Future store is one such example of how with AI, running a retail store is childs play.

At the annual Futiju Forum, held in Tokyo recently, technologylargely artificial intelligence (AI) including machine learningwas 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 childs play. In a future store, a robot roams inside a shop. 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.

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Fujitsu created a smart factory for its subsidiary INESA Display Materials. It built an intelligent system to handle data collection on production, quality, efficiency, cost control and reduction in energy consumption, besides storage, processing and visualisation to enable fast access and analysis of information in mass production operations as well as energy monitoring. In our production environment, there are numerous data sources that provide information on processes, equipment and environmental factors, which can directly affect the quality of our product, said Wei Fengrong, director, information, INESA Display Materials Co.

Another technology which has started making waves is an interface device called Ontenna. The device, worn atop the users hair, helps deaf people to perceive rhythms, patterns, and volumes of sounds through their hair as it conveys the characteristics of sounds using vibration and light.

The reporter was in Japan at theinvitation of Fujitsu.

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Artificial Intelligence: Here are the different avatars of AI at the centre of innovation - Financial Express

Lately, it seems that more and more jobs that used to be performed by humans are being taken over by one form or another of artificial intelligence, or AI. But, despite all jokes made to the contrary, there is still a need for real actual human beings, say experts. You just need to have what is required to work with a machine that thinks. Machines augmented with AI have long been replacing human workers on assembly lines, factory floors, and in manual labor. Most recently they are being added to jobs that usually were thought to require the judgment and intuition of a person, in fields like finance, law, and medicine.

But humans have not been made obsolete yet. There remain plenty of jobs for those who develop, program, manage, and market AI to work alongside it or improve its operation. As an exercise in irony, recruiters are now using AI to find employees with the correct qualifications and intelligence for these slots.

However, as job descriptions have gone from traditional roles like chief clerk to modern titles like chief digital officer the attributes for job seekers have changed too, though employers and employees alike are having difficulties effectively specifying what they are.

Most people will tell you that to work in AI you need traits like a growth mindset, you need to be adaptable and have an owner mentality those are the buzzwords. The reality is that the job itself has to be properly defined, says Caitlin MacGregor, CEO, and co-founder of Plum, an Ontario-based online recruiter.

Plum matches prospective employees with possible employers by using a specialized algorithm based on their surveys. Like a dating app but for job opportunities rather than your love-life. But, Macgregor goes on to say, no ones quite sure what to make of these opportunities in the new economy.

They default to questions like where people went to school, what degrees they have, how many years theyve worked, what titles theyve had. Those markers never really were able to predict success; in this digital age, we need to be really clear what does, she says.

We know based on 30 years of research that intelligence is the number one predictor of performance, across all roles and all industries. We need to be measuring for intelligence before we even pick up a rsum, instead of waiting until someone has been three months on the job, Ms. MacGregor adds.

At the same time, the qualities that usually accompany intelligence, like adaptability and flexibility, are desired as well, according to Marlina Kinnersly, CEO, and co-founder of Fortay.co, an AI-based hiring site similar to Plum.

You want them to be able to think fast and learn on their feet, she says.

The Toronto-based firm looks for workers who will be a good culture fit or team alignment for the corporation but also thinks independently enough to add new perspectives to the company, says Kinnersly.

However, the search isnt on until the company can truly describe what their corporate culture is. Thats their baseline to find the right candidates, she says.

In a paper for Brookings Institute, Christian Bodewig, a World Bank executive, reveals a few qualifications for employees working along AI should have. Amongst these skills, he lists cognitive skills in numeracy and literacy, creative critical thinking, and advanced problem solving, as well as contentiousness and whatever technical skills required for the specific job as all being important.

In contrast, Thomas L. Friedman, a columnist for The New York Times, says employers want someone who will get up, dress up, show up, shut up, and never give up.

Potential employees should acquire these skills as early as possible, according to Bodewig because the window for building cognitive skills closes with late adolescence.

Overall, in this new AI dominated employment market, an intelligent, independent thinker, and team player needs more than he has in the past to procure job security. Math skills help, according to Henry Kim, an associate professor of operations management and information system from Schulich School of Business at Torontos York University. A point he is always reminding his 11-year-old daughter too:

She loves Anne of Green Gables, so when she grows up, she wants to run a caf in Prince Edward Island where Anne is set. I told her that that is actually a job that AI robots cant do, so she should go for it, Dr. Kim explains.

But you have to make money to open that caf. Which means youll need a good-paying job in a workforce of the future, where a lot of the well-paying white-collar jobs we have today will not exist. However, there will definitely be well-paying jobs in the future for programming and working with AI robots.

So, I tell her do your math homework.

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Companies use AI to Find Human Employees to Work with Artificial Intelligence - TrendinTech

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.

But Garry Kasparov has a different take on the issue. On 11 May 1997, Russian chess grandmaster Kasparov became the first world champion to be defeated by a machine. Yet in his new book Deep Thinking: Where Artificial Intelligence Ends And Human Creativity Begins, he is optimistic about the future of people with skills even as he concedes the inevitability of intelligent machines becoming more prominent. The sensation of being challenged, surpassed and possibly replaced by automaton, or an invisible algorithm, is becoming a standard part of our society, he writes. So while smarter computers are one key to success, doing a smarter job of humans and machines working together is far more important.

Is it possible to beat this threat of being displaced? Theres ample research and books on the subject, and here are some of the things they suggest you could do to robot-proof your career.

Build empathy

Employers want people who are empathetic and collaborative, who can guide relationships and work in teams. Because empathy is something that even intelligent machines are incapable of. Recognizing the importance of this skill is Geoff Colvin in his book Humans Are Underrated : What High Achievers Know That Brilliant Machines Never Will. The critical 21st century skill is empathy: we empathize to survive, he says, pointing to the healthcare profession. So while machines may be superior with diagnostics, a patient still needs to have a conversation with an expert. An empathetic doctor can help the patient deal with his condition better and recover faster. This, in turn, leads to lower healthcare costs and fewer lawsuits, says Colvin.

Empathy is a skill that can be developed through learning how to study the thoughts and feelings of others, and then responding appropriately. This involves inviting people to speak about their worries and concerns, hearing them out and then reassuring them, says Colvin.

Be a good communicator

A skill like communication is less easy to automate, says Anu Madgavkar, partner with McKinsey Global Institute, the research arm of McKinsey and Co., Mumbai. Intelligent machines cannot communicate the way human beings do. So people with better communication skills will be harder to replace with AI. The bigger message for professionals is that they should learn to communicate in a more compelling way, learn to work in teams, to excel at social interactions, says Madgavkar.

Become a lifelong learner

Previously in history, even in the 20th century, life was divided into two main parts: in the first part, you mostly learned, acquired knowledge and skills, and built yourself a personal and a professional identity. In the second part, you mostly made use of those skills and those identities. The pace of change in the 21st century will be such that most of what you learn as a teenager will be completely irrelevant by the time youre 40, says Yuval Noah Harari, author of Homo Deus: A Brief History Of Tomorrow, in a February interview with Time magazine, where he emphasized the necessity of life-long learning.

The good news is that anytime, anywhere learning is a reality now. For instance, if you want to do a project on design thinking, you can go immediately to the massive open online courses at online platforms like edX and Coursera and do a course on it, says Vijay Thadani, co-founder, NIIT.

Get those number skills

Digital literacy should be taken as seriously as language literacy, says Infosys chief executive Vishal Sikka, in an Infosys commissioned study on how to amplify human potential. The most important academic subjects that decision-makers see as focus areas for future generations are computer sciences, business and management and mathematics, says the study, which looked at the skills professionals need to acquire to integrate AI in a positive way into organizations and society.

