The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In the year 1770, at the court of the Austrian Empress Maria Theresa, an inventor named Wolfgang von Kempelen presented a chess-playing machine.
One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: "So today I'm going to be talking about different approaches to building…" He stalled, as though just realising that he was stating his momentous ambition out loud. And then he said it: "AGI". AGI stands for artificial general intelligence, a hypothetical computer program that can perform intellectual tasks as well as, or better than, a human. AGI will be able to complete discrete tasks, such as recognising photos or translating languages, which are the single-minded focus of the multitude of artificial intelligences (AIs) that inhabit our phones and computers. But it will also add, subtract, play chess and speak French. It will also understand physics papers, compose novels, devise investment strategies and make delightful conversation with strangers. It will monitor nuclear reactions, manage electricity grids and traffic flow, and effortlessly succeed at everything else. AGI will make today's most advanced AIs look like pocket calculators. The only intelligence that can currently attempt all these tasks is the kind that humans are endowed with. But human intelligence is limited by the size of the skull that houses the brain. Its power is restricted by the puny amount of energy that the body is able to provide. Because AGI will run on computers, it will suffer none of these constraints. Its intelligence will be limited only by the number of processors available.
The first five days or so of SXSW in Austin are always dedicated to the "interactive" portion of the festival. The city's downtown streets swell with lanyard-laden "entrepreneurs" and "founders" wearing that familiar uniform of T-shirts screen-printed with their company's clever logo, an outfit made professional by throwing a blazer over the ensemble. They bounce from panel to panel and branded "house" to branded "house" (this year, on scooters, so many scooters) hawking their new apps and software products, each promising to be more revolutionary and life-changing and utterly necessary than the next. For years, the unspoken question at the conference seemed to be which company will become SXSW famous, like Persicope, Foursquare, or, most memorably, Twitter? But this year, on the opening Friday of SXSW, Democratic presidential hopeful Elizabeth Warren unleashed a manifesto titled "Here's How We Can Break Up Big Tech," and a new question burst onto the scene: What do you think of Warren's proposal?
Watson started its life as a TV star and now is being used by clients such as Symrise to create new perfumes.IBM It is less than fifteen years ago when IBM sold its PC business to Lenovo and did the same for its x86 server business back in 2014. It was a major shift for the company that some decades ago was virtually the same as the'PC' itself. But what was the key factor that attracted the company's attention and made it open a brand new path, different from its past business-safe lanes? Undoubtedly, both businesses that were sold had become less profitable, still, it does not sufficiently explain the reason why a new business had to replace the old one to secure the viability of the corporation.
The convergence of artificial intelligence (AI) systems with the agile world is having a disruptive effect on how we build software and the types of products that we build. By combining machine learning and deep learning we can build applications that truly learn like humans. AI bias is a very serious concern, as AI systems are only as good as the data sets used to train them. Aidan Casey, senior software engineering manager at Johnson Controls, will speak about how artificial intelligence capabilities will be used to augment and shape the agile world of tomorrow at aginext 2019. The conference will be held on March 21 - 22 in London, United Kingdom.
The evolution of artificial intelligence (AI) grew with the complexity of the languages available for development. In 1959, Arthur Samuel developed a self-learning checkers program at IBM on an IBM 701 computer using the native instructions of the machine (quite a feat given search trees and alpha-beta pruning). But today, AI is developed using various languages, from Lisp to Python to R. This article explores the languages that evolved for AI and machine learning. The programming languages that are used to build AI and machine learning applications vary. Each application has its own constraints and requirements, and some languages are better than others in particular problem domains.
For many months, artificial intelligence has been in my peripheral vision, just sitting there, ignored by me because it seemed too far in the future to be interesting now. And then, there were all these terms -- Big Data, machine learning, data science -- which circled the subject and, frankly, gave me a bit of a headache. Artificial intelligence is upon us, unleashed and unbridled in its ability to transform the world. If in the previous technological revolution, machines were invented to do the physical work, then in this revolution, machines are being invented to do the thinking work. And no field involves more thinking than medicine.
True, AI began as a sci-fi fantasy popularized by visionary writers, but AI is here and now. There are many current applications, depending on how you define artificial intelligence. Although, after solving what was once a complex AI problem, it quickly seems obvious and therefore less "intelligent." In the U.S., one of the first examples of AI being used at the edge concerned handwriting recognition of checks. The smart home is full of AI at the edge with devices that learn behavior patterns: ovens that pre-heat when you leave work; thermostats that save money by not heating the home when no one is home; and lights that learn preferences based on different activities humans are engaged in within a room.
A stubborn brother and 3,000 Russian rubles were all it took to convince Elena Tverdokhlebova to go into science and technology. "I was 10 years old when my brother, who was studying for the university admissions exams, gave me a math problem to solve," she says. He was jumping around the living room offering her 100 rubles, then 1,000, and finally 3,000 if she could do it. "To his surprise, I was able to solve it, and he gave me 3,000 rubles, about $100 at that time," she says. This small incentive and the support she received from her family convinced Tverdokhlebova to study math and later computer science.
Machine Learning (ML) models power technologies that recommend movies we might like, assist in detecting health risks, suggest routes to dodge traffic and beat world-class chess players. Over the last decade, we have witnessed an explosion of emerging ML-enabled solutions across industries from health care to supply chains, enhanced by algorithms capable of making better predictions based on past data. Yet, as the range of industry problems in which ML systems can play a role continues to expand, it is essential to separate the hype from the reality, and understand misconceptions about ML as well as its limitations. Companies also need to be aware of the skills they require to harness the benefits of ML. In his seminal paper "Computing Machinery and Intelligence" published in 1950, Alan Turing introduced a test to assess whether or not a machine is capable of learning, of representing knowledge and of performing other cognitive functions generally associated with the human mind.