The field of NeuroAI encompasses two intertwined research programs: the use of artificial intelligence (AI) to model intelligent behavior, and the application of neuroscience insights to improve AI systems.
The motivation for using neuroscience to improve AI is clear: If the ultimate goal is, in the words of AI pioneer Marvin Minsky, “to build machines that can perform any […] task that a human can do,” then the most natural strategy is to reverse-engineer the brain. The motivation for using AI—in particular, artificial neural networks (ANNs)—to model neuroscience is that they represent our best model of distributed brain-like computation; indeed, these are the only models that can solve hard computational problems.
In spite of remarkable progress over the past decade, though, modern AI still lags far behind people and other animals on some tasks. AI systems can now write essays, pass the bar exam, ace advanced physics tests, prove mathematical theorems, write complex computer programs and flawlessly recognize speech. In many other domains, however—including navigating the physical world, planning over multiple time scales and performing perceptual reasoning—AI is mediocre at best.
To read more, click here.