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

by Pedro Domingos

Even in “older” sciences like physics and astronomy, progress continues because of the flood of data pouring forth from particle accelerators and digital sky surveys.

In biology, learning algorithms figure out where genes are located in a DNA molecule, where superfluous bits of RNA get spliced out before proteins are synthesized, how proteins fold into their characteristic shapes, and how different conditions affect the expression of different genes.

and set about predicting four things for each individual voter: how likely he or she was to support Obama, show up at the polls, respond to the campaign’s reminders to do so, and change his or her mind about the election based on a conversation about a specific issue. Based on these voter models, every night the campaign ran 66,000 simulations of the election and

what worked against yesterday’s attacks is powerless against today’s.

block each one would be as effective as the Maginot Line,

if an attack is the first of its kind and there aren’t any previous exampl...

the generals will be human, but the foot soldiers will be algorithms.

The foot soldiers will be algorithms and the general human. The foot soldiers will be algorithms and the general will be algorithms.

information. If the enemy can’t hide, he can’t survive.

also the bad news.

it doesn’t have millions of staffers to eavesdrop on all these calls and e-mails or even just keep track of who’s talking to whom.

pick

few suspicious ones is ...

don’t just learn to defeat what your opponent does now; learn to parry what he might do against your learner.

Machine learning is like having a radar that sees into the future.

predict

predictive policing. By forecasting crime trends

fraud

large-scale modeling,

Today’s world filled with tons and tons of information, and some of these doesn’t apply to what your wanting to obtain, so we take loads of information and with a algorithm we differentiate the bad from the good, the good is what will be valuable to your company.

Outside of machine learning, if you have two different problems to solve, you need to write two different programs.

Adapts to new problems

In machine learning, the same algorithm can do both, provided you give it the appropriate data to learn from.

Machine learning can even use only one algorithm to solve to drastically different problem ex. One can play chess and process credit card applications, provided that you gave it a database.

Naïve Bayes

That’s probably used for... based on a problem of an user, a problem related to the usage of the users, the database of the user that has the problem it can help him. Anytime that the user can dislike the algorithm it is that kind of naive bayes.

finite

making assumptions,

diff...

assump...

some ...

in...

explicit ...

Easily transmitted to other manners

which ones to ...

limits

relevant

derived

So inventing a universal learner boils down to discovering the deepest regularities in our universe, those that all phenomena share,

Notice regularity

derived

I call this learner the Master Algorithm.

inventing it would be one of the greatest scientific achievements of all time.

Find all the regularities in the world shit one algorithm, find the path to take the greatest info, universal learner. That gathers the tings that happens regularly. Imitates the scientific mind, the theoretical mind. Going into the deepest of these comportement would let us see everything, anomalities to regularities,

right kind of data,

corresponding knowledge.

Gi...

discovers the laws of physics.

In congenitally blind people, the visual cortex can take over other brain functions. In deaf ones, the auditory

Blind people can learn to “see” with their tongues by

with high voltages corresponding to bright pixels and low vol...

echolocation to n...

clicking his tongue and listening to the echoes, he could walk around without...

areas dedicated to the different senses distinguished only by the different inputs they are connected to (e.g., eyes, ears, nose).

Examining the cortex under a microscope leads to the same conclusion. The same wiring pattern is repeated everywhere.

algorithms.

memories are formed by strengthening the connections between neurons

that algorithm can learn everything we can.

Study particularity of brain, imagination etc.