Is AI Riding a One-Trick Pony? - MIT Technology Review. I’m standing in what is soon to be the center of the world, or is perhaps just a very large room on the seventh floor of a gleaming tower in downtown Toronto. Showing me around is Jordan Jacobs, who cofounded this place: the nascent Vector Institute, which opens its doors this fall and which is aiming to become the global epicenter of artificial intelligence. We’re in Toronto because Geoffrey Hinton is in Toronto, and Geoffrey Hinton is the father of “deep learning,” the technique behind the current excitement about AI. “In 30 years we’re going to look back and say Geoff is Einstein—of AI, deep learning, the thing that we’re calling AI,” Jacobs says. Of the AI researchers at the top of the field, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team.
Vindication Reality distortion field This is the thing that has everybody enthralled. Why no learning algorithm can be good at learning everything. Not long ago, my aunt sent her colleagues an email with the subject, “Math Problem! What is the answer?” It contained a deceptively simple puzzle: She thought her solution was obvious. Her colleagues, though, were sure their solution was correct—and the two didn’t match. Was the problem with one of their answers, or with the puzzle itself? My aunt and her colleagues had stumbled across a fundamental problem in machine learning, the study of computers that learn. As a human, the challenge is to find any pattern at all. The problem only recently became of practical concern. To tackle my aunt’s puzzle, the expert systems approach would need a human to squint at the first three rows and spot the following pattern: The human could then instruct the computer to follow the pattern x * (y + 1) = z.
Despite expert systems’ early success, the manual labor required to design, tune, and update them became unwieldy. And yet. By this logic the final answer should be 40. So which pattern is right? The Great A.I. Awakening. Four days later, a couple of hundred journalists, entrepreneurs and advertisers from all over the world gathered in Google’s London engineering office for a special announcement. Guests were greeted with Translate-branded fortune cookies. Their paper slips had a foreign phrase on one side — mine was in Norwegian — and on the other, an invitation to download the Translate app.
Tables were set with trays of doughnuts and smoothies, each labeled with a placard that advertised its flavor in German (zitrone), Portuguese (baunilha) or Spanish (manzana). After a while, everyone was ushered into a plush, dark theater. Photo Sadiq Khan, the mayor of London, stood to make a few opening remarks. Pichai was in London in part to inaugurate Google’s new building there, the cornerstone of a new “knowledge quarter” under construction at King’s Cross, and in part to unveil the completion of the initial phase of a company transformation he announced last year.
Until today. The new wave of A.I. 1. 2. 3. 4. Using Artificial Intelligence to Write Self-Modifying/Improving Programs. This article is the first in a series of three. See also: Part 1, Part 2, Part 3, and research paper. Introduction Is it possible for a computer program to write its own programs? Could human software developers be replaced one day by the very computers that they master? “hello” The above programming code was created by an artificial intelligence program, designed to write programs with self-modifying and self-improving code.
All code for the AI program is available at GitHub. Artificial Intelligence Takes Up Coding Artificial intelligence has been progressing steadily over the years, along with advances in computer technology, hardware, memory, and CPU speeds. An AI Hobby It’s been somewhat of a hobby for me, dabbling with artificial intelligence programs in an attempt to write a program that can, itself, write programs. A Lot of Monkeys and Broken Typewriters What if you could guide the monkeys? On a Tangent to Genetic Algorithms This is the general idea behind a genetic algorithm. 1. 2. 3. Venture capitalist Marc Andreessen explains how AI will change the world. AI’s Language Problem. About halfway through a particularly tense game of Go held in Seoul, South Korea, between Lee Sedol, one of the best players of all time, and AlphaGo, an artificial intelligence created by Google, the AI program made a mysterious move that demonstrated an unnerving edge over its human opponent.
On move 37, AlphaGo chose to put a black stone in what seemed, at first, like a ridiculous position. It looked certain to give up substantial territory—a rookie mistake in a game that is all about controlling the space on the board. Two television commentators wondered if they had misread the move or if the machine had malfunctioned somehow. In fact, contrary to any conventional wisdom, move 37 would enable AlphaGo to build a formidable foundation in the center of the board. About the art One reason that understanding language is so difficult for computers and AI systems is that words often have meanings based on context and even the appearance of the letters and words. Machine whisperers. Paul Allen: The Singularity Isn't Near. Futurists like Vernor Vinge and Ray Kurzweil have argued that the world is rapidly approaching a tipping point, where the accelerating pace of smarter and smarter machines will soon outrun all human capabilities.
