By Deepti Pathak In Steal a Brainrot, Brainrots, or characters are the main way you earn money in the…
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By Deepti Pathak The BGIS 2026 Grand Finals will be the final stage of India’s biggest BGMI esports tournament…
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By Trevor Mogg When the Robotics and AI (RAI) Institute talks technology, it’s wise to take notice. The company was, after all, founded by Marc Raibert, who also launched robotics pioneer Boston Dynamics, the firm behind the astonishing Atlas robot and the dog-like Spot quadruped. Massachusetts-based RAI Institute has just unveiled Roadrunner, a 15 kg (33 lb) bipedal-wheeled robot prototype. […]

Source:: Digital Trends

By Ana-Maria Stanciuc The London-based platform, which already covers 1.5 million eligible lives across enterprise and healthcare deployments, is using the Series A to accelerate US market entry and deepen its clinical infrastructure, with a new CEO who sold his last company to Adobe. JAAQ, the London-based digital health engagement platform, has raised $17 million in a Series […] This story continues at The Next Web

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By Ana-Maria Stanciuc The Prague and Krakow firm, whose earliest bets include UiPath and ElevenLabs, is doubling down on pre-seed in Central and Eastern Europe and its global diaspora, with a six-partner team and a $1–5M typical cheque. Credo Ventures has closed Credo Stage 5, an $88 million fund raised in a single closing, continuing the Prague and […] This story continues at The Next Web

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“Three or four years ago, we were super excited when our [AI] models could solve eighth-grade math problems,” Jeff Dean, chief scientist, Google DeepMind and Google Research, said during a panel discussion at Nvidia’s GTC developer show last week.

By last year, Google’s Gemini had reached the gold-medal standard at the International Mathematical Olympiad and it has now won a variety of coding contests.

That’s just one example of how quickly AI has upended the world. It’s taken over businesses, warped economies, rapidly changed the job market and careers and, perhaps, could even alter the future of humanity.

Tech industry experts have described AI as more revolutionary than electricity and the internet. (It’s also been called more dangerous than nuclear weapons, because the technology in the wrong hands could wreak havoc.)

After several years of rapid evolution, what’s next for AI? Dean and Nvidia’s chief scientist, Bill Dally, shared some ideas during the panel discussion at GTC. Here’s a rundown on some of what they envision for large language models (LLMs) and agentic AI.

Autonomous models: The arrival earlier this year of OpenClaw provides a glimpse of how AI agents can complete work unsupervised without any human intervention. But the current computing pipeline — including chips, power requirements, communications and cost — are lagging. To empower these kinds of futuristic agents, things need to be faster, Dean said.

Nvidia is working to make agents faster, including the use of technology that allows data to be transferred using optical networking technologies. “We call it the speed of light,” said Dally. 

Free agents evolving on their own: Here’s a scary thought: agents could be creating the next version of themselves, or least creating updated versions that can run the latest large language models (LLMs) and genAI tools.

That’s not happening quite yet, but there are signs it’s coming, Dean said, noting that AI agents can already self-evolve by accepting and dismissing ideas.

There’s history here, too. In 2017, AI researchers came up with the concept of “meta learning,” where AI could search for models best suited for experiments and problem solving. The search parameters at the time were mostly specified in code, but now that can be done with natural language, Dean said.

Natural language interaction makes it easier for agents to find ways to get better, such as finding new information, specific algorithms, or distillation mechanisms. AI can be seen as a performance multiplier that frees researchers to think up new ideas. “It’s a partnership between super-capable researchers and super-capable agents,” Dean said.

More interactive LLMs: As AI technology progresses, LLMs could become more interactive with the real world, actually re-learning and updating themselves in real time and taking actions based on that new knowledge.

Today’s LLMs are basically strapped on a board, streamed through internet data, and then presented to the world, Dean said, with results that are largely predetermined.

But future models will learn on the fly by instantly interleaving physical and digital information. With that information, LLMs will be better able to direct robotic actions and predict answers to questions.

