Artificial intelligence is certainly one of the most interesting fields of modern science, since it involves many different issues and uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, ontology, operations research, economics, control theory, probability, optimization and logic.

Most AI books define it as “the study and design of intelligent agents,” although it’s clear that in order to reproduce intelligence through agents of any sort, it needs to be understood what the word “intelligence” actually denotes.

It’s evident that, since we define ourselves as intelligent (sometimes mistakenly) and since our intelligence is notoriously due to our brain, every discussion about intelligence can basically be reduced to a discussion about human brain. It’s nice to remember that, according to Princeton’s professor Jonathan Cohen “There are more synapses in the brain than stars in the galaxy” and “[The human brain is] the most complex device in the known universe“.

There are many different approaches to the AI-issue, depending on which sides or aspects one is interested in exploring. In this brief introduction, we will focus on an abstract yet fundamental question, which Alan Turing has already undertaken: “Can machines think?“. In order to make things easier, we need to analyze the question and, if possible, decompose it into sub-problems. What do we mean with “machine”? What does the word “think” mean?

A machine is commonly defined as any algorithm-capable automaton; I think this definition is limiting, since it assumes that intelligence is an algorithmic process, so we will assume here that a “machine” is anything human beings can build and reproduce.

To ask what the verb “think” means is the same as asking what intelligence actually  is: we’re more or less at the same point where we started. A good hint to a possible solution to the problem comes from noticing that, apparently, we just assume that other people (intelligence is perforce anthropocentric) think only according to their behaviours. So the question might become “Can a machine act displaying intelligence?”

But what about the real ability to solve problems intelligently? We just formulated an exterior question, but we want an ontological answer, we want to know if a machine can actually feel like humans, if it can share with us the same intelligence structure. To put it simply, we’re asking whether a machine can have a mind.

The first serious attempt in order to answer to the question “Can machines think?” has been taken by Alan Turing in a famous 1950 paper, where he basically reduced this question to the exterior one we pointed out earlier. Trough thin deductions and fine reasoning he concluded that “if a machine acts as intelligently as human being, then it is as intelligent as a human being.”.

Marvin Minsky gave another, though not less relevant, answer to the issue in a 1943 paper, where he claimed that “if the nervous system obeys the laws of physics and chemistry, which we have every reason to suppose it does, then we ought to be able to reproduce the behavior of the nervous system with some physical device.”

Another fundamental contribution came from Alan Newell and Herbert Simon, which proposed that “symbol manipulation” was the essence of both human and machine intelligence. They wrote: “A formal symbol system has the necessary and sufficient means of general intelligent action.”
Another, actually relevant, version of this position has been formulated by Hubert Dreyfus, who called it “the psychological assumption”: “The mind can be viewed as a device operating on bits of information according to formal rules.”; Dreyfus criticized this assumption, claiming that it rests on two others: the epistemological and ontological assumptions. The first one is that “all activity (either by animate or inanimate objects) can be formalised(mathematically) in the form of predictive rules or laws“. The ontological assumption is that “reality consists entirely of a set of mutually independent, atomic (indivisible) facts“. It’s because of the epistemological assumption that workers in the field argue that intelligence is the same as formal rule-following, and it’s because of the ontological one that they argue that human knowledge consists entirely of internal representations of reality.
According to Dreyfus, a context free psychology is a contradiction in terms, since we cannot be able to understand our own behavior in the same way as we understand objects.
Dreyfus’s arguments against this position are taken from the phenomenological and hermeneutical tradition (especially the work of Martin Heidegger). Heidegger argued that, contrary to the cognitivist views on which AI is based, our being is in fact highly context bound, which is why the two context-free assumptions are false. Dreyfus doesn’t deny that we can choose to see human (or any) activity as being ‘law governed’, in the same way that we can choose to see reality as consisting of indivisible atomic facts…if we wish. But it is a huge leap from that to state that because we want to or can see things in this way that it is therefore an objective fact that they are the case. In fact, Dreyfus argues that they are not (necessarily) the case, and that, therefore, any research program that assumes they are will quickly run into profound theoretical and practical problems. Therefore the current efforts of workers in the field are doomed to failure.

Another famous (despite its weakness) argument against symbol processing, is John Lucas‘ oen; he argued that “Gödel’s theorem seems to me to prove that mechanism is false, that is, that minds cannot be explained as machines.“ Roger Penrose “expanded” on this argument in his ridicolus 1989 book “The Emperor’s New Mind“, where he pathetically speculated that quantum mechanical processes in neurons’ structures called microtubules, gave humans this special advantage over machines.

Luckily, Douglas Hofstadter, in his pulitzer-awarded “Gödel, Escher, Bach” book, explains that these “Gödel-statements” always refer to the system itself, similar to the way the Epimenides paradox uses statements that refer to themselves, such as “this statement is false” or “I am lying”. But, of course, the Epimenides paradox applies to anything that makes statements, whether they are machines or humans, even Lucas. This shows that Lucas himself is subject to the same limits that he describes for machines, as are all people, and so Lucas’s argument is pointless.

Regarding the other question, it basically revolves around a position defined by John Searle as “strong AI“:

A physical symbol system can have a mind and mental states.

Searle distinguished this position from what he called “weak AI“:

A physical symbol system can act intelligently.

Searle introduced the terms to isolate strong AI from weak AI so he could focus on what he thought was the more interesting and debatable issue. He argued that even if we assume that we had a computer program that acted exactly like a human mind, there would still be a difficult philosophical question that needed to be answered.

Neither of Searle’s two positions are of great concern to AI research, since they do not directly answer the question “can a machine display general intelligence?” (unless it can also be shown that consciousness is necessary for intelligence). Turing wrote “I do not wish to give the impression that I think there is no mystery about consciousness … but I do not think these mysteries necessarily need to be solved before we can answer the question [of whether machines can think].” Bertand Russell and Peter Norvig agree: “Most AI researchers take the weak AI hypothesis for granted, and don’t care about the strong AI hypothesis.“

For scientists the word “consciousness” refers to the familiar everyday experience of having a “thought in your head”, like a perception, a dream, an intention or a plan, and to the way we know something, or mean something or understand something. What is mysterious and fascinating is not so much what it is but rather how it works: how does a lump of fatty tissue and electricity give rise to this (familiar) experience of perceiving, meaning or thinking?