‘US rethinks Chinese investment in AI start-ups’ – BBC News


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'US rethinks Chinese investment in AI start-ups' - BBC News

Amazon Web Services AI exec: How cloud computing is driving artificial intelligence breakthroughs – GeekWire

Artificial intelligence research is still in its infancy, at least as compared to computer science in general, but the concept of unlimited computing resources is accelerating the field.

As someone with nearly unlimited computing resources at his disposal, this is something Swami Sivasubramanian, vice president of AI at Amazon Web Services, is watching play out. Last week Sivasubramanian walked GeekWire Cloud Tech Summit attendees through the array of artificial intelligence and machine-learning services that his team has developed for AWS customers and Amazons own internal services as well.

If youve been through a few tech cycles, youve already heard a lot about artificial intelligence. Much has been promised from this research field over several decades, but the enormous amount of data now moving into cloud computing services like AWS and others allows researchers like Sivasubramanian to make real breakthroughs that werent possible when data sets were scattered and siloed.

Many of these algorithms, especially like deep learning neural nets, papers were written about even two decades ago. But what has accelerated adoption of it is that we have specialized compute infrastructure, such as GPUs, specialized CPUs, FPGAs (field programmable gate arrays), you name it, he said. The combination of huge data sets and powerful computing engines is making AI concepts previously confined to science fiction a reality.

Take two AWS customers: CSPAN and the sheriffs office of Washington County in Oregon. Using the companys Rekognition image-recognition service, both were able to automate tasks that required painstaking human labor. CSPAN can now automatically identify Congresspeople speaking on the floor of the House or Senate, saving someone from having to manually annotate those videos, and Washington County is using the service to help it process photo tips when it is looking for a person of interest in an investigation.

But its still very early days for AI applications: Sivasubramanian joked that this world is about where the field of databases was when btrees were invented in the early 1970s. Thats about to change, however, as we gain a greater understanding of how AI models work and develop more sophisticated ways of training these systems to accomplish real goals.

Watch the full video of Sivasubramanians GeekWire Cloud Tech Summit talk above, and stay tuned for more highlights from the event in the days ahead.

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Ethics and Artificial Intelligence With IBM Watson’s Rob High – Forbes


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Ethics and Artificial Intelligence With IBM Watson's Rob High
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Listen to The Modern Customer Podcast with Rob High here. Artificial intelligence seems to be popping up everywhere, and it has the potential to change nearly everything we know about data and the customer experience. However, it also brings up new ...

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The Limits of Artificial Intelligence – Bloomberg

Talking about artificial intelligence is in season for Europes corporate executives. Just dont mention its shortcomings.

The C-suite is eager to tout its abilities in riding the 21st-century wave of automation by using sophisticated machine learning or shop-floor robots. Mentions of the phrase artificial intelligence on earnings calls are surging, as Bloomberg Intelligences Michael McDonough hasnoted.

In a world where CEOs get more credit for cutting costs and buying back shares than opening factories or hiring staff, technology-driven efficiency is a carrot to dangle in front of shareholders. Stock-market valuations are stretched and spending opportunities are rarebut processing power is abundant and data storage cheap.

Thats why executives are conjuring up the promise of lower costs, more revenue or something in between. Deutsche Telekom and Royal Bank of Scotland are turning to chatbotsa digital replacement for call centers that could shave billions off costs in the next five years. Frances BNP Paribas and publisher Wolters Kluwer are trying to boost revenue, and are using machines to screen financial markets or customer databases and trigger automatic alerts.

Siemens computers are having a go at running gas turbines more efficiently than humans. And dont forget the blue-collar world: Logistics firms Deutsche Post and DHL are talking up the idea of using robots alongside workers on the warehouse floor.

But theres remarkably little talk of the limits of automation. What is the acceptable failure rate of these projects? Outside of games like Go or poker, just how suited are machines to the corporate world? Are some algorithms too expensive, as Netflix once found out? Theres a risk that disappointing results lead to an exaggerated corporate pullback, as the Harvard Business Review warned in April.

Machines can fail. Chatbots do so very publicly: Microsoft shut down a bot called Tay after pranksters pushed it to make racist, sexist and pornographic remarks. Earlier this year, Facebook went back to the drawing board after its bots hit a failure rate of 70 percent, according to The Information.

Failure is fine, but the acceptable failure rate of an intelligent vehicle or a computer-controlled turbine is probably different to a bum steer on an electricity bill. That can be the difference between an easy path to cost savings and a complex, long-term investment that doesnt work as intended.

Then theres the question of whether machines are always suitable. Machine learning works best in an environment with rules and huge numbers of data points. That might work with cars driving through heavy traffic governed by laws, or with achieving the best price for selling a big block of shares.

It might not work well in deciding where to invest a hedge funds money, for example, or recommending products to customers without much previous data to go on. The minute things get fuzzyeither due to a lack of rules, an unclear evaluation of success or a lack of dataartificial intelligence performs poorly, according to Pictet strategist Edgar van Tuyll.

These limitations mean its not yet clear that the cost of automation will be offset by savings in human capital. Hiring a data scientist can cost more than $200,000, according to Bloomberg News. Flight-bookings company Amadeus has 40 of them. Siemens says it has more than 200 A.I. specialists running various projects. And even Silicon Valley has its grunt workers: Facebook is hiring 3,000 content moderators, on top of 4,500 existing ones. A.I. cheerleader Amazon has 341,000 employeesthree times the number it had in 2012.

There are good reasons to talk about A.I. and boast of its successes. But opening up about failure will help, too.

This column does not necessarily reflect the opinion of Bloomberg LP and its owners.

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Artificial Intelligence And Its Impact On Legal Technology (Part I) – Above the Law

Artificial intelligence (AI) is just beginning to come into its own in terms of its use by lawyers and within the legal industry. Whats the impact of this technology on the legal profession? Within the next few years, we will find ourselves on the cusp of a revolution in the practice of law led by the adoption of artificial intelligence in particular, by in-house lawyers. Much like email changed the way we do business every day, AI will become ubiquitous an indispensable assistant to practically every lawyer. Those who do not adopt and embrace the change will get left behind. Those who do will ultimately find themselves freed up to do the two things there always seems to be too little time for: thinking and advising.

Like many, you may be wondering about what AI products are out there or on the way, and how you can use them. Welcome to the first installment of a four-part series on artificial intelligence and its impact on the legal industry, specifically how in-house legal departments will be affected by it. Over the course of the series, I will discuss what AI is, how it can be used by legal departments, and what you as an in-house lawyer should be doing next regarding AI.

What Is Artificial Intelligence?

Before we discuss the impact of AI on the legal profession, its important to define it. The term artificial intelligence can be a bit misleading, at least when it comes to application in the legal field. No, were not talking about some type of walking and talking robot from The Terminator with a briefcase and tie (though that would be pretty cool). Perhaps a better description, and one that is catching on, is cognitive computing. This means teaching computers how to learn, reason, communicate, and make decisions. Cognitive tools are trained vs. programmed learning how to complete tasks traditionally done by people, where the focus is looking for patterns in data, testing the data, and finding/providing results. Or, as I like to think about it, a research assistant who can sift through the dreck and tell you what it found. Why is this important? Because, according to IBM, 2.5 quintillion bytes of data are being generated every day. In case youre not up-to-date on a quint, thats 2,500,000,000,000,000,000 bytes. Every day. The ability of any human to review and comprehend that level of data without help is the definition of impossible.

