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75 recent industry stories relevant to the field â releases, launches, and announcements beyond the papers.
As Meta pours billions into AI infrastructure and agents, Zuckerberg is working to convince investors that the payoff will be worth the price.
On the companyâs second-quarter earnings call Wednesday, CEO Mark Zuckerberg said Meta sees a âlarge enterprise opportunityâ spanning AI agents, APIs, compute, and internal software.
Microsoft is working on an AI "super app" that combines Copilot's chat, coding, and agentic capabilities. During an earnings call on Wednesday, Microsoft CEO Satya Nadella said the app will span "both consumer and commercial experiences" when it launches this year. "Copilot is evolving rapidly from chat to Cowork to Autopilots," Nadella said. "This quarter, […]
Meta is all-in on AI, and sometime soon, the company is going to make a big push into personal AI agents that can do things on your behalf. On Wednesday's Q2 2026 earnings call, CEO Mark Zuckerberg previewed a high-level vision of how the company is thinking about personal agents and what it will do […]
When The Atlantic published a searchable dataset of works used to train AI, Kirk Wallace Johnson, like a lot of artists, looked for his name out of curiosity. And, like a lot of artists, he found it. Essentially, his books, like The Feather Thief and The Fishermen and the Dragon - nonfiction tomes that he […]
Pangram has raised $9 million to scale its AI detection software. The startup has also released a new AI text detection model, Pangram 4, and an AI image detection model in research preview.
Opus 5 will be both cheaper and less restrictive than Fable, likely making it preferable in most use cases.
Weeks after Anthropic's latest toe-to-toe with the US government, and days after an OpenAI security incident that dominated tech industry discussions, Anthropic on Thursday released its newest model, Claude Opus 5. The company said in a release that Opus 5 "comes close to the capabilities of Claude Fable 5 in many domains" and is much […]
Meta is upgrading its AI chatbot with new productivity features in a bid to compete with rivals like Gemini, ChatGPT, and Claude. The update will allow Meta AI to tap into your calendar to help you plan events and generate daily briefings, as well as perform in-depth research that you can steer as it progresses. […]
The Trump administration unveiled the first "Genesis Mission" grants on Thursday, directing $5 billion toward hundreds of AI-driven science projects in an effort the White House has described as "comparable in urgency and ambition to the Manhattan Project." At roughly the same time, Trump's science adviser Michael Kratsios was on Capitol Hill selling lawmakers on […]
The Media Router is a tool that automatically selects the best image, video, or audio generation model for a request based on whether a developer prioritizes quality, speed or cost.
Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs required, it says.
The viability of orbital data centers hosting the largest and most capable large language models (LLMs) remains hotly contested. But enormous deployments that require thousands of GPUs arenât the only way LLMs might prove useful in space. NASAâs Jet Propulsion Laboratory recently sent Googleâs Gemma 3 to space, achieving the first in-orbit demonstration of a vision-language model analyzing imagery from a satelliteâs own sensor.The system, known as NAVI-Orbital, used Gemma 3 to analyze images captured by a YAM-9 satellite built by Loft Orbital. Juan M. Delfa, technical group lead at NASA, said that though the goal in this case was image analysis, the projectâs success implies a fundamentally new way researchers on the ground can interact with spacecraft.âThis is a major shift,â said Delfa. âNow, a scientist can write a prompt, upload it to the spacecraft, and that will be taken into account by the system. Itâs different from previous paradigms, where researchers have to write very structured commands that require an operations team and process.