Be constructive

Many perceive AI as a threat. Prominent among them are entrepreneur Elon Musk (our biggest existential threat) and scientist Stephen Hawking (the development of full AI could spell the end of the human race). From elevator operators to bank tellers and airplane pilots, history is full of examples of how technology has made jobs redundant.

But technology has also made life safer, easier and better. Its better to accept AI as a part of development, and look at the avenues it opens up rather than see the situation as man versus machine, says Kasparov.

Start to look at tasks hard to mechanizeanything that involves human creative energy, from photography and theatre, to baking, art, running, cooking classes, teachinganything thats not linear, says Mumbai-based Gurprriet Siingh, senior client partner at consulting firm Korn Ferry Hay Group. He says skills like empathy, creativity, flexibility and the ability to communicate can never be automated, and so education today should emphasize development of those skills.

Many of the most promising jobs today didnt even exist 20 years ago, says Kasparov, pointing to the demand for talent in new professions like app designers, 3D print engineers, drone pilots, social media managers and genetic counsellors. This is a trend that will accelerate as technology continues to create different professions .

Learn to work with machines

The future of increased productivity and business success isnt men or machines. Its both, argue Thomas H. Davenport and Julia Kirby in their book Only Humans Need Apply. Augment your skills, learn to work with machines, they say. The doctor who relies on diagnostic software, the lawyer who relies on research machines, the logistics manager who works with drones or the customer service manager who works with a chatbot, all of these professionals will be able to work better by complementing their human skills of empathy, of communication and creativity with machine intelligence. As the McKinsey report states, Humans will still be needed in the workforce; the total productivity gains we estimate will only come if people work alongside machines.

At wealth management firm ORO Wealth, for instance, the role of human portfolio advisers who work with intelligent machines is important. Even though the investment recommendations are machine-based, we need humans beings to work alongside. Because only a human adviser can empathize, can sense hesitation or lack of enthusiasm for a particular investment on the clients part. In which case they will go back to the machine-based algorithm, which will recommend alternative products, says Mumbai-based Vijay Kuppa, co-founder of ORO Wealth.

The skill and flexibility to work with a machine will help the workforce to become more productive. As Kasparov puts it, Smart machines will free us all...taking over the more menial aspects of cognition and elevating our mental lives towards creativity, curiosity, beauty and joy. These are what truly make us human, not any particular activity or skill like swinging a hammeror even playing chess.

First Published: Sun, Jun 25 2017. 03 47 PM IST

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Is AI over-hyped in 2017? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by Joanne Chen, Partner at Foundation Capital, on Quora:

To quote Bill Gates We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don't let yourself be lulled into inaction.

In short, over the next ten years, I dont believe AI will be overhyped. However, in 2017, will all of our jobs be automated away by bots? Unlikely. I believe the technology has incredible potential and will permeate across all aspects of our lives. But today, my sense is that many people dont understand what the state of AI is, and thus contribute to hype.

Artificial intelligence, a concept dating back to the 50s, is simply the notion that a machine can performance tasks that require human intelligence. But AI today is not what the science fiction movies portray it to be. What we can do today falls in the realm of narrow AI (vs general intelligence), which is the idea that machines can perform very specific tasks in a constrained environment. With narrow AI, there are a variety of techniques that you may have heard of. Ill use examples to illustrate differences.

Lets say you want to figure out my age (which is 31).

1) Functional programming: what we commonly know as programming, a way to tell a computer to do something in a deterministic fashion. I tell my computer that to compute my age, it needs to solve AGE = todays date birth date. Then I give it my birth date (Dec 4, 1985). There is 0% chance the computer will get my age wrong.

2) Machine learning: an application of AI where we give machines data and let them learn for themselves to probabilitically predict an outcome. The machine improves its ability to predict with experience and more relevant data. So take age for example. What if I had 1,000 data sets of peoples ages and song preferences? Song preference is highly correlated with generation. For example, Led Zeppelin and The Doors fans are mostly 40+ and Selena Gomez fans are generally younger than 25. Then I could ask the computer given that I love the Spice Girls and Backstreet Boys, how old does it think I am? The computer then looks at these correlations and compares it with a list of my favorite songs to predict my age within x% probability. This is a very simple example of using machine learning..

3) Deep Learning: is a type of machine learning emerged in the last few years, and talked widely about in the media when Google DeepMinds AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go.

Deep learning goes a step further than ML in that it enables the machine to learn purely by providing examples. In contrast, ML requires programmers to tell the computer what it should look for. As a result, deep learning functions much more like the human brain. This especially works well with applications like image recognition.

4) Deep reinforcement learning: DRL goes one step further and combines deep learning with reinforcement learning which is the notion of learning by trial-and-error, solely from rewards or punishments. DRL mimics how children learn they see observe other people doing things, they try things out and depending on the reward, they either repeat them or not!

Machine learning technologies have become more available (and the reason why there has been increasing media hype around this space) has been driven by advancements in three areas:

1) Infrastructure to run ML algorithms massive improvements in storage, processing capabilities (i.e. GPUs that speed up parallel processing), and accessibility for rapid innovation (cloud).

2) New available algorithms developed.

3) Data proliferation to train algorithms.

Between algorithms innovation and data availability, I believe data plays a more crucial role in advancements. If you look at the chart below, breakthroughs in AI have been quickly followed by availability of datasets, while many of the corresponding algorithms have been available for over a decade.

AI will permeate our lives in the next ten years. Think of the possible time, money, and manpower saved by automating simple processes. And as the technology becomes more advanced, the use cases will get even more exciting. I think its a wonderful time as an entrepreneur to be able to leverage this technology, and I couldnt be more excited as an investor.

This question originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora on Twitter, Facebook, and Google+. More questions:

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Is Artificial Intelligence Overhyped in 2017? | HuffPost - HuffPost

Artificial intelligence has made great strides in the past few years, but its also generated much hype over its current capabilities.

Thats one takeaway from a Friday panel in San Francisco involving leading AI experts hosted by the Association for Computing Machinery for its 50th annual Turing Award for advancements in computer science.

Michael Jordan, a machine learning expert and computer science professor at University of California, Berkeley, said there is way too much hype regarding the capabilities of so-called chat bots. Many of these software programs use an AI technique called deep learning in which they are trained on massive amounts of conversation data so that they learn to interact with people.

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But despite several big tech companies and new startups promising powerful chat bots that speak like humans when prodded, Jordan believes the complexity of human language it too difficult for bots to master with modern techniques like deep learning. These bots essentially perform parlor tricks in which they respond with comments that are loosely related to a particular conversation, but they cant say anything true about the real world.

We are in era of enormous hype of deep learning, said Jordan. Deep learning has the potential to change the economy, he added, but we are not there yet."

Also in the panel, Fei-Fei Li, Googles ( goog ) machine learning cloud chief and Stanford University Professor, said We are living in one of the most exciting and hyped eras of AI. Li helped build the ImageNet computer-vision contest, which spurred a renaissance in AI in which researchers applied deep learning to identify objects like cats in photos.

But while everyone talks about ImageNets success, we hardly talk about the failures, she said, underscoring the hard work researchers have building powerful computers that can see like humans.

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Still, Li is excited that current AI milestones will eventually lead to more breakthroughs that will touch every single industry, like healthcare. We are entering a new phase in AI, she said.

