Artificial intelligence

Artificial intelligence

Computer scientists at UC Berkeley say that AI models show promise as a way to discover and optimize algorithms. In a preprint paper titled "Barbarians at the Gate: How AI is Upending Systems Research," 17 UC Berkeley researchers describe how they employed OpenEvolve, an open source implementation of Google DeepMind's AlphaEvolve, to improve a load balancing algorithm so that it significantly outperforms prior human designs.

The idea of automating tasks for desktop users is not entirely novel. Last year in October, Anthropic became the first LLM provider to showcase the possibility of controlling a computer or some parts of its operating system. That ability, which Anthropic had termed "computer use," enabled developers to instruct Claude 3.5 Sonnet, through the Anthropic API, to read and interpret what's on the display, type text, move the cursor, click buttons, and switch between windows or applications.

"I've noticed that popping up more and more,"

Cisco's recent AI Readiness Index shows that in EMEA, only 11% of companies are completely ready for AI compared to 13% worldwide. This is a number that hasn't changed much in three years. While 82% of organizations intend to use AI, and 44% anticipate a significant increase in AI workloads within a year, only 30% believe their current IT infrastructure can support today's AI technologies.

Together with SambaNova and our strategic partners, we're building a sovereign AI infrastructure powered by renewable energy, demonstrating that sustainability and scale can go hand in hand. Our goal isn't just to make AI greener, but to make it competitive, compliant and cost-effective.

Conventional wisdom is that a good certiorari petition needs to show the legal error below and also show why the case is important and timely. The petition follows this dual framing strategy: (1) a doctrinal claim that the Federal Circuit has abandoned preemption as the touchstone of patent eligibility; and (2) a policy argument tied to what I think of as the "new great game" and what the Trump Administration calls "Winning the AI Race." The case also arrives at the Supreme Court as the USPTO has begun to move aggressively toward limiting its use of eligibility in patent prosecution.

The browser would have more value if it included an on-device AI model that could run without requiring access to the internet, O'Donnell said. "This provides a channel through which they can get hundreds of millions of people to download their model," he said. In that scenario, the browser could access heavyweight AI models in the cloud to handle more demanding tasks.

When it comes to artificial intelligence, a few names dominate the conversation like Nvidia ( NASDAQ:NVDA ), Taiwan Semiconductor Manufacturing ( ), or even Intel ( NASDAQ:INTC ) in recent months. These companies rightfully claim the spotlight. These players drive the AI narrative because they deliver tangible results - record revenues, market share gains, and innovations that fuel everything from chatbots to autonomous systems. Investors flock to them, bidding up shares on every earnings beat or product launch. Yet beneath the hype, AI's foundation relies on more than just processing power and fabrication prowess. Data storage and high-speed memory are the unsung necessities that enable seamless data flow , preventing bottlenecks in the AI pipeline.

Police showed up, like eight cop cars, and then they all came out with guns pointed at me talking about getting on the ground,

From urban air taxis to hybrid combat drones, each concept seemed to inch closer to what once felt like science fiction. But every now and then, a creation comes along that feels straight out of Gotham. A machine so darkly sophisticated it blurs the line between military technology and cinematic imagination. Shield AI's X-BAT is that machine: a stealthy, autonomous VTOL jet that looks and behaves like Batman's next aircraft, only it's very much real.

GPT-5 also showed the best "utility" across the tested models, solving 58 percent of the original problems despite the errors introduced in the modified theorems. Overall, though, LLMs also showed more sycophancy when the original problem proved more difficult to solve, the researchers found. While hallucinating proofs for false theorems is obviously a big problem, the researchers also warn against using LLMs to generate novel theorems for AI solving.

"I think this is a secular growth trend for many years," Hotard said. "The reason I say that is if you look at what we are doing in AI today, we are largely using LLMs for language-based applications." "Autonomous vehicles are still in the early days of penetration. Augmented and virtual reality, smart glasses. Very low penetration. Robotics. Very low penetration. There's so many applications to come that I think we are very much in the early days," he continued.

Meta's PyTorch team has unveiled Monarch, an open-source framework designed to simplify distributed AI workflows across multiple GPUs and machines. The system introduces a single-controller model that allows one script to coordinate computation across an entire cluster, reducing the complexity of large-scale training and reinforcement learning tasks without changing how developers write standard PyTorch code. Monarch replaces the traditional multi-controller approach, in which multiple copies of the same script run independently across machines, with a single-controller model.

The two companies announced the deal on Thursday, with Anthropic pitching it as "expanded capacity" that the company will use to meet surging customer demand and allow it to conduct "more thorough testing, alignment research, and responsible deployment at scale." Google's take on the deal is that it will enable Anthropic to "train and serve the next generations of Claude models," and involves "additional Google Cloud services, which will empower its research and development teams with leading AI-optimized infrastructure for years to come."

Within its super app, the bank has launched a voice-based AI assistant that performs financial operations on behalf of entrepreneurs. Thanks to this innovation, businesses have gained access to a new level of automation. Now routine banking tasks can simply be voiced to the AI assistant, saving time on repetitive actions. The intelligent assistant automatically pays taxes, processes payroll, executes account-to-account transfers, deposits and withdraws funds, pays fines, and much more.

Instead of bolting AI onto legacy experiences, we have the chance to reimagine Outlook from the ground up,

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The Enterprise Challenge: When AI Breaks, Who Gets the 2AM Call? Consider a financial services company deploying an AI agent to handle customer inquiries. The agent needs to: Now imagine these scenarios: Prompt injections bypass traditional input validation Jailbreaks exploit model behavior, not code vulnerabilities Multi-step agent attacks emerge from seemingly innocent individual actions Data poisoning happens during training, not runtime Model extraction threatens your competitive IP