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2016 saw the completion of a significant milestone for humanity: artificial intelligence (AI) beat the world champion in the Go game. For context, Go is a board game previously thought to require too much human intuition for a computer to succeed in, and as a result, it was a North Star for AI.
For years, researchers tried and failed to create an AI system that could beat humans in the game. Until AlphaGo.
In 2016, AlphaGo, an AI system created by Google’s DeepMind, not only beat its champion human counterpart (Lee Sedol); it demonstrated that machines could find playing strategies that no human would come up with. AlphaGo shocked the world when it performed its unimaginable move #37. It was a move so counterintuitive and strange to human experts that after AlphaGo played it, it stunned and perplexed Lee and all the onlookers and world experts. It ultimately led to the technology’s triumph during that game.
AI vs. cancer: In search of Move 37
Beyond exemplifying AI’s potential in this context, the Go game demonstrated that AI could and should help humanity come up with the Move 37 for significant, real-world problems. Among these include fighting cancer.
Like board games, there is a particular element of a game in the proverbial “contest” between the human immune system and cancer. If the immune system is the policeman guarding the health of the body, cancer is like a mobster that is trying to elude capture. While the “immune system police” search for harmful cancer cells, viruses, infections and any disorders, cancer is busy coming up with various tactics of subversion, deceit and destruction.
Let data augment our intuition
Centuries ago, scientists and doctors operated largely in the dark when attempting to cure diseases and had to rely solely on their intuition. Today, however, humanity is uniquely positioned to fully utilize available resources with advancements in high throughput and measurement of biological data. We can now create AI models and use every bit of available data to allow these AIs to augment our innate intuition.
To illustrate this concept more clearly, consider the case of CAR-T cells edited with CRISPR (a genetic editing technology) to create a promising therapeutic option in treating cancer. Many current and past approaches in the field relied on a single researcher or academic group’s intuition for prioritizing which genes to test edit. For example, some of the world’s experts in genetically engineered T cells came up with the idea of trying to knock out the PD1, which did not play out to improve patient outcomes. In this case, genes were not compared head-to-head, and a lot of human intuition was required to decide how to best proceed.
Recently, with advances in high-throughput single-cell CRISPR sequencing methods, we are nearing the possibility of simply testing all genes simultaneously on equal footing and in various experimental scenarios. This makes the data a better fit for AI and, in this case, we have the opportunity of letting AI help us decide on which genes look most promising to modify in patients to fight their cancer.
The ability to run extensive AI experiments and generate data for fighting cancer is a game-changer. Biology and disease are so complex that it is improbable that current and past strategies, driven largely by human intuition, are the best approaches. In fact, we predict that in the next 10 years, we will have an equivalent of a Move 37 against cancer: a therapy that at first may seem counterintuitive (and at which human intuition alone would not arrive) but that in the end, shocks us all and wins the game for patients.
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