Dec 6, 2015 10:06 PM
(This post was last modified: Dec 8, 2015 03:07 AM by C C.)
The Physical Origin of Universal Computing: The physical nature of computers might reveal deep truths about their uniquely powerful abstract abilities
EXCERPT: [...] In 1985, the physicist David Deutsch took another important step toward understanding the nature of algorithms. [...] A human being using an abacus to multiply two numbers is obviously profoundly different from a silicon chip running a flight simulator. But both are examples of physical systems [...] With this in mind, Deutsch stated the following principle... "Every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means."
In other words, take any physical process at all, and you should be able to simulate it using a universal computer. It’s an amazing, Inception-like idea, that one machine can effectively contain within itself everything conceivable within the laws of physics. [...] In a sense, if you had a complete understanding of the machine, you’d understand all physical processes.
Deutsch’s principle goes well beyond Turing’s earlier informal arguments. [...] Furthermore, unlike Turing’s informal arguments, Deutsch’s principle is amenable to proof. In particular, we can imagine using the laws of physics to deduce the truth of the principle. [...]
In attempting this, it helps to modify Deutsch’s principle in two ways. [...] With these two modifications, Deutsch’s principle becomes: "Every finitely realizable physical system can be simulated efficiently and to an arbitrary degree of approximation by a universal model (quantum) computing machine operating by finite means."
[...] At first glance, it seems that the artificial sciences should be special cases of the natural sciences. But as Deutsch’s principle suggests, the properties of artificial systems such as computers may be just as rich as those of naturally occurring physical systems. We can imagine using computers to simulate not only our own laws of physics, but maybe even alternate physical realities. In the words of the computer scientist Alan Kay: “In natural science, Nature has given us a world and we’re just to discover its laws. In computers, we can stuff laws into it and create a world....”
How machines learn and you win
EXCERPT: “Machine learning.” You’ve heard the term, and you probably nod in agreement when someone tells you how important it is. But secretly you may not be sure what it is or how it works. Ask your data scientists to explain, and you may get lost in a sea of specialist talk about forks, leaf nodes, split points, and recursions. Forget all that. The only thing you need to know is that machine learning applies statistical models to the data you have in order to make smart predictions about data you don’t have. Those predictions can help you find signals in the noise and extract value from all the data you’re collecting. The advantage of—indeed, the imperative for—using machine learning is its speed and brute force. It can mine vast swaths of data in seconds or minutes, find patterns, and make predictions in ways that no human analyst could begin to emulate. Machine learning is, among other things, helping companies to detect that patients will have seizures long before they actually occur. Best of all, no matter how much work you throw at it, the student never gets exhausted or bored. Here’s a look at how it works....
EXCERPT: [...] In 1985, the physicist David Deutsch took another important step toward understanding the nature of algorithms. [...] A human being using an abacus to multiply two numbers is obviously profoundly different from a silicon chip running a flight simulator. But both are examples of physical systems [...] With this in mind, Deutsch stated the following principle... "Every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means."
In other words, take any physical process at all, and you should be able to simulate it using a universal computer. It’s an amazing, Inception-like idea, that one machine can effectively contain within itself everything conceivable within the laws of physics. [...] In a sense, if you had a complete understanding of the machine, you’d understand all physical processes.
Deutsch’s principle goes well beyond Turing’s earlier informal arguments. [...] Furthermore, unlike Turing’s informal arguments, Deutsch’s principle is amenable to proof. In particular, we can imagine using the laws of physics to deduce the truth of the principle. [...]
In attempting this, it helps to modify Deutsch’s principle in two ways. [...] With these two modifications, Deutsch’s principle becomes: "Every finitely realizable physical system can be simulated efficiently and to an arbitrary degree of approximation by a universal model (quantum) computing machine operating by finite means."
[...] At first glance, it seems that the artificial sciences should be special cases of the natural sciences. But as Deutsch’s principle suggests, the properties of artificial systems such as computers may be just as rich as those of naturally occurring physical systems. We can imagine using computers to simulate not only our own laws of physics, but maybe even alternate physical realities. In the words of the computer scientist Alan Kay: “In natural science, Nature has given us a world and we’re just to discover its laws. In computers, we can stuff laws into it and create a world....”
How machines learn and you win
EXCERPT: “Machine learning.” You’ve heard the term, and you probably nod in agreement when someone tells you how important it is. But secretly you may not be sure what it is or how it works. Ask your data scientists to explain, and you may get lost in a sea of specialist talk about forks, leaf nodes, split points, and recursions. Forget all that. The only thing you need to know is that machine learning applies statistical models to the data you have in order to make smart predictions about data you don’t have. Those predictions can help you find signals in the noise and extract value from all the data you’re collecting. The advantage of—indeed, the imperative for—using machine learning is its speed and brute force. It can mine vast swaths of data in seconds or minutes, find patterns, and make predictions in ways that no human analyst could begin to emulate. Machine learning is, among other things, helping companies to detect that patients will have seizures long before they actually occur. Best of all, no matter how much work you throw at it, the student never gets exhausted or bored. Here’s a look at how it works....