What is AI and autonomy (and what is it not)?
What would be the definition of AI? In general, it means a machine that exhibits some characteristics of intelligence – thinking, reasoning, planning, learning, and adapting. It can also mean a software program that can simulate thinking or reasoning. Let’s try some examples: a robot that avoids obstacles by simple rules (if the obstacle is to the right, go left) is not AI. A program that learns, by example, to recognize a cat in a video is AI. A robot arm that is operated by a joystick does not use AI, but a robot arm that adapts to different objects in order to pick them up is an application of AI.
There are two defining characteristics of AI robots that you must be aware of. First of all, AI robots are primarily trained to perform tasks, by providing examples, rather than being programmed step by step. For example, we will teach the robot’s software to recognize toys – things we want it to pick up – by training a neural network with examples of what toys look like. We will provide a training set of pictures with the toys in the images. We will specifically annotate what parts of the images are toys, and the robot will learn from that. Then we will test the robot to see that it learned what we wanted it to, somewhat like a teacher would test a student. The second characteristic is emergent behavior, in which the robot exhibits evolving actions that were not explicitly programmed into it. We provide the robot with controlling software that is inherently non-linear and self-organizing. The robot may suddenly exhibit some bizarre or unusual reaction to an event or situation that might appear to be odd, quirky, or even emotional. I worked with a self-driving car that we swore had delicate sensibilities and moved very daintily, earning it the nickname Ferdinand, after the sensitive, flower-loving bull from a cartoon, which was strange in a nine-ton truck that appeared to like plants. These behaviors are just caused by interactions of the various software components and control algorithms and do not represent anything more than that.
One concept you will hear in AI circles is the Turing test. The Turing test was proposed by Alan Turing in 1950, in a paper entitled Computing Machinery and Intelligence. He postulated that a human interrogator would question a hidden, unseen AI system, along with another human. If the human posing the questions was unable to tell which person was the computer and which was the human, then that AI computer would pass the test. This test supposes that the AI would be fully capable of listening to a conversation, understanding the content, and giving the same sort of answers a person would. Current AI chatbots can easily pass the Turing test and you may have interacted several times this week with AI on the phone without realizing it.
One group from the Association for the Advancement of Artificial Intelligence (AAAI) proposed that a more suitable test for AI might be the assembly of flatpack furniture – using the supplied instructions. However, to date, no robot has passed this test.
Our objective in this book is not to pass the Turing test, but rather to take some novel approaches to solving problems using techniques in machine learning, planning, goal seeking, pattern recognition, grouping, and clustering. Many of these problems would be very difficult to solve any other way. AI software that could pass the Turing test would be an example of general AI, or a full, working intelligent artificial brain, and, just like you, general AI does not need to be specifically trained to solve any particular problem. To date, general AI has not been created, but what we do have is narrow AI or software that simulates thinking in a very narrow application, such as recognizing objects, or picking good stocks to buy.
While we are not building general AI in this book, that means we are not going to be worried about our creations developing a mind of their own or getting out of control. That comes from the realm of science fiction and bad movies, rather than the reality of computers today. I am firmly of the mind that anyone preaching about the evils of AI or predicting that robots will take over the world has likely not seen the dismal state of AI research in terms of solving general problems or creating something resembling actual intelligence.