While that is already done in post training, what’s better is interleaving at the pre-training stage. “We sort of have this artificial distinction now…. It seems like that shouldn’t exist for the long term,” Dean said.

Continual-learning models are already emerging without any fixed parameters, with organically growing models advancing, pruning and compressing parameters, Dean said.

The Master agent: Nvidia and Google are already using AI for chip design; the next step is to figure out how to automate the process so chip designers and developers can do other things.

The process might well involve a “master” agent calling on sub-agents that specialize in creating on-chip functions or fixing bugs. Those agents would have to negotiate improvements on chips and iterate if the results aren’t good.

“They’ll have the same kind of meetings we have, but between agents,” Dally said.

Machine speed agents: AI development tools are designed for human speed, but need to perform at machine speed, the panelists said. Because agents reason, decide and act significantly faster than people, human tools like a slow-loading C++ compiler get in the way of quick progress.

“We’re going to need to start to reengineer the tools that these models [use],” Dean said.

That’s already happening for coding tools and document manipulation, which previously extracted information at human speed. Put simply, ”We’ll need new forms of tools,” Dean said.

Security experts highlighted the ability for machine-speed AI agents to tackle cyberattacks. That’s because humans might be too slow at stopping agentic AI-based  attacks.

Better educational tools: The panelists criticized universities that have restricted the use of AI in classrooms. Instead, educators need to lean into AI to accelerate learning, said Dally, who previously was a computer science professor at Stanford University.

Models will soon serve as “amazing” personalized tutors that promote learning concepts efficiently without giving away answers, Dean said. He noted how calculators removed bottlenecks around learning math, helping students quickly move to higher levels.

“Maybe I should quit my day job and go and do it myself,” Dean said.

Source:: Computer World

The M5 Max MacBook Pro is a symphony of performance. On the surface, it looks like any other MacBook Pro, but its capabilities define top-notch performance. It aces every performance benchmark and handles everything you throw at it, quietly and with 22 hours of battery life. Put simply: it’s portable computing done right.

The 14-in. MacBook Pro I’ve been working with is undoubtedly a pro machine (as it should be with a nearly $6,000 price tag). This particular unit uses Apple’s most advanced M5 Max processor (count ’em, 18 CPU cores, 40 GPU cores), has a whopping 128GB RAM and 4TB of SSD storage. Price with options is: $5,849.

A benchmark for excellence

So much of what makes these Macs great relates to the processor. The M5 Max in my test machine (thanks, Apple) is one of the most advanced chips on the planet. It introduces a new “Fusion” architecture that connects two chip dies together, forming a single system-on-a-chip (SOC) in a move that delivers amazing performance. 

Three statistics illustrate this:

Single-core processor performance: 4,351

Multi-core processor performance: 29,510

Super fast SSD performance. Blackmagic’s Disk Speed Test returns a phenomenal 12,587.3 MBps read speed, and 12,661.1 MBps write speed — twice as fast as before. That one change alone means you’ll get more done faster. 

Together, the processor and disk speed mean this Mac will crunch swiftly through even the largest and most demanding files; it’s great for video as well as CAD, complex simulations, medical imaging, on-device AI tools, data analysis, and beyond. Max Tech’s tests showed a 16-in. MacBook Pro beat an $11,238 Dell Pro Max 18 Plus in Blender 3D Rendering tests by a country mile. 

Apple’s amazing silicon journey

Apple’s work on silicon is astonishing. Each core is powerful. The 18 CPU cores host 6 “super” cores and 12 performance cores for ridiculously fast handling of the pro tasks your business is built on, from AI modelling to code compiling, effects rendering, audio compositing/mixing, CGI — even the most demanding professional will find it challenging to push these Macs beyond the wire.

The same is true for all those GPU cores, each of which includes its own dedicated Neural Accelerator. What that means is that if you are using your Mac to run AI workloads, you’ll get around 4x the performance of the last generation high-end Mac and 8x the performance of M1 Max machines. These Macs munch through professional (multi-core) tasks, while the 40 GPU cores shrug through everything else. 

