Meet your digital wingman.

(Photo: mapodile/Getty Images)

At the recentStartup Grind Global conference inCalifornia, Tinder CEO and Founder Sean Rad said the future of the popular dating app may involveartificialintelligence. And that future may come much sooner than anticipated.

Rad said that in the next five years, Tinder's millions of global users may be able to ask Siri to find them dates, which would then prompt AI to track down possible matches nearby.

"In five years time, Tinder might be so good, you might be like 'Hey [Apple voice assistant] Siri, what's happening tonight?'And Tinder might pop up and say 'There's someone down the street you might be attracted to. She's also attracted to you. She's free tomorrow night. We know you both like the same band, and it's playing - would you like us to buy you tickets?' and you have a match."

If it sounds like some sort of far-flung premise of a Black Mirror episode, that's because it pretty much is. Even Rad admits, "It's a little scary" despite the obvious convenience of the process.

But Tinder is eyeing even furtherfuturistic dating disruption. Rad added that the dating app was taking stock of"augmented reality," which overlays digital images onto the real world as you walk around (think Pokemon Go). Rad proposes that Tinder could use AR to let users know who is single or taken when walking into a room, which is really, really similar to an episode of Black Mirror.

"You can imagine how, with augmented reality, that experience could happen in the room, in real time," Rad said. "The impact is profound as these devices get closer to your senses, to your eyes, to your experiences."

And here we were thinking that Tinder adding video was a breakthrough...

Continued here:

Tinder May Incorporate Artificial Intelligence to Help You Get Some - Maxim

Todays artificial intelligence is certainly formidable. It can beat world champions at intricate games like chess and Go, or dominate at Jeopardy!. It can interpret heaps of data for us, guide driverless cars, respond to spoken commands, and track down the answers to your internet search queries.

And as artificial intelligence becomes more sophisticated, there will be fewer and fewer jobs that robots cant take care ofor so Elon Musk recently speculated. He suggested that we might have to give our own brains a boost to stay competitive in an AI-saturated job market.

But if AI does steal your job, it wont be because scientists have built a brain better than yours. At least, not across the board. Most of the advances in artificial intelligence have been focused on solving particular kinds of problems. This narrow artificial intelligence is great at specific tasks like recommending songs on Pandora or analyzing how safe your driving habits are. However, the kind of general artificial intelligence that would simulate a person is a long ways off.

At the very beginning of AI there was a lot of discussion about more general approaches to AI, with aspirations to create systemsthat would work on many different problems, says John Laird, a computer scientist at the University of Michigan. Over the last 50 years the evolution has been towards specialization.

Still, researchers are honing AIs skills in complex tasks like understanding language and adapting to changing conditions. The really exciting thing is that computer algorithms are getting smarter in more general ways, says David Hanson, founder and CEO of Hanson Robotics in Hong Kong, who builds incredibly lifelike robots.

And there have always been people interested in how these aspects of AI might fit together. They want to know: How do you create systems that have the capabilities that we normally associate with humans? Laird says.

So why dont we have general AI yet?

There isn't a single, agreed-upon definition for general artificial intelligence. Philosophers will argue whether General AI needs to have a real consciousness or whether a simulation of it suffices," Jonathan Matus, founder and CEO of Zendrive, which is based in San Francisco and analyzes driving data collected from smartphone sensors, said in an email.

But, in essence, General intelligence is what people do, says Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence in Seattle, Washington. We dont have a computer that can function with the capabilities of a six year old, or even a three year old, and so were very far from general intelligence.

Such an AI would be able to accumulate knowledge and use it to solve different kinds of problems. I think the most powerful concept of general intelligence is that its adaptive, Hanson says. If you learn, for example, how to tie your shoes, you could apply it to other sorts of knots in other applications. If you have an intelligence that knows how to have a conversation with you, it can also know what it means to go to the store and buy a carton of milk.

General AI would need to have background knowledge about the world as well as common sense, Laird says. Pose it a new problem, its able to sort of work its way through it, and it also has a memory of what its been exposed to.

Scientists have designed AI that can answer an array of questions with projects like IBMs Watson, which defeated two former Jeopardy! champions in 2011. It had to have a lot of general capabilities in order to do that, Laird says.

Today, there are many different Watsons, each tweaked to perform services such as diagnosing medical problems, helping businesspeople run meetings, and making trailers for movies about super-smart AI. Still, Its not fully adaptive in the humanlike way, so it really doesnt match human capabilities, Hanson says.

Were still figuring out the recipe for general intelligence. One of the problems we have is actually defining what all these capabilities are and then asking, how can you integrate them together seamlessly to produce coherent behavior? Laird says.

And for now, AI is facing something of a paradox. Things that are so hard for people, like playing championship-level Go and poker have turned out to be relatively easy for the machines, Etzioni says. Yet at the same time, the things that are easiest for a personlike making sense of what they see in front of them, speaking in their mother tonguethe machines really struggle with.

The strategies that help prepare an AI system to play chess or Go are less helpful in the real world, which does not operate within the strict rules of a game. Youve got Deep Blue that can play chess really well, youve got AlphaGo that can play Go, but you cant walk up to either of them and say, ok were going to play tic-tac-toe, Laird says. There are these kinds of learning that are not youre not able to do just with narrow AI.

What about things like Siri and Alexa?

A huge challenge is designing AI that can figure out what we mean when we speak. Understanding of natural language is what sometimes is called AI complete, meaning if you can really do that, you can probably solve artificial intelligence, Etzioni says.

Were making progress with virtual assistants such as Siri and Alexa. Theres a long way to go on those systems, but theyre starting to have to deal with more of that generality, Laird says. Still, he says, once you ask a question, and then you ask it another question, and another question, its not like youre developing a shared understanding of what youre talking about.

In other words, they can't hold up their end of a conversation. They dont really understand what you say, the meaning of it, Etzioni says. Theres no dialogue, theres really no background knowledge and as a resultthe systems misunderstanding of what we say is often downright comical.

Extracting the full meaning of informal sentences is tremendously difficult for AI. Every word matters, as does word order and the context in which the sentence is spoken. There are a lot of challenges in how to go from language to an internal representation of the problem that the system can then use to solve a problem, Laird says.

To help AI handle natural language better, Etzioni and his colleagues are putting them through their paces with standardized tests like the SAT. I really think of it as an IQ test for the machine, Etzioni says. And guess what? The machine doesnt do very well.

In his view, exam questions are a more revealing measure of machine intelligence than the Turing Test, which chatbots often pass by resorting to trickery.

To engage in a sophisticated dialogue, to do complex question and answering, its not enough to just work with the rudiments of language, Etzioni says. It ties into your background knowledge, it ties into your ability to draw conclusions.

