Ian King / Bloomberg: Nvidia unveils Halos, a safety-focused OS developed from autonomous vehicle tech and designed to run on IGX Thor hardware for humanoid robots, and opens a lab  —  Nvidia Corp. is working to make humanoid robots safer around people, arguing that they'll need to handle split-second decisions …

After 18 years of focusing on Europe, early-stage investor Seedcamp said that it has raised $320 million for its latest fund which will see it expanding its presence in the United States.

Huntress, HackerOne, Jamf, Recorded Future, and Tanium are among the cybersecurity companies that had data stolen following an earlier breach at market research firm Klue.

Everpure (formerly Pure Storage) may have changed its name a few months back, but the real change appears to be in the future, as the company embarks upon an aggressive […] The post Everpure Outlines Ambitious Pivot to Data-Centric Storage at Accelerate 2026 appeared first on BigDATAwire.

Enterprise Document Intelligence [Vol.1 #6bis] - Ask one focused clarification, learn the default from the answer, stay silent next time The post When RAG Users Ask Vague Questions: Clarify Once, Learn the Default appeared first on Towards Data Science.

🧠 Is this the future of product development? Real world examples with templates and guides to follow.

Sarah Basford Canales / The Guardian: In a rare joint statement, Five Eyes leaders warn AI models capable of taking down governments and businesses are mere months away, urging leaders to “act now”  —  Signal agencies in Australia, the US, the UK, New Zealand and Canada sound alarm after Trump blocks foreign nationals from Anthropic's Fable AI model

Kurt Wagner / Bloomberg: Meta invests $900M into Indian fintech Cred for a ~20% stake, and plans to appoint Cred founder Kunal Shah as the leader of WhatsApp, replacing Will Cathcart  —  Meta Platforms Inc. is investing $900 million into Indian fintech startup Cred, with plans to appoint its founder, Kunal Shah, the new leader of WhatsApp.

Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional... Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional safety which was built for structured environments can not work anymore as the spaces become more unstructured and robots move out of cages. AI-driven safety is the key. Marking a major milestone in the arrival of physical AI… Source

Table of Contents Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks Gemma 4 Model Family Overview: E2B, E4B, 31B, and MoE 26B A4B Gemma 4 Capabilities: Reasoning, Multimodal AI, and Thinking Mode Gemma 4 Thinking Mode: Chain-of-Thought Reasoning Explained… The post Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks appeared first on PyImageSearch.

