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A fascinating line of research into equipping machines with the knowledge to understand the world revolves around mathematical modelling and learning cause-effect relationships. Existing deep learning-based systems can simply learn symmetric mathematical functions, particularly correlations, but they cannot learn asymmetric relationships and are therefore unable to differentiate between causes and effects, such as the sunrise being the reason for the rooster’s crowing and not the other way around.

AI’s state of play

So what is the real state of affairs concerning AI? In reality, we have specific variants of artificial intelligence, in the sense that each one can execute a single task effectively, even though it does not really understand anything it is doing. Let’s focus on one AI technique, known as deep learning, which, over the last ten years, has achieved spectacular results based on its extraordinary capacity to identify statistical patterns by analysing large amounts of data. According to this technique, there is a programme called AlphaFold[1] that is able to predict 3D models of protein structures, i.e. how a protein folds in three-dimensional space. Solving the protein-folding problem is important for discovering and designing new drugs, because the role of proteins depends on their three-dimensional structure. Another very recent successful example of deep learning, combining neural networks and reinforcement learning, is the aforementioned generative AI, which can process natural language as well as music, images or videos from an initial description. Despite their apparent intelligence, these systems are very limited. In fact, they do not learn anything in the human sense of learning, since, unlike us, they do not learn incrementally, they cannot relate what they learn to what they have learned before and they have practically no capacity for generalising or reasoning.

So why is it widely believed that AI is on the verge of equalling human intelligence? Moreover, based on this premise, why are hypothetical artificial intelligences superior to human intelligence being predicted? In my opinion, excessive anthropomorphism is the main reason for society’s misperception of the real state of artificial intelligence. When we discover the outstanding results achieved by an AI in performing a very specific task, we tend to generalise and attribute this to its capacity to do almost anything we humans do, and even to do it much better. In reality, what today’s AI systems have is not intelligence but “competence without understanding” in the sense that Daniel Dennett describes in his book From Bacteria to Bach and Back: The Evolution of Minds[2]. That is, systems that can become highly skilled at performing specific tasks, such as recognising a series of elements in an image or generating text, but with absolutely no understanding of the nature of these elements or of the properties and connections between them.

The problem is that current AI systems do not have access to real-world reference points that inform the content of words because, as non-corporeal artificial intelligences, they cannot interact with the world. This inability to truly understand the world makes it impossible for major language models such as GPT-4 to understand the meaning of the texts they generate. In fact, the designers of these systems do not care about the absence of real-world reference points. They only care about rhetorical power, fooling listeners and readers into believing that they understand language like humans do, even though they generate a plethora of falsehoods.

Broadly speaking, the more sophisticated AI systems become, the more accountability we should demand from their designers and programmers, to ensure their compliance with legal and ethical principles. Artificial intelligence does not, and never will, have intentionality; they are a reflection of the intentions and biases of the teams of programmers and agencies involved in their implementation and deployment. We are the moral agents, not machines. This is why many experts point to the need to regulate their development and even to prohibit their use in cases of extreme risk, such as lethal weapons. But besides regulation, citizens, especially politicians, need to be educated about the benefits and risks of smart technologies. We need future citizens who are more and better informed, better able to assess technological risks, citizens with a more critical mind and capable of asserting their rights. This education process must start in schools and continue at university. In particular, science and engineering students need to be trained in ethics to better understand the social implications of the technologies they will develop. It is only by investing in education that will we achieve a society that can reap the benefits of smart technologies while minimising their risks. Artificial intelligence will thus play a major role in driving the progress of humankind.

[1] Jumper, J., Evans, R., Pritzel, A. et al. “Highly accurate protein structure prediction with AlphaFold”. Nature, 596, 583-589. 2021. http://ow.ly/XiVW50OnOjm

[2] Dennet, D. C. From Bacteria to Bach and Back: The Evolution of Minds. Penguin Books, 2017.

[3]  Bender, E. M., Gebru, T., McMillan-Major, A. i Shmitchell, S. “On the Danger of Stochastic Parrots: Can Language Models Be Too Big?”. ACM Conference on Fairness, Accountability, and Transparency, 610-623. 2021.