Lets say youre taking a test and find yourself faced with the question: what happens if you move a plant into a dark room? Youll need an understanding of language to decipher the question, scientific knowledge to inform you what photosynthesis is, and a bit of common sensethe ability to realize that if light is necessary for photosynthesis, a plant wont thrive when placed in a shady area.

Its not enough to know what photosynthesis is very formally, you have to be able to apply that knowledge to the real world, Etzioni says.

Will general AI think like us?

Researchers have gained a lot of ground with AI by using what we know about how the human brain. Learning a lot about how humans work from psychology and neuroscience is a good way to help direct the research, Laird says.

One promising approach to AI, called deep learning, is inspired by the architecture of neurons in the human brain. Its deep neural networks gather human amounts of data and sniff out patterns. This allows it to make predictions or distinctions, like whether someone uttered a P or a B, or if a picture features a cat or a dog.

These are all things that the machines are exceptionally good at, and [they] probably have developed superhuman patter recognition abilities, Etzioni says. But thats only a small part of what is general intelligence.

Ultimately, how humans think is grounded in the feelings within our bodies, and influenced by things like our hormones and physical sensations. Its going to be a long time before we can create an effective simulation of all of that, Hanson says.

We might one day build AI that is inspired by how humans think, but does not work the same way. After all, we didnt need to make airplanes flap their wings. Instead we built airplanes that fly, but they do that using very different technology, Etzioni says.

Still, we might want to keep some especially humanoid featureslike emotion. People run the world, so having AI that understand and gets along with people can be very, very useful, says Hanson, who is trying to design empathetic robots that care about people. He considers emotion to be an integral part of what goes into general intelligence.

Plus, the more humanoid a general AI is designed to be, the easier it will be to tell how well it works. If we create an alien intelligence thats really unlike humans, we dont know exactly what hallmarks for general intelligence to look for, Hanson says. Theres a bigger concern for me which is that, if its alien are we going to trust it? Is it going to trust us? Are we going to have a good relationship with it?

When will it get here?

So, how will we use general AI? We already have targeted AI to solve specific problems. But general AI could help us solve them better and faster, and tackle problems that are complex and call for many types of skills. The systems that we have today are far less sophisticated than we could imagine, Etzioni says. If we truly had general AI we would be saving lives left and right.

The Allen Institute has designed a search engine for scientists called Semantic Scholar. The kind of search we do, even with the targeted AI we put in, is nowhere near what scientists need, Etzioni says. Imagine a scientist helperthat helps our scientists solve humanitys thorniest problems, whether its climate change or cancer or superbugs.

Or it could give strategic advice to governments, Matus says. It could also be used to plan and execute super complex projects, like a mission to Mars, a political campaign, or a hostile takeover of a public company."

People could also benefit from general AI in their everyday lives. It could assist elderly or disabled people, improve customer service, or tutor us. When it comes to a learning assistant, it could understand your learning weaknesses and find your strengths to help you step up and plan a program for improving your capabilities, Hanson says. I see it helping people realize their dreams.

But all this is a long way off. Were so far away fromeven six-year-old level of intelligence, let alone full general human intelligence, let alone super-intelligence, Etzioni says. He surveyed other leaders in the field of AI, and found that most of them believed super-intelligent AI was 25 years or more away. Most scientists agree that human-level intelligence is beyond the foreseeable horizon, he says.

General artificial intelligence does raise a few concerns, although machines run amok probably wont be one of them. Im not so worried about super-intelligence and Terminator scenarios, frankly I think those are quite farfetched, Etzioni says. But Im definitely worried about the impact on jobs and unemployment, and this is already happening with the targeted systems.

And like any tool, general artificial intelligence could be misused. Such technologies have the potential for tremendous destabilizing effects in the hands of any government, research organization or company, Matus says. This simply means that we need to be clever in designing policy and systems that will keep stability and give humans alternative sources of income and occupation. People are pondering solutions like universal basic income to cope with narrow AI's potential to displace workers.

Ultimately, researchers want to beef up artificial intelligence with more general skills so it can better serve humans. Were not going to see general AI initially to be anything like I, Robot. Its going to be things like Siri and stuff like that, which will augment and help people, Laird says. My hope is that its really going be something that makes you a better person, as opposed to competes with you.

Read the rest here:

There are two kinds of AI, and the difference is important - Popular Science

Engineering | Honors and awards | News releases | Research | Technology | UW Today blog

February 23, 2017

Carlos Guestrin in the Paul G. Allen Center for Computer Science & Engineering at the UW.Dennis Wise/ University of Washington

University of Washington Computer Science & Engineering announced today the establishment of the Guestrin Endowed Professorship in Artificial Intelligence and Machine Learning. This $1 million endowment will further enhance UW CSEs ability to recruit and retain the worlds most outstanding faculty members in these burgeoning areas.

The professorship is named for Carlos Guestrin, a leading expert in the machine learning field, who joined the UW CSE faculty in 2012 as the Amazon Professor of Machine Learning. Guestrin works on the machine learning team at Apple and joined Apple when it acquired the company he founded, Seattle-based Turi, Inc. Guestrin is widely recognized for creating the high-performance, highly-scalable machine learning technology first embodied in his open-source project GraphLab.

At Apple, Guestrin is helping establish a new Seattle hub for artificial intelligence and machine learning research and development, as well as strengthening ties between Apple and UW researchers.

Appleincorporates machine learning across our products and services, and education has been a part ofApples DNA from the very beginning. said Johny Srouji, senior vice president of Hardware Technologies at Apple.

Seattle and UW are near and dear to my heart, and it was incredibly important to me and our team that we continue supporting this world-class institution and the amazing talent coming out of the CSE program, said Guestrin. We look forward to strong collaboration betweenApple, CSE and the broader AI and machine learning community for many years to come.

For more information, contact Ed Lazowska, Bill & Melinda Gates Chair in Computer Science & Engineering at lazowska@cs.washington.edu or Guestrin at guestrin@cs.washington.edu.

Read more here:

UW CSE announces the Guestrin Endowed Professorship in Artificial Intelligence and Machine Learning - UW Today

Forbes

This Cognitive Whiteboard Is Powered By Artificial Intelligence Forbes Imagine if the whiteboard in your next corporate meeting could take notes when you talked and add comments from your teammates in the meeting. The wait is over. IBM and Ricoh Europe have announced an interactive whiteboard with artificial intelligence ... and more »

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This Cognitive Whiteboard Is Powered By Artificial Intelligence - Forbes

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AI is mainstream these days. The attention it gets and the feelings it provokes cover the whole gamut: from hands-on technical to business, from social science to pop culture, and from pragmatism to awe and bewilderment. Data and analytics are a prerequisite and an enabler for AI, and the boundaries between the two are getting increasingly blurred.

Many people and organizations from different backgrounds and with different goals are exploring these boundaries, and we've had the chance to converse with a couple of prominent figures in analytics and AI who share their insights.