Are you able to tell the difference between a scientific image made by a person or by an AI model? Olga Yastremska/iStock via Getty Images A photograph of Earth glowing in deep space, the Moon’s cratered horizon stretching across its foreground, caught many people’s eyes in April 2026. Astronauts captured the image while aboard NASA’s Artemis II mission, and like the famous Apollo 8 “Earthrise” image, the picture felt instantly real and inspiring for many. But when almost anyone can fabricate a visually similar image in seconds from a text prompt using artificial intelligence, how do people decide which image is real? The proliferation of AI-generated science images in public spaces is not simply a misinformation problem. As a researcher who studies visual science communication and public trust, I believe it also contributes to a crisis of trust in science in the age of AI, and the tools scientists have long relied on to establish visual credibility are losing their grip. AI-generated images infiltrate science AI tools are already changing how scientific visuals are created, shared and publicized. Researchers use them to generate illustrations, create synthetic data, edit lab images and produce materials for education and public outreach. While AI can help scientists communicate complicated ideas more creatively and efficiently, these same tools blur the lines between illustration, enhancement and fabrication. In 2024, two papers were retracted after publishing AI-generated figures posessing biologically impossible structures. In April 2026, the New England Journal of Medicine retracted a paper after discovering that a clinical image had been manipulated with AI. These are just cases that came to mass public attention and are likely just the tip of the iceberg. Researchers have warned that AI-generated visuals pose growing threats in fields that depend heavily on visual evidence, such as materials science. Academic publishers are beginning to adopt AI-detection tools. However, systems designed to detect fake images will almost always lag behind systems designed to create them. Many detectors can identify only image patterns they were trained to recognize. As new AI models emerge, developers must constantly obtain new data and retrain detectors to catch up. The biggest concern are realistic-looking visuals that subtly distort scientific details while remaining believable enough to pass initial review. Trust in scientific images For decades, scientific images carried authority partly because they were difficult to produce. Creating microscope images, climate graphs and space photographs required expensive equipment, institutional resources and specialized expertise. Most people assumed such images represented true observations because very few people could make them. Research in science communication, including my own, suggests that people judge scientific visuals using a few mental shortcuts. Does the image look technically sophisticated? Does it come from a trusted institution? Does it match what I already believe? Generative AI is undermining all three of these heuristics, or mental shortcuts. Today, anyone can create a polished, scientific-looking image from a text prompt. Images are also detached from their original source when circulating online. When visual quality and institutional attribution become unreliable cues for judging the credibility of science images, people tend to fall back on something else: their own prior beliefs. This image of the Earth taken from the Artemis II mission in April 2026 is very much real. Does everyone believe it? NASA As a result, authentic scientific images that challenge someone’s existing beliefs can now be dismissed as AI-generated, whereas fabricated images that confirm them are easily accepted as evidence. AI, in this way, may amplify motivated reasoning – that is, people’s tendency to accept what they already agree with and question what they do not. This shift matters because visuals have long served as evidence for scientific claims. Nonexpert audiences rely on images not only to see what scientists have discovered but also to develop an emotional connection and perceive credibility in the science being presented. If audiences stop trusting visual evidence altogether, science loses one of its most powerful tools for public communication. Transparency, not restriction AI tools offer real benefits for researchers communicating their work to diverse audiences. The challenge is using these tools without quietly transferring AI’s credibility deficit onto the science the images are meant to convey. One practical path forward is for researchers to treat image provenance – where an image came from and how it was created – with the same seriousness they already apply to data provenance. Scientists routinely disclose funding resources, study methodologies and conflicts of interest. Similar standards may now be necessary for scientific images. Was AI used to generate or modify this image? Is it a direct observation, a simulation or an illustration? What exactly does the image represent, and how was it verified? Can it be replicated by other researchers? A particularly inaccurate scientific image of a rat that was published in a journal went viral. My colleagues and I found that people’s familiarity with AI significantly shapes how they judge the credibility of AI-generated visuals. Those familiar with AI tools were more likely to view AI disclosure as a sign of transparency, and some rated clearly labeled AI-generated content as more credible than unlabeled content. Transparency gives audiences the necessary context to evaluate what they are seeing, but it may not resolve every dispute about how images are made. Responsible use of AI-generated scientific images will require honesty, adherence to professional norms and the collective development of evidence-based standards across fields. Why authentic images remain powerful The original Apollo 8 “Earthrise” photograph of 1968 carries significant emotional impact. So do the Artemis II images of 2026. What makes them meaningful is not simply their beauty. It is their traceable connection to scientific reality. When people look at these photographs of planets, they also know there are astronauts, physical cameras, documented missions and verifiable observations behind the images. In this sense, authenticity is a documented relationship between an image and the world. In the age of generative AI, scientific institutions can no longer assume audiences will automatically trust their visuals. Trust now depends on transparency, documentation and clear communication about how visual evidence is produced. Without guidelines and standards, science risks entering a world where every image can be questioned and no image carries inherent credibility. Nan Li does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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. Inside the world’s deepest and longest subsea road tunnel —Niall Firth I’m currently around 1,000 feet beneath the North Sea, in a dark, dank cave. It smells weird. And I’m increasingly…

Most AI agent tutorials start with an API.

The intuition behind neural networks and why they need activation functions. The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You appeared first on Towards Data Science.

