How AI and human intelligence will beat cancer
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In 2016, an important milestone for humanity was reached: artificial intelligence (AI) defeated the world champion in the Go game. For context: Go is a board game that was previously thought to require too much human intuition for a computer to succeed, and as a result it was a North Star for AI.
For years, researchers tried to create an AI system that could beat humans in the game, but failed. See you AlphaGo.
In 2016, AlphaGo, an AI system created by Google’s DeepMind, not only defeated its champion human counterpart (Lee Sedol); it showed that machines could find play strategies that no human would come up with. AlphaGo shocked the world when it made its unimaginable move #37. It was a move so counterintuitive and strange to human experts that after AlphaGo played it, it stunned and baffled Lee and all spectators and world experts. It eventually led to the triumph of technology during that game.
AI vs. Cancer: Looking for Move 37
In addition to illustrating the potential of AI in this context, the Go game showed that AI can and should help humanity devise the Move 37 for significant, real-world problems. This includes fighting cancer.
As with board games, there is a certain element of a game in the proverbial “match” between the human immune system and cancer. If the immune system is the cop guarding the health of the body, cancer is like a gangster trying to escape. While the “immune system police” searches for harmful cancer cells, viruses, infections and any ailments, cancer is devising various tactics of subversion, deception and destruction.
Let data strengthen our intuition
Centuries ago, scientists and doctors were largely in limbo when trying to cure disease and had to rely only on their intuition. Today, however, humanity is in a unique position to fully utilize available resources with advances in high throughput and biological data measurement. We can now create AI models and use all available data to let these AIs reinforce our innate intuition.
To more clearly illustrate this concept, we can look at the case of CAR-T cells edited with CRISPR (a genetic editing technology) to create a promising therapeutic option in cancer treatment. Many current and past approaches in the field relied on the intuition of a single researcher or academic group to prioritize the genes to be tested. For example, some of the world’s experts on genetically engineered T cells came up with the idea of trying to turn off the PD1, which didn’t work to improve patient outcomes. In this case, genes were not directly compared and it took a lot of human intuition to decide how best to proceed.
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 different 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 seem most promising to modify in patients to fight their cancer.
The ability to conduct extensive AI experiments and generate data to fight cancer is a game-changer. Biology and disease are so complex that current and past strategies, largely driven by human intuition, are unlikely to be the best approaches. In fact, we predict that in the next 10 years we will have the equivalent of a Move 37 against cancer: a therapy that at first glance seems counterintuitive (and where human intuition alone wouldn’t come), but which ultimately shocks us all and wins the game for patients.
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