Intelligence Research Mission
Current AI systems lack flexibility and contextual understanding, and resist explanation in terms comprehensible by humans. Ultimately we need to develop machine intelligence that understands human language, emotions, intentions, behaviors, and interactions at multiple scales.
Today’s AI methods can perform simple, well-defined, narrow tasks well, but only after training on laboriously annotated data. While recent algorithms have enabled us to solve formerly intractable real-world problems, it remains to be seen how far they can go, and whether they can ultimately serve as the basis for a general theory of intelligence and the development of truly intelligent machines.
To create a machine-assisted — yet human-centered — world, we must develop the next generation of AI techniques that overcomes the limitations of current algorithms, expands the class of problems that can be addressed, and complements human cognitive and analytic styles. Ultimately we need machine intelligence that leads to good decisions, either acting alone or working in combination with human decision-makers. It should understand human language, emotions, intentions, behaviors, and interactions at multiple scales.
Tackling these challenges on both the theoretical and practical levels requires substantial fundamental research. Developing a next generation of human-centered machine intelligence will demand combining further research in core machine learning and artificial intelligence with approaches coming from our growing understanding of human intelligence developed in areas including neuroscience and cognitive science.
Learn more about HAI's additional research areas, Augment Human Capabilities and Human Impact.
Blog: Insights & News
Using AI to Detect Seemingly Perfect Deep-Fake Videos
But a Stanford professor says the cat-and-mouse game is far from over.
Is GPT-3 Intelligent? A Directors' Conversation with Oren Etzioni
Seeking the Next Generation of Intelligent Machines
The Geographic Bias in Medical AI Tools
Patient data from just three states trains most AI diagnostic tools.
Coded Bias: Director Shalini Kantayya on Solving Facial Recognition’s Serious Flaws
We need ‘guidelines around transparency and laws that balance Big Tech’s power.’
An Open-Source Challenger to Popular Virtual Assistants
Stanford researchers hope their open virtual assistant Almond can increase competition and better protect privacy.
Sample Research Projects
Adversarial Examples for Humans?
Gregory Valiant and Noah Goodman
Humans are generally regarded as the gold-standard for robust perception and classification, and the implicit assumption in much of the work on “adversarial examples” is that humans do not have such vulnerabilities. We hope to understand whether there are settings where nearly every natural input (e.g., pertaining to vision, speech recognition, perception, etc.) can be turned into an “illusion”. We hope that this work will yield significant insights into aspects of human perception; the results may have implications for security and safety.
Free Exploration in Human-Centered AI Systems
Mohsen Bayati and Ramesh Johari
All systems that learn from their environment must grapple with a tradeoff between making decisions that maximize current rewards ( "exploitation") and decisions that are likely to teach the system about the environment and thus potentially increase future rewards ("exploration"). Automated machine learning and AI systems leverage techniques to balance exploration and exploitation to maximize rewards over time. This dynamic becomes more complicated at the interface between ML systems and humans. Our goal is to develop a formal methodology for reasoning about the free exploration provided by humans who interact with machine learning and AI systems.
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