The late, great AI pioneer Marvin Minsky referred to us as “meat machines,” which irked many (very biased) humans. The more polite phrase subsequently coined to describe our brains in computer terms is “wetware.” Regardless of the vernacular, I think we’re essentially machines, though (for a little while longer) easily the most complex ones.
On that topic, John Pavlus of Quanta has an interesting interview with Harvard computer scientist Leslie Valiant, who believes all biology computational, that “ecorithms” underlie life the way algorithms do machines. To the researcher, learning is learning, human or AI, though there are significant differences in stimuli (external, unpredictable vs. internal, predictable). Not everyone may agree with Valiant, but we’re a far cry from the brickbats he would have received for his beliefs in the 1980s when he began working on machine learning, a field then very belittled if not verboten.
An excerpt:
Question:
So what is learning? Is it different from computing or calculating?
Leslie Valiant:
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.
Question:
And from there, you eventually arrived at the concept of an “ecorithm.” What is an ecorithm, and how is it different from an algorithm?
Leslie Valiant:
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. But in either case an ecorithm lives in an external world and interacts with that world.
Question:
So the concept of an ecorithm is meant to dislodge this mistaken intuition many of us have that “machine learning” is fundamentally different from “non-machine learning”? 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. But in either case an ecorithm lives in an external world and interacts with that world.
Leslie Valiant:
Yes, certainly. Scientifically, the point has been made for more than half a century that if our brains run computations, then if we could identify the algorithms producing those computations, we could simulate them on a machine, and “artificial intelligence” and “intelligence” would become the same. But the practical difficulty has been to determine exactly what these computations running on the brain are. Machine learning is proving to be an effective way of bypassing this difficulty.•