After several decades of developments in AI, has the inspiration that can be drawn from neuroscience been exhausted? Recent initiatives make the case for taking a fresh look at the intersection between the two fields.
The effects of neuroscience on artificial intelligence (AI), and the mutual influence of the two fields, have been discussed and debated in the past few decades. Not long after the seminal workshop at Dartmouth College in 1956, which launched the field of AI, artificial neural networks called perceptrons were introduced by Rosenblatt. He studied them as simple models of brain-inspired systems following earlier work, including from McCulloch and Pitts, who introduced formal models of biological neurons, and from Hebb, who postulated the conditions under which the connection strengths of biological neurons change. Research on hierarchical processing in the visual system in the 1960s inspired the development of convolutional neural networks in the 1980s. However, as AI research has evolved at a fast pace, progress over recent years has stirred a divergence from this original neuroscience inspiration. The pursuit of more powerful artificial neural systems in leading AI research labs, particularly those affiliated with tech companies, is currently focussed on engineering. This pursuit emphasizes further scaling up of complex architectures such as transformers, rather than integrating insights from neuroscience.
Source: https://www.nature.com/articles/s42256-024-00826-6
