Hybrid Vigor: The Intersection of Genetics and Artificial Intelligence

Why I’m Studying Some Of Our Most Dangerous Superpowers

The worlds of genetics and machine learning are both in the midst of their own Cambrian Explosions. If you have been living under a rock, you might not have noticed. From gene drives to “The Singularity”, these two fields have made waves in science and technology headlines for good reasons; these fields give humanity a small taste of what it’s like to play god. Both of these fields have seen remarkable advances in the 2010’s and the pace of research is continuing to pick up speed as we approach the 2020’s.

With genetic engineering, we can reprogram the basic building blocks of life. As we become more and more adept at manipulating genes and genomes, we may eventually have the power to create entirely new forms of life that have never existed before. On the other side, AI researchers are working towards an “artificial general intelligence”, a computer than can think and reason about the world in general, on its own. While current machine learning systems are narrow AI’s, the speed of advances has outpaced even the most optimistic predictions. Scientists and researchers have even discovered that a popular kind of machine learning algorithm — neural networks — have an unanticipated degree of generality.

I personally harbor both excitement and trepidation about humanity’s ever growing technological prowess. Machine learning has already shown great promise in automated cancer screening, but the same technology is deployed to massively automate surveillance in ways that ought to frighten us. Genetic engineering displays a similar dichotomy. The promise of eliminating genetic diseases like Huntington’s disease is counterbalanced by fears of eugenics. Clearly, futurists and sci-fi writers have plenty of fodder for the next wave of entertainment, whether they will be utopian or dystopian is left as an exercise for other writers.

One thing that is clear — the people of the world need to have a richer dialog about the state of these fields; and that dialog must be fueled by accurate and timely information regarding these challenging and complex subjects. Unfortunately, our media diets (in part thanks to machine learning) are clogged up with the banal and the outrageous. Science writing doesn’t get top billing, there is precious little of it that is accessible to the average citizen; and unfortunately some of the most accessible writing is full of errors and downright lies. But, with so many quandaries that rely on science entering the public domain, we can no longer afford to have the general public (and especially the voting public) remain uninformed about these topics.

So, I have embarked on a quest to do my part to change this state of affairs. I am going back to grad school to study the intersection of genetics and machine learning, and along the way I am going to be writing, recording, and creating other materials to spread the knowledge that I hope to gain. Starting with this article: a description of what I think is so exciting — and so important — about the intersection of these two disciplines.

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