0.8 C
Washington

Google DeepMind’s AI systems can now solve complex math problems

“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.  “We can have much greater confidence in the correctness of published results if they are able to formulate this proving system, and it can also become more collaborative,” he adds. The Gemini model works alongside AlphaZero—the reinforcement-learning model that Google DeepMind trained to master games such as Go and chess—to prove or disprove millions of mathematical problems. The more problems it has successfully solved, the better AlphaProof has become at tackling problems of increasing complexity. Although AlphaProof was trained to tackle problems across a wide range of mathematical topics, AlphaGeometry 2—an improved version of a system that Google DeepMind announced in January—was optimized to tackle problems relating to movements of objects and equations involving angles, ratios, and distances. Because it was trained on significantly more synthetic data than its predecessor, it was able to take on much more challenging geometry questions.

━ more like this

Newbury BS cuts resi, expat, landlord rates by up to 30bps  – Mortgage Strategy

Newbury Building Society has cut fixed-rate offers by up to 30 basis points across a range of mortgage products including standard residential, shared...

Rate and Term Refinances Are Up a Whopping 300% from a Year Ago

What a difference a year makes.While the mortgage industry has been purchase loan-heavy for several years now, it could finally be starting to shift.A...

Goldman Sachs loses profit after hits from GreenSky, real estate

Second-quarter profit fell 58% to $1.22 billion, or $3.08 a share, due to steep declines in trading and investment banking and losses related to...

Building Data Science Pipelines Using Pandas

Image generated with ChatGPT   Pandas is one of the most popular data manipulation and analysis tools available, known for its ease of use and powerful...

#240 – Neal Stephenson: Sci-Fi, Space, Aliens, AI, VR & the Future of Humanity

Podcast: Play in new window | DownloadSubscribe: Spotify | TuneIn | Neal Stephenson is a sci-fi writer (Snow Crash, Cryptonomicon, and new book Termination...