What will help usher more breakthroughs in deep learning will be the continuing advancements in powerful computing hardware, like Nvidia's GPUs that make it possible to crunch tremendous amounts of data faster than ever, explained Ilya Sutskever, the research director of Elon Musk-backed AI research group OpenAI . Deep learning will keep booming in tandem with advancements in computing hardware that shows no signs of slowing down .

"Compute has been the oxygen of deep learning," Sutskever said.

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Beware the Hype of Artificial Intelligence - Fortune

This kind of A.I. is spreading to thousands of domains (not just loans), and as it does, it will eliminate many jobs. Bank tellers, customer service representatives, telemarketers, stock and bond traders, even paralegals and radiologists will gradually be replaced by such software. Over time this technology will come to control semiautonomous and autonomous hardware like self-driving cars and robots, displacing factory workers, construction workers, drivers, delivery workers and many others.

Unlike the Industrial Revolution and the computer revolution, the A.I. revolution is not taking certain jobs (artisans, personal assistants who use paper and typewriters) and replacing them with other jobs (assembly-line workers, personal assistants conversant with computers). Instead, it is poised to bring about a wide-scale decimation of jobs mostly lower-paying jobs, but some higher-paying ones, too.

This transformation will result in enormous profits for the companies that develop A.I., as well as for the companies that adopt it. Imagine how much money a company like Uber would make if it used only robot drivers. Imagine the profits if Apple could manufacture its products without human labor. Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. (As it happens, my venture capital firm has invested in just such a loan company.)

We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work. What is to be done?

Part of the answer will involve educating or retraining people in tasks A.I. tools arent good at. Artificial intelligence is poorly suited for jobs involving creativity, planning and cross-domain thinking for example, the work of a trial lawyer. But these skills are typically required by high-paying jobs that may be hard to retrain displaced workers to do. More promising are lower-paying jobs involving the people skills that A.I. lacks: social workers, bartenders, concierges professions requiring nuanced human interaction. But here, too, there is a problem: How many bartenders does a society really need?

The solution to the problem of mass unemployment, I suspect, will involve service jobs of love. These are jobs that A.I. cannot do, that society needs and that give people a sense of purpose. Examples include accompanying an older person to visit a doctor, mentoring at an orphanage and serving as a sponsor at Alcoholics Anonymous or, potentially soon, Virtual Reality Anonymous (for those addicted to their parallel lives in computer-generated simulations). The volunteer service jobs of today, in other words, may turn into the real jobs of the future.

Other volunteer jobs may be higher-paying and professional, such as compassionate medical service providers who serve as the human interface for A.I. programs that diagnose cancer. In all cases, people will be able to choose to work fewer hours than they do now.

Who will pay for these jobs? Here is where the enormous wealth concentrated in relatively few hands comes in. It strikes me as unavoidable that large chunks of the money created by A.I. will have to be transferred to those whose jobs have been displaced. This seems feasible only through Keynesian policies of increased government spending, presumably raised through taxation on wealthy companies.

As for what form that social welfare would take, I would argue for a conditional universal basic income: welfare offered to those who have a financial need, on the condition they either show an effort to receive training that would make them employable or commit to a certain number of hours of service of love voluntarism.

To fund this, tax rates will have to be high. The government will not only have to subsidize most peoples lives and work; it will also have to compensate for the loss of individual tax revenue previously collected from employed individuals.

This leads to the final and perhaps most consequential challenge of A.I. The Keynesian approach I have sketched out may be feasible in the United States and China, which will have enough successful A.I. businesses to fund welfare initiatives via taxes. But what about other countries?

They face two insurmountable problems. First, most of the money being made from artificial intelligence will go to the United States and China. A.I. is an industry in which strength begets strength: The more data you have, the better your product; the better your product, the more data you can collect; the more data you can collect, the more talent you can attract; the more talent you can attract, the better your product. Its a virtuous circle, and the United States and China have already amassed the talent, market share and data to set it in motion.

For example, the Chinese speech-recognition company iFlytek and several Chinese face-recognition companies such as Megvii and SenseTime have become industry leaders, as measured by market capitalization. The United States is spearheading the development of autonomous vehicles, led by companies like Google, Tesla and Uber. As for the consumer internet market, seven American or Chinese companies Google, Facebook, Microsoft, Amazon, Baidu, Alibaba and Tencent are making extensive use of A.I. and expanding operations to other countries, essentially owning those A.I. markets. It seems American businesses will dominate in developed markets and some developing markets, while Chinese companies will win in most developing markets.

The other challenge for many countries that are not China or the United States is that their populations are increasing, especially in the developing world. While a large, growing population can be an economic asset (as in China and India in recent decades), in the age of A.I. it will be an economic liability because it will comprise mostly displaced workers, not productive ones.

So if most countries will not be able to tax ultra-profitable A.I. companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their A.I. software China or the United States to essentially become that countrys economic dependent, taking in welfare subsidies in exchange for letting the parent nations A.I. companies continue to profit from the dependent countrys users. Such economic arrangements would reshape todays geopolitical alliances.

One way or another, we are going to have to start thinking about how to minimize the looming A.I.-fueled gap between the haves and the have-nots, both within and between nations. Or to put the matter more optimistically: A.I. is presenting us with an opportunity to rethink economic inequality on a global scale. These challenges are too far-ranging in their effects for any nation to isolate itself from the rest of the world.

Kai-Fu Lee is the chairman and chief executive of Sinovation Ventures, a venture capital firm, and the president of its Artificial Intelligence Institute.

Follow The New York Times Opinion section on Facebook and Twitter (@NYTopinion), and sign up for the Opinion Today newsletter.

A version of this op-ed appears in print on June 25, 2017, on Page SR4 of the New York edition with the headline: The Real Threat of Artificial Intelligence.

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The Real Threat of Artificial Intelligence - The New York Times - New York Times

When you think of artificial intelligence, you may think of Lt. Commander Data or C-3PO, but this AI will actually be the spacecraft rather than on board.

Exploring space has some far-out challengesand this is after weve sent robots to Mars and all manner of probes and orbiters to other planets, including Venus and Saturn. Future missions will venture deeper and deeper into unexplored star systems and galaxies that have only been observed via telescope. This is easier dreamed than done. Too many unforeseen obstacles could cause a craft to glitch or break down hundreds of thousands of miles away, which is why scientists developing these future missions need to be paranoid.

Space scientists Steve Chien and Kiri Wagstaff of NASAs Jet Propulsion Laboratory suggest that programming probes with advanced artificial intelligence will largely eliminate the need for prompts from the home planet that would have increasing difficulty reaching out to them the further they ventured into space. Not to mention that probes on more daring missions will have to be able to think for themselves, because they even more of them will be required and they will probably not be able to receive any intervention from Earth. It gets creepier with the realization that the capacity to learn will need to be wired into their computerized brains to make them adaptable.

"The goal is for A.I. to be more like a smart assistant collaborating with the scientist and less like programming assembly code," said Chien, who collaborated with Wagstaff on an article recently published in the journal Science Robotics. "It allows scientists to focus on the 'thinking' thingsanalyzing and interpreting datawhile robotic explorers search out features of interest."

Autonomous probes should be able to function on a hypersensitive level that includes understanding and carrying out mission requirements, recognizing geological phenomena and identifying differences between what passes for normal planetary conditions (depending on the planet) and extreme space weather. They should also be able to reprioritize if they eye something spontaneous, like ocean plumes erupting on watery worlds similar to Enceladus. Advancing the science of AI enough may even make them able to use their findings for future studies. Not having infinite fuel means the robo-brains will also need to make the call on which regions are worth delving into the most.

