It may be mentioned here that the Allen Institute for AI (Ai2) has now released an advanced AI solution called AutoDiscovery. It is meant to revolutionize the way in which scientists discover new insights and information using AI. AutoDiscovery is available as an experimental feature in the AstaLabs environment. AutoDiscovery allows researchers to explore the datasets generated.
In traditional scientific research, it has to commence with designing specific research questions, which are then subject to assessment by the analyst. While this workflow presents a restriction on potential research and results when dealing with very large and intricate datasets, AutoDiscovery eliminates this problem by beginning with the dataset and immediately designing research questions, experiment plans, Python execution code, and new directions for research.
This capability is already delivering real-world impact. Researchers have applied AutoDiscovery across diverse fields, from identifying long-term ecosystem dynamics to detecting non-obvious cancer mutation relationships, some of which have contributed to peer-reviewed research publications.
“AutoDiscovery is almost like deep research with data, but at the speed of thought.” Sanchaita Hazra, Economist in the College of Social and Behavioral Science at the University of Utah.
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How AutoDiscovery Works
At its core, AutoDiscovery blends statistical rigor with intelligent exploration to steer its search:
- Bayesian Surprise: The system quantifies how much its confidence in any hypothesis changes after analyzing data. Larger shifts reflect more informative discoveries, including findings that contradict expectations.
- Monte Carlo Tree Search (MCTS): This search strategy dynamically balances the exploration of new ideas with deeper investigation of promising leads, enabling efficient navigation of an otherwise vast hypothesis space.
By pursuing surprising results, AutoDiscovery mimics a scientific intuition that valuable discoveries often come from unexpected evidence rather than confirming known patterns.
“Analyses that would normally require weeks or months of manual exploratory modeling were done in a single day.” – Dr. Stephen Salerno, Postdoctoral Researcher in Biostatistics at Fred Hutchinson Cancer Center
Example: Insights Into Cancer Mutation Patterns
In a collaboration between oncologists from the Paul G. Allen Research Center of the Swedish Cancer Institute, AutoDiscovery analyzed the data on breast cancer mutations. A conclusion was drawn on the frequency of less co-occurrence of PIK3CA and TP53 mutations, indicating their mutual exclusion, of particular importance for the field of genomics.
“AutoDiscovery’s ability to reveal discoveries that may be hiding in plain sight is especially valuable in cancer research.” – Dr. Kelly Paulson, Medical Oncologist and Head of the Center for Immuno-Oncology at the Swedish Cancer Institute
Getting Started With AutoDiscovery
Scientists interested in using AutoDiscovery can access it through AstaLabs by uploading structured data (CSV, JSON, Parquet, etc.) and configuring an exploratory session. As analyses run, results populate in an interactive interface showing hypothesis progression, statistical surprisal scores, and detailed execution artifacts all designed to be reproducible.
To support early experimentation, Ai2 provides 1,000 complimentary Hypothesis Credits through Feb. 28, 2026 enough to test a wide range of ideas and familiarize users with the system’s capabilities.
With AutoDiscovery, Ai2 aims to accelerate scientific breakthroughs by removing traditional barriers between data and discovery allowing researchers to focus on interpreting results and advancing knowledge.