Going Deeper

The recent explosion in AI is due to a fundamental rule of technology: Moores Law. In 1965, Gordon Moore, a scientist at Intel, made a prediction based on his observation that the number of transistors per square inch on integrated circuits had doubled every year since their invention. His law predicts that this trend will continue, and growth in computer power will double roughly every two years while the cost of that computing power will go down. Simply put, more computer for less money. When coupled with the ever-lower cost of storing electronic data, you have the basis for the rapid rise in AI capabilities and availability. In fact, experts predict that spending on AI by companies will grow from $8 billion in 2016 to $47 billion in 2020, up almost 600%.

The reason for the huge increase in AI spending is simple: There are huge productivity gains and cost savings available from freeing humans from routine tasks that computers can handle, allowing people to focus on tasks that truly add value, things that computers really cannot do or do well. As well see in future installments of this series, this goal rationale fits particularly well with the legal industry. More importantly, legal departments will need to be ready for this change and adapt quickly to the use of AI. For example, a number of M.B.A. programs are introducing AI courses. Harvard, MIT, Stanford, and Frances INSEAD School of Business along with several other top-line M.B.A. programs have added courses on AI applications. As CEOs and CFOs become more accustomed to using AI, they will expect the other members of the C-Suite including the general counsel and legal department to follow suit. In-house lawyers who embrace AI, will become more valuable to the next generation of CEOs and CFOs.

For more about the future of AI for in-house counsel, see the full version of this article. Or visit the larger Legal Department 2025 Resource Center from Thomson Reuters.

Sterling Miller spent over 20 years as in-house counsel, including being general counsel for Sabre Corporation and Travelocity. He currently serves as Senior Counsel for Hilgers Graben PLLC focusing on litigation, contracts, data privacy, compliance, and consulting with in-house legal departments. He is CIPP/US certified in data privacy.

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The Future Of Growth And Economic Development Powered By Human And Artificial Intelligence – Seeking Alpha

The recent technological advancement within artificial intelligence, the "Internet of Things", and robotics has generated significant impact on traditional businesses, causing decreasing profit margins across several sectors, whereas most of the big winners in the Wall Street IPOs are companies with innovative ideas from Facebook (NASDAQ: FB) and Twitter, (NASDAQ: TWTR) to Snapchat (NYSE: SNAP). There are two common determining factors among those successful IPOs: Ideation and User Generated Content (UGC).

In the era of big data and artificial intelligence, we will soon be able to create the tools to better capture the value from ideation and UGC, as well as spur economic growth by capitalizing on human ingenuity. With the ever-accelerating developments in technology, the world is in the process of moving from a consumer economy to a knowledge-based economy, and from a debt- based system to an equity based system, which will include movement from tangible assets to intangible assets. Hence we envision that our world economic system will operate on a new growth formula.

This growth formula is as follows:

IA > M1 IC + AI = W

which translate into Intangible Assets (IA) is greater than Money Supply ((M1)) - therefore - Intellectual Capital (IC) plus Artificial Intelligence (AI) equals Wealth (W).

With this formula, we are developing a new financial platform - The Artificial Intelligence Economic Development Corporation (AIEDC). This platform will combine artificial intelligence with human intelligence along with big data to spur long term sustainable economic development that will facilitate the discovery, as well as the advancement of ideas - from ideation to monetization, in the area of economic development through the creation of new businesses, land infrastructure projects, environmental projects, scientific research, and technological projects.

At this time, the AI community is very vibrant and open with a lot of open source projects pioneered by the likes of companies such as, Google (NASDAQ: GOOG), Facebook and Amazon (NASDAQ: AMZN). These open source projects will be extremely beneficial to the future of growth and economic development of the World.

The high performance computing technology which is quantum leaping at this time, has been fueled by the recent developments in bitcoin mining, as well as the new chips that are being released every month, such as the tensor processing unit (TPU) from Google and Volta architecture from Nvidia (NASDAQ: NVDA) that are taking AI development to the next level.

According to Accenture LLP's report "Why Artificial intelligence is The Future of Growth", research on the impact of AI in 12 developed economies reveals that AI could double annual economic growth rates by 2035, specifically by changing the nature of work and creating a new relationship between Man and Machine. The impact of AI technologies on businesses is projected to increase labor productivity by up to 40 percent and enable people to make more efficient use of their time. This additional allocation of "Time" will further seed the development of ideation and user generated content.

Government organizations around the world have been accumulating vast amounts of data - by forming public private partnerships (P3's), AIEDC's platform will help governments all over the world through strategic partnerships for economic development.

At the center of the company will reside the M.I.N.D - a Machine Intelligence Neural-Network Database. Our neural network database is a clustered computational model with artificial neurons receiving input variables similar to a biological brain in a human being. The database will be the collective sum of all the data being fed into the M.I.N.D at that time.

The M.I.N.D. enabled platform will utilize artificial intelligence to combine the aspects of incubators, venture capital firms, crowdfunding, as well as project financing. It will include, but not be limited to intellectual property registration and protection, blockchain based smart contracts, and initial cryptocurrency offerings.

Finally, the M.I.N.D. will convert the Ideas submitted by users from intellectual capital to intellectual property.

With this platform, people with innovative ideas will be able to have their ideas validated, protected and converted into capital; investors will have a new platform and mechanism to effectively invest in new ideas and or companies; industry practitioners will have more and better ideas to execute on; governments will have additional income through their stake in the platform funded projects; the society will have a long term sustainable growth model.

Here are some thoughts from industry visionaries:

DeepMind's Cofounder Mustafa Suleyman:

The following is from the Business Insider article, 'In many areas, capitalism is currently failing us'

"And yet in many areas, capitalism is currently failing us," he said. "We actually need a new kind of set of incentives to tackle some of most pressing and urgent social problems and we need a new kind of tool, a new kind of intelligence, that is distributed, that is scaled, that is accessible, to try and make sense of some of the complexity that is overwhelming us."

Facebook's Cofounder Eduardo Saverin: stated the following as well:

This quote is taken from a CNBC article title, What I Learned From Watching "The Social Network"

"Entrepreneurship involves mistakes and failures. But ultimately, if you have that intellectual capital and intimate understanding behind your project, you have a chance to succeed. Intellectual capital, and not just monetary capital, will spawn the next great product or idea. Entrepreneurs, especially in the technology sector, will create things tomorrow that we can barely imagine today".

Leonard S. Johnson, The Artificial Intelligence Economic Development Corporation.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

Business relationship disclosure: I am the CEO of the Artificial Intelligence Economic Development Corporation

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The Future Of Growth And Economic Development Powered By Human And Artificial Intelligence - Seeking Alpha

3 Steps To Embedding Artificial Intelligence In Enterprise Applications – Forbes


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3 Steps To Embedding Artificial Intelligence In Enterprise Applications
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In the context of contemporary applications, it's hard to think of an application that doesn't use a database. From mobile to web to the desktop, every modern application relies on some form of a database. Some apps use flat files while others rely on ...

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3 Steps To Embedding Artificial Intelligence In Enterprise Applications - Forbes

How artificial intelligence is revolutionizing customer management – TNW

A few years back, cloud computing transformed customer management, giving every small and medium business access to unified data and communication platforms without the need to make heavy investments in IT infrastructure and staff. This time around, the next revolution in the space is being driven by artificial intelligence algorithms that help businesses automate customer outreach and make optimal use of data.

Beneath the surface of the roiling sea of data were generating hide exceptional business and sales opportunities. But the problem is theres now more information available than limited human resources and legacy tools can handle.

Fortunately, making sense of data, both structured and unstructured, is something that artificial intelligence is becoming increasingly proficient at. While were still least decades away from human-level synthetic intelligenceAI that will match the human brain in reasoning and decision makingmachine learning algorithms, computer vision, natural language processing and generation (NLG/NLP), and other forms of narrow artificial intelligence are proving to be the best complement for human activity.