IoT: The Security Challenge

The Internet of Things is creating serious new security risks. We examine the possibilities and the dangers.

Professor Mark Bishop is a lot of things: an academic with numerous publications on AI, the director of TCIDA (Tungsten Centre for Intelligent Data Analytics), and a thinker with his own view on why there are impenetrable barriers between deep minds and real minds.

Bishop recently presented on this topic in GOTO Berlin. His talk, intriguingly titled "Deep stupidity - what deep Neural Networks can and cannot do," was featured in the Future of IT track and attracted widespread interest.

In short, Bishop argues that AI cannot become sentient, because computers don't understand semantics, lack mathematical insight and cannot experience phenomenal sensation -- based on his own "Dancing with Pixies" reductium.

Bishop however is not some far-out academic with no connection to the real world. He does, when prompted, tend to refer to epistemology and ontology at a rate that far surpasses that of the average person. But he is also among the world's leading deep learning experts, having being deeply involved in neural networks before it was cool.

"I was practically mocked when I announced this was going to be my thesis topic, and going from that to seeing it in mainstream news is quite the distance," he notes.

His expertise has earned him more than recognition and a pet topic, however. It has also gotten him involved in a number of data-centric initiatives with some of the world's leading enterprises. Bishop, about to wrap up his current engagement with Tungsten as TCIDA director, notes that going from academic research and up in the sky discussions to real-world problems is quite the distance as well.

"My team and myself were hired to work with Tungsten to add more intelligence in their SaaS offering. The idea was that our expertise would help get the most out of data collected from Tungsten's invoicing solution. We would help them with transaction analysis, fraud detection, customer churn, and all sorts of advanced applications.

But we were dumbfounded to realize there was an array of real-world problems we had to address before embarking on such endeavors, like matching addresses. We never bothered with such things before -- it's mundane, somebody must have addressed the address issue already, right? Well, no. It's actually a thorny issue that was not solved, so we had to address it."

Injecting AI in enterprise software is a promising way to move forward, but beware of the mundane before tackling the advanced

Steven Hillion, on the other hand, comes at this from a different angle. With a PhD in mathematics from Berkeley, he does not lack relevant academic background. But Hillion made the turn to industry a long time ago, driven by the desire to apply his knowledge to solve real-world problems. Having previously served as VP of analytics for Greenplum, Hillion co-founded Alpine Data, and now serves as its CPO.

Hillion believes that we're currently in the "first generation" of enterprise AI: tools that, while absolutely helpful, are pretty mundane when it comes to the potential of AI. A few organizations have already moved to the second generation, which consists of a mix of tools and platforms that can operationalize data science -- e.g. custom solutions like Morgan Stanley's 3D Insights Platform or off the shelf solutions such as Salesforce's Einstein.

In many fields, employees (or their bosses) determine the set of tasks to focus on each day. They log into an app, go through a checklist, generate a BI report, etc. In contrast, AI could use existing operational data to automatically serve up the highest priority (or most relevant, or most profitable) tasks that a specific employee needs to focus on that day, and deliver those tasks directly within the relevant application.

"Success will be found in making AI pervasive across apps and operations and in its ability to affect people's work behavior to achieve larger business objectives. And, it's a future which is closer than many people realize. This is exactly what we have been doing with a number of our clients, gradually injecting AI-powered features into the everyday workflow of users and making them more productive.

Of course, this isn't easy. And in fact, the difficult aspect of getting value out of AI is as much in solving the more mundane issues, like security or data provisioning or address matching, as it is in working with complex algorithms."

Before handing over to AI overlords, it may help to actually understand how AI works

So, do androids dream of electric sheep, and does it matter for your organization? Although no definitive answers exist at this point, it is safe to say that both Bishop and Hillion seem to think this is not exactly the first thing we should be worried about. Data and algorithmic transparency on the other hand may be.

10 types of enterprise deployments

As businesses continue to experiment with the Internet of Things, interesting use cases are emerging. Here are some of the most common ways IoT is deployed in the enterprise.

Case in point -- Google's presentation on deep learning preceding Bishop's one in GOTO. The presentation, aptly titled "Tensorflow and deep learning, without a PhD", did deliver what it promised. It was a step-by-step, hands-on tutorial on how to use Tensorflow, Google's open source toolkit for deep learning, given by Robert Kubis, senior developer advocate for the Google Cloud Platform.

Expectedly, it was a full house. Unexpectedly, that changed dramatically as the talk progressed: by the end, the room was half empty, and a lukewarm applause greeted off Kubis. Bishop's talk, by contrast, started with what seemed like a full house, and ended proving there could actually be more people packed in the room, with a roaring applause and an entourage for Bishop.

There is an array of possible explanations for this. Perhaps Bishop's delivery style was more appealing than Kubis' -- videos of AI-generated art and Bladerunner references make for a lighter talk than a recipe-style "do A then B" tutorial.

Perhaps up in the sky discussions are more appealing than hands-on guides for yet another framework -- even if that happens to be Google's open source implementation of the technology that is supposed to change everything.

Or maybe the techies that attended GOTO just don't get Tensorflow -- with or without a PhD. In all likelihood, very few people in Kubis' audience could really connect with the recipe-like instructions delivered and understand why they were supposed to take the steps described, or how the algorithm actually works.

And they are not the only ones. Romeo Kienzler, chief data scientist at IBM Watson IoT, admitted in a recent AI Meetup discussion: "we know deep learning works, and it works well, but we don't exactly understand why or how." The million dollar question is -- does it matter?

After all, one could argue, not all developers (need to) know or care about the intrinsic details of QSort or Bubble Sort to use a sort function in their APIs -- they just need to know how to call it and trust it works. Of course, they can always dig into commonly used sort algorithms, dissect them, replay and reconstruct them, thus building trust in the process.

Deep learning and machine learning on the other hand are a somewhat different beast. Their complexity and their way of digressing from conventional procedural algorithmic wisdom make them hard to approach. Coupled with vast amounts of data, this makes for opaque systems, and adding poor data quality to the mix only aggravates the issue.

It's still early days for mainstream AI, but dealing with opaqueness may prove key to its adoption.

How the cloud enables the AI revolution:

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Artificial intelligence in the real world: What can it actually do? - ZDNet

Artificial intelligence: What's real and what's not in 2017 The Business Journals I'm a big Star Wars fan, so when Rogue One: A Star Wars Story descended on theaters this month, I of course braved the crowds to see it twice in the first 18 hours. And just like all the other Star Wars movies, Rogue One stoked our geeky ... Rogue One: A Star Wars Story In-Home Trailer (Official)YouTube The Mission Comes Home: Rogue One: A Star Wars Story Arrives Soon on Digital HD and Blu-ray | StarWars.comStarWars.com all 65 news articles »

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Artificial intelligence: What's real and what's not in 2017 - The Business Journals

Artificial Intelligence (AI) is becoming part of our everyday language as more organizations integrate the technology into the products and services they offer. The latest to do so is eBay(NASDAQ:EBAY), which according to the company will help its sellers become more competitive.

