There has been shock around the world at the rapid rate of progress with ChatGPT and other artificial intelligence created with what’s known as large language models (LLMs). These systems can produce text that seems to display thought, understanding and even creativity.

Illus_man / Shutterstock

But can these systems really think and understand? This is not a question that can be answered through technological advance, but careful philosophical analysis and argument tells us the answer is no. And without working through these philosophical issues, we will never fully comprehend the dangers and benefits of the AI revolution.

In 1950, the father of modern computing, Alan Turing, published a paper which laid out a way of determining whether a computer thinks. This is now called “the Turing test”. Turing imagined a human being engaged in conversation with two interlocutors hidden from view: one another human being, the other a computer. The game is to work out which is which.

If a computer can fool 70% of judges in a five-minute conversation into thinking it’s a person, the computer passes the test. Would passing the Turing test – something which now seems imminent – show that an AI has achieved thought and understanding?

Chess challenge

Turing dismissed this question as hopelessly vague, and replaced it with a pragmatic definition of “thought”, whereby to think just means passing the test.

Turing was wrong, however, when he said the only clear notion of “understanding” is the purely behavioural one of passing his test. Although this way of thinking now dominates cognitive science, there is also a clear, everyday notion of “understanding” that’s tied to consciousness. To understand in this sense is to consciously grasp some truth about reality.

In 1997, the Deep Blue AI beat chess grandmaster Garry Kasparov. On a purely behavioural conception of understanding, Deep Blue had knowledge of chess strategy that surpasses any human being. But it was not conscious: it didn’t have any feelings or experiences.

Humans consciously understand the rules of chess and the rationale of a strategy. Deep Blue, in contrast, was an unfeeling mechanism that had been trained to perform well at the game. Likewise, ChatGPT is an unfeeling mechanism that has been trained on huge amounts of human-made data to generate content that seems like it was written by a person.

It doesn’t consciously understand the meaning of the words it’s spitting out. If “thought” means the act of conscious reflection, then ChatGPT has no thoughts about anything.

Time to pay up

How can I be so sure that ChatGPT isn’t conscious? In the 1990s, neuroscientist Christof Koch bet philosopher David Chalmers a case of fine wine that scientists would have entirely pinned down the “neural correlates of consciousness” in 25 years.

By this, he meant they would have identified the forms of brain activity necessary and sufficient for conscious experience. It’s about time Koch paid up, as there is zero consensus that this has happened.

This is because consciousness can’t be observed by looking inside your head. In their attempts to find a connection between brain activity and experience, neuroscientists must rely on their subjects’ testimony, or on external markers of consciousness. But there are multiple ways of interpreting the data.

Chess player
Unlike computers, humans consciously understand the rules of chess and the underlying strategy. LightField Studios / Shutterstock

Some scientists believe there is a close connection between consciousness and reflective cognition – the brain’s ability to access and use information to make decisions. This leads them to think that the brain’s prefrontal cortex – where the high-level processes of acquiring knowledge take place – is essentially involved in all conscious experience. Others deny this, arguing instead that it happens in whichever local brain region that the relevant sensory processing takes place.

Scientists have good understanding of the brain’s basic chemistry. We have also made progress in understanding the high-level functions of various bits of the brain. But we are almost clueless about the bit in-between: how the high-level functioning of the brain is realised at the cellular level.

People get very excited about the potential of scans to reveal the workings of the brain. But fMRI (functional magnetic resonance imaging) has a very low resolution: every pixel on a brain scan corresponds to 5.5 million neurons, which means there’s a limit to how much detail these scans are able to show.

I believe progress on consciousness will come when we understand better how the brain works.

Pause in development

As I argue in my forthcoming book “Why? The Purpose of the Universe”, consciousness must have evolved because it made a behavioural difference. Systems with consciousness must behave differently, and hence survive better, than systems without consciousness.

If all behaviour was determined by underlying chemistry and physics, natural selection would have no motivation for making organisms conscious; we would have evolved as unfeeling survival mechanisms.

My bet, then, is that as we learn more about the brain’s detailed workings, we will precisely identify which areas of the brain embody consciousness. This is because those regions will exhibit behaviour that can’t be explained by currently known chemistry and physics. Already, some neuroscientists are seeking potential new explanations for consciousness to supplement the basic equations of physics.

While the processing of LLMs is now too complex for us to fully understand, we know that it could in principle be predicted from known physics. On this basis, we can confidently assert that ChatGPT is not conscious.

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Karma Paljor

There are many dangers posed by AI, and I fully support the recent call by tens of thousands of people, including tech leaders Steve Wozniak and Elon Musk, to pause development to address safety concerns. The potential for fraud, for example, is immense. However, the argument that near-term descendants of current AI systems will be super-intelligent, and hence a major threat to humanity, is premature.

This doesn’t mean current AI systems aren’t dangerous. But we can’t correctly assess a threat unless we accurately categorise it. LLMs aren’t intelligent. They are systems trained to give the outward appearance of human intelligence. Scary, but not that scary.

Philip Goff, Associate Professor of Philosophy, Durham University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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