AI is already being prototyped for the Mars 2020 rover and could someday make once-impossible endeavors, like a mission to Alpha Centauri, possible, but even the researchers themselves admit it still has to level up.

"For the foreseeable future, there's a strong role for high-level human direction," Wagstaff said. "But A.I. is an observational tool that allows us to study science that we couldn't get otherwise."

(via Phys.org)

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Space probes of the future will have artificial intelligence, and it's ... - SYFY WIRE (blog)

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer.

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer.

Theres no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai and others.

All these companies and researchers are convinced of the technologys enormous commercial potential. If AI develops like other technologies, most of these benefits will flow to the country that builds the first good ecosystem. This is a huge opportunity for Canada.

At the same time, AI poses clear challenges to business and government. Over the next 10 to 20 years, nearly half of Canadas jobs are at high risk of being affected by automation. Women hold a lot of these jobs and are especially at risk the World Economic Forum says that globally, women will face about twice the rate of job loss as men in what it calls the fourth industrial revolution.

How can Canadian companies gain the benefits of this disruptive technology while ensuring that large segments of society are not left behind? In our view, the public and private sectors should take six steps to outsmart AI and avoid its dislocations:

Commit to building the worlds best AI ecosystem: The winning AI cluster will create many high-paying jobs and create spillover effects for the middle class but the also-rans will not. Half-measures wont work. Canada must play to win. If there is going to be a steam engine that disrupts the status quo and AI is shaping up that way then Canada should develop and build the very best steam engine it can, right here at home.

Create at-scale AI training programs: Industry can form coalitions to collect data, oversee curriculum development and rapidly retrain workers in the skills needed to succeed in nascent AI applications.

Take Generation, a McKinsey-supported initiative that works with employers to quickly train and place young workers in sectors like health care and technology. Graduates have an 84 per cent employment rate within 90 days of completing the program and earn two to six times more income than before. Similarly, Prominp in Brazil trains 30,000 youth each year for positions in the oil and gas industry, with 189 skill-profile tracks and an 80-per-cent postgrad employment rate.

In Canada, such a program could be built in partnership with new research groups such as the Vector Institute in Toronto or with incubators such as Communitech, Next Canada and the Creative Destruction Lab.

Launch innovative new training models: The government could launch and fund a venture capital lab to create innovative training programs, so new training ideas can be tested, validated and scaled up (as recommended by the Advisory Council on Economic Growth). Startups such as Ryersons Magnet have great potential to address labour-market challenges. A so-called FutureSkills Lab could help scale great ideas and share learnings across provinces.

Build real links between companies and research schools: Large companies could partner with universities and vocational schools to provide equipment, facilities and expertise to prepare students for AI. In exchange, these companies could receive preferential recruiting.

For example, TAFE SA in South Australia trains approximately 500,000 students each year in high-demand areas such as aged care and nursing, trades and information technology. It partners with hundreds of businesses each year, which provide apprenticeships and traineeships. TAFE also orchestrates reverse co-op program where large corporations and small-to-medium-sized enterprises send workers back to campus for a term to learn critical AI skills.

Urgently reinvent curriculums for software and AI: Elementary, high-school and university programs have to develop the skills that empower students to be leaders in the coming AI tsunami critical thinking, teamwork, coding, algorithmic understanding and math. Some jurisdictions (e.g., Chicago and Queensland, Australia) are already moving to make software-coding classes mandatory. Canada should consider doing the same.

Government may want to consider practising what it preaches and adopt AI itself: A technology-enabled, AI-smart public service could not only be more efficient and provide better services. It might also create a product that Canada could export to the world.

Canadian companies have a real opportunity to leverage AI for growth but not without an inclusive work force. We all have a stake in getting this right.

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Canada has a chance to monopolize the artificial intelligence industry - The Globe and Mail

Artificial intelligence might seem more the preserve of computer nerds and tech giants than pharma companies. But according to Boehringer Ingelheim's global chief data scientist, Philipp Diesinger, "the entire industry is looking at data science and AI".

This increased focus on data could drastically change the way drugs are developed and paid for. For example, AI will be vital if outcomes-based healthcare is to be successfully implemented, pointed out Philips' chief innovation & strategy officer, Jeroen Tas, who also stressed that AI really signaled a new way of handling data.

He described AI as "the way you interpret data. You constantly stream the data and add that data to the body of knowledge," he told EP Vantage during the AI Summit in London in May. "That's not the case today, because it's all in the head of the doctor."

Boehringer's Mr. Diesinger believes that what is new is the "combination of AI, big data and new perceptions of these deep analytical methods", as well as an increasing capacity for data storage and processing.

While some might question whether this marks a real change from existing approaches, Mr. Diesinger believes that "there is a perception now for data-driven decision making in businesses, and that has not been around before". He pointed out how AI has transformed the financial industry "using theoretical physicists and mathematicians to optimise trading. We're doing the same now with regards to decision-making within [Boehringer]."

The German company has been active in AI for around two years, and is using data to reduce the cost of drug development and enable earlier go/no-go decisions on pipeline candidates. According to Mr. Diesinger, the group wants to evolve from a pharma to a holistic healthcare company, with the help of AI.

Meanwhile, Philips has been narrowing its focus from technology in general to medtech alone - and has gone big on connected devices and data processing.

Improving cancer care

Oncology is one area where pharma companies are already employing AI. Notably, Novartis (NYSE:NVS), which has also been involved in AI for two or three years, recently signed a deal with IBM Watson to explore the technology's use in breast cancer care.

The collaboration's aims include identifying better treatment sequences or predictors of response, Pascal Touchon, Novartis' global head of oncology strategy, told EP Vantage.

The project will analyse data from existing electronic health records using Watson's AI expertise. So what does Novartis bring to the table? "We understand what the key questions are and what to do with the answers," Mr. Touchon replied.

The scope is not limited to patients receiving Novartis drugs as the company is interested in breast cancer generally. Mr. Touchon expects initial findings in less than a year and, if it is successful, "we believe this collaboration could then be applied to other cancers".

Another application for AI that both Novartis and Watson are exploring is clinical trial matching. A study presented at the recent Asco meeting found that using the technology reduced the time required to screen patients for eligibility by 78%.

"If you're better at scanning patients, this could lead to faster trial enrollment [and] faster development of innovation," Mr. Touchon said.

At a stroke

As for Boehringer, Mr. Diesinger would only give one example of its AI projects: the Angels Initiative, a joint venture with the European Stroke Organisation that gathers anonymous time stamp data from hospitals to reveal patterns in stroke care and identify potential pinch points. This could lead to improvements aimed at speeding up stroke treatment, ultimately resulting in better outcomes for patients.

One change in practice involves identifying stroke patients in the ambulance and carrying out simple tests, so the stroke team is waiting at the hospital entrance. "That saves something like 10 minutes right away," Mr. Diesinger said.

Also looking for patterns is London-based BenevolentAI, which hopes its machine-based learning approach to processing academic research, clinical studies and other health-related data will help identify correlations in data that could lead to new drugs and significantly speed up the process of drug development.

The company has already signed a deal worth up to $800m to develop two Alzheimer's drugs for an undisclosed US pharma group. This is good progress, but Jackie Hunter, BenevolentAI's chief executive, believes most big pharma companies, if they are doing anything in AI, are dabbling. "We need critical mass," she said.

Ms. Hunter also believes that if big pharma continues to sit on the sidelines and not integrate AI into their mainstream activities it could find itself over taken by other industries. Speaking at the Prism Series conference in London earlier this month Ms. Hunter said: "It would not surprise me if one of the top 10 companies in healthcare in 10 years will be [Alphabet's] Google or Vodafone."

