Reactive machines in AI #podcast #ailearning #artificialsuperintelligence ##aiethics #aiandhumanity

1. Reactive machines in AI

AI systems that have no memory and are task specific. Input always delivers the same output.

Artificial intelligence models that continuously interact with their environment, but they dont maintain any internal representation of it.

What they rely on is this - rules and heuristics - that helps make real-time decisions and they are able to adjust to changing environmental conditions.

USED in: real-time applications, for eg. robotic control systems and autonomous navigation systems. WHY? because they can make fast and accurate decisions without the need to process large amounts of data or maintain long-term memory.

On flip side to these reactive machines -- They are limited in their ability to learn from experience. And they cannot adapt to new or unfamiliar situations, right?
So what we do is - we use these reactive machines in combination with other machine learning approaches, Machine learning approaches such as 1 reinforcement learning and supervised learning. So essentially we take these reactive machines, use them WITH other ML approaches improve their adaptive and decision-making capabilities.