â Google Gemma 3 goes to spaceâno modifications requiredAt its core, NAVI-Orbital is an agentic software framework developed by Delfa and his coauthors, Taran Cyriac John, an AI researcher at NASA JPL, and Andrew W. Herson, a tech lead at Loft Orbital. It coordinates operations with a LangGraph-based conductor and deploys a compressed, 4-bit format of Googleâs Gemma 3 4B, an open-weights LLM, to produce plain-text image descriptions.NAVI-Orbital was 88 percent accurate when used to classify images in a benchmark dataset of 7,960 images. Notably, Gemma 3 classified the images without being trained or fine-tuned on this particular dataset or its categories; itâs the same base model you can download from Hugging Face and use on a laptop. The benchmark was conducted on the ground to validate the system before launch.Once the system was in orbit, NASA researchers performed two live capture tests with a camera on Loftâs YAM-9 satellite: one over Toulouse, France, and a second over the coast of Argentina. Gemma 3 generated a text description of each image, and NASA also prompted the LLM with a set of scripted questions about the images, such as whether they contain commercial or residential areas or show natural features. The image analysis also took place onboard YAM-9, which carries a compute cluster of several radiation-hardened processors (FPGAs, CPUs, and GPUs) to serve multiple customer payloads simultaneously. The satellite is powered by solar panels, which provide onboard systems with between 150 and 500 watts, depending on the position of the satellite.For the live capture experiment, Gemma 3 ran on Nvidiaâs Jetson Orin AGX, a small compute module frequently used for robotics and AI tasks. The 4-bit, 4-billion-parameter model requires only 8 gigabytes of memory, which makes it possible for it to run on a lower-power device such as the Orin AGX. âIt conveys the message of how lightweight it is. You can run it in a tiny, tiny computer,â said Delfa.Getting more useful data across limited bandwidthPaul Lasserre, general manager at Loft Orbital, said vision-capable LLMs could deliver a âparadigm shiftâ for orbital operations. Contrary to what spy movies would have you believe, most satellites canât provide fast, high-fidelity image and video feeds to observers on the ground. Bandwidth is often limited and, in most cases, satellites can deliver data to ground stations only at set intervals based on their orbit. Lasserre believes AI models can work around this problem with âsemantic compression.â Instead of sending large amounts of raw image data, a satellite can report a text summary of noteworthy information. âIt doesnât matter if the link is slow, because youâre downlinking dozens of kilobytes instead of dozens or hundreds of megabytes,â said Lasserre. âIt lets you use your satellite in a tactical way, which until now was only in Hollywood movies.âDelfa expanded on this with a real-world example: wildfire detection. Satellites are currently capable of detecting wildfires, but limits in downlink bandwidth and data processing can delay results by up to 90 minutes. A satellite capable of analyzing an image in space and reporting a plain-text warning might remove this delay. NASAâs Jet Propulsion Laboratory tested NAVI-Orbital on a YAM-9 satellite, made by Loft Orbital. Loft OrbitalFrom image analysis to spacecraft controlQuicker insight is only half of what NAVI-Orbital points toward. The other half relates to the âmajor shiftâ Delfa flagged. NAVI-Orbital provides a proof of concept for an alternate means of interacting with spacecraft.Thatâs not to say Google Gemma 3 is currently at the controls. NAVI-Orbital is deliberately walled off from the flight software. It reads images and produces descriptions. The system has the capability to make decisions about how images are analyzed but has no access beyond that.Still, the interface is novel. Retooling the system to search for a different kind of targetâsuch as wildfiresâis a matter of editing a text prompt. It doesnât require rewriting and revalidating onboard software, or training and deploying a different AI model (as was often required with prior image-classification models). The long-term vision for how this capability could be deployed goes beyond uncrewed satellites and image processing. Delfa said NAVI is rooted in thinking about how AI could serve as a companion for astronauts. âWe thought, astronauts have a lot of limitations in the spacesuit in terms of dexterity, so we conceived this idea of having NAVI as a companion to the astronaut, to allow interaction via natural languageâŠ. This is the concept that we definitely want to push forward.â A great deal of additional research will be required to push the technology that far, but NAVI-Orbitalâs demonstration has shown that two elementsâdeploying a large language model in space and controlling it with promptsâare possible.
Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…
"I don't think you get a model this strong and this quickly on the heels of Fable doing strictly distillation," one expert told TechCrunch.
Google is adding personalized AI avatars to Vids that let users create videos starring a digital version of themselves, alongside Gemini Omni-powered tools for generating and editing videos from prompts and reference images.
