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2016 saw the completion of a milestone for humanity: artificial intelligence (AI) beat the world champion at the game of Go. For context, Go is a board game that was previously thought to require too much human intuition for a computer to succeed, and therefore it was a North Star for AI.
For years, researchers have tried and failed to create an AI system that could beat humans in gaming. Alpha Go.
In 2016, AlphaGo, an AI system created by Google DeepMind, not only beat his champion human counterpart (Lee Sedol); he demonstrated that machines could come up with game strategies that no human would come up with. AlphaGo shocked the world when it performed its unimaginable stunt #37. It was such a counter-intuitive and strange move to human experts that after AlphaGo played it, it stunned and perplexed Lee and all onlookers and experts in the world. This ultimately led to the triumph of technology during this game.
AI against cancer: in search of Move 37
Beyond illustrating the potential of AI in this context, the game of Go demonstrated that AI could and should help humanity deliver Move 37 for meaningful real-world problems. Among these is the fight against 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 protecting the health of the body, cancer is like a gangster trying to evade capture. While the “immune system police” are looking for harmful cancer cells, viruses, infections and any other disorder, Cancer is busy coming up with various tactics of subversion, deception and destruction.
Let the data augment our intuition
Centuries ago, scientists and doctors largely operated in obscurity when trying to cure diseases and had to rely solely on their intuition. Today, however, humanity is in a unique position to make full use of available resources with advances in broadband and the measurement of biological data. We can now create AI models and use all available data to enable these AIs to augment our innate intuition.
To illustrate this concept more clearly, consider the case of CAR-T cells edited with CRISPR (a gene editing technology) to create a promising therapeutic option in the treatment of cancer. Many current and past approaches in the field have relied on the intuition of a single researcher or academic group to prioritize which genes to test. For example, some of the world’s genetically engineered T cell experts came up with the idea of trying to eliminate PD1, which failed to improve patient outcomes. In this case, the genes weren’t compared head-to-head, and it took a lot of human intuition to decide the best way to go.
Recently, with advances in high-throughput single-cell CRISPR sequencing methods, we are approaching the possibility of simply testing all genes simultaneously on an equal footing and in various experimental scenarios. This makes the data better suited for AI and, in this case, we have the opportunity to let AI help us decide which genes look most promising to modify in patients to fight their cancer.
The ability to conduct large AI experiments and generate data to fight cancer is a game-changer. Biology and disease are so complex that current and past strategies, guided largely by human intuition, are unlikely to be the best approaches. In fact, we predict that within the next 10 years we will have the equivalent of a Move 37 against cancer: a therapy that at first glance may seem counter-intuitive (and which human intuition alone would not achieve). ) but which in the end shocks us all and wins the game for the patients.
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