Institute Output
Towards a Theory for the Speed of Biological Evolution
Willem Nielsen
An exploration of whether general principles govern the speed of evolution. Modified cellular automata models inspired by Stephen Wolfram are used to examine how genome dimensionality and the number of viable solutions influence rapid evolutionary bursts resembling punctuated equilibrium.
What If We Had Bigger Brains? Imagining Minds beyond Ours
Stephen Wolfram
We humans have perhaps 100 billion neurons in our brains. But what if we had many more? Or what if the AIs we built effectively had many more? What kinds of things might then become possible? At 100 billion neurons, we know, for example, that compositional language of the kind we humans use is possible. At the 100 million or so neurons of a cat, it doesn’t seem to be. But what would become possible with 100 trillion neurons? And is it even something we could imagine understanding?
What Can We Learn about Engineering and Innovation from Half a Century of the Game of Life Cellular Automaton?
Stephen Wolfram
Things are invented. Things are discovered. And somehow there’s an arc of progress that’s formed. But are there what amount to “laws of innovation” that govern that arc of progress?
There are some exponential and other laws that purport to at least measure overall quantitative aspects of progress (number of transistors on a chip; number of papers published in a year; etc.). But what about all the disparate innovations that make up the arc of progress? Do we have a systematic way to study those?
Nature's Compass: A visual exploration of hierarchy in biology and beyond
Willem Nielsen
A discussion of the computational essence of hierarchy in biology and its potential implications for everyday life.
Towards a Computational Formalization for Foundations of Medicine
Stephen Wolfram
As it’s practiced today, medicine is almost always about particulars: “this has gone wrong; this is how to fix it”. But might it also be possible to talk about medicine in a more general, more abstract way—and perhaps to create a framework in which one can study its essential features without engaging with all of its details?
Foundations of Biological Evolution: More Results & More Surprises
Stephen Wolfram
A few months ago I introduced an extremely simple “adaptive cellular automaton” model that seems to do remarkably well at capturing the essence of what’s happening in biological evolution. But over the past few months I’ve come to realize that the model is actually even richer and deeper than I’d imagined. And here I’m going to describe some of what I’ve now figured out about the model—and about the often-surprising things it implies for the foundations of biological evolution.
Why Does Biological Evolution Work? A Minimal Model for Biological Evolution and Other Adaptive Processes
Stephen Wolfram
Why does biological evolution work? And, for that matter, why does machine learning work? Both are examples of adaptive processes that surprise us with what they manage to achieve. So what’s the essence of what’s going on? I’m going to concentrate here on biological evolution, though much of what I’ll discuss is also relevant to machine learning—but I’ll plan to explore that in more detail elsewhere.