New research from Accenture (NYSE:ACN) reveals that artificial intelligence (AI) could accelerate China's economic growth rate from 6.3 percent to 7.9 percent by 2035, by transforming the nature of work and opening new sources of value and growth. This Smart News Release features multimedia. AI is poised to boost China's GVA by USD $7,111 billion by 2035 (Graphic: Business Wire) The report, titled "How Artificial Intelligence Can Drive China's Growth," explores new insights into AI and its impact on China's economy. Based on analysis and modeling by Accenture Research, in collaboration with Frontier Economics, there is dramatic impact on China's growth when AI is added as a completely new factor of production to the economic growth model. "China has already made great leaps in the development of AI and our research shows that it has the potential to be a powerful remedy for slowing growth," said Chuan Neo Chong, Accenture Greater China Chairwoman.

Last month, Sudhakar Choudhari took the company bus as usual from his one-bedroom apartment to the suburban offices of Tech Mahindra, a major employer of workers in India that powers the global technology machine behind the scenes. Then a manager took him into a conference room and asked him to resign. "It was a terrible scene for me," said Mr. Choudhari, 41, who had been with the company for 11 years and most recently maintained software for a British client. As the manager spoke, he thought: "I have an 11-year-old child. My wife is not working. How to pay the home loans?"

Too often the answer to this question resembles the plot of a sci-fi thriller. People worry that developments in A.I. will bring about the "singularity" -- that point in history when A.I. surpasses human intelligence, leading to an unimaginable revolution in human affairs. Or they wonder whether instead of our controlling artificial intelligence, it will control us, turning us, in effect, into cyborgs. These are interesting issues to contemplate, but they are not pressing. They concern situations that may not arise for hundreds of years, if ever.

To read the original article visit my blog: www.globalfuturist.org The rise of Artificial Intelligence (AI), and an increasingly connected society has already, according to the UK's MI5 made it "much harder for spies to hide in the shadows", but now, if Robert Cardillo has his way, so called robo-automation tools will perform 75 percent of the tasks currently done by the new front line of American intelligence spies – the analysts who collect, analyse, and interpret images beamed from drones, satellites, and other feeds around the globe. Cardillo, the director of the National Geospatial-Intelligence Agency, (NGA), announced his push toward "automation" and "AI" at a conference this week in San Antonio. The annual conference, hosted by the United States Geospatial Intelligence Foundation, brings together technologists, soldiers, and intelligence professionals to discuss national security threats, changes in technology, and data collection and processing. AI is on the rise, and last year former President Barack Obama's White House created a Defcon Scale for Cyberattacks, and released a white paper on its potential future impacts in the final months of the administration, and police forces around the world are increasingly using preliminary "pre-crime" technologies to predict when, where and by whom crimes will likely be committed.

AI promises to transform the world. Companies like this one will pave the way. He's been called one of the "foremost thinkers on the topic of artificial intelligence," so it's no surprise that Andrew Ng -- the cofounder of Coursera, the lead developer of Stanford University's main Massive Open Online Course (MOOC) platform, and the founder of the Google Brain project -- is starting another AI company of his own now that he's left Baidu. The resume of this impressive entrepreneur reads like a laundry list of some of the most impressive achievements in AI technology, and it seems safe to assume that Ng's newest venture, known only as deeplearning.ai, Hope will help many of you: deeplearning.ai

In recent years, Artificial Intelligence (AI) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB. We can think of blockchains as "blue ocean" databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space. Famous blue ocean examples are Wii for video game consoles (compromise raw performance, but have new mode of interaction), or Yellow Tail for wines (ignore the pretentious specs for wine lovers; make wine more accessible to beer lovers). By traditional database standards, traditional blockchains like Bitcoin are terrible: low throughput, low capacity, high latency, poor query support, and so on. But in blue-ocean thinking, that's ok, because blockchains introduced three new characteristics: decentralized / shared control, immutable / audit trails, and native assets / exchanges.

At the annual Futiju Forum, held in Tokyo recently, technology--largely artificial intelligence (AI) including machine learning--was at the centre of all innovation, with the aim of helping solve everyday problems. Future store is one such example of how with AI, running a retail store is child's play. Through the use of AI, it collects a combination of various types of data for analysis, including product shelf video data, point-of-sale (POS), and shelf arrangement data. Based on the collected data, the robot suggests which products are to be displayed on shelves and at what time. This is enabled by sending real-time product shelf condition data to the shop staff and therefore, improving operational efficiency in the store. Another example is its work for the Instruments and Electronics (Shanghai) Associates Group (INESA), a state-owned firm in China that provides smart city solutions.

Intelligent machines are taking over thousands of jobs, and being qualified is no longer enough to keep your job. Earlier this year, consulting firm McKinsey and Co. released a study that said 51% of all jobs could be automated in the next 20 years. Even specialized professions like medicine, law and banking are feeling the heat of Artificial Intelligence (AI). A few months ago, investment bank JP Morgan made the news by introducing intelligent machines to review financial deals that once kept employees busy for thousands of hours. Diagnostics and other decision-making skills previously thought of as the exclusive preserve of human beings, will soon be better handled by machines.

The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off.In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. You'll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything. On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can'... It was, for decades, primitive technology. But it now has abilities we never expected. It can learn through experience -- much the way humans do -- and it won't be long before machines, like their human creators, begin thinking for themselves, creatively. Independently with judgment -- sometimes better judgment than humans have. As we first reported last fall, the technology is so promising that IBM has staked its 106-year-old reputation on its version of artificial intelligence called Watson -- one of the most sophisticated computing systems ever built.

This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different ways to assess the value of these systems. In so doing, both designers and users could take this intrinsic characteristic of medicine more seriously and consider alternative approaches that do not "sweep uncertainty under the rug" within an objectivist fiction, which everyone can come up by believing as true.