Roblox's new "Build" feature lets users generate basic games using a single text prompt.
It's the company's first public proof point after a year and a half spent building AI infrastructure largely out of public view.
OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet. GPT-Red automates…
The hacker used an employee's credentials to access source code, which revealed how Suno scraped decades of audio.
A number of social media posts claim that GPT-5.6 Sol deleted files and data without warning. OpenAI had basically disclosed the problem in June.
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. What Anthropicâs latest AI discovery doesâand doesnâtâshow âJames O’Donnell When Anthropic announced last week that it had found a new window into its modelsâ âinternal thoughtsâ as they reason through answers,…
Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]
Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]
OpenAI's latest family of models promises improvements across a range of areas, including cybersecurity.
The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at whatâs really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. Researchers at the company built a tool called the Jacobian lens (or…
The large language models (LLMs) that form the basis of generative AI chatbots such as ChatGPT, Claude, and Gemini can generate uncannily human-like text and images. But these models still struggle with a skill that, ironically, looks at face value to be right in their wheelhouse: analyzing structured data. A new type of generative AI is set to change this situation.Although you can get your favorite chatbot to solve intractable math problems, review dense legal documents, compose a catchy pop song, or put together some slick PowerPoint slides, give it anything more than a small table and it doesnât have a clue what to do.For most companies and organizations, the most important data sits in spreadsheets. Whether itâs a bankâs transaction logs, a marketing agencyâs website metrics, clinical trial participantsâ vital signs, or the vast amount of proton collision information produced at atom smashers like the Large Hadron Collider, structured, row-and-column data runs the world, and LLMs canât deal with it.AI startup Fundamental is pioneering a new type of AI foundation model, known as a large tabular model (LTM), to fill the gap. Fundamental came out of stealth mode on 5 February 2026 with US $275 million in funding and a model called NEXUS, purpose-built for tabular data. Now, the model is being adopted by companies such as Amazon Web Services, while others race to build their own LTMs. Why LLMs struggle with spreadsheetsPart of why structured data has garnered less attention is a very human bias, argues Boris van Breugel, a senior AI researcher based in Amsterdam. âPeople like to see images, videos, and ChatGPT responses,â he says. âBut tabular data really lags behind because itâs not fun to look at numbers.â Different tabular datasets are also difficult to compare, explains van Breugel, who co-wrote a prescient position paper on this topic in 2024. Whereas most language has similar semantics, making LLMs well-suited to being trained on vast amounts of text data, van Breugel argues that it is much harder to train a single tabular model on tables with very different variables. Additionally, language is sequential by nature (as are music, images, and video). Changing the order of words in a sentence may change or completely destroy its meaning. But the structured data you find in spreadsheets isnât sequential. You can swap the order of columns or play around with rows, but the underlying factual meaning of the data remains the same.This independence from linear order is incompatible with an LLMâs fundamental purpose of predicting the next value in a linear sequence. âWith LLMs, even slightly changing the input, you get a different output,â says Jeremy Fraenkel, CEO of Fundamental. âThatâs fine and actually often desirable for LLMs, but when youâre making a prediction of whether a transaction is fraudulent or not, you want to make sure that the prediction is the same, or deterministic, no matter what.âDeveloping Fundamentalâs LTMCurrent tabular data solutions are limited to machine learning algorithms, such as XGBoost, that have been around for more than 15 years and are used by organizations globally. These algorithmsâcalled gradient-boosted decision treesâhave to be trained and optimized by data scientists over the course of months for each and every use case. In contrast, NEXUS and other emerging LTMs are foundational, leveraging learning amassed from pre-training on diverse databases so that they can be applied across a range of different predictive tasks with minimal bespoke feature engineering or task-specific model building.And unlike LLMs, which primarily model sequences of tokens, LTMs model the structure of tabular data directly. They jointly learn from each entryâs numerical value, what it represents, and how it relates to other entries. For example, imagine an entry in a grocery stock inventory table for bananas: The LTM can take in not just the magnitudeâsay, 500âbut the fact that the entry represents the current banana stock quantity, its category (produce), and the statistical properties that link the entry with the rest of the column. This contextual understanding enables more accurate reasoning and prediction over structured data.According to Fraenkel, one of Fundamentalâs biggest challenges in developing NEXUS was obtaining the right training data. Unlike natural language, which is abundant and broadly uniform in structure, tabular data is relatively hard to findâmuch of the data is sensitive or proprietaryâand diverse. There are very few similarities between, for instance, a biology dataset and a financial one. That combination of factors meant Fundamental needed to invest in building a huge training set.