The companyhas been supporting its sellers, many of whom are small to medium sized businesses, with AI driven investments for the past five years. To date, it has been embedded and distributed across 30 domains to help sellers with everything from delivery time to fraud detection, wrote President and CEO of eBay, Devin Wenig in a post on he companys official blog.

The pricing and inventory AI solution is a great example. It can identify gaps in inventory of a particular product and alert sellers of that item to stock up. Based on demand, it will make price recommendations so they wont price themselves out during a hot market. And the beauty of this solution is, it is seamless and non-intrusive, giving you recommendations automatically when events are trending.

The way new AI solutions are helpingTanya Crew, a single mom who started selling on eBay in 2003, is by optimizing the price for the items she sells. It predicts shifts in consumer behavior, along with more featuresso she can be more competitive.

The work on AI will also help optimize listing and online images featuring different types of consumer behavior to help Mohamed Taushif Ansariof Mumbai. He started with just a laptop and a sewing machine, and now exports the products he makes to 30 countries around the world.

Additionally, his products are being featured on social platforms through the eBay ShopBot, which is powered by AI and is currently in beta.

Even though there is a passionate debate going on regarding AI with polarizing views. eBays CEO said it best in his blog post:

I believe our greatest days are ahead of us. But this rests on embracing our most promising technologies and shaping them to lift people up and create opportunity at all levels.

Image: eBay

Originally posted here:

eBay Deploys Artificial Intelligence to Benefit Sellers - Small Business Trends

February 22, 2017

Quantum biomimetics consists of reproducing in quantum systems certain properties exclusive to living organisms. Researchers at University of the Basque Country have imitated natural selection, learning and memory in a new study. The mechanisms developed could give quantum computation a boost and facilitate the learning process in machines.

Unai Alvarez-Rodriguez is a researcher in the Quantum Technologies for Information Science (QUTIS) research group attached to the UPV/EHU's Department of Physical Chemistry, and an expert in quantum information technologies. Quantum information technology uses quantum phenomena to encode computational tasks. Unlike classical computation, quantum computation "has the advantage of not being limited to producing registers in values of zero and one," he said. Qubits, the equivalent of bits in classical computation, can take values of zero, one or both at the same time, a phenomenon known as superposition, which "gives quantum systems the possibility of performing much more complex operations, establishing a computational parallel on a quantum level, and offering better results than classical computation systems," he added.

The research group to which Alvarez-Rodriguez belongs decided to focus on imitating biological processes. "We thought it would be interesting to create systems capable of emulating certain properties exclusive of living entities. In other words, we were seeking to design quantum information protocols whose dynamics were analogous to these properties." The processes they chose to imitate by means of quantum simulators were natural selection, memory and intelligence. This led them to develop the concept of quantum biomimetics.

They recreated a natural selection environment in which there were individuals, replication, mutation, interaction with other individuals and the environment, and a state equivalent to death. "We developed this final mechanism so that the individuals would have a finite lifetime," said the researcher. So by combining all these elements, the system has no single clear solution: "We approached the natural selection model as a dispute between different strategies in which each individual would be a strategy for resolving the problem, the solution would be the strategy capable of dominating the available space."

The mechanism to simulate memory, on the other hand, consists of a system governed by equations. But equations display a dependence on their previous and future states, so the way in which the system changes "does not only depend on its state right now, but on its state five minutes ago, and where it is going to be in five minutes' time," explained Alvarez-Rodriguez.

Finally, in the quantum algorithms relating to learning processes, they developed mechanisms to optimize well-defined tasks, to improve classical algorithms, and to improve the error margins and reliability of operations. "We managed to encode a function in a quantum system but not to write it directly; the system did it autonomously, we could say that it 'learned' by means of the mechanism we designed so that it would happen. That is one of the most novel advances in this research," he said.

From computational models to the real world

All these methods and protocols developed in his research have provided the means to resolve all kinds of systems. Alvarez-Rodriguez says that the memory method can be used to resolve highly complex systems: "It could be used to study quantum systems in different ambient conditions, or on different scales in a more accessible, more cost-effective way."

With respect to natural selection, "more than anything we have come up with a quantum mechanism on which self-replicating systems could be based and which could be used to automate processes on a quantum scale." And finally, as regards learning, "we have come up with a way of teaching a machine a function without having to insert the result beforehand. This is something that is going to be very useful in the years to come, and we will get to see it," he said.

All the models developed in the research were computational models. But Alvarez-Rodriguez has made it clear that one of the main ideas of his research group is that "science takes place in the real world. Everything we do has a more or less direct application. Despite having been conducted in theoretical mode, the simulations we have proposed are designed so that they can be carried out in experiments, on different types of quantum platforms, such as trapped ions, superconducting circuits and phototonic waveguides, among others. To do this, we had the collaboration of the experimental groups."

Explore further: Quantum RAM: Modelling the big questions with the very small

More information: Quantum Machine Learning without Measurements. arxiv.org/abs/1612.05535

Unai Alvarez-Rodriguez et al. Artificial Life in Quantum Technologies, Scientific Reports (2016). DOI: 10.1038/srep20956 , http://www.nature.com/articles/srep20956

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Artificial intelligence in quantum systems, too - Phys.Org

Artificial intelligence is not going away. But we have a choice whether to embrace it or fear it. PHOTO: ambermb

The argument against artificial intelligence (AI) is driven by fear. Fear of the unknown fear of intelligence.

According to Stephen Hawkings we do have reason to beware of the consequences of artificial intelligence (AI) including the possibility of the end of the human race.

The rise of the machines wont be happening imminently. After all, AI is still in its infancy. The most realistic fear today is that AI will take peoples jobs.

Undoubtedly technology is taking peoples jobs in droves. Anytime you self-checkout in the grocery store you might be conveniencing yourself but youre also doing something that just 15 years ago someone would have been paid to do for you.

The trend is also happening in casual type restaurants such as Red Robin, where machines are on the table that do everything but bring you the food itself.

Airlines use self-serve kiosks to print luggage tags and boarding passes. Banks use intelligent automated voices to route calls and do practically everything unless you specifically ask for a representative.

It doesnt exactly take a forward thinker to envision a time when cars are self-driving. And with the technological advancement of drones, its not hard to imagine that commercial planes will one day be pilotless.

While Moores Law implies technology doubles every two years, the reality is humans are notoriously slow at adopting it.

Weve been trained to think of new technology as cost prohibitive and buggy. We let tech savvy pioneers test new things and we wait until the second or third iteration, when the technology is ready, before deciding to adopt it.

