ST EK List:
No EK's found

What is AI?

In this lab, you will learn about advances in artificial intelligence and the ethics of artificial intelligence and robotics technology.

On this page, you will do online research on image recognition.

Artificial intelligence (AI) is a field of computer science with a vague definition of "trying to get computers to think." This definition has led to a lot of arguments about whether a computer can ever really think, so John McCarthy, one of the founders of AI, defined AI as "getting computers to do things that, when done by human beings, are said to involve intelligence." This definition allows AI researchers to work instead of spending time arguing about what "thinking" means.

Interestingly, tasks that human beings generally consider to be hard to do (like playing chess) have turned out to be easier for computers than tasks people think of as being so easy that we do them "without thinking," like walking. Another example is seeing, that is, recognizing images, which is a big field of research in AI.

  1. Do your own research to learn more about getting computers to see.

    Some suggested sites:

  2. Discuss some of the social implications of teaching computers to understand images. For example, some articles above include positive implications such as helping the blind or aiding law enforcement; others warn of potentially negative consequences of image recognition.
    1. What implications of this area of AI do you find exciting?
    2. What do you find surprising or unexpected?
    3. Do you think computers will ever be able to understand images as humans do?
  1. In your research, you probably came across terms like machine learning or neural networks or deep learning. Research these ways of thinking about artificial intelligence by searching for articles and videos like these:
  2. The history of artificial intelligence goes back further than you might realize. The ideas of robots and artificial intelligence existed before the 1950s, but the field become more established in the 1950's and 1960's. Research some early contributions to AI to see both how far we've come and also how some early challenges still exist today.