Arnold Gold/Connecticut Post via Getty Images This post originally appeared in the Business Insider Today newsletter. You can sign up for Business Insider's daily newsletter here. Rush hour is turning into every hour. The fight for fully remote jobs is dead, but a small victory for many employees has been a flexible schedule. A 9-to-5 doesn't necessarily mean being at the office from 9-to-5. But with no standard arrival or departure times, people's non-traditional commutes have meant we're sitting in traffic or on congested trains no matter what time of day it is, writes BI's Juliana Kaplan. The distance people travel to get to work is also on the rise, which could be why they're keen to leave the office early. In 2024, 9.3% of workers traveled an hour or more to work compared to 7.7% in 2021. That statistic seems likely to keep rising as young people seeking affordable homes move farther from metropolitan hubs. As a card-carrying member of the suburban commuters, I'm here to help. I've got a roughly 80-minute commute that includes a car, a train, and a subway. Couple that with two little ones at home and a spouse who also commutes, and I'm well aware of the importance of commutemaxxing. Here's my advice: Failure to prepare is preparing to fail: Outfit picked. Work bag packed. Gas tank full. Put the work in the night before so you can leave less to chance the morning of. Know the ebbs and flows of your commute: I know what time I need to turn off my street to beat a local bus that'll slow me down. I know the right spot to mobile-order my Starbucks coffee so it's ready when I arrive. Turning your commute into a formula might only save you a little bit of time, but it adds up to a lot. Don't leave your bag on an open seat in a crowded train: This isn't really a commuting tip. I just hate people who do this. Don't think that'll stop me from asking to sit there. Get those steps in: Sneaking in a bit more walking on your way to work can do wonders for your fitness and longevity. Maybe take the stairs (within reason) or opt for the long route on your way in. Don't run for that train: I know I said get some exercise in, but not like this. No one wants to see you pull a hammy in the middle of Grand Central Terminal. Leave some work for the ride home: Nothing makes a commute go by faster than having to wrap up some stuff on the train ride home. This is a bit trickier for people driving, but maybe that's where AI comes into play. Build an AI agent that you can dictate end-of-day tasks to. Read the original article on Business Insider

Tokenmaxxing within some companies is creating a new divide, fueling burnout among some software engineers. Samyukta Lakshmi/Bloomberg via Getty Images AI tokenmaxxing is creating a new divide among engineers, Menlo Ventures partner Deedy Das says. 'Lazy' engineers heavily rely on AI to generate code, while the 'craftsmen' review and fix it. It's driving many engineers to face an 'identity crisis bordering on depression,' he said. Software engineers may be paying the price for Silicon Valley's tokenmaxxing frenzy. As companies push developers to use AI coding tools in the hopes of driving productivity up, a growing divide is emerging inside some engineering teams, says Deedy Das, a partner at venture capital firm Menlo Ventures, where he invests in AI and enterprise software companies. First are what he calls the "lazy" engineers — workers who rely heavily on AI to write code, answer questions, prepare updates, and complete tasks with minimal engagement. Then there are the "craftsmen," experienced engineers who bear the burden of understanding, reviewing, and fixing the growing flood of AI-generated code. "Most software engineers are facing an identity crisis bordering on depression," Das said in an X post over the weekend. 'The craft they loved is dead' Das' comments tap into a growing debate over how AI coding tools are reshaping software engineering, a theme at the center of Business Insider's latest series, The Great Coding Reset. As companies encourage developers to generate more code with AI, some engineers say their role is shifting from writing software to reviewing, managing, and validating machine-generated work — a change that is forcing some to rethink where their value lies. The broader problem is what Business Insider's Amanda Hoover recently described as "AI sprawl": workers juggling multiple AI tools, duplicating work, and generating ever-increasing volumes of output without clear evidence that companies are becoming substantially more effective. "The craftsmen are tired. Very tired," Das wrote. "The entire burden of review falls on the craftsman. The burden of understanding." As AI makes code generation easier, reviewing and maintaining that code has become the new bottleneck. The shift echoes the rise of "botsitting" — supervising AI, fixing its mistakes, and validating its work. Das, who previously said many software engineers feel "their life's skill is no longer useful" amid the AI boom, said some engineers now often find themselves buried under pull requests while bugs increasingly slip into production. "The craft they loved is dead," he wrote. While Das said many companies are successfully integrating AI into software development, he said the tension is common at large organizations where AI-generated output is growing faster than teams' ability to evaluate it. "It tends to happen in bigger companies that are 10+yrs old with a higher talent variance," he said. "But it happens. A lot." Read the original article on Business Insider

Anthropic’s abrupt Fable 5 withdrawal due to US export controls underscores the urgent need for UK and European sovereign AI infrastructure. The post Anthropic’s Fable 5 Withdrawal Underscores Importance and Difficulty of ‘sovereign AI’ Strategies appeared first on TechRepublic.

The next wave of software will be AI-native, industry-specific platforms, writes guest author Richard de Silva who believes the biggest winners will be vertical AI companies with deep domain expertise, proprietary data and strong customer relationships, as these advantages create durable competitive moats that generic horizontal SaaS products cannot match.

Globally, robotics startups have so far raised $18.8 billion in 2026, compared to $15 billion in the full year of 2025. The figure also handily surpasses the $14.1 billion raised in the peak venture funding year of 2021, and we still have more than six months of fundraising left. We use Crunchbase data to see where the funding went.