CXMT said it had made good progress on new DRAM architecture that would boost chip memory density. CXMT said it had made good progress on new DRAM architecture that would boost chip memory density. Tue 13 Oct 2026 05.48 EDTFirst published on Tue 13 Oct 2026 04.01 EDT Shares in semiconductor companies fell on Tuesday after the boss of Broadcom compared many AI investors to "casino" gamblers, and the Chinese memory chip maker CXMT said it had made good progress on a new chip architecture. Japan's Nikkei fell 1.8% and the Taiwan exchange lost almost 2%, with the country's chip maker TSMC down by a similar amount. Samsung Electronics shares dropped 7.4% on the South Korean exchange, while the memory chip maker SK Hynix was down 10%.

What can we learn from the brain about building better computers? Oliver Rhodes discusses how neuromorphic computing draws on the brain's architecture to develop new approaches to more efficient information processing. Can you give me an overview of your background and the research that you're involved with? Neuromorphic computing is quite a broad subject, which essentially looks to biology as inspiration to develop next-generation computing systems. We work off this principle: we know the brain is this really amazing computer, and it's able to do things that a lot of modern computing systems aren't able to do, and it also does them in an incredibly energy efficient way. We'd like to try to replicate that with some of the systems we build. In the context of the current climate around AI, while we've seen that it's made really big steps forward and is able to do really impressive things, there are certain things that it still can't do that the brain is able to do. So we look to the brain for inspiration to try and solve some of those next generation challenges. Neuromorphic computing covers all aspects of this: from looking at algorithms that might be solving a particular problem, to the systems and subsystems that would be running those algorithms, right down to a chip or devices level. We often use neural networks, which are also employed in artificial intelligence models, such as in deep learning and transformer models.

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With AI inference now driving enterprise workloads, organizations must rethink infrastructure for speed, efficiency, scalability, and performance per watt to unlock AI's real-world potential. The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while also supporting an increasingly intelligent edge of IoT and consumer devices. However, in this inference-driven landscape, every delay, bottleneck, or wasted watt directly affects human outcomes and operating costs. This shift changes what infrastructure must deliver.

Agentic AI is no longer a research artifact. These systems are embedded in live infrastructure, clinical workflows, and legal processes: deleting databases, advising patients, and generating legal documents. Wrong outputs cannot simply be taken back. The central question has shifted from whether these systems are ready to make consequential decisions to who is accountable when they get those decisions wrong. The standard apparatus of fault-finding (identifying an actor, establishing a duty, connecting a breach to an injury) was built for a world in which agents are human, decisions are sequential, and causation is legible.

Tech Visionary Says the Big AI Labs Don't Get What People Want Tim O'Reilly built a publishing empire that AI is helping to destroy. Yet he loves AI--as long as it's open source. Tim O'Reilly's yardstick for measuring the worth of a company, person, or society has long been It's no surprise that O'Reilly-- publisher, internet pioneer, VC, conference organizer, and dispenser of tech wisdom --is applying that metric to the way people design and use AI. Specifically, he's pushing for a future where open-source AI is an elixir for the masses. He worries that, like Microsoft in the 1990s, today's hyperscalers are trying to lock users into their products.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Don't throw it away - Sparky Linux has decided not to give up on the aging architecture. Every PC sold today is 64-bit. In fact, one of the last true 32-bit-only CPUs was the Pentium 4 Northwood chip, which was available for purchase between 2000 and 2008. I can't remember the last time I actually saw a 32-bit-only machine. Couple that with Microsoft ending support for Office 365 on 32-bit architecture this December, and it's pretty simple to conclude that 32-bit PCs are done.

CogTwin is a hybrid cognitive architecture framework designed to bring autonomous reasoning and real-time adaptation to digital twin systems. Presented at IJCAI 2025, this work aims to advance the state of digital twin technology by addressing key gaps in autonomy, cognition, and real-time decision-making. Digital twin technology has transformed how complex systems are managed, from smart cities to industrial processes. However, most current digital twins remain fundamentally reactive: they rely on pre-programmed rules and static data-driven models, and therefore struggle when confronted with unforeseen events or evolving conditions. Real-time learning, reasoning, and adaptation - hallmarks of human cognition - are largely absent.

Nvidia's Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company's growing ambition to power every layer of AI infrastructure. Nvidia is hyping up its new Vera Rubin chip system this week, revealing new performance benchmarks for the GPU and CPU combo ahead of rival AMD's annual product event in San Francisco on Thursday. During a lengthy technical workshop last week at the company's headquarters in Santa Clara, California, Nvidia executives boasted to a small group of journalists about the chip system's increased power and efficiency capabilities. The biggest takeaway: Nvidia, which has long specialized in making GPUs, is increasingly trying to position itself as a supplier of CPUs that can power AI agents. While GPUs are still the main hardware that companies use to train and run their AI models, the industry's shift toward more complex, agentic systems has increased demand for CPUs, which can orchestrate data flows, networking, and other software tasks.

Babies are tremendous learning machines, and key advances for AI may soon be found in the architecture of their little brains. If you think an artificial intelligence model running on thousands of cutting-edge computer chips is smart, allow me to introduce you to the concept of a 1-year-old. OK, so babies might not be able to write computer programs, solve advanced math problems, or debate philosophical ideas. But unlike today's AI models, which consume an ocean's worth of training data and as much energy as a small country, babies learn to make sense of the world with amazing efficiency. They identify new objects after seeing them once or twice, and they learn through fleeting observation and physical interaction. When it comes to improving AI, babies--and the architecture of their brains--might hold crucial insights.