It is important to consider the cadence of Apple’s processor improvements; the M4 Max appeared just 18 months ago in October 2024. Apple has already exceeded the records set by that machine. Today, excepting any act of force majeure, Apple seems on track to beat this latest set of records by summer 2027. 

Thanks for the memory

The MacBook Pro is no MacBook Neo, which is limited to 8GB RAM. My test ‘Pro held the maximum 128GB RAM, and to get a loose sense of how it performs, I ran some Passmark tests:

19,479 thousand operations per second for database operations.

30,108 MBps for memory read cached (or 33,676 MBps uncached).

Memory write benchmark of 33,512 MBps and 14 nanoseconds memory latency. 

For comparison, a MacBook Neo can deliver 4,939 thousand operations per second with lower memory write speeds and higher (22 nanoseconds) memory latency.

Pro users often handle huge files, which is why these Macs are perfect for that kind of task. You get a serious 614GBps of memory bandwidth, which means you can multitask to your heart’s content, run some of the largest generative AI (genAI) models on device, and work with massive files. If creating, manipulating, or understanding huge chunks of data is your jam, this Mac will help you earn more of it.

And if you need to use any external accessories (including displays) the decision to include three Thunderbolt 5 USB-C ports means those big chunks of data will continue to fly at 120GBps. That interconnectivity means that if you have multipole MacBooks that support RDMA, you can cluster them together using Exolabs to run LLMs at speeds that exceed cloud-based AI. Everything about these Macs is focused on performance.

Apple

In the hood

It’s also important to recognize the extent to which Apple is leaning into AI. It understands that the one thing people will need as AI proliferates will be systems capable of running that infrastructure outside the cloud. These Macs are more than capable of doing so. When it comes to building AI models, MacBook Pro has you covered there, too — thanks to the on-board. Neural Accelerators. They give AI developers the kind of performance they can get from a desktop workstation in a system they can take with them to their local coffee shop. (Of course, at this price, only the very brave will choose to use these systems anywhere near liquids.)

Apple also supports its power focus with support for key production codecs including HEVC, H.264, ProRes, 4k and 8K video, and more. The company’s hardware designers have obviously thought about what their customers will need and how to deliver the goods for them. This is also why the company equipped these machines with such fast SSDs, capable of handling speeds up to 12GBps. Not only can the processor race through data, but the overall system can sift through it quickly as well.

And when doing those tasks, you’ll find yourself enjoying the 12MP Center Stage Camera, excellent, studio-quality microphone, and six-speaker immersive sound stage. The sound is particularly impressive, and maintains the company’s track record for great sounding kit.

Here’s looking at you

As noted, these top-of-the-line models look just like other MacBook Pros, featuring beautiful slabs of rounded, milled aluminium with an Apple logo and a display to match. The screen is critical to the experience of using these Macs, and the Liquid Retina XDR screen offers exceptional, super-bright performance (1,600 nits peak brightness), with support for a billion colors.

The review unit in hand also had the nano-texture display, which noticeably reduces glare, reflections, and smudges. You can work effectively on your Mac in all kinds of lighting conditions, including when you work outside on bright, sunny days. And Apple promises up to 22 hours of battery life, though my real-world testing shows if you are handling huge files and demanding workflows you’ll certainly get less than that. How much less is hard to state. But I believe a typical Mac videographer working on video outdoors should have a full working day on the MacBook pro, with some left over for watching the world unravel on social media.

Buying advice

What you have in this box proves that everything in these Macs is about performance. That’s as true of the processor as it is to the internal components, the ports, the speedy SD 4.0 SDXC card slot, and even the inclusion of Wi-Fi 7, which supports speeds up to 2.4Gbps in the 6GHz band. 

These Macs are also about workflow, both in terms of the power they bring and the accessories you’ll need, including support for not one, not two, but four external displays in my test unit. Two of these displays will dance happily with your content at full 5K resolution at 120Hz refresh rates. The user experience is made even more lovely by the backlit Magic Keyboard.