Hurdles

AI could come into its own in outcomes-based pricing, an increasing focus for cost-conscious healthcare systems. While several outcomes-based deals have been announced, the approach still faces barriers.

"You might ask, why is it not happening? One reason is that's not the way care is being reimbursed today," said Philips' Mr. Tas.

Current practice involves paying for discrete events: "Consultation, procedure, medication". In contrast, outcomes-based strategies rely on continuous care. "You continuously monitor and you intervene at the moment it's needed, so you need another way to reimburse it."

Mr. Tas concluded that outcomes-based pricing was "not going to happen overnight because it's such a big shift. But it's happening, and we see it everywhere."

With plenty of other companies clamoring to get into healthcare, including tech giants like IBM Watson and Alphabet, how will medtech and pharma groups compete in the AI space?

"We're at the point of care," Mr. Tas said. "It's not only that we have the devices; it's that we're on the floor. We're working with clinicians on the ground, and they get the insight into what's needed, which perhaps someone who's set back from that is not going to be able to gain."

Boehringer's Mr. Diesinger agreed: "IBM Watson has some nice cases where it is diagnosing patients better than doctors, but to make it to a highly regulated traditional market there's a long way to go. We're not a technology company obviously, but we already have all this regulatory burden and access to healthcare figured out."

There are still issues to be ironed out, including cybersecurity dangers, illustrated by the ransomware attack in May that hit the UK's NHS as well as a recent report by the US Health Care Industry Cybersecurity Task Force highlighting the challenges the industry faces.

In AI we trust?

Even if cybersecurity is assured, others in the industry believe that one of the biggest hurdles AI in healthcare will have to overcome is patient trust.

Josh Sutton of Sapientrazorfish, a digital and AI consultancy group, says the big problem for health-based AI is that patients often want the answers about their health explained.

"In certain industries, like advertising for example, people don't care how you came up with an answer. In healthcare people are passionately obsessed, justifiably so, with how a decision was made to diagnose someone with cancer or recommend they have heart surgery."

This desire for transparency around diagnosis could require AI companies to give details of the algorithms used in their technology, something they might be reluctant to consider - or even enabling the technology to provide direct explanations to patients.

Mr. Sutton believes that this will become more of a focus as AI becomes more prevalent in the industry and could be a limiting step for the global adoption of the approach as a standalone outside of the human-plus-machine construct many see for the industry in the short term.

"The full automation of work that is done in the industry today will take a significantly longer time than [in] other industries simply because of how critical it is we get it right, and our need, correctly in my opinion, to understand how the decisions get made and why they get made," he said.

Mr. Diesinger of Boehringer agrees that overall, the pharma sector is a "couple of years behind other industries" in terms of using AI. But he feels that that could soon begin to change, particularly if healthcare spending comes under more pressure, forcing the sector to become more streamlined.

He said: "Managers are now much more interested in these new technologies and much more open to trying new things."

Editor's Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.

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Artificial Intelligence Is The Real Thing For Pharma And Medtech - Seeking Alpha

As the CEO of Silicon Valley Artificial Intelligence, Pete Kane has founded multiple startups such as Healthcare Minnesota and Startup Venture Loft, which led to his most recent collaborative creation Silicon Valley Artificial Intelligence. The community group uses machine learning (ML) and artificial intelligence (AI) to collaborate on research projects that can make landmark discoveries in science and healthcare. Silicon Valley AI will host the Genomics Hackathon fromFriday through Sunday at Google Launchpad in San Francisco. We spoke to Kane to get the skinny on what AI means for the future, and whether we should be afraid of the machines turning on us.

Why should people be excited about AI?

AI is exciting because were all exploring it at the same pace. Its possibilities have captured undivided attention of the world's smartest and most innovative people. Its exciting because were early on in this field. Everyone can get involved. Everyone can dream up ways to use machine intelligence.

What are the biggest benefits of AI now, and in the future?

I think of AI in terms of healthcare, medicine and life sciences research. Right now there are fantastic algorithms for imaging analysis like radiology and dermatology. In the future, I believe AI will play a leading role in areas like drug discovery, personalized medicine and cancer genomics.

Should we fear singularity?

No.The singularity question is a bit overhyped. I feel like we should focus on using AI to increase our understanding of medicine and biology.

What intentions did the original founders have for Silicon Valley Artificial Intelligence?

Our original intention was to build community in the SF Bay Area AI scene. We wanted to build sustainable non-profit organization, where people could learn from one another and make meaningful connections on a regular basis.

What was the first thing that got you interested in AI?

When I realized the AI scene wanted healthcare data, I was all in. The previous organization I started was a healthcare innovation community in Minnesota (Healthcare.mn), so I knew I could add a lot to the emerging AI scene here.

What response has the group received from the Silicon Valley community?

Strong! Weve have built wonderful relationships with researchers, students, and industry. The gatherings we host draw a serious, motivated crowd and I think weve built a great culture.

How does genomics play into AI and affect everyday people?

Very little at the moment. The cost and accessibility of high-resolution genomic sequencing excludes the general population. Moreover, it is still largely exploratory how AI/ML and Deep Learning is being applied to genomics, and the interpretability of those results.

What results could be a product of the Genomics Hackathon on June 23?

Participants will be analyzing drug treatment pathways, creating mutation ranking algorithms and simulating drug interventions. When 150 of the smartest people in AI, Genomics, Bioinformatics and Computer Science come together to hack on a rare cancer (NF2) genomics dataset, amazing things are going to happen. Stay tuned.

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Q&A: Pete Kane, CEO of Silicon Valley Artificial Intelligence - San Jose Inside (blog)

We should all be blessed with friends like my pal Stafford Masie. Energetic, enthusiastic and hugely knowledgeable, on a scale of smartness hes the only one of my pals whose intellect is in the same league as Simon Marais, the late chairman of Allan Gray. Stafford is deeply plugged into the tech world, a great advantage for his friends as he willingly helps the rest of us understand the big trends shaping the world.

Our breakfast was a celebration of sorts. Staffords most recent venture, the multibillion mobile payments product Thumbzup, is a huge success with its Absa relationship expanded to now include clients like Mr Price and even Uber. After a substantial investment from Visa my pal has at last been able to take the foot off the accelerator. Which is good news for me, as we were able to spend a rare couple of relaxing, thoughtful hours together yesterday.

Too many takeaways to list. But the thing which stays with me is his assertion that South Africa is full of entrepreneurial talent, rough diamonds that with a bit of polish are sure to become world beaters. He has proved the point with Thumbzup while the Paddock brothers of Cape Town did likewise with their recent $133m sale of edutech business Getsmarter to Nasdaq-listed 2U. The next big global winner, Stafford reckons, will be the company that becomes the gorilla in artificial intelligence. With all that talent around, who says it wont be born in the RSA?

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Seeking the world's new artificial intelligence gorilla maybe born in the RSA? - BizNews

Alan Turing, dubbed the Father of Computer Science, is credited with opening the doors to artificial intelligence as early as 1950, with his paper, Computing Machinery and Intelligence. Using that as a starting point, humans have been working on replicating human-like intelligence for over 60 years.

So how far have we come? How close are we to simulating human intelligence? Or, how close have we come to surpassing human capability? And will artificial intelligence be replacing jobs, or creating them?

When it comes to AI, there seem to be more questions than answers. Next week, at the panel Artificial Intelligence for Learning: Is it Human Enough?, three experts in artificial intelligence in education will discusswhats happening in AI right now, and what we can expect in the future. The conversationwill focus on the use of AI technology for lifelong learning and workforce development, and its use in education overall.

