aims to democratize access to the technology across the organization. It will be open to all staff looking to understand or build AI agents, he explains, with informative classes and one-on-one training. “We’re getting agentic AI into everybody’s hands,” he says. This echoes research by Deloitte, which found that leading agentic AI adopters in health care were far more likely to have opted for multiagent solutions, redesigning end-to-end workflows rather than sticking to narrow solutions or individual use cases. The key, it appears, is to integrate AI agents across the entire enterprise, treating them as a general-purpose technology. As Dr. Barad puts it: “It’s wrong to think of agentic AI in use cases… It’s a general-purpose technology, analogous to electricity.” In practice, this means health-care providers need to set the right foundation to achieve value with agentic AI. This includes creating a unified data strategy, one that integrates fragmented data sources across an organization to create a single, comprehensive source of truth. In health care, data is often split across multiple departments and providers, each with their own legacy IT system. In systems that rely on fragmented data sources, metrics often lack standardized definitions too. For example, Dr. Barad says that each hospital he’s worked in has had a slightly different definition for “time to start surgery,” a metric commonly used to gauge operating room efficiency. This level of fragmentation impedes AI agents from retrieving information from different sources or applications and assimilating the tacit knowledge that differentiates them from other technologies. By creating greater interoperability of

In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .

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 small businesses can leverage AI From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. Large companies can hire experts to handle these tasks, but small businesses don’t always have that luxury. That’s where AI comes in. Today’s models can already take on a range of basic administrative work, from organizing notes and summarizing meetings to invoicing, goal-setting, and social media planning. Find out how small-business owners can put AI to work. —Peter Hall 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. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Anthropic has confidentially filed for IPO ahead of OpenAIIt aims to go public as early as this fall. (CNN)+ The company did not disclose its target valuation. (Guardian)+ It’s expected to list shortly after a trillion-dollar IPO by SpaceX. (BBC)+ Beating OpenAI in the IPO race could have a big impact. (WSJ $) 2 The EU may exclude US cloud giants from critical contractsThe likes of Amazon, Microsoft, and Google could be shut out. (Reuters $)+ The EU aims to reduce its dependence on US tech. (FT $)+ Trump supercharged this sovereignty push. (Politico $) 3 Florida has become the first state to sue OpenAIThe lawsuit targets ChatGPT’s alleged child safety risks. (NPR) + Florida says OpenAI put profit ahead of safety. (Reuters $)+ Chatbots are now starting to check user ages. (MIT Technology Review) 4 Hackers stole Instagram accounts just by asking Meta AI for themThey easily broke into a host of celebrity profiles. (404 Media)+ The exploit shows the risk of offloading support to AI. (TechCrunch)+ AI is making online crimes easier. (MIT Technology Review) 5 Chinese universities with military ties are seeking Nvidia chipsTwo are blacklisted by the US Commerce Department. (Bloomberg $)+ The Chinese military has sought restricted Nvidia chips for years. (NYT $)+ US senators have slammed a loophole in chip export rules. (Reuters $) 6 Blue Origin and NASA disagree on a crucial rocket’s next flight+ Blue Origin says the rocket will fly again this year. (Engadget)+ But NASA is less optimistic. (CNBC)+ The rocket’s failure cast doubt on NASA’s moon plans. (BBC) 7 Moderna has won funding to develop an Ebola mRNA vaccineThe CEPI has pledged over $60 million to the effort. (Ars Technica)+ To fight an outbreak raging out of control. (MIT Technology Review) 8 China is using AI to predict future political dissentA company called Geedge Networks is developing the tech. (NYT $) 9 Geoengineering can thicken Arctic ice, but melt results are mixedTrials show the tech has had a limited impact. (New Scientist $) 10 Top AI labs are expanding research into machine ‘consciousness’Meta, Anthropic, and DeepMind are increasing their investments. (FT $)+ A new tool could show how consciousness works. (MIT Technology Review) Quote of the day “Sam Altman and ChatGPT have chosen the AI race over the safety and security of our kids. They have chosen profit over public safety, and we’re not going to stand for it in here in Florida.”  —Florida Attorney General James Uthmeier tells reporters why his state is suing OpenAI, the LA Times reports. One More Thing The entrance to the Moscow storage facility of KrioRus, which was until recently the only cryonics company in Eurasia.ALESSANDRO GANDOLFI Why the sci-fi dream of cryonics never died Cryonics is best known for its appearance in sci-fi films like 2001: A Space Odyssey. But its adherents have held on to a dream that advances in medicine will one day allow for resuscitation and additional years on Earth. Around 500 people are preserved in liquid nitrogen globally, while another 4,000 are on waiting lists. Despite scant evidence that cryonics can work, believers remain optimistic that future science could eventually revive them. Discover why the hope of human reanimation refuses to die. —Laurie Clarke 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.) + Hear Dolly Parton reimagined through this spot-on Dire Straits-style cover of “Jolene”.+ Find out which birds people search for most in this interactive visualization of bird popularity.+ Explore thousands of Q&As between students and astronauts on the ISS at this interactive site.+ Paris’s oldest bridge disappeared beneath a giant inflatable cave in this surreal public art installation.