Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Machine learning technique teaches power-generating kites to extract energy from turbulent airflows more effectively, ...
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 ...
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, ...
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 ...
Positive reinforcement involves rewarding an employee for doing a good job. An example would be giving an employee a paid vacation day for exceeding monthly production goals. Negative reinforcement ...
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 ...
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