The convergence of machine learning and neuroscience has sparked debate about whether AI can advance the field without traditional “understanding.”
In an article in The Transmitter, experts consider how the two fields are essentially interchangeable. Neuroscience is increasingly focused on prediction, while machine learning is moving toward causal explanations, News.Az reports, citing The Transmitter.
Large-scale neural basis models are now generalizable across different species and brain regions, suggesting that machine-learnable rules govern the activity of neural populations.
But some researchers, such as Anthony Zador, argue that while AI can find structure in vast data sets and automate analysis, true understanding may require “creating” or replicating the brain’s calculations. This concept is rooted in Richard Feynman’s famous quote, “I can’t understand what I can’t create.” Others have suggested that the future of the field, or “NeuroAI,” needs to go beyond pure data processing and embrace “embodiment,” recognizing that brain function is inexorably shaped by the body and its interactions with the physical world.
News.AZ
Written by Leila Sirinova
