Vishweshwar Joshi

The field of Artificial Intelligence (AI) is confronted with a fundamental challenge: how to design programs that exhibit rational behavior from compact algorithms rather than relying on vast tables or exhaustive databases. This challenge is reminiscent of the revolutionary impact of Isaac Newton’s method for calculating square roots. In the 1970s, engineers transitioned from using extensive tables of square roots to employing a succinct, efficient program based on Newton’s method. This shift significantly streamlined calculations, enhancing computational efficiency. The central question now is whether AI can replicate this paradigm shift for general intelligent behavior, much like Newton did for square roots.

The Challenge of AI Efficiency: Can Small Programs Match Newton’s Legacy? - Startupsgurukul The field of Artificial Intelligence (AI) is confronted with a fundamental challenge: how to design programs that exhibit rational behavior from compact

In Artificial Intelligence (AI) and Machine Learning (ML), rational agents are entities designed to perceive their environment, make decisions, and take actions to maximize a certain goal or objective. The performance of these rational agents is critical to their success and requires continuous measurement and evaluation to ensure they are functioning optimally in dynamic environments

Scalability, Fairness, and Emotional Intelligence: New Metrics for Rational Agents - Startupsgurukul In Artificial Intelligence (AI) and Machine Learning (ML), rational agents are entities designed to perceive their environment, make decisions, and take

In the world of artificial intelligence (AI) and machine learning (ML), the concept of a “rational agent” is foundational. A rational agent is an entity that consistently makes decisions to achieve its objectives based on the information it has and the actions available to it. Conceptually, this means that every entry in the agent function—essentially a table that maps percepts to actions—is filled out correctly. But what does “doing the right thing” really mean in the context of AI? How do we determine whether an agent’s actions are truly rational? This blog post explores these questions by diving deep into the nature of rationality, the importance of consequences, and the complexities involved in defining and evaluating rational behavior in AI systems.

Beyond Logic: How AI Agents Make Decisions in Complex Environments - Startupsgurukul Understanding and implementing rationality in intelligent agents involves a complex interplay of decision-making theories, ethical considerations,