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 News: DeepMind AI with built-in fact-checker makes mathematical discoveries



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The AI company DeepMind claims it has developed a way to harness the creativity of chatbots to solve mathematical problems while filtering out mistakes


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Google DeepMind claims to have made the first ever scientific discovery with an AI chatbot by building a fact-checker to filter out useless outputs, leaving only reliable solutions to mathematical or computing problems.

Previous DeepMind achievements, such as using AI to predict the weather or protein shapes, have relied on models created specifically for the task at hand, trained on accurate and specific data. Large language models (LLMs), such as GPT-4 and Google’s Gemini, are instead trained on vast amounts of varied data to create a breadth of abilities. But that approach also makes them susceptible to “hallucination”, a term researchers use for producing false outputs.

Gemini – which was released earlier this month – has already demonstrated a propensity for hallucination, getting even simple facts such as the winners of this year’s Oscars wrong. Google’s previous AI-powered search engine even made errors in the advertising material for its own launch.


One common fix for this phenomenon is to add a layer above the AI that verifies the accuracy of its outputs before passing them to the user. But creating a comprehensive safety net is an enormously difficult task given the broad range of topics that chatbots can be asked about.

Alhussein Fawzi at Google DeepMind and his colleagues have created a generalised LLM called FunSearch based on Google’s PaLM2 model with a fact-checking layer, which they call an “evaluator”. The model is constrained to providing computer code that solves problems in mathematics and computer science, which DeepMind says is a much more manageable task because these new ideas and solutions are inherently and quickly verifiable.

The underlying AI can still hallucinate and provide inaccurate or misleading results, but the evaluator filters out erroneous outputs and leaves only reliable, potentially useful concepts.


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