So, who is this for?

Well, to use a tired analogy, if MacBook Neo is the Volkswagen Beetle, then MacBook Air could be a Tesla, and this MacBook Pro is absolutely a Porsche. You’ll choose your drive for many reasons, but if you need a system to deliver consistent, capable performance for the very toughest of tasks, I have no hesitation recommending this Mac to you. 

Please follow me on Twitter, or join me in the AppleHolic’s bar & grill and Apple Discussions groups on MeWe. Also, now on Mastodon.

Source:: Computer World

By Rachit Agarwal Korean researchers successfully transmitted voice signals 100 meters underground using magnetic fields, a world first that could transform rescue operations and underground communication forever.

Source:: Digital Trends

By Partner Content For many players, one of the biggest frustrations in Pokémon GO is that the Pokémon they…
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By Deepti Pathak If you have spent some time in the World of Warcraft Classic: Season of Discovery, you…
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By Rachit Agarwal Elon Musk has announced TeraFab, a chip factory in Austin built as a joint venture between Tesla and SpaceX, to produce chips for powering its cars, humanoid robots, and spaceships.

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By Ana-Maria Stanciuc A quieter week by headline standards, but one that reveals a great deal about where European venture capital is quietly concentrating: AI agents for physical industries, agritech automation, and the growing operator-to-VC pipeline. What the week of 16-22 March delivered was something different in texture rather than volume: smaller rounds, more specific theses, and a […] This story continues at The Next Web

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By Vikhyaat Vivek AI is emerging as an unlikely hero, helping pet owners find lost animals by scanning massive databases and matching visual features across platforms.
The post In a sea of slop and risks, AI is helping distraught humans find their lost pets appeared first on Digital Trends.

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By Ana-Maria Stanciuc The indictment of Super Micro’s co-founder exposes not just a $2.5 billion scheme, it exposes a system that was never built to stop one. Somewhere in a rented warehouse in Southeast Asia, a man was using a hair dryer on a server box. Not to dry it. To loosen the adhesive on a serial-number sticker, […] This story continues at The Next Web

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By Vikhyaat Vivek Hermès’ new leather Apple Watch charger lineup pushes luxury to the extreme, with a top-tier model costing more than a high-end MacBook Pro.
The post This luxury leather-clad Apple Watch charging kit from Hermès costs than a MacBook Pro appeared first on Digital Trends.

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US President Donald Trump’s administration today released its National Policy Framework for Artificial Intelligence: Legislative Recommendations, a document that reads less like the AI safety blueprints that states are increasingly adopting and more like a playbook for asserting federal control over AI governance.

It is part of a coordinated push with congressional allies, most notably Republican Senator Marsha Blackburn, to translate federal preemption of state regulations into law. “We need one national AI framework, not ​a 50-state patchwork,” Michael Kratsios, science and technology adviser to Trump, told one publication.

“The administration’s proposal represents a smart starting point for a pro-innovation AI policy legislative framework,” Adam Thierer, a senior fellow at the R Street Institute, said in an interview. “It is now time for Congress to do its job and step up to the plate with a sensible policy framework based on those principles.”

An effort to block fragmented state laws

At its core, the White House framework is an attempt to prevent a state-by-state regulatory system from becoming the default architecture of AI governance.

States are already moving ahead, drafting and passing their own AI laws in the absence of federal action. The longer that continues, the more those rules solidify into a fragmented regulatory system that will be difficult to unwind.

The centerpiece of the White House strategy is federal preemption, the legal mechanism that would allow Congress to override state AI laws and establish a single national standard.

Blackburn’s companion legislation translates that idea into statutory form, envisioning a single federal rulebook that would replace the emerging patchwork of state policies. If enacted, it would not simply harmonize regulation; it would strip states of primary authority over AI policy and consolidate that power in Washington.

