Artificial intelligence research has a lot to learn from nature. My work links biology with computation every day, but recently the rest of the world was reminded of the connection: The 2018 Nobel Prize in Chemistry went to Frances Arnold together with George Smith and Gregory Winter for developing major breakthroughs that are collectively called "directed evolution." One of its uses is to improve protein functions, making them better catalysts in biofuel production. Another use is entirely outside chemistry - outside even the traditional life sciences. [...]

In the Nobel laureates' work, the natural principle at work is evolution - which is also the approach I use to develop artificial intelligence. My research is based on the idea that evolution led to general intelligence in biological life forms, so that same process could also be used to develop computerized intelligent systems.

When designing AI systems that control virtual cars, for example, you might want safer cars that know how to avoid a wide range of obstacles - other cars, trees, cyclists and guardrails. My approach would be to evaluate the safety performance of several AI systems. The ones that drive most safely are allowed to reproduce - by being copied into a new generation.

Yet just as nature does not make identical copies of parents, genetic algorithms in computational evolution let mutations and recombinations create variations in the offspring. Selecting and reproducing the safest drivers in each new generation finds and propagates mutations that improve performance. Over many generations, AI systems get better through the same method nature improves upon itself - and the same way the Nobel laureates made better proteins.