Originally featured: The Economist
Language: Finding a voice
Computers have got much better at translation, voice recognition and speech synthesis, says Lane Greene. But they still don’t understand the meaning of language
I’M SORRY, Dave. I’m afraid I can’t do that.” With chilling calm, HAL 9000, the on-board computer in “2001: A Space Odyssey”, refuses to open the doors to Dave Bowman, an astronaut who had ventured outside the ship. HAL’s decision to turn on his human companion reflected a wave of fear about intelligent computers.
When the film came out in 1968, computers that could have proper conversations with humans seemed nearly as far away as manned flight to Jupiter. Since then, humankind has progressed quite a lot farther with building machines that it can talk to, and that can respond with something resembling natural speech. Even so, communication remains difficult. If “2001” had been made to reflect the state of today’s language technology, the conversation might have gone something like this: “Open the pod bay doors, Hal.” “I’m sorry, Dave. I didn’t understand the question.” “Open the pod bay doors, Hal.” “I have a list of eBay results about pod doors, Dave.”
Creative and truly conversational computers able to handle the unexpected are still far off. Artificial-intelligence (AI) researchers can only laugh when asked about the prospect of an intelligent HAL, Terminator or Rosie (the sassy robot housekeeper in “The Jetsons”). Yet although language technologies are nowhere near ready to replace human beings, except in a few highly routine tasks, they are at last about to become good enough to be taken seriously. They can help people spend more time doing interesting things that only humans can do. After six decades of work, much of it with disappointing outcomes, the past few years have produced results much closer to what early pioneers had hoped for.
Digital assistants on personal smartphones can get away with mistakes, but for some business applications the tolerance for error is close to zero, notes Nikita Ivanov. His company, Datalingvo, a Silicon Valley startup, answers questions phrased in natural language about a company’s business data. If a user wants to know which online ads resulted in the most sales in California last month, the software automatically translates his typed question into a database query. But behind the scenes a human working for Datalingvo vets the query to make sure it is correct. This is because the stakes are high: the technology is bound to make mistakes in its early days, and users could make decisions based on bad data.
This process can work the other way round, too: rather than natural-language input producing data, data can produce language. Arria, a company based in London, makes software into which a spreadsheet full of data can be dragged and dropped, to be turned automatically into a written description of the contents, complete with trends. Matt Gould, the company’s chief strategy officer, likes to think that this will free chief financial officers from having to write up the same old routine analyses for the board, giving them time to develop more creative approaches.
Machines that relieve drudgery and allow people to do more interesting jobs are a fine thing. In net terms they may even create extra jobs. But any big adjustment is most painful for those least able to adapt. Upheavals brought about by social changes—like the emancipation of women or the globalisation of labour markets—are already hard for some people to bear. When those changes are wrought by machines, they become even harder, and all the more so when those machines seem to behave more and more like humans. People already treat inanimate objects as if they were alive: who has never shouted at a computer in frustration? The more that machines talk, and the more that they seem to understand people, the more their users will be tempted to attribute human traits to them.
That raises questions about what it means to be human. Language is widely seen as humankind’s most distinguishing trait. AI researchers insist that their machines do not think like people, but if they can listen and talk like humans, what does that make them? As humans teach ever more capable machines to use language, the once-obvious line between them will blur.
Above article is an excerpt from the original article.
Originally featured: The Economist
Originally featured: Fast Company
Don't fear artificial intelligence. It's what's going to help you do your job faster and better in 2017.
Artificial intelligence is growing fast. Recent research puts it as a $5 billion market by 2020, and Gartner estimates that 6 billion connected "things" will require AI support by 2018. Connected machines, wearables, and other business tools like voice assistants are already boosting productivity at work and at home.
Two reports just surfaced that tackle the troubling predictions that automation, artificial intelligence, and robots are going to supplant human workers. The McKinsey Global Institute and Glassdoor research indicates that we don’t have to worry that we humans will become obsolete.
Matt Gould, chief strategy officer at Arria NLG, U.K.-based company offering AI technology in data analytics and information delivery, explains that AI has the ability to distill expertise into the machine. "Knowledge work, for the first time, can be produced at volume from NLG-AI systems," Gould says. "Far from killing the jobs of knowledge workers, this tends to free them up to do what they are paid to do—innovate, model, refine, and improve on the expertise of their business."
Originally featured: ITBusinessEdge
The new era of interconnected life is here. And, as our technology advances, we are surrounded by more and more sensors gathering data — allowing our machines to speak to each other and speak to us via the Internet of Things (IoT).
Data is being generated faster than any one person or company is able to analyze it. In fact, less than 0.5 percent of all data is ever analyzed. The real problem lies in the shortage of experts who have the ability to analyze, unlock, and communicate its value. Automation and natural language generation platforms are able to solve the data capacity crisis by gleaning actionable insights in the most efficient and effective way.
The real value that the IoT brings is at the intersection of gathering data and leveraging it. We need to analyze that data, often in real time, if we are to extract its value, and then we need to communicate the results of that analysis to those who can make use of it and take action. In the slides below, Matt Gould, chief strategy officer of Arria NLG, a global leader in the development and deployment of natural language generation (NLG) software technologies, advises on the benefits and path needed for successful IoT data analysis.