Measuring progress toward AGI: A cognitive framework

Google DeepMind Blog Papers

Summary

Google DeepMind released a paper proposing a cognitive framework to measure progress toward AGI, identifying ten key cognitive abilities and launching a Kaggle hackathon to build relevant evaluations.

We’re introducing a framework to measure progress toward AGI, and launching a Kaggle hackathon to build the relevant evaluations.
Original Article Export to Word Export to PDF
View Cached Full Text

Cached at: 05/08/26, 09:11 AM

# Measuring progress toward AGI: A cognitive framework Source: [https://blog.google/innovation-and-ai/models-and-research/google-deepmind/measuring-agi-cognitive-framework/](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/measuring-agi-cognitive-framework/) We’re introducing a framework to measure progress toward AGI, and launching a Kaggle hackathon to build the relevant evaluations\. ![orankelly](https://storage.googleapis.com/gweb-uniblog-publish-prod/images/orankelly.max-244x184.format-webp.webp)Oran Kelly Product Manager, Google DeepMind ## General summary Google DeepMind wants to help measure the progress of Artificial General Intelligence \(AGI\) using cognitive science\. Their new paper, "Measuring Progress Toward AGI: A Cognitive Taxonomy," presents a framework for understanding AI systems' cognitive capabilities\. You can participate by designing evaluations for key cognitive abilities in their Kaggle hackathon for a chance to win from a prize pool of $200,000\. Summaries were generated by Google AI\. Generative AI is experimental\. ![Several rectangles in lines diagonally across the image. Each rectangle has swirls.](https://storage.googleapis.com/gweb-uniblog-publish-prod/images/agi_cognitive-framework_header.width-200.format-webp.webp) Your browser does not support the audio element\. Listen to article This content is generated by Google AI\. Generative AI is experimental \[\[duration\]\] minutes 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](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/measuring-progress-toward-agi/measuring-progress-toward-agi-a-cognitive-framework.pdf),” 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](http://kaggle.com/competitions/kaggle-measuring-agi), 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: 1. **Perception**: extracting and processing sensory information from the environment 2. **Generation**: producing outputs such as text, speech and actions 3. **Attention**: focusing cognitive resources on what matters 4. **Learning**: acquiring new knowledge through experience and instruction 5. **Memory**: storing and retrieving information over time 6. **Reasoning**: drawing valid conclusions through logical inference 7. **Metacognition**: knowledge and monitoring of one's own cognitive processes 8. **Executive functions**: planning, inhibition and cognitive flexibility 9. **Problem solving**: finding effective solutions to domain\-specific problems 10. **Social cognition**: processing and interpreting social information and responding appropriately in social situations ![Bubbles all connecting to the central bubble "Cognitive faculties". Each bubble list a cognitive faculty.](https://storage.googleapis.com/gweb-uniblog-publish-prod/images/agi_cognition_framework_blog_inli.width-100.format-webp.webp) To understand AI capabilities across these cognitive abilities, we propose a three\-stage evaluation protocol that benchmarks system performance in relation to human capabilities: 1. Evaluate AI systems across a broad suite of cognitive tasks covering each ability, using held\-out test sets to prevent data contamination 2. Collect human baselines for the same tasks from a demographically representative sample of adults 3. Map each AI system’s performance relative to the distribution of human performance in each ability ## Going from theory to practice Defining these cognitive abilities is a crucial first step, but we need more than a framework to measure progress\. To put this theory into practice, we are launching a new Kaggle hackathon — “[Measuring progress toward AGI: Cognitive abilities](http://kaggle.com/competitions/kaggle-measuring-agi)”\. The hackathon encourages the community to design evaluations for five cognitive abilities where the evaluation gap is the largest: learning, metacognition, attention, executive functions and social cognition\. Participants can use Kaggle's newly launched[Community Benchmarks](https://blog.google/innovation-and-ai/technology/developers-tools/kaggle-community-benchmarks/)platform to build and test their evaluations against a lineup of frontier models\. We are offering a total prize pool of $200,000: $10,000 awards for the top two submissions in each of the five tracks, and $25,000 grand prizes for the four absolute best overall submissions\. Submissions are open March 17 through April 16, and we’ll announce the results June 1\. Head over to the[Kaggle website](http://kaggle.com/competitions/kaggle-measuring-agi)to start building\. ## Get more stories from Google in your inbox\. Done\. Just one step more\. Check your inbox to confirm your subscription\. You are already subscribed to our newsletter\. You can also subscribe with a ### Related stories

Similar Articles

Rethinking how we measure AI intelligence

Google DeepMind Blog

Google DeepMind and Kaggle introduced Kaggle Game Arena, an open-source AI benchmarking platform where large language models compete head-to-head in strategic games to provide dynamic and verifiable evaluation of their capabilities. The platform addresses limitations of traditional benchmarks by offering clear winning conditions and unambiguous performance signals.

Planning for AGI and beyond

OpenAI Blog

OpenAI outlines its strategy for preparing for AGI, emphasizing gradual deployment with real-world feedback loops, increasing caution as systems approach AGI capabilities, and development of better alignment techniques to ensure AI systems remain steerable and safe.

Taking a responsible path to AGI

Google DeepMind Blog

DeepMind publishes a comprehensive approach to AGI safety and security, outlining a systematic framework to address misuse, misalignment, accidents, and structural risks as artificial general intelligence approaches reality within the coming years.

Evaluating potential cybersecurity threats of advanced AI

Google DeepMind Blog

DeepMind published a comprehensive framework for evaluating offensive cybersecurity capabilities of advanced AI models, analyzing over 12,000 real-world AI-powered cyberattack attempts across 20 countries and creating a 50-challenge benchmark covering the entire attack chain to help defenders prioritize security resources.

Google ramps up agentic AI efforts amid pressure from Anthropic

Reddit r/singularity

Google has formed a dedicated strike team to improve its coding AI models, ramping up agentic AI efforts amid competitive pressure from Anthropic. This signals an intensifying race in AI coding capabilities between major AI labs.