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"Life is a game between you and the learners that surround you. You can refuse to play, but then you'll have to live a twentieth-century life in the twenty-first. Or you can play to win. What model do you want the computer to have? And what data can you give it that will produce that model? Those two questions should always be in the back of your mind whenever you interact with a learning algorithm - as they are when you interact with other people." - pg 264 ln 16
"The molecular biology of living cells is such a mess that molecular biologists often quip that only people who don't know any of it could believe in intelligent design."
"The master algorithm" by Pedro Domigo, p. 141
I aspire to this level of pettiness
"In farming, we plant the seeds, make sure they have enough water and nutrients, and reap the grown crops. Why can't technology be more like this? It can, and that's the promise of machine learning. Learning algorithms are the seeds, data is the soil, and the learned programs are the grown plants. The machine-learning expert is like a farmer, sowing the seeds, irrigating and fertilizing the soil, and keeping an eye on the health of the crop but otherwise staying out of the way." - pg 7 ln 7
People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.
Pedro Domingos
Algorithms: The Future That Already Happened
Michael S. Evans reviews Pedro Domingos’s book, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.
ONE DAY IN LATE MARCH, Microsoft made a chatbot named Tay. Tay began the day tweeting love to everyone. A few hours later, Tay was quoting Adolf Hitler and offering filthy sex on demand. To borrow a phrase from John Green, Tay fell in love with Hitler and filthy sex the way you fall asleep: slowly, and then all at once. That’s because Tay learned from humans, and humans are awful.
Machine-learning algorithms try to make sense of human activity from the data we generate. Usually these algorithms are invisible to us. We see their output as recommendations about what we should do, or about what should be done to us. Netflix suggests your next TV show. Your car reminds you it’s time for an oil change. Siri tells you about a nearby restaurant. That loan you wanted? You’re approved!
In a sense, you’re making these recommendations yourself. Machine-learning algorithms monitor information about what you do, find patterns in that data, and make informed guesses about what you want to do next. Without you, there’s no data, and there’s nothing for machine learning to learn. But when you provide your data, and when the guesses are correct, machine learning operates invisibly, leaving you to experience life as an endless stream of tiny, satisfying surprises.
Or at least that’s how things could be, according to computer scientist Pedro Domingos. In The Master Algorithm, Domingos envisions an individually optimized future in which our digital better halves learn everything about us, then go out into the world and act for us, thereby freeing us to be our best non-digital selves. In this vision, machine-learning algorithms replace tedious human activities like online shopping, legal filing, and scientific hypothesis testing. Humans feed data to algorithms, and algorithms produce a better world for humans.
It sounds like science fiction. And it is, notably in Charles Stross’s novel Accelerando. But is this future possible?
If you’re skeptical, maybe it’s because you think we’re not capable of creating good enough machine-learning algorithms. Maybe you got a bad Netflix recommendation. Maybe Siri can’t understand your instructions. The technology, you might think, just isn’t very good.
The Master Algorithm seeks to prove you wrong. Over the course of several chapters on the current state of machine-learning research, Domingos explains that we are close to creating a single, universal learning algorithm that can discover all knowledge, if given enough data. And he should know. In a research field dominated by competition, Domingos has long championed a synthetic approach to machine learning: take working components from competing solutions, find a clever way to connect them, then use the resulting algorithm to solve bigger and harder problems. The algorithms are good enough, or soon will be.
No, the problem isn’t the technology. But there are good reasons to be skeptical. The algorithmic future Domingos describes is already here. And frankly, that future is not going very well for most of us.
Read full review here.
Inside the Hunt for the 'Master Algorithm' of Artificial Intelligence
For nearly 30 years, Pedro Domingos has been working within artificial intelligence communities, both as a researcher and a developer. In that time, the University of Washington professor has become an expert on all things machine learning. His new book, The Master Algorithm, traces the beginnings of machine learning, and provides a road-map to where it may be headed in the future. We talked to him by phone about what lies ahead for machine learning, how the master algorithm might change everything, and why he thinks the idea of the singularlity is overstated.
(via Inside the Hunt for the 'Master Algorithm' of Artificial Intelligence)
