Walter Pitts was used to being bullied. He’d been born into a tough family in Prohibition-era Detroit, where his father, a boiler-maker, had no trouble raising his fists to get his way. The neighborhood boys weren’t much better. One afternoon in 1935, they chased him through the streets until he ducked into the local library to hide. The library was familiar ground, where he had taught himself Greek, Latin, logic, and mathematics—better than home, where his father insisted he drop out of school and go to work. Outside, the world was messy. Inside, it all made sense. ... Not wanting to risk another run-in that night, Pitts stayed hidden until the library closed for the evening. Alone, he wandered through the stacks of books until he came across Principia Mathematica, a three-volume tome written by Bertrand Russell and Alfred Whitehead between 1910 and 1913, which attempted to reduce all of mathematics to pure logic. Pitts sat down and began to read. For three days he remained in the library until he had read each volume cover to cover—nearly 2,000 pages in all—and had identified several mistakes. Deciding that Bertrand Russell himself needed to know about these, the boy drafted a letter to Russell detailing the errors. Not only did Russell write back, he was so impressed that he invited Pitts to study with him as a graduate student at Cambridge University in England. Pitts couldn’t oblige him, though—he was only 12 years old. But three years later, when he heard that Russell would be visiting the University of Chicago, the 15-year-old ran away from home and headed for Illinois. He never saw his family again. ... Though they started at opposite ends of the socioeconomic spectrum, McCulloch and Pitts were destined to live, work, and die together. Along the way, they would create the first mechanistic theory of the mind, the first computational approach to neuroscience, the logical design of modern computers, and the pillars of artificial intelligence. But this is more than a story about a fruitful research collaboration. It is also about the bonds of friendship, the fragility of the mind, and the limits of logic’s ability to redeem a messy and imperfect world. ... “He was absolutely incomparable in the scholarship of chemistry, physics, of everything you could talk about history, botany, etc. When you asked him a question, you would get back a whole textbook … To him, the world was connected in a very complex and wonderful fashion.”
As self-help workshops go, Applied Rationality’s is not especially accessible. The center’s three founders — Julia Galef, Anna Salamon and Smith — all have backgrounds in science or math or both, and their curriculum draws heavily from behavioral economics. Over the course of the weekend, I heard instructors invoke both hyperbolic discounting (a mathematical model of how people undervalue long-term rewards) and prospect theory (developed by the behavioral economists Daniel Kahneman and Amos Tversky to capture how people inaccurately weigh risky probabilities). But the premise of the workshop is simple: Our minds, cobbled together over millenniums by that lazy craftsman, evolution, are riddled with bad mental habits. ... Some of these problems are byproducts of our brain’s reward system. ... logical errors may be easy to spot in others, the group says, they’re often harder to see in ourselves. The workshop promised to give participants the tools to address these flaws, which, it hinted, are almost certainly worse than we realize. ... Most self-help appeals to us because it promises real change without much real effort, a sort of fad diet for the psyche. ... CFAR’s focus on science and on tiresome levels of practice can seem almost radical. It has also generated a rare level of interest among data-driven tech people and entrepreneurs who see personal development as just another optimization problem, if a uniquely central one. Yet, while CFAR’s methods are unusual, its aspirational promise — that a better version of ourselves is within reach — is distinctly familiar. The center may emphasize the benefits that will come to those who master the techniques of rational thought, like improved motivation and a more organized inbox, but it also suggests that the real reward will be far greater, enabling users to be more intellectually dynamic and nimble. ... CFAR’s original mandate was to give researchers the mental tools to overcome their unconscious assumptions. ... What makes CFAR novel is its effort to use those same principles to fix personal problems: to break frustrating habits, recognize self-defeating cycles and relentlessly interrogate our own wishful inclinations and avoidant instincts.
Since its release seven years ago, Minecraft has become a global sensation, captivating a generation of children. There are over 100 million registered players, and it’s now the third-best-selling video game in history, after Tetris and Wii Sports. In 2014, Microsoft bought Minecraft — and Mojang, the Swedish game studio behind it — for $2.5 billion. ... There have been blockbuster games before, of course. But as Jordan’s experience suggests — and as parents peering over their children’s shoulders sense — Minecraft is a different sort of phenomenon. ... For one thing, it doesn’t really feel like a game. It’s more like a destination, a technical tool, a cultural scene, or all three put together: a place where kids engineer complex machines, shoot videos of their escapades that they post on YouTube, make art and set up servers, online versions of the game where they can hang out with friends. It’s a world of trial and error and constant discovery, stuffed with byzantine secrets, obscure text commands and hidden recipes. And it runs completely counter to most modern computing trends. ... Minecraft culture is a throwback to the heady early days of the digital age. In the late ’70s and ’80s, the arrival of personal computers like the Commodore 64 gave rise to the first generation of kids fluent in computation. They learned to program in Basic, to write software that they swapped excitedly with their peers. It was a playful renaissance that eerily parallels the embrace of Minecraft by today’s youth. ... Today it costs $27 and sells 10,000 copies a day. (It’s still popular across all age groups; according to Microsoft, the average player is between 28 and 29, and women make up nearly 40 percent of all players.)
