If there’s a theory of AI, computer science is unlikely to provide it Premium

The possibility of AI thinking like humans, the Turing test, and the limitations of current AI systems.

The popular understanding seems to be that the day is not far off when an artificial intelligence (AI) will be able to think like humans and interact, at least through languages, in a way that is indistinguishable from real humans. Such a day has been called “the singularity”, a pivotal moment for the human race. With the recent success of large language models (LLM) like ChatGPT, which are capable of interpreting language use and composing sentences, many think this day is imminent.

When confronted with such a possibility, Ludwig Wittgenstein, one of the most influential philosophers of the 20th century, famously said, “But a machine surely cannot think!” He perhaps meant the concepts of thinking and intelligence can only apply to living objects; it would be grammatically and logically incorrect otherwise. Nevertheless, machines can indeed share some traits of human behaviour, so even without precise definitions of these terms, their increasing use for machines is perhaps germane. In fact, in the eventuality that we do go past the “singularity” – a proposition that sounds frightening – a machine may have to be treated someday like a person.

Most folks trained in computer science believe such AI must be possible. This is because central to the accepted theory of computation – as obtained among others by Alan Turing in 1936 – is the existence of an abstract algorithmic concept of a universal computer, a device that can simulate the actions of all other computers.

At the risk of some over-simplification, we can think of this universal computer as one that can execute any program written in any modern programming language given unbounded memory and time. Of course, it may not be able to do so “efficiently”, but that is only because we may not yet have discovered a sufficiently efficient model of computation. Given adequate time and memory, the universal computer can, in principle, simulate with arbitrary precision all physical and chemical processes of the brain and other parts of the human body, and actually all of nature’s, provided their theories are understood. The physicist, philosopher, and computer scientist David Deutsch calls this a fundamental law of physics and computer science.

Of course, Turing fully understood universality and believed AI must be possible. If it is, it will also need sensorimotor perception because it cannot possibly rely on external intelligence to provide it with the essential methods to survive and exchange signals with the outside world. Turing also estimated the resources required to simulate a human brain, which he argued must also be a universal computer, will not be very large – in fact, less than that of a typical modern laptop. After all, the average size of the human brain is not all that much. And the fact that there must exist computational problems that can’t be solved by a universal computer – as established by Gödel’s incompleteness theorem and Turing’s own results on computability – did not deter his arguments because humans also can’t solve many problems.

He also formulated a test for AI where a human judge should be unable to tell whether it is a human or a program based on interacting with it. Many believe that current state-of-the art LLM-based AI software like ChatGPT, built using deep neural networks, may have come close to passing this Turing test.

Thus, the question arises: do we know how the brain works to be able to program a universal simulator for AI? That is, can a parametrised neural network model with parameters estimated using a purely data-driven inductive method become a program for the universal simulator? Unfortunately, the answers to these have to be a resounding ‘no’. We are not even close.