While AI seems like a futuristic concept, its actually something that many people use daily, although 63 percent of users dont realize theyre using it.

Google is a great example of machine learning that many people use every day and it truly does make life easier. Marketers use artificial intelligence for a variety of functions, not the least of which include personalization. The reason that Netflix or Amazon are able to give you personalized suggestions is because the technology that runs their software uses AI.

While the fear of job loss is understandable, there is another point to make: because of artificial intelligence many people are currently doing jobs that werent available even just a few years back.

Lets circle back to marketers for example. The technological know-how is now a full-time job, so alongside designers and copywriters is a new breed of marketer that is trained to purposefully promote content to a uniquely tailored audience.

Even so, when you Google which new jobs will AI produce, you only get a list of articles saying AI will eliminate jobs.

Of course, fear typically drives more clicks than positivity, so its not surprising that more articles focus on the negative aspects of AI than the good that many people proclaim will come from it.

Were currently in a situation where the new US Presidential administration that has made a mantra out of saving American jobs.

To date, the jobs the administration is focusing on are jobs that will be taken over by intelligent machines in the not-to-distant future.

Retaining jobs is important, but with a strategy around educating people on the coming technology, long-term retention of jobs would be a lot more realistic. Manufacturing is becoming less about screwing parts together and more about robotic maintenance and foresight.

No leader should want to stop this advancement, but a leader should recognize the future and see to a long-term solution rather than a short-term one.

The previous administration did study the impact of AI on our economy. The White House study, Artificial Intelligence, Automation, and the Economy doesnt sugarcoat the fact that AI will take peoples jobs as many as 47 percent in the next decade. It also goes on to emphasize that these jobs will be replaced with others, and that a focus on education and investments in the industry are vital.

AI informed intelligence software will always learn from current scenarios. It is only as good as the programmers, according to Kitty Parr, founder and CEO of Social Media Compliance (SMC), in ComputerWeekly.com. If thats the case, certainly programmers have a bright future.

Even software companies not at the scale of Google or Amazon are already using AI and creating jobs at the same time. Take my company, censhare, a Munich-based digital experience company. We've been running a semantic network, a fancy term for AI, since 2001. Besides the jobs at censhare that AI produces, its customer base needs people who can run the software as well.

You can extract from the above paragraph that there are many companies on the forefront of this new technology and they all need developers, marketers, sales, support, leadership and everyone else involved in running a company.

Intelligent machines arent going to start running companies, people will continue making the glue that holds corporations together.

Artificial intelligence is not going away.

We have a choice whether to embrace it or fear it.

People who embrace it from the start will inevitably end up ahead, while those who choose to fear or even ignore it will be left playing catch-up. The latter is who will end up losing jobs while the former will continue doing what they love, just maybe in a slightly different way.

Douglas Eldridge has worked in marketing/communications since 2003. As marketing manager for censhare US, he is tasked with strategizing and implementing digital marketing efforts in the US, utilizing both inbound and outbound methods.

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Why the Benefits of Artificial Intelligence Outweigh the Risks - CMSWire

Irish companies are seen to be particularly aware of the changing role for AI with 71 per cent of those surveyed saying they believe it will revolutionise the way they gain information from and interact with customers

As many as three quarters of Irish companies believe artificial intelligence (AI) will have a major impact on their industry in the coming years, with 25 per cent expecting it to completely transform their sector.

Thats according to Accentures annual Technology Vision 2017 report, which reports on the most disruptive tech trends for businesses.

The survey of more than 5,400 business and IT executives across 16 industries and 31 countries, including Ireland, indicates that AI is moving far beyond being a back-end tool to take on a more sophisticated role within companies.

Irish companies are seen to be particularly aware of the changing role for AI with 71 per cent of those surveyed saying they believe it will revolutionise the way they gain information from and interact with customers. Almost three quarters of those surveyed also expect AI interfaces to become their primary interface for interacting with the outside world.

However, the rise of AI is not without challenges with 41 per cent of Irish companies expecting compatibility issues to impact take-up within firms. Other potential problems cited included privacy issues, a lack of sufficient usable data and the newness of such technology.

When it comes to AI investment over the next three years, the most significant areas where Irish businesses plan to invest capabilities are in natural language processing, computer vision, machine learning, deep learning, and in embedded AI solutions such as IPsofts Amelia in call centre services, or IBMs Watson embedded in healthcare diagnostics.

The research indicates that many Irish organisations are racing to keep up with advances in technology, with one in five surveyed saying their industry is facing complete disruption and a further 48 per cent experiencing moderate disruption over the next three years.

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Irish companies preparing for artificial intelligence revolution - Irish Times

Charles-Antoine Beyney is Co-Founder and CEO of Etix Everywhere.

Data centers are proliferating to meet the relentless demand for IT capacity and seeking greater efficiency everyday, and each new innovation is a major step. To meet these requirements, Artificial Intelligence (AI) has arrived, holding tremendous promise for the industry.

Facility administrators and IT managers have several critical objectives for their data center operations, but none are as important as uptime and energy efficiency. According to 2016 research by the Ponemon Institute, the average cost of a single data center outage today is approximately $730,000. Unplanned outages are costly and time-consuming affairs that can be detrimental not just to a data center, but to an organizations business operations and bottom line. Meanwhile, figures concerning data center energy consumption are equally compelling. Worldwide, data centers consume approximately three per cent of the global electricity supply.

See also: This Data Center is Designed for Deep Learning

Fortunately, AI is mitigating data centers energy consumption, while improving uptime and reducing costs without compromising on performance.

AI is technology that enables machines to execute processes that would otherwise require human intelligence. A machine endowed with AI is capable of interpreting data to form its own conclusions and make reasonable operating decisions automatically.

Many progressive businesses today are using AI to optimize resource management and to gain a leg up on the competition. A smart, AI-enabled data center is now a necessity for any business that wants to achieve an operationally efficient, high-performance computing environment.

Common use cases of AI include:

Long-Term Planning: Research and development teams may use AI to predict the short- and long-term implications of strategic business decisions. In a manufacturing setting, for example, AI could be used to make accurate, long-term environmental predictions. This data could be very useful for planning eco friendly business initiatives.

Game Theory: Some executives are using AI to predict how markets will react to certain business decisions. An AI engine can compile data from many different sources and help executives to better understand how customers and investors will respond to corporate announcements.

Collective Robot Behavior: Imagine a scenario where an unmanned drone has to land on an aircraft carrier. A successful landing would require many different connected systems to act as one, exchanging data in real-time from a variety of sensors monitoring ocean conditions, temperature, the speed of the craft and other vehicles that are attempting to land. In this case, AI is used to control the collective behavior of the different systems.

These diverse business cases make AI one of the hottest branches of computer science and a top focal point for technology providers today. According to MarketsandMarkets, its estimated that the global AI market will grow at an astounding rate of 62.9 percent from 2016 to 2022 when it will reach $16.06 billion, much of the increase driven by technology companies, including IBM, Intel and Microsoft, which serve high performance, data center computing environments.

