But the news item that really stood out to me was one that didn't get as much attention as it should have. It has the potential to usher in more powerful AI and scientific discovery than previously possible. Last Thursday, Google DeepMind announced it had built AI systems that can solve complex math problems. The systems--called AlphaProof and AlphaGeometry 2--worked together to successfully solve four out of six problems from this year's International Mathematical Olympiad, a prestigious competition for high school students. Their performance was the equivalent of winning a silver medal.

"It is often easier to train a model for mathematics if you have a way to check its answers (e.g., in a formal language), but there is comparatively less formal mathematics data online compared to free-form natural language (informal language)," says Katie Collins, an researcher at the University of Cambridge who specializes in math and AI but was not involved in the project. Bridging this gap was Google DeepMind's goal in creating AlphaProof, a reinforcement-learning-based system that trains itself to prove mathematical statements in the formal programming language Lean. The key is a version of DeepMind's Gemini AI that's fine-tuned to automatically translate math problems phrased in natural, informal language into formal statements, which are easier for the AI to process. This created a large library of formal math problems with varying degrees of difficulty. Automating the process of translating data into formal language is a big step forward for the math community, says Wenda Li, a lecturer in hybrid AI at the University of Edinburgh, who peer-reviewed the research but was not involved in the project.

The term DNA immediately calls to mind the double-stranded helix that contains all our genetic information. But the individual units of its two strands are pairs of molecules bonded with each other in a selective, complementary fashion. Turns out, one can take advantage of this pairing property to perform complex mathematical calculations, and this forms the basis of DNA computing. Since DNA has only two strands, performing even a simple calculation requires multiple chemical reactions using different sets of DNA. In most existing research, the DNA for each reaction are added manually, one by one, into a single reaction tube, which makes the process very cumbersome.