Understanding Artificial Intelligence

The Programming Question

Classical programming begins with rules written by humans. The computer receives those rules and data, follows the rules, and produces answers. Machine learning begins by asking a different question: can a computer examine data, discover useful rules, and apply those rules to information it has not seen before?

Two Routes from Input to Answer

In classical programming, a human supplies two things: a program containing rules and data for those rules to process. The computer follows the supplied rules and produces answers. The path from input to output is designed by the programmer before the computer runs the program.

Machine learning rearranges these inputs and outputs. Humans provide data together with the answers expected for that data. The computer uses this material to produce rules. Those learned rules can then be applied to new data, producing answers for cases that were not part of the original material.

From Examples to Predictions

The important point is not that a human has written every decision in advance. The human has supplied material from which the computer produces rules. The rules are then used beyond the original examples, on new data.

The Historical Question

The shift toward machine learning connects to a historical question about what computers can do. In 1843, Ada Lovelace argued that Charles Babbage’s Analytical Engine could perform whatever people knew how to order it to perform, but could not originate anything. Alan Turing later discussed this idea as Lady Lovelace’s objection while considering whether general-purpose computers could learn and show originality.

Machine learning addresses the learning part of this question by changing where the rules come from. Instead of requiring people to explicitly order every rule, it asks whether a computer can examine data, discover useful rules, and use them on information it has not seen before. This does not remove the role of human-provided data and expected answers; it changes the route by which rules are produced.

What Artificial Intelligence Includes

This definition makes artificial intelligence a broad field rather than the name of one particular technique. When asking whether a computer is intelligent, a useful starting point is to ask what kind of human intellectual work the computer is being designed to perform, rather than whether it thinks like a person.

Rules and Fuzzy Problems

Symbolic AI depends on explicit rules handcrafted by programmers. This approach is suitable for problems whose logic can be described clearly. When the decisions and knowledge can be stated directly, explicit rules provide a direct way to describe the solution.

The difficulty changes when a problem is complex and fuzzy. For image classification, speech recognition, and language translation, it proved intractable to work out all the explicit rules needed to describe the solution. Machine learning arose as an approach for these kinds of problems.

Common Misunderstandings

• Treating artificial intelligence and machine learning as synonyms.

  Artificial intelligence is the broader field. It includes machine learning, symbolic AI, deep learning, and approaches that do not involve learning.

  Fix: Describe machine learning as one approach within artificial intelligence.

• Assuming machine learning means that humans provide no guidance.

  The machine learning model described here begins with data and expected answers supplied by humans.

  Fix: Remember that humans provide the data and expected answers, while the computer produces rules from them.

• Assuming that every problem should be solved by explicit rules.

  For complex and fuzzy problems, working out all the explicit rules needed to describe the solution proved intractable.

  Fix: Recognize machine learning as an approach that arose for these complex, fuzzy problems.

• Defining artificial intelligence as a computer that thinks like a person.

  A useful starting point is to ask what human intellectual task the computer is designed to automate.

  Fix: Define AI in terms of automating intellectual tasks normally performed by humans.

Check Your Understanding

Key Takeaways

1. Classical programming uses human-written rules to process data and produce answers.
2. Machine learning uses data and expected answers to produce rules that can be applied to new data.
3. The machine learning idea connects to the historical question of whether computers can learn rather than merely follow explicitly ordered instructions.
4. Artificial intelligence is the effort to automate intellectual tasks normally performed by humans.
5. Symbolic AI uses explicit handcrafted rules, while machine learning arose for complex and fuzzy problems where describing every rule is difficult.
6. Machine learning is one approach within the broader field of artificial intelligence.