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December 5 - December 17, 2018
This is what nature does: evolution creates brain structures, and individual experience modulates them.
Another issue is that, if you have an endless stream of data coming in, you can’t wait to see it all before you commit to some decisions. One solution is to use the sampling principle: if you want to predict who will win the next presidential election, you don’t need to ask every voter who he or she will vote for; a sample of a few thousand suffices, if you’re willing to accept a little bit of uncertainty.
When Alice’s cancer mutates, it repeats the whole process. Even before the cancer mutates, the model predicts likely mutations, and CanceRx prescribes drugs that will stop them dead in their tracks. In the game of chess between humanity and cancer, CanceRx is checkmate.
Most important, however, CanceRx would incorporate data from millions of cancer patients, with the help of their doctors and hospitals. Without that data, we can’t cure cancer; with it, we can.
Because every cancer is different, it takes machine learning to find the common patterns. And because a single tissue can yield billions of data points, it takes machine learning to figure out what to do for each new patient.
Like the red pill in The Matrix, the Master Algorithm is the gateway to a different reality: the one you already live in but didn’t know it yet.
To succeed with fewer dates and less work, your two main tools are your profile and your responses to suggested matches.
The better the learner, the more it’s worth your time to teach it about you. But as a rule of thumb, you want to differentiate yourself enough so that it won’t confuse you with the “average person” (remember Bob Burns from Chapter 8), but not be so unusual that it can’t fathom you.
And if you’re about to watch some videos of a kind that you ordinarily have no interest in, log out first.
for when you don’t want the current session to influence future personalization.
you don’t like a company, click on their ads: this will not only waste their money now, but teach Google to waste it again in the future by showing the ads to people who are unlikely to buy the products.
Self-improvement aside, probably the first thing you’d want your model to do is negotiate the world on your behalf: let it loose in cyberspace, looking for all sorts of things for you.
Everyone will have a detailed model of him- or herself, and these models will talk to each other all the time.
If you’re looking for a job and company X is looking to hire, its model will interview your model.
In the world of the Master Algorithm, “my people will call your people” becomes “my program will call your program.” Everyone has an entourage of bots, smoothing his or her way through the world.
Your bot’s job is to see through their claims, just as you see through TV commercials, but at a much finer level of detail, one that you’d never have the time or patience for.
Your digital half will be like power steering for your life: it goes where you want to go but with less effort from you.
Part of its brief is to leave some things open to chance, to expose you to new experiences, and to look for serendipity.
It shares its most important findings with you. (“You believe you like X, but in reality you tend to go for Y.”)
Your digital half has a model of the world: not just of the world in general but of the world as it relates to you.
Every party to an interaction learns from it and applies what it’s learned to its next interactions. You have your model of every person and organization you interact with, and they each have their model of you.
Tomorrow’s cyberspace will be a vast parallel world that selects only the most promising things to try out in the real one. It will be like a new, global subconscious, the collective id of the human race.
(Imagine depositing your money with Bank of America and not knowing if you’ll be able to transfer it to Wells Fargo somewhere down the line.)
return for a subscription fee. It would anonymize your online interactions, routing them through its servers and aggregating them with its other users’. It would store all the data from all your life in one place—down to your 24/7 Google Glass video stream, if you ever get one. It would learn a complete model of you and your world and continually update it.
the value of a user to the Internet advertising industry is more like $1,200 per year.
despite their head start, companies like Google and Facebook are not well suited to being your digital home because they have a conflict of interest.
You wouldn’t let the first or second half of your brain have divided loyalties, so why would you let the third?
When people have to trade off privacy against other benefits, as when filling out a profile on a website, the implied value of privacy that comes out is much lower than if you ask them abstract questions like “Do you care about your privacy?”
The more context a job requires, the less likely a computer will be able to do it soon. Common sense is important not just because your mom taught you so, but because computers don’t have it.
Data and intuition are like horse and rider, and you don’t try to outrun a horse; you ride it.
And one very important job for the technically minded will remain: keeping an eye on the computers. In fact, this will require more than engineers; ultimately, it may be the full-time occupation of all mankind to figure out what we want from the machines and
The need to earn a living will be a distant memory, another piece of humanity’s barbaric past that we rose above.
letting robots learn ethics by observing humans may not be such a good idea. The robot is liable to get seriously confused when it sees that humans’ actions often violate their ethical principles.
Teaching ethics to robots, with their logical minds and lack of baggage, will force us to examine our assumptions and sort out our contradictions.
Recall the three components of every learning algorithm: representation, evaluation, and optimization.
starts with robot rights, which seem absurd to me but not to everyone. After all, we already give rights to animals, who never asked for them.
Your job in a world of intelligent machines is to keep making sure they do what you want, both at the input (setting the goals) and at the output (checking that you got what you asked for). If you don’t, somebody else will.
then Homo technicus will evolve into a myriad different intelligent species, each with its own niche, a whole new biosphere as different from today’s as today’s is from the primordial ocean.
Download some data sets from the UCI repository (archive.ics.uci.edu/ml/) and start playing. When you’re ready, check out Kaggle.com,
If one day you invent the Master Algorithm, please don’t run to the patent office with it. Open-source it. The Master Algorithm is too important to be owned by any one person or organization.
Machine learning touches the lives of every one of us, and it’s up to all of us to decide what we want to do with it.