Our own Hannah Nyren will be moderating the panel taking place at NYUs edtech incubator in Washington Square, and we will publish highlights from the conversation after the event.

What: Artificial Intelligence for Learning: Is it Human Enough?

When: Wednesday, June 28, 2017, 6:30 PM

Where: NYU Edtech Incubator 35 W. 4th St. 2nd Floor, New York, NY

Who: Amir Banifatemi, Lead at IBM Watson AI XPRIZE Kathy Benemann, CEO at EruditeAI Marissa Lowman, Education Practice Lead at Village Capital

See you there!

A Texan by birth but a Bostonian at heart, Hannah is an educational writer, AmeriCorps alum, and one-time StartupWeekend EDU (SWEDU) winning team member. She started her career at a Pearson-incubated edtech startup, but has since covered travel, food & culture, and even stonemasonry in addition to education.

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Artificial Intelligence for Learning: Is It Human Enough? Join the Conversation in New York Next Week - EdTech Times

Senraj Soundar, CEO, ConnectLeader discusses how AI-driven software can eliminate a great deal of manual work, helping sales reps make decisions about how to approach prospects, personalize conversations, and most importantly, focus on the leads that deserve follow-up

In 1996, IBM introduced a supercomputer called Deep Blue to the world. Deep Blue challenged world chess champion Garry Kasparov to a series of chess matches and Deep Blue won. In 2011, IBM introduced Watson, another supercomputer, which beat two of Jeopardy's greatest champions. In 2016, Go Master Lee Sedol played a match against an ominous new challenger, a super computer from Google named, DeepMind, which won the match, making it the first time a computer had defeated a Go master.

Watson was the beginning of a new era in computing. Computing started with mechanical switches that counted things. It then moved to programmable systems. The computer was told what to do and it improved our productivity. Watson was the first computer built for big data, for extracting an understanding from massive amounts of data to help humans make better decisions. As it was given data and given outcomes, it learned. The more data it got, the smarter it got. And it never forgot. As Watsons father John Kelly, senior vice president of IBM said, We wanted to help humans that were basically in this cognitive overload because of information. We wanted to help them make better decisions.

Yet, Stephen Hawking, Bill Gates and Elon Musk have all raised concerns about the threat that artificial intelligence (AI) poses. According to Musk, advanced AI could be more dangerous than nukes, while Hawking suggested that it could lead to the end of humanity. But, humans have always been masters of technology--steam power, coal, electricity, and now computerization--and it seems every new technology comes with its scare. Every day now we hear stories about AI, data and robotics, about the jobs threatened in manufacturing, retail, transportation, even in the legal, medical, and high finance worlds. The research firm PwC found that nearly 4 out of 10 jobs in the United States could be vulnerable to replacement by robots in the next fifteen years. Ford plans to invest $1 billion into AI. The goal is to have a fully autonomous vehicle on the commercial market by 2021.

Jeff Bezos, the founder of Amazon, seems to have infiltrated every aspect of our lives, especially with his personal assistant, Alexa, that delivers us the news, weather, and the fastest way to get to work. Buoy Health has launched a digital symptom-checker designed to simulate a conversation with a real doctor. Scientists at MIT have developed a wearable wrist device that can read the emotions of a conversation. AI seems to be everywhere--the computer part of all we do.

According to computer scientist, inventor and futurist, Ray Kurzweil, AI outperforms humans because of several aspects unique to machines:

In the business world, with sales, its all about understanding behaviors and motivations. An AI-based system can use computing power to find the best prospects. The computer can use masses of market data which can be compared and matched with ideal customer profiles, saving the sales rep hours and hours of manual labor.

Todays best reps use predictive analytics, a form of AI that optimizes decision making around sales efforts. Salesforce and Microsoft have AI-driven tools and investment in AI startups is at an all-time high. This type of software uses techniques that gather customer and prospect data from multiple sources, run it through machine learning models to predict which leads are most likely to convert, and present the findings to a sales team, in the form of best prospects and accounts. AI-driven software can eliminate a great deal of manual work, helping sales reps make decisions about how to approach prospects, personalize conversations, and most importantly, focus on the leads that deserve follow-up.

Here are a few data points which can be taken into consideration by an AI-based platform: size of company; location; recent ventures; length of sales cycle; revenue; growth rate; financial health; recruitments; relocations; funding rounds; installed technologies; intent to buy; social media activity.

AI engines can provide sales reps with quality connects and conversations with qualified buyers from all that data residing in the companys CRM system. By sourcing and analyzing the data coming from different sales channels (emails, calls, social media), the AI algorithms can provide optimal personalized propositions for customers. And when propelled by calling tools, the best leads will be reach at the most optimal time.

And at this point, it may be best to remember the story of John Henry. Unlike those in the beginning of this article who lost their games to supercomputers and had to suffer embarrassment, when John Henry took on the steam shovel and lost, he died. So, . Let advanced AI technology help you stop wasting valuable time and energy, and help make you optimize your lead generation by making you win more sales.

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How Artificial Intelligence Makes Lead Generation Smarter - MarTech Advisor

The reckless rise of artificial intelligence is going to be much more disruptive for the London technology scene in the longer run than Britains departure from the EU, according to musician, entrepreneur and philanthropist will.i.am.

Speaking at an event celebrating his collaboration with Atom Bank, an app-based digital-only bank launched last year, the founding member of The Black Eyed Peas said that by 2030, Brexit will be an old school thought for the UKs rapidly evolving tech industry and AI will present a much more acute challenge.

At the moment no one really understands the things that we should care about, he said. We need to invest in AI in order to stay ahead.

Echoing similar remarks made by Chinese business magnate Alibaba chairman Jack Ma this week, will.i.am said that historically, technology and industrialisation have caused wars.

Technology today hasnt caused turmoil [yet] but we need to make sure it doesnt, he said. We need to work closer together to be inspiring and encouraging, and to protect the youth.

A time when we do everything on our phones from banking to screening our medical health and even voting in elections is just around the corner.

Multimillionaire will.i.am shot to fame in the early 2000s with hip-hop group The Black Eyed Peas. Hes since released several solo albums, collaborated with scores of artists, including Michael Jackson, Justin Bieber, Britney Spears and Lady Gaga. Hes broken into television, with talent show The Voice and has also dabbled in fashion.

Hes a founding shareholder of Beats Electronics, which makes high-end headphones, and an avid philanthropist through his foundation dedicated to providing education to underprivileged students. In the UK, his foundation collaborates with The Princes Trust.

Earlier this year, Durham-based Atom announced that will.i.am had been appointed as the banks first strategic board advisor.

As a consumer technology investor, Atom at the time said that the 42-year old, whose real name is William James Adams, would provide an external perspective on culture, philanthropy and technology.

At this weeks event, hosted in a Shoreditch hotel, Anthony Thomson, founder and chairman of Atom, elaborated on the perhaps not quite obvious partnership.

He said that he had pitched to the musician around two years ago after trying to determine who Atom would be if it were a person.

Hes a guy who has all the qualities were looking for [in the bank], Mr Thomson, who is also the founder and chairman of Metro Bank, said.

will.i.am told The Independent that he had chosen to work with Atom because of its progressive approach to banking and the way in which it strives to educate especially young people about saving, in a prescient manner.

When he got his first pay cheque after securing a record deal at 20 years old, he had no clue how to manage his finances. He said that he developed a habit of stashing the cheques he was receiving in the locked glove compartment of his car. That was my idea of saving.

