The Business of Artificial Intelligence. DCS latest. DCS latest. Artificial general intelligence AGI is the intelligence of a machine that could successfully perform any intellectual task that a human being can. A true AI might ruin the worldbut that assumes its possible at all. Artificial Intelligence The Very Idea Pdf ConverterLatest. For more than 2. The most important of these are what economists call general purpose technologies a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big box retailers, shopping centers, cross docking warehouses, new supply chains, and, when you think about it, suburbs. Mexican International Conference on Artificial Intelligence. Celebrating 30 years of the SMIA. October 23 to 28 Ensenada, Baja California, Mexico. Artificial intelligence is infiltrating our daily lives, with applications that curate your phone pics, manage your email, and translate text from any language into. Recently, there has been growing alarm about the potential dangers of artificial intelligence. Famous giants of the commercial and scientific world have expressed. Some say that artificial intelligence threatens to automate away all the work that people do. But what if theres a way to rethink the concept of work that not only. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models. The most important general purpose technology of our era is artificial intelligence, particularly machine learning ML that is, the machines ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks its given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own. Why is this such a big deal Two reasons. First, we humans know more than we can tell We cant explain exactly how were able to do a lot of things from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldnt automate many tasks. Now we can. Second, ML systems are often excellent learners. They can achieve superhuman performance in a wide range of activities, including detecting fraud and diagnosing disease. Excellent digital learners are being deployed across the economy, and their impact will be profound. In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general purpose technologies. Download Boeing 747 Systems Manual. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination. Like so many other new technologies, however, AI has generated lots of unrealistic expectations. We see business plans liberally sprinkled with references to machine learning, neural nets, and other forms of the technology, with little connection to its real capabilities. Simply calling a dating site AI powered, for example, doesnt make it any more effective, but it might help with fundraising. This article will cut through the noise to describe the real potential of AI, its practical implications, and the barriers to its adoption. What Can AI Do Today The term artificial intelligence was coined in 1. John Mc. Carthy, a math professor at Dartmouth who organized the seminal conference on the topic the following year. Ever since, perhaps in part because of its evocative name, the field has given rise to more than its share of fantastic claims and promises. In 1. 95. 7 the economist Herbert Simon predicted that computers would beat humans at chess within 1. It took 4. 0. In 1. Marvin Minsky said, Within a generation the problem of creating artificial intelligence will be substantially solved. Simon and Minsky were both intellectual giants, but they erred badly. Thus its understandable that dramatic claims about future breakthroughs meet with a certain amount of skepticism. Lets start by exploring what AI is already doing and how quickly it is improving. The biggest advances have been in two broad areas perception and cognition. In the former category some of the most practical advances have been made in relation to speech. Voice recognition is still far from perfect, but millions of people are now using it think Siri, Alexa, and Google Assistant. The text you are now reading was originally dictated to a computer and transcribed with sufficient accuracy to make it faster than typing. A study by the Stanford computer scientist James Landay and colleagues found that speech recognition is now about three times as fast, on average, as typing on a cell phone. The error rate, once 8. Whats striking is that this substantial improvement has come not over the past 1. Although AI is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. Image recognition, too, has improved dramatically. You may have noticed that Facebook and other apps now recognize many of your friends faces in posted photos and prompt you to tag them with their names. An app running on your smartphone will recognize virtually any bird in the wild. Image recognition is even replacing ID cards at corporate headquarters. Vision systems, such as those used in self driving cars, formerly made a mistake when identifying a pedestrian as often as once in 3. The error rate for recognizing images from a large database called Image. Net, with several million photographs of common, obscure, or downright weird images, fell from higher than 3. See the exhibit Puppy or MuffinThe speed of improvement has accelerated rapidly in recent years as a new approach, based on very large or deep neural nets, was adopted. The ML approach for vision systems is still far from flawless but even people have trouble quickly recognizing puppies faces or, more embarrassingly, see their cute faces where none exist. The second type of major improvement has been in cognition and problem solving. Machines have already beaten the finest human players of poker and Go achievements that experts had predicted would take at least another decade. Googles Deep. Mind team has used ML systems to improve the cooling efficiency at data centers by more than 1. Intelligent agents are being used by the cybersecurity company Deep Instinct to detect malware, and by Pay. Pal to prevent money laundering. A system using IBM technology automates the claims process at an insurance company in Singapore, and a system from Lumidatum, a data science platform firm, offers timely advice to improve customer support. Dozens of companies are using ML to decide which trades to execute on Wall Street, and more and more credit decisions are made with its help. Amazon employs ML to optimize inventory and improve product recommendations to customers. Infinite Analytics developed one ML system to predict whether a user would click on a particular ad, improving online ad placement for a global consumer packaged goods company, and another to improve customers search and discovery process at a Brazilian online retailer. The first system increased advertising ROI threefold, and the second resulted in a 1. Machine learning systems are not only replacing older algorithms in many applications, but are now superior at many tasks that were once done best by humans. Although the systems are far from perfect, their error rate about 5 on the Image. Net database is at or better than human level performance. Voice recognition, too, even in noisy environments, is now nearly equal to human performance. Reaching this threshold opens up vast new possibilities for transforming the workplace and the economy. Our Fear of Artificial Intelligence. Years ago I had coffee with