AI-powered tools are now helping scale the efforts of sales teams by gleaning useful patterns from data, finding successful courses of action, and taking care of the bulk of the work in addressing customer needs and grievances.

Main providers of Customer Relationship Management (CRM) solutions have started to invest in the added value of AI. Last year, SalesForce, the leader in the CRM industry, announced Einstein, an AI assistant that, when launched, will be omnipresent across its platform. Einstein puts its AI chops to work to continuously study the flood of data SalesForces collects from sales, e-commerce activity, emails, IoT generated data and social media streams among others. The AI engine will then make suggestions across different use cases. For instance, it helps sales reps focus on the most promising leads based on engagement data analysis, or gives advice on when to trigger email campaigns according to customer response history.

SAP, another top competitor in the field, is also joining the fray by adding AI functionalities to its S/4HANA cloud ERP. The tacked-on features will give automated insights into the business data the system collects. This includes monitoring accounts or preparing lists of top vendors for an organization based on their pricing, past performance and ability to deliver.

Oracle also declared its cloud AI project earlier this year, called Adaptive Intelligence. The initiative involves a series of add-on applications that integrate with its cloud suite. These apps combine third-party data with real-time analytics to optimize decisions and recommendations in various domains. For example, the AI Offers app merges data from the company cloud and the Oracle Data Cloud to extract contextual insights into individual customer behaviors and provide personalized offers as visitors browse websites powered by the Commerce Cloud.

Other players are focusing on verticals and optimizing specific disciplines. Growbots, a lead generation platform, uses machine learning algorithms to automate the prospecting process and marketing campaigns. The platform uses machine learning algorithms to scan websites and gather publicly available information, and enrich its database of profiles about people and businesses. Growbots incorporates this information with client CRM data to identify new potential customers and create tailored prospect lists. The platform integrates with SalesForce and uses AI to enhance and automate email marketing, creating tailored emails for customers, scheduling campaigns and sending follow ups at opportune times. The AI engine uses Natural Language Processing to parse responses and channel positive replies to the sales team.

Such solutions can be a boon to salespersons who are under constant pressure to meet quotas. By enhancing and automating the routine-based parts of the business process, AI-powered tools enable sales teams to focus on their efforts on better serving the more complicated and human-demanding needs of customers. Over time, as these solutions continue to process company and customer data, they become more efficient in their functionality.

Another example is Conversica, an AI-powered assistant that functions like a sales employee and reaches out to anyone who has shown interest in the company, such as by downloading a whitepaper or requesting information from the website. The assistant processes replies from customers, determines feedback and potential questions and crafts a meaningful reply. The assistant passes off the lead to a human salesperson when the time is right.

Another interesting development in the space is the advent of customer service chatbots, which have become more popular in recent years. Powered by AI algorithms, these bots are becoming much more efficient at independently identifying and resolving customer problems through natural conversation. These assistants free up staff time for more critical and complicated tasks.

Amelia is a virtual customer assistant that uses natural language processing to understand customer queries and provide answer based on data gathered from previous interactions and the company knowledge base. According to estimates, Amelia solves 55 percent of incidents. When it doesnt have an answer or senses a frustration or hostility, it will pass on to a human operator.

Soul Machines, a eerily named startup based in New Zealand, is creating chatbots with expressive digital faces that understand and manifest human emotion. Nadia, the first iteration of their technology, uses AI algorithms to discern human tone and facial expression. The developers believe these chatbots will eventually create a richer experience and be able to engage customers at a much more personal level.

These are some of the trends that are transforming the ways businesses interact with their customers. AI-powered customer support and management will surely result in more satisfied and less frustrated customers, and more productive sales teams. We expect to see more exciting developments in the space in the coming months.

Read next: Thieves use Facebook tricks to steal your money and turn it into Bitcoin

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How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017 – Forbes


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How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017
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These and many other fascinating insights are from the Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science (142 pp., PDF, client access reqd). The study is based on interviews with 146 ...

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Emerging Artificial Intelligence (AI) Leaders: Rana el Kaliouby, Affectiva – Forbes


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Emerging Artificial Intelligence (AI) Leaders: Rana el Kaliouby, Affectiva
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Without our emotions, we can't make smart decisions, says Rana el Kaliouby. In the field of artificial intelligence, this is sheer heresy. Isn't the goal of AI to create a machine with human-level intelligence but without the human baggage of ...

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China is outsmarting America in artificial intelligence – The Australian Financial Review

Soren Schwertfeger, centre, and his team of assistants work on an automated arm at his artificial intelligence lab in Shanghai.

Soren Schwertfeger finished his postdoctorate research on autonomous robots in Germany and seemed set to continue his work in Europe or the United States, where artificial intelligence was pioneered and established.

Instead, he went to China.

"You couldn't have started a lab like mine elsewhere," Schwertfeger said.

The balance of power in technology is shifting. China, which for years watched enviously as the West invented the software and the chips powering today's digital age, has become a major player in artificial intelligence, what some think may be the most important technology of the future. Experts widely believe China is only a step behind the United States.

China's ambitions mingle the most far-out sci-fi ideas with the needs of an authoritarian state: Philip K. Dick meets George Orwell. There are plans to use it to predict crimes, lend money, track people on the country's ubiquitous closed-circuit cameras, alleviate traffic jams, create self-guided missiles and censor the internet.

Beijing is backing its artificial intelligence push with vast sums of money. Having already spent billions on research programs, China is readying a new multibillion-dollar initiative to fund moonshot projects, start-ups and academic research, all with the aim of growing China's AI capabilities, according to two professors who consulted with the government on the plan.

China's private companies are pushing deeply into the field as well, although the line between government and private in China sometimes blurs. Baidu often called the Google of China and a pioneer in artificial-intelligence-related fields, like speech recognition this year opened a joint company-government laboratory partly run by academics who once worked on research into Chinese military robots.

China is spending more just as the United States is cutting back. This past week, the Trump administration released a proposed budget that would slash funding for a variety of government agencies that have traditionally backed artificial intelligence research.

"It's a race in the new generation of computing," said James Lewis, a senior fellow at the Centre for Strategic and International Studies. "The difference is that China seems to think it's a race and America doesn't."

For Schwertfeger, the money mattered. He received a grant six times larger than what he might have gotten in Europe or America. That enabled him to set up a full artificial intelligence lab, with an assistant, a technician and a group of PhDstudents.

"It's almost impossible for assistant professors to get this much money," he said. "The research funding is shrinking in the USand Europe. But it is definitely expanding in China."

Schwertfeger's lab, which is part of ShanghaiTech University, works on ways for machines, without aid from humans, to avoid obstacles. Decked out with wheeled robots, drones and sensors, the lab works on ways for computers to make their own maps and to improve the performance of robots with tasks like finding objects specifically, people during search-and-rescue operations.

Much of China's artificial intelligence push is similarly peaceful. Still, its prowess and dedication have set off alarms within the USdefence establishment. The Defense Department found that Chinese money had been pouring into USartificial intelligence companies some of the same ones it had been looking to for future weapons systems.

Quantifying China's spending push is difficult, because Chinese authorities disclose little. But experts say it looks to be considerable. Numerous provinces and cities are spending billions on developing robotics, and a part of that funding is likely to go to artificial intelligence research. For example, the city of Xiangtan, in China's Hunan province, has pledged $US2 billion toward developing robots and artificial intelligence. Other places have direct incentives for the AI industry. In Suzhou, leading artificial intelligence companies can get about $US800,000 in subsidies for setting up shop locally, while Shenzhen, in southern China, is offering $US1 million to support any AI project established there.

On a national level, China is working on a system to predict events like terrorist attacks or labour strikes based on possible precursors like labour strife. A paper funded by the National Natural Science Foundation of China showed how facial recognition software could be simplified so that it could be more easily integrated with cameras across the country.

