Review of the new book Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World by the Harvard computer scientist Leslie Valiant:
Nature speaks in algorithms. Dr. Valiant believes that phenomena like evolution, adaptation and learning are best understood in terms of “ecorithms,” a term he has coined for algorithms that interact with and benefit from their environment. Ecorithms are at play when children learn how to distinguish cats from dogs, when we navigate our way in a new city — but more than that, Dr. Valiant writes, when organisms evolve and when brain circuits are created and maintained.
An organism can adapt to a new environment by starting with a hypothesis about the environment, then testing it against new data and, based on the feedback, gradually improving the hypothesis by using an ecorithm, to behave more effectively.
The evolution of species, as Darwin taught us, relies on natural selection. But Dr. Valiant argues that if all the mutations that drive evolution were simply random and equally distributed, it would proceed at an impossibly slow and inefficient pace.
Darwin’s theory “has the gaping gap that it can make no quantitative predictions as far as the number of generations needed for the evolution of a behavior of a certain complexity,” he writes. “We need to explain how evolution is possible at all, how we got from no life, or from very simple life, to life as complex as we find it on earth today. This is the BIG question.”
Dr. Valiant proposes that natural selection is supplemented by ecorithms, which enable organisms to learn and adapt more efficiently. Not all mutations are realized with equal probability; those that are more beneficial are more likely to occur. In other words, evolution is accelerated by computation.
Nature speaks in algorithms. Dr. Valiant believes that phenomena like evolution, adaptation and learning are best understood in terms of “ecorithms,” a term he has coined for algorithms that interact with and benefit from their environment. Ecorithms are at play when children learn how to distinguish cats from dogs, when we navigate our way in a new city — but more than that, Dr. Valiant writes, when organisms evolve and when brain circuits are created and maintained.
An organism can adapt to a new environment by starting with a hypothesis about the environment, then testing it against new data and, based on the feedback, gradually improving the hypothesis by using an ecorithm, to behave more effectively.
The evolution of species, as Darwin taught us, relies on natural selection. But Dr. Valiant argues that if all the mutations that drive evolution were simply random and equally distributed, it would proceed at an impossibly slow and inefficient pace.
Darwin’s theory “has the gaping gap that it can make no quantitative predictions as far as the number of generations needed for the evolution of a behavior of a certain complexity,” he writes. “We need to explain how evolution is possible at all, how we got from no life, or from very simple life, to life as complex as we find it on earth today. This is the BIG question.”
Dr. Valiant proposes that natural selection is supplemented by ecorithms, which enable organisms to learn and adapt more efficiently. Not all mutations are realized with equal probability; those that are more beneficial are more likely to occur. In other words, evolution is accelerated by computation.
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