Study in US: NeuroAI field building momentum at Columbia University

NeuroAI is a combination of Neuroscience and Artificial Intelligence. The Columbia University statement said that it is a great time to be a neuroscientist.

Columbia University asked about the perspectives of people at the university on the NeuroAI. Three PhD students from the university including Kenneth Miller, Richard Zemel, and Nikolaus Kriegeskorte talked about NeuroAI.

On the question of Why is NeuroAI emerging now, Dr. Kriegeskorte said, "About a decade ago, broadly brain-inspired neural network models began to deliver on their promises. Neural network models have since revolutionized AI. In parallel, the modern neuroscience revolution gave us much richer ways to measure and manipulate brain activity. These two complementary revolutions have disrupted both fields. Some researchers in each field are looking to the other field for guidance."

While responding to the same question Dr. Zemel said that technical advances are making it easier to manipulate and observe brains and quantify complex behaviours.

"AI systems have also made remarkable progress in solving tasks, often at superhuman levels of accuracy. The latest AI systems have surprised even the researchers in the field with their abilities, such as adapting to new domains and engaging in reasonable dialogues," added Dr. Zemel.

How Columbia University contributing to NeuroAI progress

The Centre for Theoretical Neuroscience at Columbia University is key to all this development. The centre provides a platform to coordinate mathematical modelling, neuroscience, machine learning and statistics, said Dr. Kriegeskorte.

ARNI brings engineers and biologists together and helps me to learn my own study on computational neuroscience, said Dr. Kriegeskorte.

"I’ll add that ARNI is opening pathways to the field’s future by funding postdoctoral fellows who will build the bridges between those of us who until now have been focusing either on natural or artificial intelligence alone, but who now are finding common ground in NeuroAI," Dr. Miller.

"Making a generalized AI with the versatility of our own brains is a famously hard problem. That’s because there’s no right way to generalize. Different AI models embody different biases and different prior assumptions. An AI that works well in some domains, like in writing tasks or recognizing objects, will likely not work well in others. The key question is whether NeuroAI will deliver AI that, like the human brain, can do it all," said Dr. Kriegeskorte.

Read more:

• Study in Germany: TUM offers numerous opportunities for vocational training
• Yale study reveals molecular mechanism behind autoimmune diseases