They call this tipping point the singularity, because they believe it is impossible to predict how the human future might unfold after this point. Once these machines exist, Kurzweil and Vinge claim, they’ll possess a superhuman intelligence that is so incomprehensible to us that we cannot even rationally guess how our life experiences would be altered. Vinge asks us to ponder the role of humans in a world where machines are as much smarter than us as we are smarter than our pet dogs and cats. Kurzweil, who is a bit more optimistic, envisions a future in which developments in medical nanotechnology will allow us to download a copy of our individual brains into these superhuman machines, leave our bodies behind, and, in a sense, live forever.
It’s heady stuff. The AI Approach. The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe. In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines.
Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. But there is a problem. There is no mathematical reason why networks arranged in layers should be so good at these challenges. Today that changes thanks to the work of Henry Lin at Harvard University and Max Tegmark at MIT. First, let’s set up the problem using the example of classifying a megabit grayscale image to determine whether it shows a cat or a dog. Such an image consists of a million pixels that can each take one of 256 grayscale values. Now Lin and Tegmark say they’ve worked out why. The laws of physics have other important properties. The Business Implications of Machine Learning – Free Code Camp. As buzzwords become ubiquitous they become easier to tune out. We’ve finely honed this defense mechanism, for good purpose. It’s better to focus on what’s in front of us than the flavor of the week.
CRISPR might change our lives, but knowing how it works doesn’t help you. VR could eat all media, but it’s hardware requirements keep it many years away from common use. But please: do not ignore machine learning. Yes, machine learning will help us build wonderful applications. You should pay attention to machine learning because it has been prioritized by the companies which drive the technology industry, namely Google, Facebook, and Amazon.
To understand the impact of machine learning, let’s first explore it’s nature. (I am going to use deep learning and machine learning interchangeably. Machine Learning Makes Everything Programmatic The goal of machine learning, or deep learning, is to make everything programmatic. In a nutshell, deep learning is human recognition at computer scale. Human and Artificial Intelligence May Be Equally Impossible to Understand. Dmitry Malioutov can’t say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM’s corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm.
When it came time to describe the results to his client, though, there was a wrinkle. “We couldn’t explain the model to them because they didn’t have the training in machine learning.” In fact, it may not have helped even if they were machine learning experts. That’s because the model was an artificial neural network, a program that takes in a given type of data—in this case, the insurance company’s customer records—and finds patterns in them. As exciting as their performance gains have been, though, there’s a troubling fact about modern neural networks: Nobody knows quite how they work. Also in Artificial Intelligence Robots Can’t Dance Aaron M.
True AI is both logically possible and utterly implausible | Aeon Essays. Suppose you enter a dark room in an unknown building. You might panic about monsters that could be lurking in the dark. Or you could just turn on the light, to avoid bumping into furniture. The dark room is the future of artificial intelligence (AI). Unfortunately, many people believe that, as we step into the room, we might run into some evil, ultra-intelligent machines.
This is an old fear. It dates to the 1960s, when Irving John Good, a British mathematician who worked as a cryptologist at Bletchley Park with Alan Turing, made the following observation: Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Once ultraintelligent machines become a reality, they might not be docile at all but behave like Terminator: enslave humanity as a sub-species, ignore its rights, and pursue their own ends, regardless of the effects on human lives. If this sounds incredible, you might wish to reconsider. Mapping the Brain to Build Better Machines. Take a three year-old to the zoo, and she intuitively knows that the long-necked creature nibbling leaves is the same thing as the giraffe in her picture book.
That superficially easy feat is in reality quite sophisticated. The cartoon drawing is a frozen silhouette of simple lines, while the living animal is awash in color, texture, movement and light. It can contort into different shapes and looks different from every angle. Humans excel at this kind of task. Visual identification is one of many arenas where humans beat computers. An ambitious new program, funded by the federal government’s intelligence arm, aims to bring artificial intelligence more in line with our own mental powers. Time is short. Koch and his colleagues are now creating a complete wiring diagram of a small cube of brain — a million cubic microns, totaling one five-hundredth the volume of a poppy seed. No one has yet attempted to reconstruct a piece of brain at this scale. The Brain’s Processing Units. Is AlphaGo Really Such a Big Deal? In 1997, IBM’s Deep Blue system defeated the world chess champion, Garry Kasparov. At the time, the victory was widely described as a milestone in artificial intelligence.
But Deep Blue’s technology turned out to be useful for chess and not much else. Computer science did not undergo a revolution. Will AlphaGo, the Go-playing system that recently defeated one of the strongest Go players in history, be any different? QuantizedA monthly column in which top researchers explore the process of discovery. This month’s columnist, Michael Nielsen, is a computer scientist and author of three books. I believe the answer is yes, but not for the reasons you may have heard. In other words, these arguments don’t address the core question: Will the technical advances that led to AlphaGo’s success have broader implications?
In chess, beginning players are taught a notion of a chess piece’s value. You can use these values to assess potential moves. The notion of value is crucial in computer chess. Kurzweil Interviews Minsky: Is Singularity Near? The Hidden Algorithms Underlying Life. To the computer scientist Leslie Valiant, “machine learning” is redundant.
