The road to intelligent machines has been difficult, filled with hairpin curves, steep hills, crevices, potholes, intersections, stop signs, and occasionally smooth and straight sections. The initial over-the-top optimism of AI founders John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon set unrealistic expectations. According to their predictions, by now every household should have its own humanoid robot to cook, clean, and do yard work and every other conceivable household task we humans perform.

During the course of my career, I have managed hundreds of scientists and engineers. In my experience they are, for the most part, overly optimistic as a group. When they say something was finished, it usually means it’s in the final stages of testing or inspection. When they say they will have a problem solved in a week, it usually means a month or more. Whatever schedules they give us—the management—we normally have to pad, sometimes doubling them, before we use the schedules to plan or before we give them to our clients. It is just part of their nature to be optimistic, believing the tasks associated with the goals will go without a hitch, or the solution to a problem will be just one experiment away. Often if you ask a simple question, you’ll receive the “theory of everything” as a reply. If the question relates to a problem, the answer will involve the history of humankind and fingers will be pointed in every direction. I am exaggerating slightly to make a point, but as humorous as this may sound, there is more than a kernel of truth in what I’ve stated.

This type of optimism accompanied the founding of AI. The founders dreamed with sugarplums in their heads, and we wanted to believe it. We wanted the world to be easier. We wanted intelligent machines to do the heavy lifting and drudgery of everyday chores. We did not have to envision it. The science-fiction writers of television series such as Star Trek envisioned it for us, and we wanted to believe that artificial life-forms, such as Lieutenant Commander Data on Star Trek: The Next Generation, were just a decade away. However, that is not what happened. The field of AI did not change the world overnight or even in a decade. Much like a ninja, it slowly and invisibly crept into our lives over the last half century, disguised behind “smart” applications.

After several starts and stops and two AI winters, AI researchers and engineers started to get it right. Instead of building a do-it-all intelligent machine, they focused on solving specific applications. To address the applications, researchers pursued various approaches for specific intelligent systems. After accomplishing that, they began to integrate the approaches, which brought us closer to artificial “general” intelligence, equal to human intelligence.

Many people not engaged in professional scientific research believe that scientists and engineers follow a strict orderly process, sometimes referred to as the “scientific method,” to develop and apply new technology. Let me dispel that paradigm. It is simply not true. In many cases a scientific field is approached via many different angles, and the approaches depend on the experience and paradigms of those involved. This is especially true in regard to AI research, as will soon become apparent.

The most important concept to understand is that no unifying theory guides AI research. Researchers disagree among themselves, and we have more questions than answers. Here are two major questions that still haunt AI research.

Should AI simulate human intelligence, incorporating the sciences of psychology and neurology, or is human biology irrelevant? Can AI, simulating a human mind, be developed using simple principles, such as logic and mechanical reasoning, or does it require solving a large number of completely unrelated problems?

Why do the above questions still haunt AI? We will discuss this in the next post (Part 2)

Source: The Artificial Intelligence Revolution (2014)