Core AI technologies and applications
At SRI, our researchers work together across disciplines to deliver on the promise of AI and machine learning. We create leading-edge technologies and explore their deployment across a broad range of application areas.
SENSING AND ANALYTICS
Scene recognition and understanding
Person recognition and tracking
Mapping and localization
Text understanding and summarization
REASONING AND PROBLEM SOLVING
Adaptive planning and resource allocation
Decision-making under uncertainty
Deep learning for language, image processing
Learning from demonstration
Hybrid logic/learning architectures
Learning causal models
Our workmore +
Is it real news or manipulated? SRI taps AI to spot manipulated media
In the age of disinformation, SRI researchers are helping develop cutting-edge AI tools that can tell manipulated news from real.
SRI taps AI to hunt for linguistic DNA that proves authorship
With bad information running rampant, the need to assign authorship is essential. SRI is developing the tools to make it happen.
SRI researchers are working to build the knowledge management technology of the future
Funded by a $10.8 million Defense Advanced Research Projects Agency contract, the initiative aims to transform how organizations store and manage data.
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
The Artificial Intelligence Center
Founded in 1966, SRI International’s Artificial Intelligence (AI) Center has played a key role in AI research since the field’s early days. The AI Center has been the source of many seminal contributions to AI, spanning mobile robotics (Shakey), search (the A* algorithm), planning (STRIPS), image understanding (the RANSAC algorithm, TerraVision) and information extraction from text (FASTUS).
The center continues to push the boundaries of AI as it develops innovative technologies and explores their applications across a range of problem domains. Machine learning is a major focus, both as a core research area and as an enabler for natural language understanding, computer vision, autonomy and personalization. Important innovations in machine learning include learning when data is scarce and understanding the limits of learned models. Effective man-machine interaction is a second focus, given the need for humans to understand and direct AI systems. The AI Center’s research leverages symbolic reasoning for areas such as planning and bioinformatics, neural architectures for data-rich learning tasks and hybrid neural-symbolic architectures for tasks that require a combination of learning from data and human expertise.
Artificial intelligence in the real world
The AI Center has an outstanding track record of moving research into the real world. Technologies created in the center have been licensed to numerous SRI venture spin-offs. Spin-offs include: Siri (acquired by Apple), Trapit, Tempo AI (acquired by Salesforce), Meta (acquired by the Chan-Zuckerberg Initiative), Summly (acquired by Yahoo), and Social Kinetics (acquired by Redbrick Health). Recently hatched companies include: Vitrina AI, Kasisto, Pulse, and Confidencial. Our BioCyc family of bioinformatics knowledge bases and tools has been licensed by over 11,000 users to support biological research at numerous universities and corporate labs.
Artificial intelligence leadership
Latest publicationsmore +
Notes on Refactoring Exponential Macros in Common Lisp Or: Multiple @Body Considered Harmful
I will first illuminate the problem, and then demonstrate two potential solutions in terms of macro refactoring techniques. These techniques can be applied in related scenarios.
BioCyc: Metabolic Pathway Databases and Informatics Tools
This article describes a coordinated set of bioinformatics databases and software tools designed to solve multiple problems faced by metabolic engineers and microbiologists related to metabolic pathways.
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning
We approach the problem of understanding how people interact with each other in collaborative settings via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team’s behavior during some task.
Reviewing Knowledgebase and Database Grant Proposals in the Life Sciences: The Role of Innovation
This article offers thoughts on reviewing grant proposals for biological knowledgebases and databases (KDs) in the hope of aiding grant reviewers and applicants in addressing the issue of innovation.
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments
We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments.
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
Our new framework provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms.
“SRI’s got that rare mix of people, creativity, technical diversity and excellence, and flexibility to pursue longer-range goals. Of course there are no guarantees, but SRI provides the tools for a motivated person to make things happen.”
Senior Computer Scientist