a friend who ran a startup. He had just turned 4. His father was ill, his back was sore, and he found himself overwhelmed by life. Dont laugh at me, he said, but I was counting on the singularity. My friend worked in technology hed seen the changes that faster microprocessors and networks had wrought. It wasnt that much of a step for him to believe that before he was beset by middle age, the intelligence of machines would exceed that of humansa moment that futurists call the singularity. A benevolent superintelligence might analyze the human genetic code at great speed and unlock the secret to eternal youth. At the very least, it might know how to fix your back. But what if it wasnt so benevolent Nick Bostrom, a philosopher who directs the Future of Humanity Institute at the University of Oxford, describes the following scenario in his book Superintelligence, which has prompted a great deal of debate about the future of artificial intelligence. Imagine a machine that we might call a paper clip maximizerthat is, a machine programmed to make as many paper clips as possible. Now imagine that this machine somehow became incredibly intelligent. Given its goals, it might then decide to create new, more efficient paper clip manufacturing machinesuntil, King Midas style, it had converted essentially everything to paper clips. This story is part of our MarchApril 2. Issue. See the rest of the issue. Subscribe. No worries, you might say you could just program it to make exactly a million paper clips and halt. But what if it makes the paper clips and then decides to check its work Has it counted correctlyIt needs to become smarter to be sure. The superintelligent machine manufactures some as yet uninvented raw computing material call it computronium and uses that to check each doubt. But each new doubt yields further digital doubts, and so on, until the entire earth is converted to computronium. Except for the million paper clips. Things ReviewedSuperintelligence Paths, Dangers, StrategiesBy Nick Bostrom. Oxford University Press, 2. Bostrom does not believe that the paper clip maximizer will come to be, exactly its a thought experiment, one designed to show how even careful system design can fail to restrain extreme machine intelligence. But he does believe that superintelligence could emerge, and while it could be great, he thinks it could also decide it doesnt need humans around. Or do any number of other things that destroy the world. The title of chapter 8 is Is the default outcome doomIf this sounds absurd to you, youre not alone. Critics such as the robotics pioneer Rodney Brooks say that people who fear a runaway AI misunderstand what computers are doing when we say theyre thinking or getting smart. From this perspective, the putative superintelligence Bostrom describes is far in the future and perhaps impossible. Yet a lot of smart, thoughtful people agree with Bostrom and are worried now. Why Volition. The question Can a machine think has shadowed computer science from its beginnings. Alan Turing proposed in 1. John Mc. Carthy, inventor of the programming language LISP, coined the term artificial intelligence in 1. As AI researchers in the 1. Even beyond the oft referenced HAL from 2. A Space Odyssey, the 1. Colossus The Forbin Project featured a large blinking mainframe computer that brings the world to the brink of nuclear destruction a similar theme was explored 1. War. Games. The androids of 1. Wpe Pro No Virus. Westworld went crazy and started killing. Extreme AI predictions are comparable to seeing more efficient internal combustion engines and jumping to the conclusion that the warp drives are just around the corner, Rodney Brooks writes. When AI research fell far short of its lofty goals, funding dried up to a trickle, beginning long AI winters. Even so, the torch of the intelligent machine was carried forth in the 1. Vernor Vinge, who popularized the concept of the singularity researchers like the roboticist Hans Moravec, an expert in computer vision and the engineerentrepreneur Ray Kurzweil, author of the 1. The Age of Spiritual Machines. Whereas Turing had posited a humanlike intelligence, Vinge, Moravec, and Kurzweil were thinking bigger when a computer became capable of independently devising ways to achieve goals, it would very likely be capable of introspectionand thus able to modify its software and make itself more intelligent. In short order, such a computer would be able to design its own hardware. As Kurzweil described it, this would begin a beautiful new era. Such machines would have the insight and patience measured in picoseconds to solve the outstanding problems of nanotechnology and spaceflight they would improve the human condition and let us upload our consciousness into an immortal digital form. Intelligence would spread throughout the cosmos. You can also find the exact opposite of such sunny optimism. Stephen Hawking has warned that because people would be unable to compete with an advanced AI, it could spell the end of the human race. Upon reading Superintelligence, the entrepreneur Elon Musk tweeted Hope were not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable. Musk then followed with a 1. Future of Life Institute. Not to be confused with Bostroms center, this is an organization that says it is working to mitigate existential risks facing humanity, the ones that could arise from the development of human level artificial intelligence. No one is suggesting that anything like superintelligence exists now. In fact, we still have nothing approaching a general purpose artificial intelligence or even a clear path to how it could be achieved. Recent advances in AI, from automated assistants such as Apples Siri to Googles driverless cars, also reveal the technologys severe limitations both can be thrown off by situations that they havent encountered before. Artificial neural networks can learn for themselves to recognize cats in photos. But they must be shown hundreds of thousands of examples and still end up much less accurate at spotting cats than a child. This is where skeptics such as Brooks, a founder of i. Robot and Rethink Robotics, come in. Even if its impressiverelative to what earlier computers could managefor a computer to recognize a picture of a cat, the machine has no volition, no sense of what cat ness is or what else is happening in the picture, and none of the countless other insights that humans have. In this view, AI could possibly lead to intelligent machines, but it would take much more work than people like Bostrom imagine. And even if it could happen, intelligence will not necessarily lead to sentience. Extrapolating from the state of AI today to suggest that superintelligence is looming is comparable to seeing more efficient internal combustion engines appearing and jumping to the conclusion that warp drives are just around the corner, Brooks wrote recently on Edge. Malevolent AI is nothing to worry about, he says, for a few hundred years at least. Insurance policy. Even if the odds of a superintelligence arising are very long, perhaps its irresponsible to take the chance. One person who shares Bostroms concerns is Stuart J. Russell, a professor of computer science at the University of California, Berkeley.