I started subscribing to Ben Thompson’s site a few weeks ago. Daily emails on technology that I’ve found very helpful to understand what’s going on, and weekly articles like today’s that is a great overview/introduction to the issue of Artificial Intelligence and machine learning. It starts with Chris Dixon’s recent article How Aristotle Created the Computer:

Dixon goes on to describe the creation of Boolean logic (which has only two variables: TRUE and FALSE, represented as 1 and 0 respectively), and the insight by Claude E. Shannon that those two variables could be represented by a circuit, which itself has only two states: open and closed. [Shannon’s insight underscores that] the logical [abstract] and the physical [circuit] layers depends on the realization that they can be two pieces of a whole. That is, Shannon identified how the logical and the physical could be fused into what we now know as a computer.

Our technology shapes human experience. An obvious point, but one with consequences when one realizes that there are different forms of logic. Boolean logic applied to circuits has shaped our experience of reality to something other than what it was before the advent of the computer. The logical and physical layers were Eureka’d as two pieces of a whole in producing what we call the computer. As Ben Thompson points out, this has allowed human beings to extend their power in new and important ways. But it’s easy to lose sight of the fact that this is all the result of one particular form of logic (new forms of logic could in time reshape our understanding of computing machines specifically or technology generally).

And while humans are “logic/thinking machines,” we’re also “consciousness/feeling” machines. These are the two pieces that make up the whole for the species homo. Those writing about our technology almost universally neglect to remember our whole nature, and act as if homo sapiens is purely a “logic/thinking machine.” In thinking this way, we risk destroying ourselves by making us less than we are. By reducing other forms of logic or knowledge (philosophy, theology, aesthetics, ethics, etc.) relative to a single, narrow understanding of logical knowledge. Interestingly, it’s also in only a narrow way that our Artificial Intelligence/machine learning is advancing so far:

Artificial intelligence is very difficult to define for a few reasons. First, there are two types of artificial intelligence: the artificial intelligence described in that Vanity Fair article is Artificial General Intelligence, that is, a computer capable of doing anything a human can. That is in contrast to Artificial Narrow Intelligence, in which a computer does what a human can do, but only within narrow bounds. For example, specialized AI can play chess, while a different specialized AI can play Go.

Ben Thompson notes that as Artificial Narrow Intelligence makes these advances, we typically stop calling it AI and start calling it simply “technology.” It remains narrow, but that might change:

Recall that while logic was developed over thousands of years, it was only part way through the 20th century that said logic was fused with physical circuits. Once that happened the application of that logic progressed unbelievably quickly.

Technology, meanwhile, has been developed even longer than logic has. However, just as the application of logic was long bound by the human mind, the development of technology has had the same limitations, and that includes the first half-century of the computer era. Accounting software is in the same genre as the spinning frame: deliberately designed by humans to solve a specific problem.

Machine learning is different. Now, instead of humans designing algorithms to be executed by a computer, the computer is designing the algorithms. It is still Artificial Narrow Intelligence — the computer is bound by the data and goal given to it by humans — but machine learning is, in my mind, meaningly different from what has come before. Just as Shannon fused the physical with the logical to make the computer, machine learning fuses the development of tools with computers themselves to make (narrow) artificial intelligence.

This is not to overhype machine learning: the applications are still highly bound and often worse than human-designed systems, and we are far, far away from Artificial General Intelligence. It seems clear to me, though, that we are firmly in Artificial Narrow Intelligence territory: the truth is that humans have made machines to replace their own labor from the beginning of time; it is only now that the machines are creating themselves, at least to a degree.

We’re nowhere near close to Artificial General Intelligence, but apparently we’re breaking through to an Artificial Narrow Intelligence that can build upon itself. In the words of our time, “Big if true.”

It would be a perverse thing if, in our quest to create a more general form of machine logic/thinking, we ended up also creating a less conscious/feeling humanity in the process.