The next big wave: AI and the value of interdisciplinary thinking

AI and the value of interdisciplinary thinking

By Chelsea Vadnie (OWU Assistant Professor of Psychology and Neuroscience

In 1950, mathematician Alan Turing published “Computing Machinery and Intelligence” in SPIRITa journal originally intended for work in psychology and philosophy. Several disciplines still engage with his questions and ideas. Turing begins by asking, “Can machines think?” In 2024, we would probably all agree that machines can “think,” but now we are concerned with what that means for the future. In his article “What is Artificial Intelligence?” John McCarthy, one of the founders of AI, said, “AI need not be limited to methods that are biologically observable.” In other words, machines/programs can be designed to perform mental functions in a different way than the human brain.

The building blocks of the brain, neurons, are thought to have evolved hundreds of millions of years ago. If biology long ago selected neurons as the computational units of life, why not use principles of neurobiology to create artificial intelligence? Not surprisingly, scientists began applying principles of neurobiology to computer science in the 1950s.

We can think of neurons as computational nodes that integrate and send information. Much like neural networks in simple organisms, these nodes can be assembled into communicating networks to perform simple functions. Specifically, in the 1950s, psychologist Frank Rosenblatt built a neural network-like machine, a perceptron, to make binary decisions, such as right or left. The perceptron integrated multiple inputs and then changed the weights of the inputs to make predictions. In other words, it learned.

This was similar to one of Turing’s ideas that intelligent machines could be built like a “child’s brain” and subjected to a “suitable course of education”.

Similarly, modern AI uses artificial neural networks and “learns” from large amounts of data to produce original results. Thus, psychology, neuroscience, philosophy, biology and other fields (interdisciplinary thinking) have helped inspire computer science.

As a neuroscientist at OWU, I am interested in how AI will impact education, research, and healthcare. AI has the potential to help educators use their time more efficiently to better meet the individual needs of students. AI shows promise in aiding drug discovery. AI helps us understand how the brain encodes information. In healthcare, there is hope for AI-powered precision psychiatry, with AI tools to help doctors diagnose and decide on treatments. Brain-computer interfaces are being paired with AI to create devices that improve the quality of life for people with injuries or impairments.

We are full of hope for AI.

We are also right to be cautious. AI is a useful but imperfect tool. It produces results based on its “training”. We should be skeptical of AI and try to understand the logic behind its results.

What does the future hold for AI? Turing proposed that three components should be considered when designing a machine that resembles the adult human brain. These components are the state of the brain at birth, the education a person receives, and the experiences a person has had. These components are complex: brain development, brain circuit function, encoding and storage of information, and the lasting effects of experiences on biology.

We hope that AI will advance science and education and expand our knowledge of these components. Our new knowledge of Turing’s components could in turn be used to inspire advances and applications of AI.

Turing concluded his lecture by saying: “Although we can only see a short distance into the future, we can see that there is much to be done there.”

What is certain, however, is that we all need interdisciplinary thinking to accomplish the “rich work.”