An AI system developed by Google DeepMind, Google’s leading AI research lab, appears to have surpassed the average gold medalist in solving geometry problems in an international mathematics competition.

The system, called AlphaGeometry2, is an improved version of a system, AlphaGeometry, that DeepMind released last January. In a newly published study, the DeepMind researchers behind AlphaGeometry2 claim their AI can solve 84% of all geometry problems over the last 25 years in the International Mathematical Olympiad (IMO), a math contest for high school students.

Why does DeepMind care about a high-school-level math competition? Well, the lab thinks the key to more capable AI might lie in discovering new ways to solve challenging geometry problems — specifically Euclidean geometry problems.

In December, the company gave access to developers and trusted testers, as well as wrapping some features into Google products, but this is a “general release,” according to Google.

The suite of models includes 2.0 Flash, which is billed as a “workhorse model, optimal for high-volume, high-frequency tasks at scale,” as well as 2.0 Pro Experimental for coding performance, and 2.0 Flash-Lite, which the company calls its “most cost-efficient model yet.”

Gemini Flash costs developers 10 cents per million tokens for text, image and video inputs, while Flash-Lite, its more cost-effective version, costs 0.75 of a cent for the same. Tokens refer to each individual unit of data that the model processes.

Researchers create genetically engineered mice with human-like telomeres, allowing effects of interventions to be observed in mammals.

A supercapacitor made from sargassum seaweed nanofibers powers medical devices inside the body, kills bacteria, and naturally fragments after use.

A breakthrough by Chinese researchers could help solve complex problems in industries ranging from aerospace to bridge design.

Capcom is experimenting with generative AI to create the “hundreds of thousands” of ideas needed for in-game environments.

As video game development costs rise, publishers are increasingly looking to controversial AI tools to speed up work and cut costs. Call of Duty reportedly sold an “AI-generated cosmetic” for Call of Duty: Modern Warfare 3 in late 2023, and fans accused Activision of using generative AI again for a loading screen last year. EA said in September that AI was “the very core” of its business.

In a new interview with Google Cloud Japan, Kazuki Abe, a technical director at Capcom who has worked on huge titles like Monster Hunter: World and Exoprimal, explained how the company is experimenting with implementing AI in its game development processes.

DeepSeek-R1 performs reasoning tasks at the same level as OpenAI’s o1 — and is open for researchers to examine.

The computer science behind translating speech from 100 source languages.

Id say ASI by 2029 or sooner, but we re now in uncharted waters.

Amodei: “I think progress really is as fast as people think it is.”

On a broader level, by pushing AI toward more human-like processing, Titans could mean AI that thinks more deeply than humans — challenging our understanding of human uniqueness and our role in an AI-augmented world.

At the heart of Titans’ design is a concerted effort to more closely emulate the functioning of the human brain. While previous models like Transformers introduced the concept of attention—allowing AI to focus on specific, relevant information—Titans takes this several steps further. The new architecture incorporates analogs to human cognitive processes, including short-term memory, long-term memory, and even the ability to “forget” less relevant information. Perhaps most intriguingly, Titans introduces a concept that’s surprisingly human: the ability to prioritize surprising or unexpected information. This mimics the human tendency to more easily remember events that violate our expectations, a feature that could lead to more nuanced and context-aware AI systems.

The key technical innovation in Titans is the introduction of a neural long-term memory module. This component learns to memorize historical context and works in tandem with the attention mechanisms that have become standard in modern AI models. The result is a system that can effectively utilize both immediate context (akin to short-term memory) and broader historical information (long-term memory) when processing data or generating responses.