Video gamers were among the first to grumble when supplies of random access memory (RAM) chips began to run short last year, causing prices to soar. But the ongoing crisis ' which has been dubbed RAMmageddon and is expected to linger well into 2027 ' is affecting some scientists, as well. The shortage is driven by the rise of artificial-intelligence systems, which has created a voracious demand for high-speed memory chips. Over the course of 2025, some forms of RAM tripled in price, causing problems for resource-constrained laboratories that already faced barriers to accessing powerful computing tools. The shortage is also pushing researchers to develop more efficient algorithms and hardware, to reduce the amount of memory needed. 'Scientific research increasingly relies on large-scale computing infrastructure,' says Matteo Rinaldi, director of the Institute for NanoSystems Innovation at Northeastern University in Boston, Massachusetts. 'And many of these workloads require substantial memory capacity.'...

Advanced Machine Intelligence, a startup co-founded by computer science pioneer and former Meta AI chief Yann LeCun, said Tuesday that it has raised $1.03 billion to develop 'world models,' or AI designed to learn from and interact with the physical world. The funding for Paris-based AMI represents the largest seed round ever for a European startup and one of the region's largest fundings for an AI startup overall, per Crunchbase data. Bezos Expeditions, Cathay Innovation, Greycroft, Hiro Capital and HV Capital led the funding, which reportedly values AMI at $3.5 billion. 'My prediction is that 'world models' will be the next buzzword,' AMI Labs CEO Alexandre LeBrun told TechCrunch after the funding. 'In six months, every company will call itself a world model to raise funding.' His co-founder LeCun is considered one of the pioneers of the large language model approach to AI. In 2018, LeCun was one of the computer scientists who received the industry's prestigious A.M. Turing Award for his work on neural networks and learning algorithms....

Let me tell you something that took me an embarrassingly long time to truly internalize: the reason deep learning works as well as it does in 2026 is only maybe 40% algorithms. The rest is hardware. We got lucky ' spectacularly lucky ' that the GPU, a chip originally designed to make triangles pretty in Quake III, turned out to be almost exactly the right computational substrate for training neural networks. But 'almost exactly right' is doing a lot of heavy lifting in that sentence. The story of AI chips is the story of closing that gap, and it's one of the most fascinating engineering stories of our time....

During a summer internship at MIT Lincoln Laboratory, Ivy Mahncke, an undergraduate student of robotics engineering at Olin College of Engineering, took a hands-on approach to testing algorithms for underwater navigation. She first discovered her love for working with underwater robotics as an intern at the Woods Hole Oceanographic Institution in 2024. Drawn by the chance to tackle new problems and cutting-edge algorithm development, Mahncke began an internship with Lincoln Laboratory's Advanced Undersea Systems and Technology Group in 2025. Mahncke spent the summer developing and troubleshooting an algorithm that would help a human diver and robotic vehicle collaboratively navigate underwater. The lack of traditional localization aids ' such as the Global Positioning System, or GPS ' in an underwater environment posed challenges for navigation that Mahncke and her mentors sought to overcome. Her work in the laboratory culminated in field tests of the algorithm on an operational underwater vehicle. Accompanying group staff to field test sites in the Atlantic Ocean, Charles River, and Lake Superior, Mahncke had the opportunity see her software in action in the real world....