The same AI applications and strategies that are being used to guide larger business decisions are now making their way into the data center. AI is being used in conjunction with data center infrastructure management (DCIM) technologies to analyze power, cooling and capacity planning, as well as the overall health and status of critical backend systems.

In 2014, Google, for instance, acquired an AI startup, DeepMind, and started to use it to slash costs and improve efficiencies in its data centers. Its AI engine automatically manages power usage in certain parts of Googles data centers by discovering and reporting inefficiencies across 120 data center variables, including fans, cooling systems and windows.

Using AI, Google was able to reduce its total data center power consumption by 15 percent, which will save the company hundreds of millions of dollars over the next several years. Additionally, the company has already saved 40 percent alone on power consumed for cooling purposes.

DCIM tools are software and technology products that converge IT and building facilities functions to provide engineers and administrators with a holistic view of a data centers performance to ensure that energy, equipment and floor space are used as efficiently as possible. In large data centers, where electrical energy billing comprises a large portion of the cost of operation, the insight these software platforms provide into power and thermal management accrue directly to an organizations bottom line while reducing its carbon footprint.

Leveraging AI, automated software platforms such as DCIM and smart devices, businesses can ensure their data centers provide stringent security and are eco-friendlier, while improving uptime and reducing costs without compromising performance. With that, why shouldnt companies follow suit or at least make AI a discussion point in 2017?

Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Penton.

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Artificial Intelligence and the Evolution of Data Centers - Data Center Knowledge

The rapidity of recent Chinese advances in artificial intelligence indicates that the country is capable of keeping pace with, or perhaps even overtaking, the United States in this critical emerging technology. The successes of major Chinese technology companies, notably Baidu Inc., Alibaba Group and Tencent Holding Ltd.and even a number of start-upshave demonstrated the dynamism of these private-sector efforts in artificial intelligence. From speech recognition to self-driving cars, Chinese research is cutting edge. Although the military dimension of Chinas progress in artificial intelligence has remained relatively opaque, there is also relevant research occurring in the Peoples Liberation Army research institutes and the Chinese defense industry. Evidently, the PLA recognizes the disruptive potential of the varied military applications of artificial intelligence, from unmanned weapons systems to command and control. Looking forward, the PLA anticipates that the advent of artificial intelligence will fundamentally change the character of warfare, ultimately resulting in a transformation from todays informationized () ways of warfare to future intelligentized () warfare.

The Chinese leadership has prioritized artificial intelligence at the highest levels, recognizing its expansive applications and strategic implications. The initial foundation for Chinas progress in artificial intelligence was established through long-term research funded by national science and technology plans, such as the 863 Program. Notably, Chinas 13th Five-Year Plan (201620) called for breakthroughs in artificial intelligence, which was also highlighted in the 13th Five-Year National Science and Technology Innovation Plan. The new initiatives focus on artificial intelligence and have been characterized as the China Brain Plan (), which seeks to enhance understandings of human and artificial intelligence alike. In addition, the Internet Plus and Artificial Intelligence, a three-year implementation plan for artificial intelligence (201618), emphasizes the development of artificial intelligence and its expansive applications, including in unmanned systems, in cyber security and for social governance. Beyond these current initiatives, the Chinese Academy of Engineering has proposed an Artificial Intelligence 2.0 Plan, and the Ministry of Science and Technology of the Peoples Republic of China has reportedly tasked a team of experts to draft a plan for the development of artificial intelligence through 2030. The apparent intensity of this support and funding will likely enable continued, rapid advances in artificial intelligence with dual-use applications.

Chinas significant progress in artificial intelligence must be contextualized by the national strategy of civil-military integration or military-civil fusion () that has become a high-level priority under President Xi Jinpings leadership. Consequently, it is not unlikely that nominally civilian technological capabilities will eventually be utilized in a military context. For instance, An Weiping (), deputy chief of staff of the PLAs Northern Theater Command, has highlighted the importance of deepening civil-military integration, especially for such strategic frontier technologies as artificial intelligence. Given this strategic approach, the boundaries between civilian and military research and development tend to blur. In a notable case, Li Deyi () acts as the director of the Chinese Association for Artificial Intelligence, and he is affiliated with Tsinghua University and the Chinese Academy of Engineering. Concurrently, Li Deyi is a major general in the PLA who serves as deputy director of the Sixty-First Research Institute, under the aegis of the Central Military Commission (CMC) Equipment Development Department.

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China May Soon Surpass America on the Artificial Intelligence Battlefield - The National Interest Online

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In five questions or less, an industry expert defines and explains a technology, term or trend with this installment seeing Heather Richards, CEO of Transversal, tackle Artificial Intelligence.

HR: AI is a broad term describing machines that operate with some degree of intelligence. It can refer to a machine that mimics human thought processes or to a machine that achieves a level of creative autonomy, for example by being able to learn things beyond its original programming. AI encompasses many methods and applications, including natural language processing, problem solving, playing games and even recognizing emotions. In our business, self-service knowledge management, it helps make user interactions more intuitive.

HR:Most AI applications are designed for specific tasks. At a basic level, the designer maps out how an intelligent entity might solve a problem, and breaks down that process into steps that can be expressed as computer calculations. AI often uses nonlinear processing techniques, such as neural networks, to approximate more closely how a living mind works. It requires significant processing power and sometimes large volumes of background data to enable the computer to form judgements. Thats why AI has leapt forward during the past decade alongside the upsurge in data storage capacity.

HR:Deep learning and cognitive computing are both divisions of AI. Deep learning is a technique that enables a machine to learn more like a person does, by using a neural network of multiple layers through which calculations pass in succession with a cumulatively sophisticated result. Cognitive computing describes AI that mimics human thought processes in order to facilitate interactions between a person and a computer. For example, a cognitive application might understand input in natural language, or deduce what a user wants by interpreting disparate clues.

HR:Luckily, robots are still some way from overthrowing humanity! The effects of more benign automation are going to vary between sectors. In our business, we want AI to help employees rather than replace them. If routine, repetitive requests are handled by automated knowledge management, users can solve problems faster and employees can give more time to issues that need personal attention. AI can thus reduce effort and make work more interesting, but the human factor is still very much required.

HR:Many AI functions are small things, invisibly embedded in larger applications, that people might not even notice much less find a threat. Regarding the news-worthy manifestations of AI, like robots and self-learning machines, there is of course concern about how such machines might behave were they to become more powerful than their builders. Industry thought leaders are already developing guidelines about the ethics of AI and how AI should be steered for the benefit of humanity. From our perspective, though, AI is simply a helper. It doesnt supplant anyone; it makes for a more effortless experience.

