innovating in
Artificial Intelligence
Working across disciplines to deliver on AI’s promise
We’ve engaged in AI research for more than 50 years, creating and delivering world-changing advances like the first mobile robot with the ability to perceive and reason, commercial-quality speech recognition, and the Siri virtual personal assistant.
Today, AI technology — and the creation of new AI technology — are core to the research of many groups across SRI. Our researchers collaborate to build systems that sense, model, and learn to create ever more capable applications. Our approach is human-centered, emphasizing system transparency and explainability to address the critical issues of predictability and reliability of AI-based systems. Contact us to learn more.
Innovating in
“We work across disciplines and in close collaboration with our government and commercial customers to create new technologies, while strengthening the guardrails, so we can all truly benefit from AI’s extraordinary promise.”
Real-world impact
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PARC Forum: How to AI
The Wall Street Journal tech columnist Christopher Mims and SRI Education’s Susan Patrick discuss how AI can strengthen human agency.
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Designing better human-machine teams
SRI is discovering how AI can organize humans and machines into complex, collaborative, high-functioning units.
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Where is AI taking us?
As the co-chair of Stanford’s AI Index Steering Committee, SRI’s Ray Perrault shapes a monumental annual effort to document the global AI ecosystem.
Core AI technologies and applications
Bioinformatics databases
BioCyc database collection
A comprehensive website for sharing fundamental information about biochemical pathways and genomes with researchers around the world
EcoCyc: encyclopedia of E. coli genes and metabolism
A bioinformatics database that describes the genome, metabolic network, and regulatory network of Escherichia coli
Recent publications
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Interpolative Decoding: Exploring the Spectrum of Personality Traits in LLMs
Abstract Recent research has explored using very large language models (LLMs) as proxies for humans in tasks such as simulation, surveys, and studies. While LLMs do not possess a human psychology, they often can emulate human behaviors with sufficiently high fidelity to drive simulations to test human behavioral hypotheses, exhibiting more nuance and range than…
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Building a Virtual Member of a Community of Practice
Abstract We describe a virtual member of a knowledge management Community of Practice (CoP), called ATHENA, that knows an individual, his tasks, his organization, and the community. ATHENA employs an agentic chat capability that combines embeddings with knowledge-based faceted search to provide accurate responses to technical questions along with rationale and citations for efficient validation.…
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AI as Collaborative Partner: Rethinking Human-AI Teaming for the Real World
Abstract Much work in human-AI teaming today involves collaboration under fairly constrained settings. Humans supervise AI agents, who are relegated to following orders. The division of tasks is relatively superficial, with independent actions executed in parallel or with simple, linear dependencies between them. Communication is rigid and turn-based, with both parties speaking in complete, unambiguous…