The so-called cognitive revolution started small, but as computers became standard equipment in psychology labs across the country, it gained broader acceptance. By the late 1970s, cognitive psychology had overthrown behaviorism, and with the new regime came a whole new language for talking about mental life. Psychologists began describing thoughts as programs, ordinary people talked about storing facts away in their memory banks, and business gurus fretted about the limits of mental bandwidth and processing power in the modern workplace. ... This story has repeated itself again and again. As the digital revolution wormed its way into every part of our lives, it also seeped into our language and our deep, basic theories about how things work. Technology always does this. During the Enlightenment, Newton and Descartes inspired people to think of the universe as an elaborate clock. In the industrial age, it was a machine with pistons. (Freud’s idea of psychodynamics borrowed from the thermodynamics of steam engines.) Now it’s a computer. Which is, when you think about it, a fundamentally empowering idea. Because if the world is a computer, then the world can be coded. ... Code is logical. Code is hackable. Code is destiny. These are the central tenets (and self-fulfilling prophecies) of life in the digital age. ... In this world, the ability to write code has become not just a desirable skill but a language that grants insider status to those who speak it. They have access to what in a more mechanical age would have been called the levers of power. ... whether you like this state of affairs or hate it—whether you’re a member of the coding elite or someone who barely feels competent to futz with the settings on your phone—don’t get used to it. Our machines are starting to speak a different language now, one that even the best coders can’t fully understand.
Cooking, as a physical activity, doesn’t come naturally to me. It never has. To compensate for my lack of dexterity, speed, and technique, I think about food constantly. In fact, I’m much stronger at thinking about food than I am at cooking it. And recently I started seeing patterns in our most successful dishes that suggested our hits weren’t entirely random; there’s a set of underlying laws that links them together. I’ve struggled to put this into words, and I haven’t talked to my fellow chefs about it, because I worry they’ll think I’m crazy. But I think there’s something to it, and so I’m sharing it now for the first time. I call it the Unified Theory of Deliciousness. ... A chef can go crazy figuring out how much salt to add to a dish. But I believe there is an objectively correct amount of salt, and it is rooted in a counterintuitive idea. Normally we think of a balanced dish as being neither too salty nor undersalted. I think that’s wrong. When a dish is perfectly seasoned, it will taste simultaneously like it has too much salt and too little salt. It is fully committed to being both at the same time.
The most remarkable thing about neural nets is that no human being has programmed a computer to perform any of the stunts described above. In fact, no human could. Programmers have, rather, fed the computer a learning algorithm, exposed it to terabytes of data—hundreds of thousands of images or years’ worth of speech samples—to train it, and have then allowed the computer to figure out for itself how to recognize the desired objects, words, or sentences. ... Neural nets aren’t new. The concept dates back to the 1950s, and many of the key algorithmic breakthroughs occurred in the 1980s and 1990s. What’s changed is that today computer scientists have finally harnessed both the vast computational power and the enormous storehouses of data—images, video, audio, and text files strewn across the Internet—that, it turns out, are essential to making neural nets work well. ... That dramatic progress has sparked a burst of activity. Equity funding of AI-focused startups reached an all-time high last quarter of more than $1 billion, according to the CB Insights research firm. There were 121 funding rounds for such startups in the second quarter of 2016, compared with 21 in the equivalent quarter of 2011, that group says. More than $7.5 billion in total investments have been made during that stretch—with more than $6 billion of that coming since 2014. ... The hardware world is feeling the tremors. The increased computational power that is making all this possible derives not only from Moore’s law but also from the realization in the late 2000s that graphics processing units (GPUs) made by Nvidia—the powerful chips that were first designed to give gamers rich, 3D visual experiences—were 20 to 50 times more efficient than traditional central processing units (CPUs) for deep-learning computations. ... Think of deep learning as a subset of a subset. “Artificial intelligence” encompasses a vast range of technologies—like traditional logic and rules-based systems—that enable computers and robots to solve problems in ways that at least superficially resemble thinking. Within that realm is a smaller category called machine learning, which is the name for a whole toolbox of arcane but important mathematical techniques that enable computers to improve at performing tasks with experience. Finally, within machine learning is the smaller subcategory called deep learning.
- Also: FiveThirtyEight - Some Like It Bot < 5min
- Also: Vox - Venture capitalist Marc Andreessen explains how AI will change the world 5-15min
- Also: Nautilus - Moore’s Law Is About to Get Weird < 5min
- Also: Edge - AI & The Future Of Civilization < 5min
- Also: Medium - Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning 5-15min
- Also: Rolling Stone - Inside the Artificial Intelligence Revolution: Pt. 1 5-15min
- Also: Rolling Stone - Inside the Artificial Intelligence Revolution: Pt. 2 5-15min