Neuroscience and artificial intelligence can help improve each other

Despite their names, artificial intelligence technologies and their component systems, such as artificial neural networks, don’t have much to do with real brain science. I’m a professor of bioengineering and neurosciences interested in understanding how the brain works as a system – and how we can use that knowledge to design and engineer new machine learning models.

In recent decades, brain researchers have learned a huge amount about the physical connections in the brain and about how the nervous system routes information and processes it. But there is still a vast amount yet to be discovered.

At the same time, computer algorithms, software and hardware advances have brought machine learning to previously unimagined levels of achievement. I and other researchers in the field, including a number of its leaders, have a growing sense that finding out more about how the brain processes information could help programmers translate the concepts of thinking from the wet and squishy world of biology into all-new forms of machine learning in the digital world.

“Machine learning” is one part of technologies that are often labeled “artificial intelligence.” Machine learning systems are better than humans at finding complex and subtle patterns in very large data sets.

These systems seem to be everywhere – in self-driving cars, facial recognition software, financial fraud detection, robotics, helping with medical diagnoses and elsewhere. But under the hood, they’re all really just variations on a single statistical-based algorithm.

Artificial neural networks, the most common mainstream approach to machine learning, are highly interconnected networks of digital processors that accept inputs, process measurements about those inputs and generate outputs. They need to learn what outputs should result from various inputs, until they develop the ability to respond to similar patterns in similar ways.

If you want a machine learning system to display the text “This is a cow” when it is shown a photo of a cow, you’ll first have to give it an enormous number of different photos of various types of cows from all different angles so it can adjust its internal connections in order to respond “This is a cow” to each one. If you show this system a photo of a cat, it will know only that it’s not a cow – and won’t be able to say what it actually is.

But that’s not how the brain learns, nor how it handles information to make sense of the world. Rather, the brain takes in a very small amount of input data – like a photograph of a cow and a drawing of a cow. Very quickly, and after only a very small number of examples, even a toddler will grasp the idea of what a cow looks like and be able to identify one in new images, from different angles and in different colors.

Because the brain and machine learning systems use fundamentally different algorithms, each excels in ways the other fails miserably. For instance, the brain can process information efficiently even when there is noise and uncertainty in the input – or under unpredictably changing conditions.

You could look at a grainy photo on ripped and crumpled paper, depicting a type of cow you had never seen before, and still think “that’s a cow.