Measuring progress toward AGI: A cognitive framework

Skip to main content

Measuring progress toward AGI: A cognitive framework

• Feed

Measuring progress toward AGI: A cognitive framework

[]

Newsletter

Mar 17, 2026

|

We’re introducing a framework to measure progress toward AGI, and launching a Kaggle hackathon to build the relevant evaluations.

Ryan Burnell

Research Scientist, Google DeepMind

Oran Kelly

Product Manager, Google DeepMind

Your browser does not support the audio element.

Listen to article

[[duration]] minutes

This content is generated by Google AI. Generative AI is experimental

Artificial General Intelligence (AGI) has the potential to accelerate scientific discovery and help solve some of humanity’s most pressing problems. But it can be difficult to know how close we are to this key milestone, because there’s a lack of empirical tools for evaluating systems’ general intelligence. Tracking progress toward AGI will require a wide range of methods and approaches, and we believe cognitive science provides one important piece of the puzzle.

That’s why today, we’re releasing a new paper, “Measuring Progress Toward AGI: A Cognitive Taxonomy,” that presents a scientific foundation for understanding the cognitive capabilities of AI systems.

Alongside the paper, we are partnering with Kaggle to launch a hackathon, inviting the research community to help build the evaluations needed to put this framework into practice.

Deconstructing general intelligence

Our framework draws on decades of research from psychology, neuroscience and cognitive science to develop a cognitive taxonomy. It identifies 10 key cognitive abilities that we hypothesize will be important for general intelligence in AI systems:

To understand AI capabilities across these cognitive abilities, we propose a three-stage evaluation protocol that benchmarks system performance in relation to human capabilities:

Going from theory to practice

Participants can use Kaggle's newly launched Community Benchmarks platform to build and test their evaluations against a lineup of frontier models.