A new age type of banking company, as will.i.am describes Atom, will ensure that young peopleespecially those from a deprived background who have little understanding of personal finance are given the opportunity to learn how to manage their savings. He said that he grew up in a poor neighbourhood in east Los Angeles and could relate to youngsters today trying to make living.

As a celebrity, he said, he can help Atom raise awareness and drive adoption and help the lender be to the larger, established banks what a true disruptor, like Uber is to the traditional taxi industry.

This is all about preparing for tomorrow, he said. No one wants to play catch up.

Atom, which received its banking licence in June 2015, currently offers several savings products and has entered the mortgage market by partnering with brokers. In March it raised 83m from major institutional investors, including Spains BBVA, veteran fund manager Neil Woodford and Toscafund. It said that it intends to launch further products this year.

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Brexit: will.i.am says artificial intelligence will be more disruptive to ... - The Independent

While some fear artificial intelligence may take jobs from humans, technology company SAP sees it as a way to potentially make better hires and increase diversity.

The German technology company, which employs more than 1,500 people in the Chicago area, is introducing a tool that will allow recruiters to use machine learning to sift through thousands of applications much faster.

SAP Resume Matching applies machine learning to the process of matching resumes and job descriptions, said DJ Paoni, SAPs Chicago-based Midwest managing director. SAP will roll the product out to its own recruiters this year and will also sell it to clients.

Paoni said the tool extracts information such as skills and experience from resumes and scores them against particular open positions. That can allow a recruiter to more easily whittle down a pool of a thousand to several dozen that are worth further consideration, he said.

It really allows recruiters to focus on the important part of that whole process, which is interacting with the candidates, as opposed to poring through resumes and trying to match job descriptions, Paoni said.

He said SAP plans to use this tool to help make better hires. Over the past eight years or so, the company has shifted to taking on more young, entry-level employees for the first time. As a result, its paying more attention to hiring and retention trends, such as the impact of employee well-being on productivity, diversity and inclusion, the use of part-time or supplemental workers and continuous feedback rather than annual reviews.

To make SAP a better workplace based on those trends, it needs to be quicker and better at finding the best hires, he said.

Automating the resume sorting process could also help remove bias from the hiring process, Paoni said, creating a more diverse candidate pool.

SAP also plans to use machine learning to score job descriptions themselves a process that could help identify unconscious bias in listings. For example, terms like rockstar or ninja may be more attractive to men than women. The company is developing a new machine learning tool that would detect this kind of language using sentiment analysis, then suggest alternatives.

Its similar to a grammar check that you might do on a document, Paoni said. The system will recommend alternative words for terms that might hint at a pattern of unconscious bias.

He said that will help SAP as it pushes for more diversity and inclusion. But Paoni said the machine learning tools are intended to speed up and supplement the recruiting process, not replace it.

If you rely too much on the technology, you lose that personal feel, he said. It's a delicate balance.

aelahi@chicagotribune.com Twitter @aminamania

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Can resume-reading software help companies make better hires? - Chicago Tribune

Photo: John Carl D'Annibale, Albany Times Union

Director of robotics at GE Global Research looks over his team's Euclid aerial inspection system autonomous drone Tuesday June 20, 2017 in Niskayuna, NY. (John Carl D'Annibale / Times Union)

Director of robotics at GE Global Research looks over his team's Euclid aerial inspection system autonomous drone Tuesday June 20, 2017 in Niskayuna, NY. (John Carl D'Annibale / Times Union)

GE Global Research advanced robotics' Euclid aerial inspection system autonomous drone during a test flight Tuesday June 20, 2017 in Niskayuna, NY. (John Carl D'Annibale / Times Union)

GE Global Research advanced robotics' Euclid aerial inspection system autonomous drone during a test flight Tuesday June 20, 2017 in Niskayuna, NY. (John Carl D'Annibale / Times Union)

Pilot in command Doug Forman monitors GE Global Research's Euclid aerial inspection system autonomous drone during a test flight Tuesday June 20, 2017 in Niskayuna, NY. (John Carl D'Annibale / Times Union)

Pilot in command Doug Forman monitors GE Global Research's Euclid aerial inspection system autonomous drone during a test flight Tuesday June 20, 2017 in Niskayuna, NY. (John Carl D'Annibale / Times Union)

Members of GE Global Research advanced robotics team pose with their Euclid aerial inspection system autonomous drone Tuesday June 20, 2017 in Niskayuna, NY. (John Carl D'Annibale / Times Union)

Members of GE Global Research advanced robotics team pose with their Euclid aerial inspection system autonomous drone Tuesday June 20, 2017 in Niskayuna, NY. (John Carl D'Annibale / Times Union)

GE mixing drones and artificial intelligence in Niskayuna

Niskayuna

In a picnic area at General Electric Co.'s Global Research Center, a group of scientists and engineers are working on a new industrial revolution that will involve robots, drones and artificial intelligence.

GE has been developing robot and artificial intelligence technologies for many years now.

But these researchers in Niskayuna are part of GE's latest effort to monetize that technology with the launch of Avitas Systems, a new GE-created company being incubated in Boston with help from scientists here in the Capital Region.

Avitas is creating technologies that will be artificial intelligence, or AI, combined with robots and predictive data analytics and software to provide high-tech inspection services to energy and transportation companies.

On Tuesday, a team supervised by John Lizzi, director of robotics at GE Global Research, and Judy Guzzo, a project leader, were performing drone testing on a simulated oil rig flare stack.

"Really the concept for the business and the technology came out of the Global Research Center here," Lizzi said. "We've been experimenting with drones and other types of robotics for a while. Eventually that gained momentum as a real business opportunity."

Currently, oil and gas companies use human workers hooked onto harnesses to inspect flare stacks for wear and damage. The inspections are dangerous and require the drilling companies to temporarily pause their operations, costing them valuable time away from drilling.

GE's drone technology being offered by Avitas eliminates all of that human work that is so costly and dangerous. And GE's software creates so-called digital twins of industrial equipment that can predict when the actual equipment will break down or need servicing.

The technology is currently being targeted for customers of GE's oil and gas business. Guzzo spent two months in the Gulf of Mexico on an oil rig a year ago testing sensor technology that is also used by Avitas.

"Unplanned asset downtime is a top issue for the oil and gas industry, and can cost operators millions of dollars," Kishore Sundararajan, the chief technology officer of GE Oil & Gas, said. "Avitas Systems will help enhance the efficiency of inspections, and can help our customers and others avoid significant costs by reducing downtime and increasing safety."

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GE mixing drones and artificial intelligence in Niskayuna - Times ... - Albany Times Union

Artificial intelligence can predict the behavior of Congress.

schools/iStock Photo

By Matthew HutsonJun. 21, 2017 , 2:30 PM

The health care bill winding its way through the U.S. Senate is just one of thousands of pieces of legislation Congress will consider this year, most doomed to failure. Indeed, only about 4% of these bills become law. So which ones are worth paying attention to? A new artificial intelligence (AI) algorithm could help. Using just the text of a bill plus about a dozen other variables, it can determine the chance that a bill will become law with great precision.

Other algorithms have predicted whether a bill will survive a congressional committee, or whether the Senate or House of Representatives will vote to approve itall with varying degrees of success. But John Nay, a computer scientist and co-founder of Skopos Labs, a Nashville-based AI company focused on studying policymaking, wanted to take things one step further. He wanted to predict whether an introduced bill would make it all the way through both chambersand precisely what its chances were.

