Information; a computer scientist's take (Introduction)

by David Turell , Thursday, February 04, 2016, 15:21 (2997 days ago)

A Harvard professor's approach to learning and its possible application to evolution:-https://www.quantamagazine.org/20160128-ecorithm-computers-and-life/-"Valiant, a computer scientist at Harvard University, is hardly the only scientist to assume a fundamental equivalence between the capabilities of brains and computers. But he was one of the first to formalize what that relationship might look like in practice: In 1984, his “probably approximately correct” (PAC) model mathematically defined the conditions under which a mechanistic system could be said to “learn” information. Valiant won the A.M. Turing Award — often called the Nobel Prize of computing — for this contribution, which helped spawn the field of computational learning theory.-***-"He broadened the concept of an algorithm into an “ecorithm,” which is a learning algorithm that “runs” on any system capable of interacting with its physical environment. Algorithms apply to computational systems, but ecorithms can apply to biological organisms or entire species. The concept draws a computational equivalence between the way that individuals learn and the way that entire ecosystems evolve. In both cases, ecorithms describe adaptive behavior in a mechanistic way.-"Valiant's self-stated goal is to find “mathematical definitions of learning and evolution which can address all ways in which information can get into systems.” If successful, the resulting “theory of everything” — a phrase Valiant himself uses, only half-jokingly — would literally fuse life science and computer science together. Furthermore, our intuitive definitions of “learning” and “intelligence” would expand to include not only non-organisms, but non-individuals as well.-***-"It is a kind of calculation, but the goal of learning is to perform well in a world that isn't precisely modeled ahead of time. A learning algorithm takes observations of the world, and given that information, it decides what to do and is evaluated on its decision. A point made in my book is that all the knowledge an individual has must have been acquired either through learning or through the evolutionary process. And if this is so, then individual learning and evolutionary processes should have a unified theory to explain them.-***-"An ecorithm is an algorithm, but its performance is evaluated against input it gets from a rather uncontrolled and unpredictable world. And its goal is to perform well in that same complicated world. You think of an algorithm as something running on your computer, but it could just as easily run on a biological organism. -***-"Biology is based on protein expression networks, and as evolution proceeds these networks become modified. The PAC learning model imposes some logical limitations on what could be happening to those networks to cause these modifications when they undergo Darwinian evolution. If we gather more observations from biology and analyze them within this PAC-style learning framework, we should be able to figure out how and why biological evolution succeeds, and this would make our understanding of evolution more concrete and predictive.-***-"We know what we're looking for. We are looking for a learning algorithm obeying Darwinian constraints that biology can and does support. It would explain what's happened on this planet in the amount of time that has been available for evolution to occur."-Comment: Matt might consider this. How does biologic evolution learn and add information? Epigenetics?