Is There a Clear Path to General AI?

People frequently mix up two pairs of terms when talking about artificial intelligence: Strong vs. Weak AI, and General vs. Narrow AI. The key to understanding the difference lies in which perspective we want to take: are we aiming for a holy grail that, once found, will mean solving one of mankind’s biggest questions … or are we merely aiming to build a tool to make us more efficient at a task?

The Strong vs. Weak AI dichotomy is largely a philosophical one, made prominent in 1980 by American philosopher John Searle. Philosophers like Searle are looking to answer the question of whether we can — theoretically and practically — build machines that truly think and experience cognitive states, such as understanding, believing, wanting, hoping. As part of that endeavor, some of them examine the relationship between these states and any possibly corresponding physical states in the observable world of the human body: when we are in the state of believing something, how does that physically manifest itself in the brain or elsewhere?

Searle concedes that computers, the most prominent form of such machines in our current times, are powerful tools that can help us study certain aspects of human thought processes. However, he calls that “Weak AI,” as it’s not “the real thing.” He contrasts that with “Strong AI” as follows: “But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states.”

Which AI Do You Mean? It’s a Matter of Perspective

While this philosophical perspective is fascinating in and of itself, it remains largely elusive to modern day practical efforts in the field of AI. Philosophers are thinkers, meant to raise the right questions at the right time to help us think through the implications of our doings. They are rarely builders. The builders among us, the engineers, seek to solve practical problems in the physical world. Note that this is not a question of whose aims are more noble, but merely a question of perspective.

Engineers seeking to build systems that are of practical use today are more interested in the distinction of General vs. Narrow AI. That distinction is one of the applicability of a system at hand. We call something “Narrow AI” if it is built to perform one function, or a set of functions in a particular domain, and that alone. In reality, that is the only form of AI we have at our disposal today. All of the currently available systems are built for one task alone.

The biggest revelation for any non-expert here is that an AI system’s performance in one task does not generalize. If you’ve built a system that has learned to play chess, your system cannot play the ancient Chinese game of Go, not even with some additional modifications. And if you have a system that plays Go better than any human, no matter how hard that task seemed before such a program finally got built in 2017, that system will NOT generalize to any other task. Just because a system performs one task well does not mean it will “soon” (a term used often by people writing and talking about technology in general) perform seemingly related tasks well, too. Each new task that is different in nature (and there are many of those “different natures”) is a tedious and laborious job for the engineers and designers who build these systems.

So if the opposite of Narrow AI is General AI, you’re essentially talking about a system that can perform any task you throw at it. The original idea behind General AI was to build a system that could learn any kind of task through self-training, without requiring examples pre-labeled by humans. (Note that this is still different from Searle’s notion of Strong AI, in that you could theoretically build General AI without building “true thinking” — it could still just be a simulation of the “real thing.”)

Is it Possible to Jerry-Rig General AI? …

Read The Full Article