A strategy tailored for a stalled Congress

Congress has spent years debating AI regulation without producing a comprehensive framework. The White House is attempting to break that deadlock by pairing its proposal with children’s online safety measures, one of the few areas where bipartisan agreement is still possible.

The framework organizes its substantive proposals around what it calls the “4 Cs”: children, creators, conservatives, and communities, borrowing from Blackburn’s draft legislation. The first pillar of the framework states, “AI services and platforms must take measures to protect children, while empowering parents to control their children’s digital environment and upbringing.”

The focus on children translates into obligations around safety and exposure to harmful content. The emphasis on creators reflects growing concern over how AI systems use copyrighted material and replicate human likenesses.

The inclusion of “conservatives” points to ongoing debates about bias and perceived censorship in AI outputs. And the broader category of communities serves as a catch-all for localized or societal impacts.

Deregulation—with consequences

Throughout the strategy document, the administration stresses that AI policy should be “minimally burdensome,” favoring a lighter-touch approach to regulation.

One of the strategy’s pillars states, “The United States must lead the world in AI by removing barriers to innovation, accelerating deployment of AI applications across sectors, and ensuring broad access to the testing environments needed to build world-class AI systems.”

Blackburn’s proposal moves toward a system in which liability plays a central role, opening the door to legal claims against AI developers and platforms when harm occurs. In doing so, it shifts enforcement away from regulators and toward the courts.

A liability-driven system produces standards through litigation rather than rulemaking, and favors companies with the resources to absorb legal risk, potentially accelerating consolidation in the AI sector.

The First Amendment strategy

One of the most consequential elements of the framework is its emphasis on protecting AI outputs as a form of speech. The administration suggests that certain types of regulation, particularly those that would require altering or constraining outputs, may raise First Amendment concerns.

According to one of the strategy’s pillars, “American creators, publishers, and innovators should be protected from AI-generated outputs that infringe their protected content, without undermining lawful innovation and free expression.”

Another pillar states, “The federal government must defend free speech and First Amendment protections, while preventing AI systems from being used to silence or censor lawful political expression or dissent.”

These statements reflect a strategic move to anchor AI policy within constitutional doctrine. If courts accept this framing, it could significantly limit the scope of future regulation, particularly in areas such as misinformation, bias mitigation, and content moderation.

Congress is the weakest link

For all its ambition, the framework depends on a single institution, Congress, which has so far remained divided and slow-moving even as AI technologies race ahead.

While the government’s executive branch can set direction, coordinate with allies, and apply pressure through enforcement and funding mechanisms, it cannot, on its own, establish a binding national standard or fully preempt state law.

Progressives and Democrats in Congress oppose the effort to create a federal preemption or moratorium on state AI legislation. They say that any legislation enacted at the federal level, rather than blocking state action, should “focus on setting a strong federal floor of protections, including prohibitions on the most dangerous uses of AI, while preserving state authority to go further in addressing new harms.”

That tension between a federal framework that overrides states and one that builds on them is likely to define the next phase of the AI policy fight in Washington.

This article originally appeared on CIO.com.

Source:: Computer World

By Ana-Maria Stanciuc Automattic has added write capabilities to WordPress.com’s MCP integration, giving AI agents like Claude and ChatGPT the ability to create posts, build pages, manage comments, and restructure content, all through natural conversation, with human approval at every step. For most of the past six months, connecting an AI agent to your WordPress.com site has meant […] This story continues at The Next Web

Source:: The Next Web

OpenAI is reportedly planning to fold its ChatGPT application, Codex coding platform, and AI-powered browser into a single desktop ‘superapp’, a move that signals a shift toward enterprise and developer audiences and away from the consumer market that made the company a household name.

The unified product will merge the ChatGPT interface, the Codex coding tool, and OpenAI’s browser known internally as Atlas into a single desktop application, the Wall Street Journal reported Thursday. The mobile version of ChatGPT is not part of the consolidation and will remain unchanged. OpenAI President Greg Brockman will temporarily oversee the product overhaul and associated organizational changes, while Chief of Applications Fidji Simo leads the commercial effort to bring the new app to market, the report added.