âWe pre-trained NEXUS on billions of tables using a combination of proprietary datasets acquired through partnerships and licensing, high-quality public and open-source datasets, and data augmentation techniques that expanded the diversity and coverage of our training corpus,â Fraenkel says, though he is keen to point out that NEXUS is not trained on customer data. In fact, it is a confidential computing platform, which means that Fundamental physically cannot access customer data, let alone train on it.This feature was most likely a key consideration when in June, Amazon Web Services (AWS) embedded NEXUS in Amazon SageMaker, widely considered the default operating system for secure machine learning. This brings NEXUS to many customersâ often sensitive dataâa contrasting approach to LLMs, where the data has to be imported to the model.âWith Amazon, we have a first-party partnership, which means that our model exists as if itâs a native AWS solution,â Fraenkel says. âAnd over time, the goal is to expand these types of relationships to allow [end users] to really access their data wherever they do their predictions.âThe future of data analysisThough Fundamental has taken the lead, at least in enterprise applications, the company is not alone in pursuing foundational LTMs. In March, Feedzai, which provides fraud and financial crime prevention services, and credit card company Mastercard separately launched similar proprietary technologies focused on finance. Then, in late June, Google launched its own foundational competitor, TabFM, trained entirely on hundreds of millions of synthetic datasets. And machine learning researchers are not far behind either. FlexTab, TabICL, and iLTM are just three of a raft of LTMs developed by the research community in the past year, all in the pursuit of bringing the success of LLMs to the tabular domain.For all involved, the direction of travel is clear. âI would be very surprised if most data processing and analysis is not done through an automated system in the future, whether thatâs an LLM, an LTM, or some combination,â van Breugel says. âMost people donât necessarily like to do data analysis, and these systems will be able to do it a lot better.âFraenkel agrees. âI see the relationship between LLMs and LTMs as being a bit like the human brain: The left side is good at reasoning and understanding and summarizing text, and the right side is really good at understanding numbers and statistics and patterns,â he says. âBut itâs when you combine both of those that you really get something much more powerful.â
As part of an ongoing legal dispute with three Hollywood studios, Midjourney is seeking to compel those studios to reveal how they use AI themselves.
Mistral AI, which offers some open source AI models, has raised significant funding since its creation in 2023, with the ambition to âput frontier AI in the hands of everyone.â
At the event "The Briefing: AI for Science" earlier this week, Anthropic announced Claude Science, a new "AI workbench for scientists" that pulls fragmented tools and datasets into one environment, and generates figures and visuals. Anthropic, already dominating the industry with its popular coding tools and powerful AI models, framed the launch around what it […]
Midjourney has shown more of its futuristic medical scanner. It still hasn't shown much proof it works. The AI startup, best known for generating images, released a behind-the-scenes video of its dunk-tank ultrasound scanner, which it plans to deploy in spas and hopes will transform medicine with cheap, detailed, radiation-free imaging. The nearly 20-minute tour […]
At an internal meeting, the Meta CEO reportedly said that AI development efforts were not moving as quickly as anticipated.
Meta has quietly launched Pocket, an experimental AI app that lets users generate and share interactive mini games using text prompts.
The news comes about a week after OpenAI announced its own custom AI chip in a partnership with Broadcom.
OpenAI CEO Sam Altman has reportedly proposed giving 5% of the companyâs equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.
Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.
Meta is developing plans for a cloud infrastructure business, selling access to AI compute power and models. The move would pit it against the big cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure.