China is preparing a concerted nationwide push, according to the two professors who advised on the effort but declined to be identified, because the effort had not yet been made public. While the size wasn't clear, they said, it would most likely result in billions of dollars in spending.

Trump's proposed budget, meanwhile, would reduce the National Science Foundation's spending on intelligent systems by 10 per cent, to about $US175 million. Research and development in other areas would also be cut, although the proposed budget does call for more spending on defence research and some supercomputing. The cuts would essentially shift more research and development to private UScompanies like Google and Facebook.

"The previous administration was preparing for a future with artificial intelligence," said Subbarao Kambhampati, president of the Association for the Advancement of Artificial intelligence. "They were talking about increasing basic research for artificial intelligence. Instead of increases, we are now being significantly affected."

China's money won't necessarily translate into dominance. The government's top-down approach, closed-mouth bureaucracy and hoarding of information can hobble research. It threw a tremendous amount of resources toward curing severe acute respiratory syndrome, the deadly virus known as SARS, when it swept through the country 15 years ago. Yet the virus was eventually sequenced and tamed by a small Canadian lab, said Clay Shirky, a professor at NYU Shanghai and a technology writer.

"It wasn't that anyone was trying to stop the development of a SARS vaccine," Shirky said. "It's the habit that yes is more risky than no."

Authorities in China are now bringing top-down attention to fixing the problem of too much top-down control. While that may not sound promising, Wang Shengjin, a professor of electronic engineering at China's Tsinghua University, said he had noticed some improvement, such as professional groups sharing information, and authorities who are rolling back limits on professors claiming ownership of their discoveries for commercial purposes.

"The lack of open sources and sharing of information, this has been the reality," Wang said. "But it has started to change."

At the moment, cooperation and exchanges in artificial intelligence between the United States and China are largely open, at least from the USside. Chinese and USscholars widely publish their findings in journals accessible to all, and researchers from China are major players in USresearch institutions.

Chinese tech giants like Baidu, Tencent and Didi Chuxing have opened artificial intelligence labs in America, as have some Chinese start-ups. Over the past six years, Chinese investors helped finance 51 USartificial intelligence companies, contributing to the $US700 million raised, according to the recent Pentagon report.

It's unclear how long the cooperation will continue. The Pentagon report urged more controls. And while there are government and private pushes out of China, it is difficult to tell which is which, as Baidu shows.

Baidu is a leader in China's artificial intelligence efforts. It is working on driverless cars. It has turned an app that started as a visual dictionary take a picture of an object, and your cellphone will tell you what it is into a site that uses facial recognition to find missing people, a major problem in a country where child kidnapping has been persistent. In one stunning example, it helped a family find a child kidnapped 27 years earlier. DNA testing confirmed the family connection.

Baidu's speech-recognition software which can accomplish the difficult task of hearing tonal differences in Chinese dialects is considered top of the class. When Microsoft announced last October that its speech recognition software had surpassed human-level language recognition, Baidu's head of research at the time playfully reminded the UScompany that his team had accomplished a similar feat a year earlier.

In an apparent effort to harness Baidu's breakthroughs, China said this year that it would open a lab that would cooperate with the company on AI research. The facility will be headed by two professors with long experience working for government programs designed to catch up to and replace foreign technology. Both professors also worked on a program called the Tsinghua Mobile Robot, according to multiple academic papers published on the topic. Research behind the robot, which in one award is described as a "military-use intelligent ground robot", was sponsored by funding to improve Chinese military capabilities.

Li Wei, a professor involved in the Baidu cooperative effort, spent much of his career at Beihang University, one of China's seven schools of national defense.

A company spokeswoman said: "Baidu develops products and services that improve people's lives. Through its partnership with the AI research community, Baidu aims to make a complicated world simpler through technology."

Still, there are advantages in China's developing cutting-edge AI on its own. National efforts are aided by access to enormous amounts of data held by Chinese companies and universities, the large number of Chinese engineers being trained on either side of the Pacific and from government backing, said Wang, of Tsinghua.

Driving that attention is a breakthrough from a UScompany largely banned in China: Google. In March 2016, a Google artificial intelligence system, AlphaGo, beat a South Korean player at the complicated strategy game Go, which originated in China. This past week, AlphaGo beat the best player in the world, a Chinese national, at a tournament in Wuzhen, China.

The Google event changed the tenor of government discussions about funding, according to several Chinese professors.

"After AlphaGo came out and had such a big impact on the industry," said Zha Hongbin, a professor of machine learning at Peking University, "the content of government discussions got much wider and more concrete."

Shortly afterward, the government created a new project on brain-inspired computing, he added.

For all the government support, advances in the field could ultimately backfire, Shirky said. Artificial intelligence may help China better censor the internet, a task that often blocks Chinese researchers from finding vital information. At the same time, better AI could make it easier for Chinese readers to translate articles and other information.

"The fact is," Shirky said, "unlike automobile engineering, artificial intelligence will lead to surprises. That will make the world considerably less predictable, and that's never been Beijing's favourite characteristic."

Additional research by Carolyn Zhang in Shanghai

The New York Times

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China is outsmarting America in artificial intelligence - The Australian Financial Review

Artificial Intelligence for Good sees development applications – Devex

Sophia, a human-like robot, being interviewed during the AI for Global Good Summit in Geneva, Switzerland. Photo by: International Telecommunication Union/ CC BY

Perhaps the most photographed individual at the AI for Global Good Summitin Geneva, Switzerland, last week was not a human but a humanoid called Sophia.

As I get smarter, I hope to understand people better help you, work with you as a friend, to imagine and build a better future for us all, Sophia, an uncannily human-like robot, said in a Facebook Liveinterview.

In that interview and onstage at the summit, her eyebrows lifted, she smiled gently, and her eyes lit up as she answered questions from the audience, with moments where only the glimpse of cords behind her face revealed that she is a machine.

Hanson Robotics developed Sophia as part of its mission to create genius machines that live and love, and work together with humans to build a a smarter and better future. Her creator, Dr. David Hanson, appeared alongside Sophiaat the summit, presenting her as an example of his work to develop robots that can learn creativity, empathy and compassion. These are the traits he thinks must be combined with artificial intelligence so that robots can solve problems too complex for people to solve on their own.

Sophias appearance placed her amongst some of the leading minds across academia and industry who are helping humanitarian agencies examine how AI can help meet global goals.

Discussions centered around how AI is already changing the world, ways to harness the technologys potential for good, and challenges ranging from policy to security to privacy in advancing the contributions of AI to the Sustainable Development Goals. Sessions explored applications of AI, including advancing the personalization of education, augmenting medical practice and health care policy, and improving smart cities. The event was convened by the XPRIZE Foundationand the United Nations International Telecommunications Unionin partnership with 20 U.N. agencies.

How artificial intelligence might help achieve the SDGs

Global development professionals are working on complex problems that might appeal to machine learning experts looking to use their artificial intelligence skills for good. A growing number of efforts are bringing these communities together.

Artificial Intelligence has the potential to accelerate progress towards a dignified life, in peace and prosperity, for all people, saidU.N. Secretary-General Antnio Guterres. The time has arrived for all of us governments, industry and civil society to consider how AI will affect our future.

The summit represents the beginnings of the U.N.s efforts to ensure advances in AI can benefit all of humanity,he said. The event was part of a broader conversation as a number of actors in the development community are looking at how AI could augment their work, for example by helping organizations to analyze and act upon the enormous volumes of data they are collecting.

Last week, chief strategy officers gathered for the World Economic Forums Industry Strategy Meetingin San Francisco asked whether AI needs a Hippocratic oath, and discussed ways to train computer scientists to understand the ethical implications of the technologies they are developing.

