AI evolution Google DeepMind creates super-advanced AI that can invent new algorithms AlphaEvolve has already made Google's data centers more efficient and improved Tensor chips. Ryan Whitwam – May 14, 2025 5:01 pm | 17 Credit: Google DeepMind Credit: Google DeepMind Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more Google's DeepMind research division claims its newest AI agent marks a significant step toward using the technology to tackle big problems in math and science. The system, known as AlphaEvolve, is based on the company's Gemini large language models (LLMs), with the addition of an "evolutionary" approach that evaluates and improves algorithms across a range of use cases. AlphaEvolve is essentially an AI coding agent, but it goes deeper than a standard Gemini chatbot. When you talk to Gemini, there is always a risk of hallucination, where the AI makes up details due to the non-deterministic nature of the underlying technology. AlphaEvolve uses an interesting approach to increase its accuracy when handling complex algorithmic problems. According to DeepMind, this AI uses an automatic evaluation system. When a researcher interacts with AlphaEvolve, they input a problem along with possible solutions and avenues to explore. The model generates multiple possible solutions, using the efficient Gemini Flash and the more detail-oriented Gemini Pro, and then each solution is analyzed by the evaluator. An evolutionary framework allows AlphaEvolve to focus on the best solution and improve upon it. Many of the company's past AI systems, for example, the protein-folding AlphaFold, were trained extensively on a single domain of knowledge. AlphaEvolve, however, is more dynamic. DeepMind says AlphaEvolve is a general-purpose AI that can aid research in any programming or algorithmic problem. And Google has already started to deploy it across its sprawling business with positive results. The team turned AlphaEvolve loose on Google's Borg cluster management system for its data centers. The AI suggested a change to the scheduling heuristics, which has been implemented to save Google 0.7 percent on its computing resources globally. For a company the size of Google, that's a significant financial benefit. AlphaEvolve may also be able to make generative AI more efficient, which is necessary if anyone is ever going to make money on the technology. The internal workings of generative systems are based on matrix multiplication operations. The most efficient way to multiply 4×4 complex-valued matrices was devised by mathematician Volker Strassen in 1969, and that held for decades, but DeepMind says AlphaEvolve has discovered a new algorithm that's even more efficient. DeepMind has worked on this problem before with narrowly trained AI agents like AlphaTensor. Despite being a general AI, AlphaEvolve came up with a better solution than AlphaTensor. Google's next-generation Tensor processing hardware will also benefit from AlphaEvolve. DeepMind reports that the AI created a change to the chip's Verilog hardware description language that dropped unnecessary bits to increase efficiency. Google is still working to verify the change but expects this to be part of the upcoming processor. So far, only Google has been able to tinker with AlphaEvolve. While it uses fewer computing resources than AlphaTensor did, it's still too complex to provide publicly. That may change in the future, but the evaluation approach that makes it so capable could also be integrated with smaller AI tools for research. Ryan Whitwam Senior Technology Reporter Ryan Whitwam Senior Technology Reporter Ryan Whitwam is a senior technology reporter at Ars Technica, covering the ways Google, AI, and mobile technology continue to change the world. Over his 20-year career, he's written for Android Police, ExtremeTech, Wirecutter, NY Times, and more. He has reviewed more phones than most people will ever own. You can follow him on Bluesky, where you will see photos of his dozens of mechanical keyboards. 17 Comments