Simo confirmed the plan the same day in a post on X. “Companies go through phases of exploration and phases of refocus; both are critical,” she wrote. “But when new bets start to work, like we’re seeing now with Codex, it’s very important to double down on them and avoid distractions.”

The superapp announcement follows an all-hands meeting on March 16, in which Simo told employees the company needed to stop being distracted by “side quests” and orient aggressively toward coding and business users.

“We realized we were spreading our efforts across too many apps and stacks, and that we need to simplify our efforts,” the Journal reported that day, citing Simo’s address to the employees. “That fragmentation has been slowing us down and making it harder to hit the quality bar we want.” At the same meeting, Simo outlined the commercial imperative plainly: “Our opportunity now is to take those 900 million users and turn them into high-compute users. We’ll do that by transforming ChatGPT into a productivity tool.”

More than a product refresh

The superapp is being designed around agentic AI, systems capable of autonomously executing multi-step tasks such as writing and debugging software, analyzing data, and completing complex workflows without continuous human instruction, the Journal reported. That positions it less as a consumer chatbot and more as an AI-powered work environment aimed at developers and enterprise knowledge workers.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the move goes beyond product consolidation. “This is not a clean enterprise pivot — it is a forced convergence driven by internal fragmentation, competitive pressure, and the need to monetized where value is actually realized,” he said. “The real value is shifting to where intent becomes action. That is workflows, not conversations.”

The announcement is the latest in a series of enterprise-facing moves. In February, OpenAI launched Frontier, an agent orchestration platform, and announced partnerships with Accenture, BCG, Capgemini, and McKinsey to embed its technology into business workflows.

The numbers behind the pivot

The urgency behind these moves becomes clear when the competitive data is examined. According to enterprise spend management software vendor Ramp, a year ago only one in 25 businesses on its platform paid for Anthropic; today that figure has jumped to nearly one in four. In new enterprise deals, Anthropic is now winning approximately 70% of head-to-head matchups against OpenAI, it said.

Gogia, however, flagged a structural risk. ChatGPT’s dominance was built on simplicity and universal accessibility, qualities a workflow-centric superapp trades away. “In trying to serve consumers, developers, and enterprises within a single interface, OpenAI risks diluting the very clarity that made ChatGPT dominant,” he said.

That risk is compounded by a governance challenge that enterprise IT leaders are only beginning to reckon with.

The governance gap

For IT leaders evaluating OpenAI tooling, Gogia pointed to a deeper challenge the superapp introduces. “The biggest constraint on agentic AI is not capability. It is control,” he said. “Identity management is not designed for non-human actors. Audit trails are incomplete. And there is no mature control plane that governs how agents act, what they access, and how those actions can be reversed or contained.”

Microsoft and Google hold a structural advantage here: Their AI is embedded within platforms that already manage identity, access, and compliance at enterprise scale, a gap enterprise buyers have repeatedly flagged as a persistent concern with OpenAI’s approach. It is precisely that trust deficit that has given Anthropic its opening.

“The battle is no longer about who builds the best chatbot. It is about who owns how work gets done,” Gogia said. “Enterprises are making platform decisions now — and those decisions will not be based on who is most advanced. They will be based on who is most dependable.”

OpenAI did not immediately respond to a request for comment.

Source:: Computer World

By Moinak Pal Google is testing AI-generated headlines in Search, replacing publisher-written titles with rewritten versions, raising concerns about accuracy, transparency, and how news is presented to users.
The post Google experiments with AI-generated headlines in search results appeared first on Digital Trends.

Source:: Digital Trends

By Paulo Vargas Pixel Watch users report inflated steps, calories, and missing health data after the March 2026 update, raising concerns about tracking reliability and leaving many waiting for a fix.
The post Pixel Watch update issues could be skewing your daily activity data appeared first on Digital Trends.

Source:: Digital Trends