I actually find it hard to name a major industry that I dont think AI can transform in the next several years.

Industry experts predict that AI could have a huge disrupting effect, with all the benefits and risks that follow.

AI is the new electricity, Andrew Ng, one of the leading thinkers on artificial intelligence, told Devex at a recent eventat Stanford University on the role of AI in achieving the SDGs. I actually find it hard to name a major industry that I dont think AI can transform in the next several years.

Ng is the co-founder of the education technology company Coursera, having recently led the AI efforts at Baidu, the Chinese search engine, and at Google, where he founded and led the Google Brain project, developing deep learning algorithms. Now he says he wants to advance AI beyond the tech world.

The most obvious cases for AI use include anything a typical human can do in less than a second of thought, he said. Image recognition is one common example, but AI also has powerful applications in education and health care in both the developed and developing world, said Ng.

We believe that AI is a transformative platform that will improve everything, Zenia Tata, executive director of global development at XPRIZE, told Devex via email. As such, the implications for development professionals are huge. Whether you are working in health care, water quality, market access for smallholder farmers or financial services for the underserved, AI will help you do it better in so many ways, as an example it will help with rapidly analyzing data and create predictive models.

As they have done with other technologies, developing countries may be able to leapfrog with some AI applications, because there are fewer regulatory barriers there for entrepreneurs looking to test these technologies, Ng said.

Yet while developing countries stand to benefit, they also face the greatest risk of being left behind, Guterres of the U.N. said.

One of the sessions at the summit in Geneva centered around promoting equality in access to AI. Discussions centered around how to ensure access to potential beneficial applications, such as diagnosing disease or promoting democracy.

Among the risks going forward will be the potential impact on the labor force. Several speakers at the AI for Global Good summit urged policymakers to begin preparing the workforce for new jobs in a future that is about human machine collaboration.

Technologists such as Ng have argued for the need to rethink social safety nets, and his preferred option is a conditional basic income, through which people are paid if they are unemployed, with the expectation that they study.

The global development community can be a strong partner in working to ensure that AI benefits everyone, said Ruchit Garg, the founder of Harvesting, which applies AI techniques to satellite imagery to drive financial inclusion for farmers.

This starts with understanding what AI is, how this may or may not work for various sections of society in different settings, creating platforms like this global summit to create dialogue between relevant global players, and finally facilitating standards and guidelines for industry to adopt which can ensure development of AI in [a way that] brings good to humanity in inclusive way, he said.

There is a precedent, he said: The ITU helped define telecommunications industry standards such as 3G, 5G, and Long Term Evolution. The organization could play a similar role in standardizing AI.

Christopher Fabian of UNICEFsaid in an interview at the summitthat he hopes to see development organizations launch partnerships with technology companies, as UNICEF has done with drones and UAVs, to move from conversation to action on AI for good.

If we can help to define some of the greatest needs in the world, and also a path to profit, how business can be involved with those needs, he said, we can create both more consistent and solid businesses but also help those who most need it.

Read more international development newsonline, and subscribe to The Development Newswireto receive the latest from the worlds leading donors and decision-makers emailed to you free every business day.

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Artificial Intelligence for Good sees development applications - Devex

Data Scientists use Artificial Intelligence to Predict Suicide Attempts – The Merkle

There are many different use cases for artificial intelligence, even though most of them have yet to be explored. A Vanderbilt University data scientist has come up with a bold and radical plan to deploy AI as a way to predict suicide. That is a rather remarkable turn of events, as it could yield quite positive results. Giving others a chance to prevent people from committing suicide is invaluable, that much is evident.

On paper, it makes a lot of sense to use artificial intelligence as well as any other form of technology to prevent suicide attempts from happening. Colin Walsh, data scientists at Vanderbilt University Medical Center, is thinking along the same lines. To be more specific, he feels AI can play a key role in the future of predicting suicide risk and giving loved ones a chance to stop people from ending their life prematurely.

As of right now, Walsh and other scientists have successfully developed a machine-learning algorithm to predict the likelihood of people attempting suicide. As one would expect from such innovative technology, the algorithm is more than capable of accurately predicting these attempts. In fact, some people claim this algorithm is unnervingly accurate, which is both good and bad.

To be put this into numbers people can understand, the algorithm is between 80% and 90% accurate. It is not a bad thing to get some false positives, though, as long as it means the patient will not attempt suicide whatsoever. Failing to predict when someone would effectively attempt suicide is a factor to be a quite concerned about, though the much is evident. These results pertain to the patients likelihood to commit suicide in the next two years.

When reducing the timespan associated with this investigation, the results become a lot more accurate. More specifically, when assessing if a patient is likely to attempt suicide within the next week, the algorithm has a 92% accuracy rate. Do keep in mind all of these results are based on data widely available from hospital admissions, including patients age, gender, medications, and prior diagnoses.

So far, the team has gathered enough data from 5,617 patients to develop this algorithm. A total of 3,250 instances of suicide attempts has been recorded as a result. All of the patients in question were admitted with signs of self-harm, which is a primary indicator of future suicide attempts. Although this is still a relatively small sample size, it also goes to show the algorithm developed by the team of data scientists is definitely worth keeping an eye on.

It is evident artificial intelligence can be a valuable tool when it comes to preventing people from attempting suicide. Although this experiment is still in the early stages of development, it will be interesting to see if and whether researchers can improve upon it moving forward. Interestingly enough, a different algorithm was created to conduct similar tests looking at over 12,000 randomly selected patients with no documented history of self-harm. In this case, the algorithm was even more accurate, which is rather surprising.

Rest assured some people will feel the usage of artificial intelligence is an invasion of privacy, even if it can reduce the number of suicide attempts. There is a lot of data gathered by hospitals, which can be used for this purpose, without having to collect additional information from patients. It will be interesting to see how these algorithms evolve over time, and whether or not artificial intelligence will effectively be used to prevent suicide attempts in the future.

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Data Scientists use Artificial Intelligence to Predict Suicide Attempts - The Merkle

Adobe CEO Hints at Artificial Intelligence on Photoshop – Fortune

Age: 54

From: Mumbai

In cloud we trust: CEO since 2007, Shantanu Narayen has overseen a period of explosive growth for the San Jose software company. As Adobe ( adbe ) has embraced a cloud-based subscription model, its stock has been on a tear, up 43% (to $142) since late May, with annual revenues of $5.85 billion.

Foggy bottom: When Narayen became CEO, you could see there were some dark clouds on the horizon, he says. The global financial crisis was just around the corner, and Adobe was not landing new customers as fast as desired. I didnt time that very well, Narayen jokes.

Outside the box: By 2009, Adobe embarked on an ambitious mission to overhaul the way it shipped popular products like Photoshop. A crisis is a terrible thing to waste, Narayen says. Adobe switched to a subscription model, opening the door to a new way to deliver software in which customers could more easily receive updates and new features.

Finding Wall Street: Investors were concerned Adobe was spending too much on data centers, but Narayen convinced them it would pay off. I think we did a good job of that, Narayen says. By going to the cloud , Adobe ended up saving money with the switch from one-time licenses to recurring subscriptions. Narayen adds that ditching packaging also helped.

The next frontier: Narayen sees artificial intelligence as a game changer, but he warns, Many companies just say A.I. without understanding how they want to apply it. Adobes A.I. plans start with voice commands. Imagine brightening colors on photos just by speaking.

Double Duty: Adobes board elected Narayen as its chairman this year on top of his CEO duties. Narayen is quick to mention Adobe couldnt be successful without his staffs hard work. But, he says, maybe it is recognition of some of the contributions Ive made in the company.

A version of this article appears in the June 15, 2017 issue of Fortune with the headline "Flash Forward."

