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Cornell University

Cornell AI Initiative

Leading the way in artificial intelligence research


Cornell is committed to building upon its existing leadership in Artificial Intelligence (AI) to shape and drive the development and practice of AI for a sustainable future. In particular, we envision an AI that is designed to engage with humans, and that sustainably serves humans and humanity. We conceptualize this vision into the following three components:

AI fields graphic (see text)

  • AI Algorithmic Capabilities: We aim to develop new algorithmic capabilities in Learning, Reasoning, Perception, Language and Actuation, since they enable new applications for AI and new economic opportunities.
  • Human-AI Engagement: We aim to understand how humans and AI engage at all scales (e.g. individual, groups, society) to inform technology practice and design. This requires connecting the technical AI Algorithmic Capabilities with Ethics, Law & Policy, Social Science, Cognition and Design.
  • Application Impact Areas: We are bringing AI to important problems where we can draw on Cornell’s broad excellence (e.g., Society & Institutions, Autonomous Systems, Scientific Discovery, Health, Culture), ensuring impact and driving algorithm development. Sustainability (e.g. climate, society) serves as a cross-cutting set of goals and values.

A schematic overview is provided in the graphic, with more detailed descriptions of the three components in the following sections.

AI Algorithmic Capabilities

Most AI systems require all or at least multiple of the following AI Algorithmic Capabilities:

  • Learning: supervised learning, unsupervised learning, reinforcement learning, causal inference, statistics, etc.
  • Reasoning: planning, control, multi-agent interaction, knowledge representation, optimization, etc.
  • Perception: images, video, depth cameras, sensors, bio-medical imaging, haptics, on-body sensors, etc.
  • Language: discourse, sentiment, summarization, speech recognition, translation, question answering, generation, etc.
  • Actuation: autonomous robot, medical device, smart city, deep fakes, recommender system, extended reality, etc.

For leadership in AI, Cornell needs research strength in all AI Algorithmic Capabilities. However, learning and data play a special role, because they serve as the methodological basis for large parts of perception, language, reasoning and actuation.

Human-AI Engagement

AI engages with individual humans, groups, and society far beyond current computing technology. This means that AI systems do not succeed based on their algorithmic capabilities alone. We need to understand how humans interact with AI and sustainably design for humans and their needs. We posit that effective AI requires deep integration of the AI Algorithmic Capabilities with the following human-centered questions and methodologies:

  • Ethics, Law & Policy: AI raises new ethical and legal questions that have deep implications on the technology and its use (e.g., algorithmic bias, fairness, privacy, explanations, regulation, national security).
  • Social Science: AI systems act in the human world and we must understand human behavior in the context of AI (e.g., human bias, human decisions, social behavior, game theory, markets).
  • Design: We must carefully design AI systems in a complex space of affordances and requirements (e.g., human-robot interaction, interaction design, value-based design).
  • Cognition: Our understanding of natural intelligence will both inform and be informed by AI (e.g., bio-artificial analogies, cognitive psychology, brain-machine interfaces).

Application Impact Areas

The following application areas combine high potential for AI impact with existing research excellence at Cornell, which provides multiple avenues for recruiting faculty to deepen connections with AI technology:

  • Society & Institutions: AI is making small and increasingly big decisions affecting retail, political discourse, labor, law, education and many other areas of our society. How can AI be used to strengthen our institutions by improving effectiveness, fairness and transparency?
  • Autonomous Systems: AI is moving into the physical space that is shared with humans, with new applications in transportation, farming, climate, medicine, manufacturing, assistive technologies and others. How can we build highly reliable and effective physical systems that suitably share autonomy with humans?
  • Scientific Discovery: AI not only supports the scientific process (e.g., literature analysis), but has shown to be highly effective when optimizing complex objectives with expensive measurements. How can AI more generally accelerate the discovery of new climate interventions, materials, drugs, crops, chemoresponsive sensors, solvents and others?
  • Health: Newly available data (e.g., clinical, multi-omics, behavioral) and perception capabilities (e.g., imaging) can inform treatment and care. Tele-health provides new opportunities to support physicians, patients and caregivers. How can we bring AI to bear on both precision medicine and improved care?
  • Culture: Nascent questions have arisen around AI-augmented creativity, neural style transfer, aesthetic feature classification and others. Can AI (and its limitations in art, literature, poetry, etc.) shine new light on what may be uniquely human?

We further assert that Sustainability is an overarching goal that can inform the AI technology we build, direct the values that this technology embodies, and guide the application problems we tackle.