In his opinion, a toddler fumbling with a rubber ball and a deep-learning network classifying cat photos are both learning; calling the latter system a “machine” is a distinction without a difference. Valiant, a computer scientist at Harvard University, is hardly the only scientist to assume a fundamental equivalence between the capabilities of brains and computers. But he was one of the first to formalize what that relationship might look like in practice: In 1984, his “probably approximately correct” (PAC) model mathematically defined the conditions under which a mechanistic system could be said to “learn” information. Valiant won the A.M. Valiant’s conceptual leaps didn’t stop there.
He broadened the concept of an algorithm into an “ecorithm,” which is a learning algorithm that “runs” on any system capable of interacting with its physical environment. So what is learning? Katherine Taylor for Quanta Magazine. A Google DeepMind Algorithm Uses Deep Learning and More to Master the Game of Go. Google has taken a brilliant and unexpected step toward building an AI with more humanlike intuition, developing a computer capable of beating even expert human players at the fiendishly complicated board game Go. The objective of Go, a game invented in China more than 2,500 years ago, is fairly simple: players must alternately place black and white “stones” on a grid of 19 horizontal and 19 vertical lines with the aim of surrounding the opponent’s pieces, and avoiding having one’s own pieces surrounded.
Mastering Go, however, requires endless practice, as well as a finely tuned knack of recognizing subtle patterns in the arrangement of the pieces spread across the board. Google’s team has shown that the skills needed to master Go are not so uniquely human after all. Their computer program, called AlphaGo, beat the European Go champion, Fan Hui, five games to zero. And this March it will take on one of the world’s best players, Lee Sedol, in a tournament to be held in Seoul, South Korea. How close are we to creating artificial intelligence... It is uncontroversial that the human brain has capabilities that are, in some respects, far superior to those of all other known objects in the cosmos. It is the only kind of object capable of understanding that the cosmos is even there, or why there are infinitely many prime numbers, or that apples fall because of the curvature of space-time, or that obeying its own inborn instincts can be morally wrong, or that it itself exists.
Nor are its unique abilities confined to such cerebral matters. The cold, physical fact is that it is the only kind of object that can propel itself into space and back without harm, or predict and prevent a meteor strike on itself, or cool objects to a billionth of a degree above absolute zero, or detect others of its kind across galactic distances. But no brain on Earth is yet close to knowing what brains do in order to achieve any of that functionality. Why? Despite this long record of failure, AGI must be possible. Turing fully understood universality. Marvin Minsky Reflects on a Life in AI. Processors That Work Like Brains Will Accelerate Artificial Intelligence. Beyond Zero and One: Machines, Psychedelics, and Consciousness by Andrew Smart review – inside the minds of computers. Machine Learning Inspired by Human Learning. Big Data’s Mathematical Mysteries. Why Self-Driving Cars Must Be Programmed to Kill.
Rock-Paper-Scissors: You vs. the Computer. Here's How Artificial Intelligence Could Kill Us All. How Relying on Algorithms and Bots Can Be Really, Really Dangerous | Wired Opinion. Miguel Nicolelis Says the Brain is Not Computable, Bashes Kurzweil’s Singularity. Philosophy will be the key that unlocks artificial intelligence | David Deutsch | Science. Blueprint for an artificial brain: Scientists experiment with memristors that imitate natural nerves.
Engineers solve a biological mystery and boost artificial intelligence. Siri’s Inventors Are Building a Radical New AI That Does Anything You Ask | Enterprise. Why Cognition-as-a-Service is the next operating system battlefield. Scientists See Advances in Deep Learning, a Part of Artificial Intelligence. What we read about deep learning is just the tip of the iceberg. Going Deeper into Neural Networks. The Trouble with Teaching Computers to Think for Themselves, by David Berreby. The Next Big Thing You Missed: The Quest to Give Computers the Power of Imagination | Business. Computer smart as a 4-year-old | UIC News Center. We’re on the cusp of deep learning for the masses. You can thank Google later. CAPTCHA Busted? AI Company Claims to Have Broken the Internet's Favorite Protection System. Ray Kurzweil Plans to Create a Mind at Google—and Have It Serve You. Inside the Artificial Brain That’s Remaking the Google Empire | Enterprise.
Google Creates Learning Brain, Turns It Loose On The Internet. IBM researchers get closer to brain-like computing. Why IBM’s New Brainlike Chip May Be “Historic” IBM Scientists Show Blueprints for Brainlike Computing. So It Begins: Darpa Sets Out to Make Computers That Can Teach Themselves | Danger Room. DARPA Building Robots With ‘Real’ Brains. Human Brain Project - Home. Can robots be creative? The Believers.