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Adobe CEO Hints at Artificial Intelligence on Photoshop - Fortune

artificial intelligence: War of the machines: The opportunities in … – Economic Times

The theatrical release of James Camerons sci-fi film Terminator 2, featuring Arnold Schwarzenegger as a cyborg with a computer brain, had a crucial scene deleted. The scene, part of the extended release of the movie, shows young John Connor and his mother opening up the head of the cyborg to switch its computer brain from read only to learning mode. The cyborg (Schwarzenegger) then picks up human values and mannerisms as the movie progresses.

For movie buffs, the deleted scene is worth seeing for special effects and also to catch a glimpse of Linda Hamilton (playing Johns mother Sarah Connor) with her twin sister Leslie playing her image in a mirror. In the theatrical release, where the scene is omitted, the cyborg just tells John that its brain is a neural-net processor, a learning computer, without mentioning any on/off options. That was back in 1991. Today, in 2017, a learning computer is much more of a reality.

While artificial intelligence (AI) and machine learning (ML) concepts have been around since the 1940s and 1950s (See ABC of AI, ML and Deep Learning), the availability of huge amounts of data is making the difference now. A learning computer does not need to travel back in time like in the movie and many are solving real problems in India. For example, in healthcare, ML is helping oncologists sift through huge amounts of cancer cases and suggesting preferred treatment; in education it is predicting who might drop out of school; and in fashion it is forecasting colours that can dominate the next season. Retail, transportation and financial services have adopted ML in different forms. The learning switch is turned on in India. Every large organisation was sitting on data. The cloud is bringing computing power to it and ML is creating actionable intelligence, says Anil Bhansali, MD, Microsoft India (R&D) Pvt Ltd.

Machine vs machine A war of machines scenario seems appropriate to discuss it. Consider this example. In October 2016, K Sandeep Nayak booked three flight tickets for his wife and children to fly to Mangaluru from Mumbai during the Christmas holidays two months later, hoping to get a low fare. He spent Rs 7,500 per ticket. Later, when he decided to join his family for the trip, just a day before the journey on December 25, he could book himself into the same flight at Rs 4,000 only. I wish I could find out if airfare could fall, says Nayak, an executive director with Centrum Broking.

Actually, there is a way.

Today, most airlines follow a sinusoidal graph (S curve) for pricing tickets, often dictated by an algorithm to maximise revenues pushing up prices following buying behaviour.gregator app Ixigo. It can predict whether the price of an air ticket on a particular date is likely to fall. When a customer enters the date of journey, the app predicts, with more than 80 per cent accuracy, how much the airfare may drop for the sector on that date and how the prices could vary over that period. (Ixigo also has a railway app that predicts if a rail ticket on a wait list may get confirmed.)

We have a huge data set created by 4 million active users, 50 million sessions per month, Aloke Bajpai, Ixigo

Ixigos global peer Kayak is one of the pioneers in fare prediction. If airfare prediction seems like a machine-vs-machine scenario, there are more such examples: programmatic advertising algorithms that compete for advertising spots, or algorithmic trading applications that compete to get the best trades in the securities market.

Here is something a little more interesting.

Arya.ai is a Mumbaibased startup, founded by Vinay Kumar and Deekshith Marla, both IIT-Bombay grads. In 2016, Arya.ai was selected by French innovation agency Paris&Co, from 21 global companies, for an international innovation award. Kumar still looks like a college student and moves around Mumbai on his motorbike. One of the current projects that Arya.ai is working on involves creating an ML application for selling securities without letting prices crash. The client, with a mandate to sell a large block of stock or bonds in the market, wants Arya.ai to create an algorithm for selling so that it does not lead to prices of the security dropping.

At the same time, there are ML algorithms as well as human intelligence trying to buy the security at the lowest price possible, says Kumar. Algorithmic trading has been around for a while and brokers with proprietary trading arms often use it to gain a few seconds advantage. Now research is focused on whether an ML layer can be built on top of the algo. Can the machines be allowed to alter the trading algorithm on their own and what will this mean for the securities markets?

Last month, JP Morgan released a report in New York, Big Data and AI Strategies, with the subhead, Machine Learning and Alternative Data Approach to Investing

Written by Marko Kolanovic and Rajesh T Krishnamachari, the report suggests that analysts and market operators need to master ML techniques as usual indicators like company quarterly reports and GDP growth data will soon be predicted early by ML programs. It says that just as machines with ML are able to replace humans for short-term trading decisions, they can also do better than humans in the medium term. Machines have the ability to quickly analyse news feeds and tweets, process earnings statements, scrape websites and trade on these instantaneously. Back in India, here is another scenario. Vertoz is a Mumbai-based programmatic advertising company that works with clients (advertisers) and online media in placing digital advertising, targeting the advertisements and bidding for the best spots.

We need to find which inventory is good for us, says founder Ashish Shah, referring to spots on popular media websites. If we had to do it manually it would be like finding needles in a haystack. Vertozs programs compete with the likes of Google, bidding for top slots in global digital media.

Man Fridays While the buzz on big data analytics came first, the focus on ML has been facilitated by larger players like Google, Intel, Microsoft and Amazon making off-the-shelf modules available in India. But, then, some platforms have been around for decades. Says Shah: Most of our work is based on Java and Python that are 1980s technologies. We have built our layers on top of that.

Ixigos chief technology officer Rajnish Kumar mentions Googles TensorFlow and Amazons AWS Machine Learning as examples of off-the-shelf modules. Microsoft offers its Azure platform for others to create their own ML offerings. A Google spokesperson told ET Magazine that in future it expects to offer non-experts the ability to create and deploy ML modules: At Google, we have applied deep learning models to many applications from image recognition to speech recognition to machine translation. In our approach a controller neural net can propose a child model architecture, which can then be trained and evaluated for quality on a particular task. This is machine to machine learning.

Going forward, we will work on careful analysis and testing of these machine-generated architectures to refine our understanding. If we succeed, we think this can inspire new types of neural nets and make it possible for non-experts to create neural nets tailored to their particular needs, allowing machine learning to have a greater impact on everyone, adds the Google spokesperson. Google offers some simple applications of ML. CESC Ltd, Kolkata-based flagship of the RP-Sanjiv Goenka Group, is using a Google API (application programming interface) which records the reading of the electrical metre when the numbers are read out loud. Instead of keying the reading in or taking a photo of it, the staff can speak into their phone app chaar-shunyo-teen-paanch (4, 0, 3, 5), says Debashis Roy, vice-president (information technology ), CESC Ltd.

Roy says that when the project started, the app showed only 40% accuracy, but it is learning to recognise more and more Bengali dialects as well as Hindi and English. No matter what the dialect of the staff, the reading can be recorded. We will launch it fully when we get to 95% accuracy, says Roy.

Another Google partner is Pune-based Searce, a 12-year-old operation led by founder Hardik Parekh, who finds it convenient to work with Googles APIs as he feels the company almost embodies the open source or democratic spirit. Parekhs ML offering HappierHR tries to automate much of the routine HR operations right from initial interviews of job applicants and induction of new employees to creation of their email ids and leave approvals.

Supervisors also get suggestions to give leave to subordinates on, say, their wedding anniversaries, if there arent any important meetings scheduled for that day,says Parekh. While Google, Amazon and Microsoft offer platforms for others to use, IBM has its own ML suite called Watson, a complete offering at the premium end of the market for end-users. One of the earliest projects IBM took up in India was with Manipal Hospitals in oncology. Manipal was an early adopter: it was globally the second or the third hospital to adopt it, says Prashant Pradhan, chief developer advocate for IBM in India and South Asia.

