NYU Tandon receives Google DeepMind grant to advance AI adaptation
Assistant Professor Eugene Vinitsky leads research using meta-learning to create AI that adapts to human behaviors
AI systems that can dynamically adjust to human norms and behaviors may soon become reality, thanks to a NYU Tandon School of Engineering project that has received prestigious grant funding from Google DeepMind.
The research, led by NYU Tandon Assistant Professor Eugene Vinitsky in collaboration with Google DeepMind scientist Edward Hughes, aims to overcome the limitations of today's rigid AI algorithms. Vinitsky is part of Tandon's Civil & Urban Engineering Department and is also on the faculty of C2SMARTER, NYU Tandon's U.S. Department of Transportation-funded Tier 1 University Transportation Center.
Their project, "Adapting to Partners Quickly and Safely in Unforeseen Situations," focuses on a novel technique called meta-learning, or "learning to learn," where AI agents are trained to interact with various synthetic partners. This approach helps AI develop adaptive strategies that generalize to new, unseen partners — including humans. Most AI today operates with predefined algorithms, making it inflexible in real-world interactions.
The goal is to create AI that can proactively infer human behaviors, a crucial advancement for applications like autonomous vehicles and robotic assistants. A primary focus is ensuring AI adapts safely. Human drivers may follow unwritten norms of local driving cultures, for instance, but current AI-powered vehicles struggle with these subtle differences, often leading to overly cautious or rigid driving.
By training AI agents against a diverse set of synthetic driving styles, the research aims to enable AI to transition from a maximally safe starting posture to fluid adaptation as it learns more about the specific environment. The implications extend beyond driving.
"This research is about creating AI that understands and adapts to human behavior in a natural way," said Vinitsky. "By building AI that can quickly learn and adjust, we're paving the way for safer autonomous systems, more intuitive robotic assistants, and even AI that can collaborate effectively as a copilot in workplaces."
Unlike static AI models that require frequent manual updates, adaptive AI could continuously refine itself, making it more reliable. Vinitsky and Hughes believe this adaptability is a crucial missing capability in AI systems today.
While promising, the research also presents challenges. AI must infer human intentions responsibly to avoid reinforcing biases or making unsafe decisions. To address these concerns, the team will integrate safety checks and test their approach in diverse real-world scenarios, including human-in-the-loop simulations.
Google DeepMind, which was formed by merging two pioneering AI research labs — Google Brain and DeepMind (acquired by Google in 2014) — is an artificial intelligence research lab specializing in machine learning and AI systems development.
