DeepMind founder thinks AI will need to build on neuroscience

DeepMind founder thinks AI will need to build on neuroscience


A leading artificial intelligence expert has said he believes research in the field must start to build on existing areas of neuroscience. Describing the two areas as having a "long and intertwined history," he said AI will need to learn human qualities.

Demis Hassabis founded the artificial intelligence startup firm DeepMind. The business was sold to Google for $650 million in 2014. This week, Hassabis published a research paper in which he explores the connection between AI and neuroscience. His view is that the two fields are already aligned but closer work is required to continue AI's development.

Most modern neural networks are predominantly focused on problems that are solved by mathematical solutions. Areas such as speech recognition and image identification are some of the most promising areas of artificial intelligence. These rely on mathematical algorithms which, while complex, are relatively simple for experienced developers to build.

The harder challenge is in creating an AI that reflects the way the brain works. This would allow qualities such as creativity and imagination to be imbued in the system. The development of neural networks with these attributes could give rise to a new generation of artificial intelligence that can balance its mathematical prowess with moral and humanitarian concerns.

In the paper, published in the Neuron journal, Hassabis explained his belief that more study of neuroscience is required to facilitate the coming breakthrough in AI. The report, written with three co-authors, makes the case that we should first understand our own intelligence before attempting to replicate it in machines.

Hassabis' argument is based on two main principles. He believes neuroscience will serve to inspire and then validate new developments in artificial intelligence. The evolution of conscious AI with human qualities will eventually come to reflect biological mechanisms that have a proven role in enabling intelligence.

"The benefits to developing AI of closely examining biological intelligence are two-fold. First, neuroscience provides a rich source of inspiration for new types of algorithms and architectures," the paper explains. "Second, neuroscience can provide validation of AI techniques that already exist. If a known algorithm is subsequently found to be implemented in the brain, then that is strong support for its plausibility as an integral component of an overall general intelligence system."

The remainder of the paper focuses on the parallels between research and artificial intelligence. Arguing that a closer relationship between the two will be essential if AI's to play the societal role many expect, Hassabis suggested a "virtuous circle" should be setup where advances in either field benefit the other. He acknowledged that being an expert in either area is difficult though, let alone trying to identify ways to make your work relevant in the other.


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