This is how it works. For a medical board on breast cancer, the Watson program is made a member along with other doctors. Given a specific case, Watson gives its opinion and preferred treatment after going through millions of cases that are loaded on to it. Entire cancer research can run to 50 million pages, and 40,000 papers are added every year.

It is impossible for a doctor to go through all of that. The ratio of cases to oncologists is 16,000:1, adds Pradhan, stressing why ML is a great application to use in cancer treatment. Microsoft, too, has used its ML offerings in Indias healthcare. In Hyderabad, it has helped LV Prasad Eye Institute treat avoidable blindness. A second project it has worked on is helping children who wear glasses.

The work started in India has gone global, and LV Prasad Eye Institute is now part of the Microsoft Intelligent Network for Eyecare, which includes five other eyecare facilities from across the world. Microsoft has also studied 50,000 students in Class X in Chittoor in Andhra Pradesh to predict which ones may drop out. It allows the schools to send them for counselling.

Machine radiologists and bankers There is enough indication that ML bots or apps can often deliver better results than humans. Last month, IT services giant Wipro said it got productivity of 12,000 people out of 1,800 bots (software programs that perform automated tasks). Automated bots are not quite ML, but are an indicator of what may come. Rizwan Koita, serial entrepreneur and founder CEO of Citius Tech, a healthcare-focused tech company, recalls a conversation with his niece two months ago. She had qualified to pursue a course in radiology or anaesthesiology and was seeking my advice. I had to tell her that in a few years a radiologist may not have a job, says Koita. He argues that a radiologists job is to interpret images. Therefore millions of existing images (X-rays, sonograms, scans) and their interpretations can be fed into an ML algorithm; it may be a matter of time before a machine gives better interpretations than a human radiologist.

From healthcare to fashion. Mumbai-based designer couple Shane and Falguni Peacock have been using IBMs Watson for a couple of months now. The system helps the duo go through designs and silhouettes that have been shown at fashion events across the world over the last decade. They are using Watson for a project that uses international designs in Bollywood. Watson predicts colours that may be in vogue six months from now and warns if certain silhouettes have been overused in the last couple of years.

Watson is able to tell us what colour may be in six months from now, says Shane Peacock

Says Shane: Suppose we want to work on a Mughal theme, we can feed images of Mughal-era paintings, architectures and colours into the system, which is able to turn out its unique prints. It also reproduces Mughal prints created by other human designers, just for comparison. The designer couple have one more exciting project for which they are using Watson. A dress that changes hues according to the time of the day or the mood of the person wearing it. We can use two colours, say black and white. The dress can become fully white or fully black or a combination of black and white. An app on the wearers phone can control it. The change can happen on the go, while the dress is worn. You can get into a car in white and come out in black. In financial services, Kumar of Arya.ai points out that the loan approval process is an area where he sees a lot of human effort being bested by machines. In fact, Arya has implemented a program where an ML app sifts through loan applications.

ICICI Lombard and Birla Sun Life Insurance too have created bots as the first interface with customers Not to be left behind, the Indian IT biggies, TCS, Infosys and Wipro, have their own ML and AI offerings (See Machine Learning in India). Google announced in March that it will mentor half a dozen AI startups. A report by Tracxn, a venture capital research platform, noted that there are at least 300 startups in India using ML and AI technologies. An opportunity also presents a threat. Before ML can replace humans in core functions, it will need humans to create applications. Says Bhansali of Microsoft: These are still early days: technologies are on trial and talent is scarce. Ixigo CEO Aloke Bajpai echoes him when he says there are no trained engineers in AI and ML in India, and his team is entirely trained in-house.

There is definitely a shortage of talent for AI technologies. Only 4 per cent of AI professionals in India have worked on core AI technologies such as deep learning and neural networks, says Akhilesh Tuteja, partner at KPMG. Bridging the gap will be key in turning a potential weakness into a strength.

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artificial intelligence: War of the machines: The opportunities in ... - Economic Times

Artificial Intelligence Will Put Spies Out of Work, Too – Foreign Policy (blog)

If Robert Cardillo has his way, robots will perform 75 percent of the tasks currently done by American intelligence analysts who collect, analyze, and interpret images beamed from drones, satellites, and other feeds around the globe.

Cardillo, the director of the National Geospatial-Intelligence Agency, known by the acronym NGA, announced his push toward automation and artificial intelligence 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.

Artificial intelligence is on the rise; former President Barack Obamas White House released a white paper on its potential future impacts in the final months of the administration. Police officers are using preliminary programs to predict the likelihood someone will commit a crime in a specific neighborhood based on crime statistics data. And companies like Amazon and Netflix use machine learning to calculate what movie you will want to watch or which book you may buy.

Yet this sort of automation is also seen as a threat to workers, who fear being put out of jobs, particularly in the private sector.

The fear that artificial intelligence will take over jobs, or fail catastrophically along the way, is palpable in the intelligence community as well, and Cardillo admitted that the workforce is skeptical, if not cynical or downright mad, about the prospect of automation intruding on their day-to-day lives, potentially replacing them.

The coming revolution in artificial intelligence has been hyped for years, often falling short of expectations. But if it does happen, analysts worry theyll become obsolete.

Cardillo, who called it a transforming opportunity for the profession, said hes working on showing the workforce that artificial intelligence is not all smoke and mirrors. The message hes sending to workers at the agency is that the goal of automation isnt to get rid of you its there to elevate you. Its about giving you a higher-level role to do the harder things.

In Cardillos eyes, the profession of geospatial intelligence monitoring and exploiting commercial and proprietary video and imagery feeds around the world is on the precipice of a data explosion similar to when the internet took off. At that point, the National Security Agency, which is responsible for collecting and analyzing digital communications, had to figure out ways to vacuum up and glean specific conclusions from an explosion of communications traveling back and forth on the web.

Just as the NSA employs algorithms to trawl through millions of messages, Cardillo wants machine learning to help with large volumes of imagery. Instead of analysts staring at millions of images of coastlines and beachfronts, computers could digitally pore over images, calculating baselines for elevation and other features of the landscape. NGAs goal is to establish a pattern of life for the surfaces of the Earth to be able to detect when that pattern changes, rather than looking for specific people or objects.

NGA is responsible for tracking potential threats, such as military testing sites in North Korea. When something at a site changes, like large groups of people or cars arriving, it may indicate preparations for a missile test. We dont have a higher priority, Cardillo told Foreign Policy. We put everything we can into North Korea.

But the number of sensors, images, and video feeds is exploding and will continue to grow in the coming years, he predicted. A significant chunk of the time, I will send [my employees] to a dark room to look at TV monitors to do national security essential work, Cardillo told reporters. But boy is it inefficient.

The agency is also turning to academia and the private sector for help. Cardillo hired Anthony Vinci, the founder and former CEO of Findyr, a company that crowdsources data from countries around the world, to head up the agencys machine-learning efforts within NGA.

Companies exhibiting at the conference were clearly on the artificial bandwagon, boasting flashy datasets and advanced algorithms. But not everyone was convinced relying on computers for the bulk of data crunching and analysis was such a great idea for intelligence work.

Justin Cleveland, a former intelligence official who works for the security company Authentic8 which created a secure browser called Silo that also allows intelligence professionals to disguise their cybertracks was skeptical of the automation boom. It can be helpful, he said in an interview at the conference. But you could have one bad algorithm and youre at war.

Taking humans out of the bulk of the process is bound to lead to errors. At the end of the day, you have to trust the person who wrote the algorithm over the analyst, Cleveland said.

Jimmy Comfort, a deputy director at the National Reconnaissance Office, was enthusiastic about certain applications for artificial intelligence in some areas like facial recognition. There are so many parallels with what the commercial guys are doing, he said in an interview.

