Is AI Riding a One-Trick Pony? - MIT Technology Review. 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. 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. 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? 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. 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.
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. 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. 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. As exciting as their performance gains have been, though, there’s a troubling fact about modern neural networks: Nobody knows quite how they work.
True AI is both logically possible and utterly implausible. 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). 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. 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. 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. 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. 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.
Marvin Minsky Reflects on a Life in AI. Processors That Work Like Brains Will Accelerate Artificial Intelligence. Picture a person reading these words on a laptop in a coffee shop. Beyond Zero and One: Machines, Psychedelics, and Consciousness by Andrew Smart review – inside the minds of computers. Machine Learning Inspired by Human Learning. Fig. 1. People can learn rich concepts from limited data. (A and B) A single example of a new concept (red boxes) can be enough information to support the (i) classification of new examples, (ii) generation of new examples, (iii) parsing an object into parts and relations (parts segmented by color), and (iv) generation of new concepts from related concepts.
Taking inspiration from the way humans seem to learn, scientists have created AI software capable of picking up new knowledge in a far more efficient and sophisticated way. Big Data’s Mathematical Mysteries. Why Self-Driving Cars Must Be Programmed to Kill. When it comes to automotive technology, self-driving cars are all the rage. Standard features on many ordinary cars include intelligent cruise control, parallel parking programs, and even automatic overtaking—features that allow you to sit back, albeit a little uneasily, and let a computer do the driving. 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. Miguel Nicolelis Says the Brain is Not Computable, Bashes Kurzweil’s Singularity.
Philosophy will be the key that unlocks artificial intelligence. To state that the human brain has capabilities that are, in some respects, far superior to those of all other known objects in the cosmos would be uncontroversial. Blueprint for an artificial brain: Scientists experiment with memristors that imitate natural nerves. Scientists have long been dreaming about building a computer that would work like a brain. This is because a brain is far more energy-saving than a computer, it can learn by itself, and it doesn't need any programming. Privatdozent [senior lecturer] Dr. Engineers solve a biological mystery and boost artificial intelligence. Siri’s Inventors Are Building a Radical New AI That Does Anything You Ask. 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. Computer smart as a 4-year-old. 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. 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. DARPA Building Robots With ‘Real’ Brains. Human Brain Project - Home. Can robots be creative? The Believers.