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Artificial Intelligence Explained - Computer Business Review

Forbes

Go Big With These Two Artificial Intelligence Stocks Forbes AI plays a critical role in the future of automobiles. The best way to invest in the AI revolution today is through big companies with scale and a proven capacity to bring lab work to life. Nvdia (NVDA) invested $2 billion in a deep-learning AI chip ...

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Go Big With These Two Artificial Intelligence Stocks - Forbes

Emanate Wireless, a vendor of systems that continuously monitor key clinical assets at healthcare facilities, introduced at HIMSS17 two new temperature sensors for its PowerPath Temp Solution. Emanate expects commercial shipment of both new temperature sensors to begin in the second quarter.

These two sensors add value to our current offering by supporting additional types of devices, and by making installation faster, which leads to cost savings for our customers, said Neil Diener, co-founder and CEO of Emanate Wireless.

The first new temperature sensor is an expanded range device capable of measuring down to -200 Celsius. This ultra-low temperature sensor enables the PowerPath Temp Solution to be used to monitor Cryogenic freezers and other deep freezers used in hospitals and other healthcare facilities. The expanded range sensor works with all PowerPath Temp Monitors.

The second new temperature sensor is a wireless Bluetooth Low Energy device. The wireless sensor makes it simple to deploy the PowerPath Temp Solution: Just plug in the monitor in-line with the AC power cord of the refrigerator to monitor AC current and the operation of the refrigeration unit, then place the wireless sensor inside the refrigerator to monitor temperature, the vendor said. This gets rid of the need for temperature cabling the wireless sensor communicates with the monitor device using Bluetooth Low Energy.

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Emanate Wireless unveils artificial intelligence-powered ... - Healthcare IT News

Slide: 1 / of 1. Caption: Getty Images

Heres how science works: You have a question about some infinitesimal sliver of the universe. You form a hypothesis, test it, and eventually gather enough data to support or disprove what you thought was going on. Thats the fun part. The next bit is less glamorous: You write a manuscript, submit it to an academic journal, and endure the gauntlet of peer review, where a small group of anonymous experts in your field scrutinize the quality of your work.

Peer review has its flaws. Human beings (even scientists) are biased, lazy, and self-interested. Sometimes they suck at math (even scientists). So, perhaps inevitably, some people want to remove humans from the processand replace them with artificial intelligence. Computers are, after all, unbiased, sedulous, and lack a sense of identity. They are also, by definition, good at math. And scientists arent just waiting around for some binary brain to manifest a set of protocols for identifying experimental excellence. Journal publishers are already building this stuff, piecemeal.

Recently, a competition called ScienceIE challenged teams to create programs that could extract the basic facts out of sentences in scientific papers, and compare those to the basic facts from sentences in other papers. The broad goal of my project is to help scientists and practitioners gain more knowledge about a research area more quickly, says Isabelle Augenstein, a post-doctoral AI researcher at University College of London, who devised the challenge.

Thats a tiny part of artificial intelligences biggest challenge: processing natural human language. Competitors designed programs to tackle three subtasks: reading each paper and identifying its key concepts, organizing key words by type, and identifying relationships between different key phrases. And its not just an academic exercise: Augenstein is on a two-year contract with Elsevier, one of the worlds largest publishers of scientific research, to develop computational tools for their massive library of manuscripts.

She has her work cut out for her. Elsevier publishes over 7,500 different journals. Each has an editor, who has to find the right reviewer for each manuscript. (In 2015, 700,000 peer reviewers reviewed over 1.8 million manuscripts across Elseviers journals; 400,000 were eventually published.) The number of humans capable of reviewing a proposal is generally limited to the specialists in that field, says Mike Warren, AI veteran and CTO/co-founder of Descartes Labs, a digital mapping company that uses AI to parse satellite images. So, youve got this small set of people with PhDs, and you keep dividing them into disciplines and sub-disciplines, and when youre done there might only be 100 people on the planet qualified to review a certain manuscript. Augensteins work is part of Elseviers work to automatically suggest the right reviewers for each manuscript.

Elsevier has developed a suite of automated tools, called Evise, to aid in peer review. The program checks for plagiarism (although thats not really AI, just a search and match function), clears potential reviewers for things like conflicts of interest, and handles workflow between authors, editors, and reviewers. Several other major publishers have automated software to aid peer reviewSpringer-Nature, for instance, is currently trialing an independently-developed software package called StatReviewer that ensures that each submitted paper has complete and accurate statistical data.

But none seem as open about their capabilities or aspirations as Elsevier. We are investigating more ambitious tasks, says Augenstein. Say you have a question about a paper: A machine learning model reads the paper and answers your question.

Not everyone is charmed by the prospect of Dr. Roboto, PhD. Last month, Janne Hukkinen, professor of environmental policy at University of Helsinki, Finland, and editor of the Elsevier journal Ecological Economics wrote a cautionary op-ed for WIRED, premised on a future where AI peer review became fully autonomous:

I dont see why learning algorithms couldnt manage the entire review from submission to decision by drawing on publishers databases of reviewer profiles, analyzing past streams of comments by reviewers and editors, and recognizing the patterns of change in a manuscript from submission to final editorial decision. Whats more, disconnecting humans from peer review would ease the tension between the academics who want open access and the commercial publishers who are resisting it.

By Hukkinens logic, an AI that could do peer review could also write manuscripts. Eventually, people become a legacy system within the scientific methodredundant, inefficient, obsolete. His final argument: New knowledge which humans no longer experience as something they themselves have produced would shake the foundations of human culture.

But Hukkinens dark vision of machines capable of outthinking human scientists is, at the very least, decades away. AI, despite its big successes in games like chess, Go, and poker, still cant understand most normal English sentences, let alone scientific text, says Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence. Consider this: The winning team from Augensteins ScienceIE competition scored 43 percent across the three subtasks.

And even non-computer brains have a hard time comprehending the passive-voiced mumbo jumbo common in scientific manuscripts; it is not uncommon for inscriptions within the literature to be structured such that the phenomenon being discussed is often described, after layers of prepositional preamble, and in vernacular that is vague, esoteric, and exorbitant, as being acted upon by causative factors. Linguists call anything written by humans, for humans, natural language. Computer scientists call natural language a hot mess.

One large category of problems in natural language for AI is ambiguity, says Ernest Davis, a computer scientist at NYU who studies common sense processing. Lets take a classic example of ambiguity, illustrated in this sentence by Stanford University emeritus computer scientist Terry Winograd:

The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.

To you and me, the verbs give away who they refers to: the city council fears; the demonstrators advocate. But a computer brain would have a hell of a time figuring out which verb indicates which pronoun. And that type of ambiguity is just one thread in the tangled knot of natural languagefrom simple things like understanding homographs to unraveling the logic of narratives.

