The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos

On Intelligence.

How to Create a Mind.

the brain is phenomenally complex,

deciphering

Always take a “prudence” and reality.

Not all neuroscientists believe in the unity of the cortex;

Measure the logc of your assumptions.

iterative search,

Multiples tries and errors to get to a possible best outcome.

selecting and modifying the best ones, and repeating these steps as many times as necessary.

Like my cost function

given only a description of this algorithm, that it could produce you and me?

I can see that just a simple phenomenon can be transferred to an algorithm. An algorithm is an series of operations use to control the world around us, to our liking.

evolving programs by simulating natural selection is a popular endeavor in machine learning.

Is it a algorithm, yes : we can recreate it into a computer.

Its input is the experience and fate of all living creatures that ever existed.

the most powerful computer on Earth: Earth itself.

Evolution

evolution or the brain?

The brain can be so abstract thinking that learning his error could be a defaults., but also is error can be abstract. Program human like robots.

And, just as nature and nurture combine to produce us, perhaps the true Master Algorithm contains elements of both.

Win win

scant

apply far beyond them?

Applied way more than they anticipated to be.

more precise than the data they a...

mathematics can accurately capture so much of our infinitely complex world?

Does an algorithm regroup the entirety of this abstract language to judge every phenomenon of our world.

But if everything we experience is the product of a few simple laws,

Simple laws like depending on the environment.

All the Master Algorithm has to do is provide a shortcut to the laws’ consequences, replacing impossibly long mathematical derivations with much shorter ones based on actual observations.

inferences

percolates up to higher domains like biology and sociology remains to be seen, but the study of chaos provides many tantalizing examples of very different systems with similar behavior,

How much of theses laws from other domain coms to this world.

rivers, clouds, and trees of the world are all the result of such procedures—and fractal geometry shows they are—perhaps those procedures are just different parametrizations of a single one that we can induce from them.

Get the algorithm of the highest spot in the tree. But we need to discover, experience.

once we discover it in one field, we can more readily discover it in others;

and once we’ve learned how to solve it in one field, we know how to solve it in all.

Moreover, all these equations are quite simple and involve the same few derivatives of quantities with respect to space and time.

One example of derivative and make tree

Optimization

constraints

constraints

Machine learning is what you get when the unreasonable effectiveness of mathematics meets the unreasonable effectiveness of data.

evidence.

Bayes’ theorem is a machine that turns data into knowledge. According to Bayesian statisticians,

class NP,

heuristic

satisfiability,

or is it self-contr...

in other words, it can be programmed to do anything.

What about all the things the experts don’t know but you can discover from data?

cognitive

partly because of the similarity between its structure and the structure of the world.

Modern learning algorithms can learn rich internal representations, not just pairwise associations

Different ways to try it

In fact, it’s the other way around: human

intuition can’t replace data. Intuition is what you use when you don’t know the facts, and since you often don’t, intuition is precious. But when the evidence is before you, why would you deny it?

Évidence is data

recording the positions of the planets

To which the answer is: indeed. Wouldn’t it be nice if, instead of trying hundreds of variations of many algorithms, we just had to try hundreds of variations of a single one?

If we can figure out what’s important and not so important in each one, what the important parts have in common and how they complement each other, we can, indeed, synthesize a Master Algorithm from them.

The human hand is simple—four fingers, one opposable thumb—

The environment forms our complexity. GANS of forming a environment adoption. Not really gans but like. Dunno Something little regroups everything

As Isaiah Berlin memorably noted, some thinkers are foxes—they know many small things—and some are hedgehogs—they know one big thing.

Before we can discover deep truths with machine learning, we have to discover deep truths about machine learning.

for you but not most other people. No doctor can keep track of all the information needed to predict the best treatment for you, given your medical history and your cancer’s genome. It’s an ideal job for machine learning, and yet today’s learners aren’t up to it.

Technological pills. I apply gans because I know gans. We have an example How machine learning is better than human. And how human comportement compare etc. Difference between human and ai