RT News recently shared the ponderings of artificial intelligence expert Ray Kurzweil. The article begins, “Most people would probably agree that computers are man-made technologies that function inside the strict boundaries of man-made borders. For technologists like Google engineering director Ray Kurzweil, however, the moment when computers liberate themselves from their masters will occur in our lifetime. By the year 2029, computers and robots will not only have surpassed their makers in terms of raw intelligence, they will understand us better than we understand ourselves, the futurist predicts with enthusiasm. Kurzweil, 66, is the closest thing to a pop star in the world of artificial intelligence, the place where self-proclaimed geeks quietly lay the grid work for what could be truly described as a new world order.” Read more
Posts Tagged ‘artificial intelligence’
David Shamah of the Times of Israel recently wrote, “The world may be a global village, but each neighborhood in that village still has its own language or dialect. Working to bridge those neighborhoods is Lexifone. With its computer learning system and smart algorithms, the company’s goal is to enable people to speak in their own tongue, with the party on the other end hearing them in their language. Lexifone works in English (the US, British, or Australian versions) Spanish (European and Mexican), Portuguese (European and Brazilian), French (European and Canadian), Mandarin (Chinese and Taiwanese), Russian, Polish, Italian, German, and Hebrew. Using the platform, anyone can call speakers of those languages, and make themselves understood in any of them.” Read more
Dean Evans of Tech Radar reports, “How, for example, does a computer know what a car looks like? We just know. We’ve built up that knowledge over time by observing lots of cars. Consequently, we know that not all cars look the same. We know that they come in different shapes, sizes and colours. But we can generally recognise a car because they have consistent and definable elements – wheels, tyres, an engine, windscreen and wing mirrors, they travel on roads, and so on. Could a computer learn all this information in the same way? A team working at Carnegie Mellon University in the United States believes so. It has developed a system called NEIL (Never Ending Image Learner), an ambitious computer program that can decipher the content of photos and make visual connections between them without being taught. Just like a human would.” Read more
Scott Raynovich of CMS Wire recently wrote, “Boston Dynamics, Nest and DeepMind. In the past month, Google has gone on yet another acquisition binge, spending at least $4 billion on a trio of startups that seem only loosely connected — robotics, home automation and artificial intelligence, respectively. Is there a central strategy, and what does it mean to the future of Google, the Internet of Things and Customer Experience? Based on a pattern of deals and feedback from leading experts, it appears Google believes the future is heavily connected to data gathering, machine learning and automation, which all of these companies have in common. ‘In a broader pattern, if Google is focusing on artificial intelligence (AI) and machine learning, how is this kind of semantic understanding going to help us make decisions faster and do our jobs,’ said David Schubmehl, a research director with International Data Corp. (IDC).” Read more
Google’s letting the cash flow. Fresh off its $3.2 billion acquisition of “conscious home” company Nest, which makes the Nest Learning Thermostat and Protect smoke and carbon monoxide detector, it’s spending some comparative pocket change — $400 million – on artificial intelligence startup DeepMind Technologies.
The news was first reported at re/code here, where one source describes DeepMind as “the last large independent company with a strong focus on artificial intelligence.” The London startup, funded by Founders Fund, was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman, with the stated goal of combining machine learning techniques and neuroscience to build powerful general purpose learning algorithms.
Its web page notes that its first commercial applications are in simulations, e-commerce and games, and this posting for a part-time paid computer science internship from this past summer casts it as “a world-class machine learning research company that specializes in developing cutting edge algorithms to power massively disruptive new consumer products.”
Naomi Eterman of McGill Daily recently discussed a technology developed in 2012 by scientists at the University of Waterloo: “Spaun, short for Semantic Pointer Architecture Unified Network, is the largest computer simulation of a functioning brain to date. It is the brainchild of Chris Eliasmith, a professor in philosophy and systems design engineering at the University of Waterloo, who developed the system as a proof-of-principle supplement to his recent book: How to Build a Brain. The model is composed of 2.5 million simulated neurons and four different neurotransmitters that allow it to ‘think’ using the same kind of neural connections as the mammalian brain. Read more
Charles Silver of Algebraix recently shared his opinions on artificial intelligence‘s recently revamped popularity and growing plausibility. Silver writes, “Just a few months ago, the phrase ‘artificial intelligence’ suddenly started being tossed around presentations, blogs, headlines, seminars — even a Facebook earnings meeting — as if it were the most benign concept in the world. AI could actually win an Oscar, thanks to Scarlett Johansson’s riveting voice-only performance as Samantha, the AI-enabled OS in the new movie ‘Her’. One reason for AI’s new respectability: Big steps have been made in solving the problems of artificial intelligence, especially in speech recognition and concept communication. Just think about how casually we now accept machines that can understand and talk, from Apple’s Siri to IBM’s ‘Jeopardy’-winning Watson.” Read more
Mark van Rijmenam of BigData-Startups.com recently wrote, “We have all heard the sentence ‘This call may be recorded for quality and training purposes’ when you call the call-centre of a company. Although some calls are indeed used for training purposes, more often they are used to improve natural language processing algorithms. The data from these calls help to create statistical models of phrases and words to improve the automated services to customers calling. This Natural Language Processing is gaining enormous traction and has massive potential for organisations.” Read more
Will Oremus of Stuff.co.nz writes, “Google just bought a fearsome fleet of robots. The company confirmed a New York Times report that it has acquired Boston Dynamics, the Massachusetts-based maker of such noted mechanical beasts as BigDog, Atlas, Petman, Cheetah and Wildcat. The company’s robots are among the world’s most advanced two- and four-legged machines. Some are humanoid, while others resemble predatory animals. Most have been developed under contract with military agencies, including the Defense Advanced Research Projects Agency, or DARPA. What might Google want with an army of military robots? At first gasp, the answer might seem to be, ‘conquering the world’. But that doesn’t seem to be the goal – at least, not in a military sense.” Read more
The Plante Moran 2013 Innovation Survey that was recently released doesn’t have anything specifically to do with semantic, Linked Data, AI, machine learning or related technologies. But it’s hard to ignore their place in innovation, which 94 out of 100 business leaders responded is a priority for them.
The survey reported that more than 90 percent of leaders saw innovation as being important to sustainability and growth; 85 percent recorded that it matters to new or improved processes; and more than 70 percent saw its value for improved products or services, to name just a few critical areas. Most readers of this blog likely will recognize that such outcomes are often realized by companies that follow semantic and other smart and innovative technologies down paths of innovation to new offerings and other key returns (Google anyone?).
Companies still experience constraints on making innovation happen, though, one of them being lack of access to new technology, according to the survey. But the report also finds that collaboration was considered a possible jumpstarter for innovation among three out of four of the respondents.
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