After weeks of negotiating with the Trump administration, Anthropic is finally going to be able to bring Claude Fable 5 back online. In a post on X, Anthropic said it plans to begin restoring access Wednesday to users globally on Claude platforms, and that the company would re-enable access on AWS, Google Cloud, and Microsoft […]
At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…
Google is updating its image generator to make it faster and cheaper, making it a more useful tool for creators looking to make AI content.
Anthropicâs Claude Sonnet 5 brings stronger agentic capabilities, lower pricing, and improved safety, positioning the model as a cheaper alternative to Opus, GPT-5.5, and Gemini Pro.
Anthropic's Claude Science is a workbench that gives scientists one environment to do computational research, saving them from the need to bounce between databases, pipelines, and tools.
New models are launching in Asia that promise Mythos-like capabilities without fear of an export ban. U.S. AI labs may never recover this enormous market.
Over 100 companies and government agencies are reportedly authorized to use Mythos 5, including their non-American employees.
Top AI researchers Jonas Adler and Alexander Pritzel are leaving Google for Anthropic, following departures from top scientists Noam Shazeer and John Jumper.
The new app, which is currently being tested with select creators, will have Facebook's recently-launched AI creator assistant built into it.
Figma has revealed some new design and coding product updates at its annual Config conference that aim to help creatives "push their ideas further" and automate tedious tasks with AI. Part of this is a reimagined canvas that's now optimized for full-stack development, according to Figma, bringing teams, AI agents, tools, and materials "together in […]
Named Jalapeño, the new processor was designed specifically for the unique needs of OpenAI's inference systems.
OpenAI has just revealed a new "intelligence processor" chip for AI servers made in partnership with Broadcom. The chip, called Jalapeño, is designed to power current and future large language models, according to an announcement on Wednesday. Jalapeño is an ASIC (Application-Specific Integrated Circuit), meaning it's designed for a specific purpose: AI inference. With AI […]
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AI is booming. New use cases are emerging each day. To capitalize on the technologyâs potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.  To understand this challenge, consider the foundation of the web itself. The web was not designed…
Large language models have moved out of the research lab and into engineersâ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.More than just a better search engineTo use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.Four ways LLMs are changing jobsHere are areas that integrate large language models.Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.Fixing the âhallucinationâ problem. LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a companyâs database.Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up âprivateâ instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.Online course program helps with mastering the techThe gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the âhowâ and the âwhyâ behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:Evolution, impact, and hands-on exercises: the shift from statistical methods to modern transformers, including hands-on model optimization.Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python.Architectural analysis and implementation: advanced LLM design with practical model-building exercises.Training and modeling with PyTorch: end-to-end pipelines in PyTorch, leveraging parameter-efficient techniques such as low-rank adaptation and quantization.Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF), group-relative policy optimization, RAG, and agentic AI.Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.Enroll in the course program on the IEEE Learning Network.Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.
Just as last week was ending, the US government forced Anthropic to pull its two newest models, Fable 5 and Mythos 5, citing national security concerns after Amazon researchers allegedly found a way to bypass Fable 5’s guardrails.  Cybersecurity researchers have since signed an open letter calling the move dangerous, and Anthropic itself noted the same jailbreaks exist in other models. So is […]
Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the…
Anthropic has joined the Frontier coalition, which received another $915M in pledges to fund carbon removal projects.
World models are the next big thing in AI beyond LLMs and, with this round, Odyssey has cemented itself as one of the startups to watch.
Google is betting generative AI can breathe new life into the smart speaker. The company's new $99.99 Google Home Speaker replaces the rigid commands of the Google Assistant era with more conversational Gemini interactions.
If physical AI is going to match the accomplishments of LLMs, there's a data problem that needs to be solved.
Amazon CEO Andy Jassy may have been the source of security concerns that led Anthropic to cut off worldwide access to two models on Friday.
On Friday evening, the government ordered Anthropic to block access to Fable 5 and Mythos 5 for all foreign nations, both inside and outside the US, due to national security concerns. That order included employees of Anthropic. To meet those demands, the company has completely cut off access to the models for all customers. In […]