Can machines think? Or are humans machines?

This dimension for understanding AI refers to how a computer program reaches its conclusion. Symbolic AI
refers to the fact that all steps are based on “symbolic”
human-readable representations of the problems which use logic and search
to solve problems. Expert Systems are a typical example of symbolic AI
as the knowledge is encoded in IF-THEN rules which are understandable by
people. NLP systems which use grammars to parse language are also symbolic AI systems. Here the symbolic representation is the grammar of
the language.

The main advantage of symbolic AI is that the reasoning process can
be understand by people, which is a very important factor for taking
important decisions. A symbolic AI program can explain why a certain
conclusion is reached and what the intermediate reasoning steps have
been. This is key for using AI systems that give advice on medical
diagnosis; if doctors cannot understand why an AI system comes to its conclusion, it is harder for them to accept the advice.

Non-symbolic AI systems do no
manipulate a symbolic representation to find solutions to problems.
Instead, they perform calculations according to some principles which have
demostrated their capability to solve problems without exactly understanding how
to arrive at their solutions. Examples include genetic algorithms,
neural networks and deep learning. The origin of non-symbolic AI comes
from the attempt to mimic the workings of the human brain; a complex
network of highly interconnected cells whose electrical signal flows
decide how we, humans, behave. Figure 2 illustrates the difference between a symbolic and non-symbolic representation of an apple. Obviously, the symbolic representation is easy to understand by humans, whereas the symbolic representation isn’t.

Today, non-symbolic AI,
through deep learning and other machines learning algorithms, is
achieving very promising results, championed by IBM’s Watson, Google’s
work on automatic translation (which has no understanding of the
language itself, it “just” looks at co-occurring patterns), Facebook’s
algorithm for face recognition, self-driving cars, and the popularity of
deep learning. The main disadvantage of non-symbolic AI systems is that
no “normal” person can understand how those systems come to their conclusions or
actions, or take their decisions. See for example Figure 2: in the left part we can understand easily why something is an apple, but looking at the right part, we cannot easily understand why the system concludes that it’s an apple. When non-symbolic (aka connectionist) systems are applied to
critical tasks such as medical diagnosis, self-driving cars, legal
decisions, etc, understanding why they come to a certain conclusion
through a human-understandable explanation is very important. In the end, in the
real world, somebody needs to be accountable or liable for the decisions
taken. But when an AI program takes a decision and no-one understands
why, then our society has an issue (see FATML, an initiative that investigates Fairness, Accountability, and Transparency in Machine Learning).

It is
now clear that machines certainly can perform complex tasks that would
require “thinking” if performed by people. But can computers have
consciousness? Can they have, feel or express emotions? Or, are we,
people, machines? After all our bodies and brains are based on a very
complex “machinery” of mechanical, physical and chemical processes, that
so far, nobody has fully understood. There is a research field called
“computational emotions” which tries to build programs that are able to
express emotions. But maybe expressing emotions is different than feeling
them? (See Intentional Stance in this post).

Another critical issue for the final question is whether machines can have consciousness. This is an even trickier question than whether machines can think. I will leave you with this MIT Technology Review interview with Christof Koch about “What It Will Take for Computers to Be Conscious”, where he says: “Consciousness is a property of complex systems that have a particular “cause-effect” repertoire. They have a particular way of interacting with the world, such as the brain does, or in principle, such as a computer could.”

In my opinion, currently, there are no
scientific answers to those questions, and whatever you may think about
it, is more a belief or conviction than a commonly accepted truth or a scientific result.
Maybe we have to wait until 2045, which is when Ray Kurzweil predicts technological singularity
to occur: the point when machines become more intelligent than humans.
While this point is still far away and many believe it will never
happen, it is a very intriguing theme evidenced by movies such as 2001: A
Space Odyssey, A.I. (Spielberg), Ex Machina and Her, among others.