AI is "not my thing," President Donald Trump admitted during a speech in Pittsburgh on Tuesday. However, the president said during his remarks at the Energy and Innovation Summit, his advisors had told him just how important energy was to the future of AI. "You need double the electric of what we have right now, and maybe even more than that," Trump said, recalling a conversation with "David"--most likely White House AI czar David Sacks, a panelist at the summit. "I said, what, are you kidding? That's double the electric that we have. Take everything we have and double it."

Imagine: China deploys hundreds of thousands of autonomous drones in the air, on the sea, and under the water--all armed with explosive warheads or small missiles. These machines descend in a swarm toward military installations on Taiwan and nearby US bases, and over the course of a few hours, a single robotic blitzkrieg overwhelms the US Pacific force before it can even begin to fight back. The proliferation of cheap drones means just about any group with the wherewithal to assemble and launch a swarm could wreak havoc, no expensive jets or massive missile installations required. The US armed forces are now hunting for a solution--and they want it fast. Every branch of the service and a host of defense tech startups are testing out new weapons that promise to disable drones en masse.

As I discovered while I continued that line of reporting, building new nuclear plants isn't so simple or so fast. And as my colleague David Rotman lays out in his story for the package, the AI boom could wind up relying on another energy source: fossil fuels. So what's going to power AI? Let's get into it. When we started talking about this big project on AI and energy demand, we had a lot of conversations about what to include. And from the beginning, the climate team was really focused on examining what, exactly, was going to be providing the electricity needed to run data centers powering AI models.

French company EDF is currently building a giant new nuclear plant at Hinkley Point in Somerset and a decision to build another one at Sizewell in Suffolk is pending. "Why are data centre providers turning to nuclear? They will need a lot of energy, reliably," Mr Chisholm told the BBC. "Replication of Hinkley Point C, alongside the roll out of SMRs, can power Britain's digital economy." SMRs refers to small modular reactors which are the size of a football stadium as opposed to the size of a whole town, like Sizewell or Hinkley.

Google has signed a landmark deal to use electricity produced by small nuclear reactors to power artificial intelligence (AI). Under the agreement signed with startup Kairos Power on Monday, the California-based tech giant will back the construction of seven small nuclear reactors capable of generating 500 megawatts of power. The first reactor is scheduled to come online by 2030, with others to follow in the coming years. "The grid needs new electricity sources to support AI technologies that are powering major scientific advances, improving services for businesses and customers, and driving national competitiveness and economic growth," Michael Terrell, the senior director of energy and climate at Google, said in a blog post. "This agreement helps accelerate a new technology to meet energy needs cleanly and reliably, and unlock the full potential of AI for everyone."

When the Three Mile Island power plant in Pennsylvania was decommissioned in 2019, it heralded the symbolic end of America's nuclear industry. In 1979, the facility was the site of the worst nuclear disaster in the nation's history: a partial reactor meltdown that didn't release enough radiation to cause detectable harm to people nearby, but still turned Americans against nuclear power and prompted a host of regulations that functionally killed most nuclear build-out for decades. Many existing plants stayed online, but 40 years later, Three Mile Island joined a wave of facilities that shut down because of financial hurdles and competition from cheap natural gas, closures that cast doubt over the future of nuclear power in the United States. Now Three Mile Island is coming back, this time as part of efforts to meet the enormous electricity demands of generative AI. The plant's owner, Constellation Energy, announced yesterday that it is reopening the facility.

Nvidia has unveiled its latest family of chips for powering artificial intelligence as it seeks to consolidate its position as the major supplier to the AI frenzy. So, ladies and gentlemen, I would like to introduce you to a very, very big GPU," CEO Jensen Huang said on Monday at a developers conference in California, referring to the graphics processors that are vital in the creation of generative AI. The event, dubbed the "AI Woodstock" by Wedbush analyst Dan Ives, has become a can't-miss date on big tech's calendar due to Nvidia's singular role in the AI revolution that has taken the world by storm since the introduction of ChatGPT in late 2022. "I hope you realise this is not a concert, this is a developers' conference," Huang joked as he took the stage in a packed arena usually reserved for ice hockey games and concerts. Nvidia's powerful GPU chips and software are an integral ingredient in the creation of generative AI, with rivals like AMD or Intel still struggling to match the power and efficiency of the company's blockbuster H100 product, launched in 2022.

Artificial IntelIigence (AI) and Machine Learning (ML) are driving significant changes in how the world consumes and uses data. For example, ML is accelerating scientific discoveries in the areas of particle physics, medical research, robotics etc... CERN openlab, for example, is at the cutting edge of applying new ML techniques to high-energy physics to help us understand our universe. Fully autonomous vehicles are in the not-too-distant future, and AI/ML is being currently deployed ranging from voice-activated assistants to smart manufacturing. But ML also presents major challenges to conventional compute architectures. To truly harness the power of AI/ML, new compute architectures that are tightly coupled with high-performance dense memory are required.

By analysing the data, and by leveraging the power of AI we can unlock the potential to build amazing new business opportunities Data is the fuel that powers AI, and large data sets make it possible for machine learning applications AI enables us to make sense of massive data sets, as well as unstructured data that doesn't fit neatly into database rows and columns AI enables us to make sense of massive data sets, as well as unstructured data that doesn't fit neatly into database rows and columns

Computer chips are usually small. The processor that powers the latest iPhones and iPads is smaller than a fingernail; even the beefy devices used in cloud servers aren't much bigger than a postage stamp. Then there's this new chip from a startup called Cerebras: It's bigger than an iPad all by itself. The silicon monster is almost 22 centimeters--roughly 9 inches--on each side, making it likely the largest computer chip ever, and a monument to the tech industry's hopes for artificial intelligence. Cerebras plans to offer it to tech companies trying to build smarter AI more quickly.