Artificial Intelligence Systems in Easy Words, Part 2

The core of any intelligent agent is continuous learning. Our brain tried thousands of ways that does not keep us stable when walk, but only one (or couples) of those thousands ways helped us to learn how to walk. Note that it’s continuous learning, not just learning. As we are getting older, our brain learn how to keep the stability to handle taller and heavier person. When we are learning how to ski, our brains are trying thousands ways of falling down. Each try to keep our stability is a rule in our brain. Each rule could be as simple as an if-then statement in our mind like “when your eyes detect you are getting close to uphill, you should bend at the hip” or it can be more complicated rule when we start running or skiing. Each rule can be a true or false statement in our mind. Our brains are trying thousands of rules; then brain strengthens good rules (ways that keeps our stability) and weakens bad rules (rules that leads to fall).


The beauty of any intelligent system comes from trying thousands of random rules and finding good rules. In practice, when intelligent agent acts on an environment, it gets feedback from the environment to update the strength of applied rule. In walking example, falling down is a negative feedback in our brain, and keeping stability is a positive one. But what if we want to train an artificial intelligent agent to keep the robot stability? Or to predict the stock market?

Most of artificial intelligent agents need to learn from another intelligent agent, like when your parents teach you how to speak and then you become a poet. Artificial agents are programmed by programmer and they are trained (finding good rules or true stamen) by training data. This introduction leads us to couples of important concepts in AI methods. (Photo Credit)

  • Training data: training data is a set of data used to discover potentially predictive relationships. In stock market, training data could be historical data of stock market. When you are training a robot for stability, you training data are any data that keeps your stability in any certain condition.
  • Machine learning: machine learning is a program that provides computers with the ability to learn from training data. Machine learning focuses on the development of computer programs that can change when exposed to new data.
  • Supervised learning:  Supervised learning is the machine learning task of inferring a function from training data.

Do you want to learn more? In my next blog post, I will cover more concepts of artificial intelligent systems and create a simple intelligent agent to improve the driving conditions of a passenger car.


Author: Amin Sabzehzar

MBA student Mechanical Engineer University of Nevada, Reno

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