Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

Reinforcement-learning algorithms 1,2 are inspired by our understanding of decision making in humans and other animals in which learning is supervised through the use of reward signals in response to ...

Ambuj Tewari receives funding from NSF and NIH. Understanding intelligence and creating intelligent machines are grand scientific challenges of our times. The ability to learn from experience is a ...

Negative reinforcement is a method that can be used to help teach specific behaviors. With negative reinforcement, something uncomfortable or otherwise unpleasant is taken away in response to a ...

The age of truly autonomous artificial intelligence, where systems proactively learn, adapt and optimize amid real-world complexities instead of simply reacting, has been a long-held aspiration. Now, ...

ChatGPT and other AI tools are upending our digital lives, but our AI interactions are about to get physical. Humanoid robots trained with a particular type of AI to sense and react to their world ...

The Reinforcement Theory, with its nuanced understanding of human behavior, offers leaders a structured approach to drive desired behaviors, invigorate teams, and sculpt an organizational culture that ...

Scottish philosopher James Beattie said a mouthful when he observed that "in every age and every man, there is something to praise as well as to blame." In other words, people face a choice when ...

Negative reinforcement encourages specific behaviors by removing or avoiding negative consequences or stimuli. It is different than punishment, which aims to discourage a specific behavior. Negative ...