Nay started with data on the 103rd Congress (19931995) through the 113th Congress (20132015), downloaded from a legislation-tracking website call GovTrack. This included the full text of the bills, plus a set of variables, including the number of co-sponsors, the month the bill was introduced, and whether the sponsor was in the majority party of their chamber. Using data on Congresses 103 through 106, he trained machine-learning algorithmsprograms that find patterns on their ownto associate bills text and contextual variables with their outcomes. He then predicted how each bill would do in the 107th Congress. Then, he trained his algorithms on Congresses 103 through 107 to predict the 108th Congress, and so on.

Nays most complex machine-learning algorithm combined several parts. The first part analyzed the language in the bill. It interpreted the meaning of words by how they were embedded in surrounding words. For example, it might see the phrase obtain a loan for education and assume loan has something to do with obtain and education. A words meaning was then represented as a string of numbers describing its relation to other words. The algorithm combined these numbers to assign each sentence a meaning. Then, it found links between the meanings of sentences and the success of bills that contained them. Three other algorithms found connections between contextual data and bill success. Finally, an umbrella algorithm used the results from those four algorithms to predict what would happen.

Because bills fail 96% of the time, a simple always fail strategy would almost always be right. But rather than simply predict whether each bill would or would not pass, Nay wanted to assign each a specific probability. If a bill is worth $100 billionor could take months or years to pull togetheryou dont want to ignore its possibility of enactment just because its odds are below 50%. So he scored his method according to the percentages it assigned rather than the number of bills it predicted would succeed. By that measure, his program scored about 65% better than simply guessing that a bill wouldnt pass, Nay reported last month in PLOS ONE.

Nay also looked at which factors were most important in predicting a bills success. Sponsors in the majority and sponsors who served many terms were at an advantage (though each boosted the odds by 1% or less). In terms of language, words like impact and effects increased the chances for climate-related bills in the House, whereas global or warming spelled trouble. In bills related to health care, Medicaid and reinsurance reduced the likelihood of success in both chambers. In bills related to patents, software lowered the odds for bills introduced in the House, and computation had the same effect for Senate bills.

Nay says he is surprised that a bills text alone has predictive power. At first I viewed the process as just very partisan and not as connected to the underlying policy thats contained within the legislation, he says.

Nays use of language analysis is innovative and promising, says John Wilkerson, a political scientist at the University of Washington in Seattle. But he adds that without prior predictions relating certain words to successthe word impact, for examplethe project doesnt do much to illuminate how the minds of Congress members work. We dont really learn anything about process, or strategy, or politics.

But it still seems to be the best method out there. Nays way of looking at bill text is new, says Joshua Tauberer, a software developer at GovTrack with a background in linguistics who is based in Washington, D.C., and who had been using his own machine-learning algorithm to predict bill enactment since 2012. Last year, Nay learned of Tauberers predictions, and the two compared notes. Nays method made better predictions, and Tauberer ditched his own version for Nays.

So how did the new algorithm rank the many (failed) bills to repeal the Affordable Care Act? A simple, base-rate prediction would have put their chances at 4%. But for nearly all of them, Nays program put the odds even lower.

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Artificial intelligence can predict which congressional bills will pass - Science Magazine

Artificial intelligence could set off a third world war, but humans will win the battle, according to Alibaba founder Jack Ma.

"The first technology revolution caused World War I," Ma told CNBC in an interview that aired on Tuesday. "The second technology revolution caused World War II. This is the third technology revolution."

Workers and employers are increasingly defined by data unless governments show more willingness to make "hard choices."

Ma said humans will ultimately win the battle against an artificial intelligence takeover, however, as machines will never have the wisdom and experience that comes with being human.

"Wisdom is from the heart," Ma said. "The machine intelligence is by the brain [...] You can always make a machine to learn the knowledge. But it is difficult for machines to have a human heart."

The goal of artificial intelligence should be making machines that do things humans cannot do, rather than making them like humans, Ma said. While "we know the machine is powerful and stronger than us," humans will rise above the impending wave of data and artificial intelligence.

"Humans will win," Ma said. "In 30 years ... we'll see us surviving. "

CNBC's Anita Balakrishnan contributed to this report.

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Jack Ma: Artificial intelligence could set off WWIII, but 'humans will win' - CNBC

Artificial intelligence may still be in its nascent stage, but the technology has a bright future ahead as companies big and small continue to invest in it and develop their expertise, according to the a senior executive at Chinese tech giant Tencent.

In an interview with CNBC, Dowson Tong, executive vice president and social network group president at Tencent, said he was very bullish about A.I. and a big proponent of offering A.I. as a service.

"I am very optimistic (and) I am very bullish about the future of A.I. I think by having all these players, big and small, and each with their own expertise, we're going to see the whole industry prosper," Tong said.

Tong oversees business operation for Tencent's social networking platform QQ and Qzone, the music entertainment group and cloud computing.

A.I. encompasses a number of different technologies, including robotics and autonomous vehicles, machine learning and natural language processing, and deep learning. For example, Microsoft has a team of researchers in India that are working on ways to make a virtual assistants effectively bilingual.

Tong said beyond the recognizable tech names in China Baidu, Alibaba and Tencent there are opportunities for small and medium-sized businesses in China. Some such businesses are "very active, pushing the envelope of the technology (and) coming up with new services everyday as well."

Last month, Tencent opened a new A.I. lab in Seattle and appointed a former Microsoft scientist to oversee operations and drive the company's research on speech recognition and natural language processing.

Among other big names in China, Baidu already has an A.I. lab set up in Silicon Valley. Meanwhile, Alibaba recently expanded its big data and A.I. cloud offerings in Europe the product handles huge amounts of data that lets organizations make real-time predictions. Uber rival Didi Chuxing in March announced an R&D center in Mountain View, California, to look into A.I. in security and intelligent driving technologies.

Experts agree that A.I. is set to unleash a new wave of digital disruption as adoption across various industries begins to pick up.

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Tencent's social network group president is betting on artificial intelligence - CNBC

Ill call you Spockers. Hayvan uzman/CC BY-SA 4.0

For better or worse, there is a long list of things that artificial intelligence is still unable to do. But we can finally scratch naming guinea pigs off of that list, because an animal shelter in Portland, Oregon recently proved that AI may produce the cutest names of all.

As The Mary Sue is reporting, the Portland Guinea Pig Rescue (PGPR) recently tasked a neural network with naming a group of the little fuzzballs. The organization contacted scientist Janelle Shane, who had worked with teaching neural networks in the past, asking her if she could purpose such computer thinking towards coming up with guinea pig names. As Shane outlined on her blog, she entered in over 600 existing guinea pig names, provided to her by the PGPR, and ran them through an open-source neural network. The new names that the computer produced were truly delightful.

Based on the input names, which were taken from a list of all the names of the guinea pigs the PGPR has ever given over for adoption, as well as some names taken from the internet, the crude AI dreamt up names like Hanger Dan, After Pie, Fuzzable, Stargoon, Stoomy Brown, Princess Pow, and Spockers. Many of the names were immediately given to some of the PGPRs rescues (which can be adopted here).

But it wasnt all perfect cuteness forever. Some of the less popular names produced from the experiment include, Pot, Fusty, Fleshy, Butty Brlomy, and Bho8otteeddeeceul.

The hope is that by giving the guinea pigs mathematically cuter names, they will have a higher chance of being adopted, and the PGPR is expected to continue using the algorithm to devise new names. We can only hope.

Link:

Artificial Intelligence Gave Some Adoptable Guinea Pigs Very Good Names - Atlas Obscura