But for his agency, which works mainly with satellites, the needs are different. Satellites take fewer images, from much farther away. Theres challenges for us doing that stuff from space, Comfort said.

Photo credit: CHIP SOMODEVILLA/Getty Images

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Artificial Intelligence Will Put Spies Out of Work, Too - Foreign Policy (blog)

Half Of People Who Encounter Artificial Intelligence Don’t Even Realize It – Forbes


Forbes
Half Of People Who Encounter Artificial Intelligence Don't Even Realize It
Forbes
Artificial Intelligence (AI) is no longer in the future. It's not science fiction. It's here. It's now. It's happening all around us, and actually has been for more years than most of us even know. For the past two years, I've been writing about IBM's ...

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Half Of People Who Encounter Artificial Intelligence Don't Even Realize It - Forbes

EILENBERG: Artificial intelligence will transform health care – Indianapolis Business Journal

The race is on for dominance in the artificial intelligence industry. Driven by deep-learning technology that allows AI systems to teach themselves, the overall industry is projected to be worth $16 billion by 2022. The health care AI market is projected to grow from $667 million in 2016 to nearly $8 billion by 2022. Jostling for position is expected to be fast and furious over the next five years.

The current big players (Intel, Microsoft, Google, Facebook, Canada, the United Kingdom) and up-and-comer (China) were all at the AI Summit in London last month. Google, already a heavyweight in AI, announced a new AI chip and the forthcoming launch of AI in the Cloud at its recent Google IO developer conference. The cloud service will be accessible to researchers and developers to build and operate software via the internet. No word yet on the price for AI as a service. But speculation is that, if the cost is low enough, customers will come.

Industries that do high volumes of human interactions, such as banking and health care, are expected to begin adopting AI to automate customer service. The anticipated shift will result in job losses in customer service but increases in customer satisfaction. As chatbot technology that simulates conversations with humans improves, additional health care applications, such as AI doctors, are expected. Babylon Health recently raised nearly $60 million for a smartphone chatbot that diagnoses illness.

AI is expected to disrupt health care beyond chatbots. The exact form of the disruption is not yet clear. On the floor of the AI summit in London, bold visions were in abundance.

Philips, a partner in pilot projects being carried out by Englands National Health Service, envisions wearables, including smart watches and other technology, working in concert with AI to provide 24/7 monitoring.

IBM Watson foresees cognitive assistants to augment physician expertise and ultimately the ability to diagnose and treat diseases well before symptoms arise.

IBM researchers estimate that 90 percent of health care data is in the form of images. Because deep learning does best with lots and lots and lots of data, AI initially is likely to cause the most profound disruption in imaging.

Medical startups and research centers are beginning to automate the analysis of MRIs, CT scans and X-rays. Google is using artificial intelligence with the NHS to spot eye disease using eye-scan images.

Artificial Intelligence analysis of medical images might significantly extend the reach of medical specialists to remote areas. Google is building an interface in India for doctors to input retinal images and receive a grade for diabetic retinopathy. The screening tool could save the vision of scores of people in a country where there is a shortage of 127,000 eye doctors and most patients suffer vision loss before they see a doctor.

While AI seems to hold endless possibilities for health care, for now the who, what, where and when? is up in the air. In the United States, electronic health records lack what AI needs: machine-learning capabilities and outcome data. Ultimately, the extent of adoption of artificial intelligence in health care might depend less on its potential and more on the quality of current health care options.

A recent report by PwC found that emerging markets were most open to artificial intelligence and robotics in health care. It found that, due to clinical shortages and a young, digitally savvy population, the Middle East could leap frog other countries.

__________

Eilenberg is CEO of Indianapolis-based Lodestone Logic, a global pharmaceutical and health care consultancy.

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EILENBERG: Artificial intelligence will transform health care - Indianapolis Business Journal

Are you lying about your identity? Artificial intelligence can tell by how you use your mouse – Science Magazine

By tracking cursor movement, lie detection becomes a game of cat and mouse.

DeanDrobot/iStock Photo

By Matthew HutsonJun. 9, 2017 , 3:30 PM

Every year, millions of people have their identities stolen. Theres no foolproof way to pinpoint fakers, but thanks to Italian researchers, investigators may soon have another tool at their disposala way to suss out frauds and other liars online with just a few clicks of a mouse.

Traditional methods of lie detection include face-to-face interviews and polygraphs that measure heart rate and skin conductance. But they cant be done remotely, or with large numbers of people. Researchers have come up with effective computer-based tests that measure reaction time in response to true and false personal information. For the tests to work, though, experimenters have to know the truth in advance.

To get around this obstacle, a team of Italian researchers has come up with an innovative way of figuring out the truth. They asked 20 volunteers to memorize the details of a fake identity and assume it as their own. The subjects then answered a set of yes-or-no questions using a computer, as did 20 truth-telling volunteers. Questions included things like: Is Giulia your name? and Were you born in 1995? Researchers recorded each answer and measured how the subjects mouse cursors moved, from the bottom middle of the screen to yes and no buttons in the top two corners.

Because liars can get to be as good as the rest of us at telling the truth, the researchers threw a wrench into their experiment. In addition to the 12 expected questions, they asked 12 unexpected questions based on the volunteers new identities. For example, they asked about a persons zodiac sign, based on their birth date. And they asked about the capital city of the subjects presumed region. A fraud might have memorized a fake birthday, but not known the corresponding zodiac sign, or been able to calculate it quickly enough. Weve found that if people rehearse lies, lying can be as easy as telling the truth, says Bruno Verschuere, a forensic psychologist at the University of Amsterdam who was not involved in the research, except when you ask unexpected questions.

The experimenters trained a computer to sort liars from truth tellers using the number of incorrect answers they gave. The teams four machine-learning algorithms ranged in accuracy from 77.5% to 85%. But when the researchers included features of the mouse pathssuch as deviation from a straight linein their training materials, computers were able to successfully pick out the liars 90% to 95% of the time, the researchers reported last month in PLOS ONE.

They also trained and tested the algorithms using only questions that the liars answered truthfully, such as whether they were Italian. The algorithms could still identify the fibbers with 77.5% to 80% accuracy. Jumping back and forth between telling the truth and lying seems to have a broad effect on peoples behavior, the scientists say. Having to tell a lie changes the way people tell the truth.

But would such a method work in the real world? Giuseppe Sartori, a forensic neuroscientist at the University of Paduain Italyand an author of the paper, says it could be used as a first screen to check peoples alibis in criminal investigations, verify identities online, or even cull terrorists from refugees at border checkpoints. It likely wont have the same accuracy it does in the lab, but he calls the study a good proof of concept.

Its a clever idea, says Giorgio Ganis, a cognitive neuroscientist at Plymouth University in the United Kingdom. But its not obvious that its going to be super useful. Ganis notes that in the real world, fraudsters would likely spend more time researching their backstories, making surprising questions harder to find. Youre going to catch the dumb criminals and dumb terrorists, he says, which is better than nothing, I guess. Sartori adds that even though impostors might learn their purported zodiac sign, other unexpected questions are practically unlimited. Do they know the cross streets of their purported home address? Do they know the layout of the restaurant where they say they were on the night of a crime? The study brings a whole new meaning to the game of cat and mouse.

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Are you lying about your identity? Artificial intelligence can tell by how you use your mouse - Science Magazine

New Study: Artificial Intelligence Is Coming For Your Job, Millennials – Forbes


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New Study: Artificial Intelligence Is Coming For Your Job, Millennials
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Research released by Gallup on Thursday indicates a collision between technology and business as usual is coming soon, and the fallout will be ugly, especially for Millennials. Automation and artificial intelligence (AI) are among the most disruptive ...

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New Study: Artificial Intelligence Is Coming For Your Job, Millennials - Forbes