Neuromorphic Cognitive Learning Systems: The Future of Artificial Intelligence?

Neuromorphic Cognitive Learning Systems: The Future of Artificial Intelligence?

연구 분야: Artificial Intelligence

학회: Cognitive Computation

In this position paper, I outline the caveats of the current artificial intelligence (AI) field driven by deep learning (DL) and large data volumes. Although AI/DL has demonstrated huge potential and attracted huge investments globally, it encounters big problems – it not only need to collect huge datasets and spend enormous time and resources to be trained on them, but also the trained system cannot deal effectively with any never encountered before (novel) data. From a human perspective, any current AI/DL system is completely unintelligent. It is only able to represent information but have no awareness of what this information means. I propose as an alternative the Neuromorphic Cognitive Learning Systems (NCLS), intimate imitations of animal and human brains, able to address the AI/DL limitations and achieve true artificial general intelligence. Similar to human and animal brains NCLS are unparalleled in their ability to rapidly, and on their own, adapt and learn from changing and unexpected environmental contingencies with very limited resources. I describe how NCLS driven AI inspired by human/animal brains can pave the way to new computing technologies with the potential to revolutionize the industry, economy and society. It is my strong belief that NCLS investigations will have major impact to real-time autonomous systems to achieve human-like intelligence capabilities.