1.1 What is Artificial Intelligence?

1.1.1 Artificial and Natural Intelligence

Artificial intelligence (AI) is the established name for the field, but the term “artificial intelligence” is a source of much confusion because artificial intelligence may be interpreted as the opposite of real intelligence.

For any phenomenon, you can distinguish real versus fake, where the fake is non-real. You can also distinguish natural versus artificial. Natural means occurring in nature and artificial means made by people.

Example 1.1.

A tsunami is a large wave in an ocean. Natural tsunamis occur from time to time and are caused by earthquakes or landslides. You could imagine an artificial tsunami that was made by people, for example, by exploding a bomb in the ocean, yet which is still a real tsunami. One could also imagine fake tsunamis: either artificial, using computer graphics, or natural, for example, a mirage that looks like a tsunami but is not one.

It is arguable that intelligence is different: you cannot have fake intelligence. If an agent behaves intelligently, it is intelligent. It is only the external behavior that defines intelligence; acting intelligently is being intelligent. Thus, artificial intelligence, if and when it is achieved, will be real intelligence created artificially.

This idea of intelligence being defined by external behavior was the motivation for a test for intelligence designed by Turing [1950], which has become known as the Turing test. The Turing test consists of an imitation game where an interrogator can ask a witness, via a text interface, any question. If the interrogator cannot distinguish the witness from a human, the witness must be intelligent. Figure 1.1 shows a possible dialog that Turing suggested. An agent that is not really intelligent could not fake intelligence for arbitrary topics.

Interrogator:

In the first line of your sonnet which reads “Shall I compare thee to a summer’s day,” would not ”a spring day” do as well or better?

Witness:

It wouldn’t scan.

Interrogator:

How about “a winter’s day,” That would scan all right.

Witness:

Yes, but nobody wants to be compared to a winter’s day.

Interrogator:

Would you say Mr. Pickwick reminded you of Christmas?

Witness:

In a way.

Interrogator:

Yet Christmas is a winter’s day, and I do not think Mr. Pickwick would mind the comparison.

Witness:

I don’t think you’re serious. By a winter’s day one means a typical winter’s day, rather than a special one like Christmas.

Figure 1.1: Part of Turing’s possible dialog for the Turing test

There has been much debate about the usefulness of Turing test. Unfortunately, although it may provide a test for how to recognize intelligence, it does not provide a way to realize intelligence.

Recently Levesque [2014] suggested a new form of question, which he called a Winograd schema after the following example of Winograd [1972]:

  • The city councilmen refused the demonstrators a permit because they feared violence. Who feared violence?

  • The city councilmen refused the demonstrators a permit because they advocated violence. Who advocated violence?

These two sentences only differ in one word feared/advocated, but have the opposite answer. Answering such a question does not depend on trickery or lying, but depends on knowing something about the world that humans understand, but computers currently do not.

Winograd schemas have the property that (a) humans can easily disambiguate them and (b) there is no simple grammatical or statistical test that could disambiguate them. For example, the sentences above would not qualify if “demonstrators feared violence” was much less or more likely than “councilmen feared violence” (or similarly with advocating).

Example 1.2.

The following examples are due to Davis [2015]:

  • Steve follows Fred’s example in everything. He [admires/influences] him hugely. Who [admires/influences] whom?

  • The table won’t fit through the doorway because it is too [wide/narrow]. What is too [wide/narrow]?

  • Grace was happy to trade me her sweater for my jacket. She thinks it looks [great/dowdy] on her. What looks [great/dowdy] on Grace?

  • Bill thinks that calling attention to himself was rude [to/of] Bert. Who called attention to himself?

Each of these have their own reasons why one answer is preferred to the other. A computer that can reliably answer these questions needs to know about all of these reasons, and require the ability to do commonsense reasoning.

The obvious naturally intelligent agent is the human being. Some people might say that worms, insects, or bacteria are intelligent, but more people would say that dogs, whales, or monkeys are intelligent (see Exercise 1). One class of intelligent agents that may be more intelligent than humans is the class of organizations. Ant colonies are a prototypical example of organizations. Each individual ant may not be very intelligent, but an ant colony can act more intelligently than any individual ant. The colony can discover food and exploit it very effectively as well as adapt to changing circumstances. Corporations can be more intelligent than individual people. Companies develop, manufacture, and distribute products where the sum of the skills required is much more than any individual could master. Modern computers, from low-level hardware to high-level software, are more complicated than any human can understand, yet they are manufactured daily by organizations of humans. Human society viewed as an agent is arguably the most intelligent agent known.

It is instructive to consider where human intelligence comes from. There are three main sources:

Biology

Humans have evolved into adaptable animals that can survive in various habitats.

Culture

Culture provides not only language, but also useful tools, useful concepts, and the wisdom that is passed from parents and teachers to children.

Lifelong learning

Humans learn throughout their life and accumulate knowledge and skills.

These sources interact in complex ways. Biological evolution has provided stages of growth that allow for different learning at different stages of life. Biology and culture have evolved together; humans can be helpless at birth presumably because of our culture of looking after infants. Culture interacts strongly with learning. A major part of lifelong learning is what people are taught by parents and teachers. Language, which is part of culture, provides distinctions in the world that are useful for learning.

When building an intelligent system, the designers have to decide which of these sources of intelligence need to be programmed in, and which can be learned. It is very unlikely we will be able to build an agent that starts with a clean slate and learns everything. Similarly, most interesting and useful intelligent agents learn to improve their behavior