Thats not even touching on the specific issues in scientific papers, like connecting a written argument to some pattern in the data. This is even the case in pure mathematics papers. Going from English to the formal logic of mathematics is not something we can automate, says Davis. And that would be one of the easiest things to work on because its highly restrictive and we understand the targets. Disciplines that arent rooted in mathematics, like psychology, will be even more difficult. In psychology papers, were nowhere near being able to check the reasonableness of arguments, says Davis. We dont know how to express the experiment in a way that a computer could use it.

And of course, a fully autonomous AI peer reviewer doesnt just have to outread humans, it has to outthink them. When you think about AI problems, peer review is probably among the very hardest you can come up with, since the most important part of peer review is determining that research is novel, its something that has not been done before by someone else, says Warren. A computer program might be able to survey the literature and figure out which questions remain, but would it be able to pick out research of Einsteinian proportionssome new theory that completely upends previous assumptions about how the world works?

Then again, what if everyoneAI advocates and critics alikeare looking at the problem backwards? Maybe we just need to change the way we do scientific publishing, says Tom Dietterich, AI researcher at Oregon State University. So, rather than writing our research as a story in English, we link our claims and evidence into a formalized structure, like a database, containing all the things that are known about a problem people are working on. Computerize the process of peer review, in other words, rather than its solution. But at that point its not computers youre reprogramming: Youre reprogramming human behavior.

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If AI Can Fix Peer Review in Science, AI Can Do Anything - WIRED

When Binny Bansal, co-founder of Indias largest retailer Flipkart was studying at IIT-Delhi, nobody at his institute was interested in the subject of artificial intelligence. Because nothing was happening in India, Mr. Bansal told his co-panelists Satya Nadella, chief executive of Microsoft and Infosys co-founder Nandan Nilekani during a fireside chat at an event in Bengaluru.

That was 15 years ago and a lot has changed since. On Monday, Mr. Nadella, who leads the worlds largest software maker, announced a strategic partnership with Flipkart, where the e-commerce company would adopt the company's cloud computing platform, Microsoft Azure.

Flipkart said that it planned to leverage artificial intelligence (AI), machine learning and analytics capabilities in Azure to optimise its data for innovative merchandising, advertising, marketing and customer service.

AI is the simulation of human intelligence processes by computer systems and machine learning and gives computers the ability to learn without being explicitly programmed.

We are on the right ladder this time, I don't think we are going back to AI winter, said Mr. Nadella at the event where the audience consisted of hundreds of start-up founders, investors and industry experts. He said that the real challenge in AI is understanding of the human language, which still doesn't exist. (We) don't have anything that says we have the ability to write like Rabindranath Tagore, said Mr. Nadella.

Last March, Mr. Nadella faced a public communication fiasco when Microsofts teen-girl-inspired chatbot named Tay which had been programmed to interact with Twitter users mimicked racist and misogynistic lines, which other Twitter users had prompted her to repeat.

Tech entrepreneur Nandan Nilekani said that he was excited about the advent of technologies like AI and the cloud. He said that there was a challenge of taking the country from $2,000 per capita income to $20,000 and there was also a need to fix sectors like healthcare, education and financial services.

He said the classical way of getting more doctors or building education infrastructure would take time. The only way to square the circle is by using AI and cloud to deliver personalised health, education and financial services to a billion people, said Mr. Nilekani. Through his social enterprise EkStep, he said that he aimed to fix the learning challenges of Indias 200 million children using AI and smartphones.

Microsoft also runs an accelerator in Bengaluru which counts AI startups such as Uncanny Vision, Flutura and Altizon among its portfolio companies.

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Nadella woos India Inc. with artificial intelligence - The Hindu

ORLANDO From Watson to Siri, Alexa to Cortana, consumers and patients have become much more familiar with artificial intelligence and natural language processing in recent years. Pick your terminology: machine learning, cognitive computing, neural networks/deep learning. All are becoming more commonplace in our smartphones, in our kitchens and as they continue to evolve at a rapid pace, expectations are high for how they'll impact healthcare.

Skepticism is, too. And even fear.

As it sparks equal part doubt and hope (and not a little hype) from patients, physicians and technologists, a panel of IT experts at HIMSS17 discussed the future of AI in healthcare on Sunday afternoon.

Kenneth Kleinberg, managing director at The Advisory Board Company, spoke with execs from two medical AI startups: Cory Kidd, CEO of Catalia Health, and Jay Parkinson, MD, founder and CMO of Sherpaa.

Catalia developed a small robot, the Mabu Personal Healthcare Companion, aimed at assisting with "long-term patient engagement." It's able to have tailored conversations with patients that can evolve over time as the platform developed using principles of behavioral psychology gains daily data about treatment plans, health challenges and outcomes.

Sherpaa is billed as an "on-demand doctor practice" that connects subscribers with physicians, via its app, who can make diagnoses, order lab tests and imaging and prescribe medications at locations near the patient. "Seventy percent of time, the doctors have a diagnosis," said Parkinson. "Most cases can be solved virtually." Rather than just a virtual care, platform, it enables "care coordination with local clinicians in the community," he said.

In this fast-changing environment, there are many questions to ask: "We're starting to see these AI systems appear in other parts of our lives," said Kleinberg. "How valuable are they? How capable are they? What kind of authority will these systems attain?"

And also: "What does it mean to be a physician and patient in this new age?"

Kidd said he's a "big believer when it's used right."

Parkinson agreed: "It has to be targeted to be successful."

Another important question: For all the hype and enthusiasm about AI, "where on the inflection curve are we?" asked Kleinberg. "Is it going to take off and get a lot better? And does it offer more benefits at the patient engagement level? Or as an assistant to clinicians?"

For Kidd, it's clearly the former, as Catalia's technology deploys AI to help patients manage their own chronic conditions.

"The kinds of algorithms we're developing, we're building up psychological models of patients with every encounter," he explained. "We start with two types of psychologies: The psychology of relationships how people develop relationships over time as well as the psychology of behavior change: How do we chose the right technique to use with this person right now?"

The platform also gets "smarter" as it become more attuned to "what we call our biographical model, which is kind of a catch-all for everything else we learn in conversation," he said. "This man has a couple cats, this woman's son calls her every Sunday afternoon, whatever it might be that we'll use later in conversations."

Consumer applications driving clinical innovations AI is fast advancing in healthcare in large part because it's evolving so quickly in the consumer space. Take Apple's Siri, for instance: "The more you talk to it, the better it makes our product," said Kidd. "Literally. We're licensing the same voice recognition and voice outlet technology thats running on your iPhone right now."

For his part, Parkinson sees problems with simply adding AI technology onto the doctor-patient relationship as it currently exists. Most healthcare encounters involve "an oral conversation between doctor and patient," he said, where "retention is 15 percent or less."

For AI to truly be an effective augmentation of clinical practices, that conversation "needs to be less oral and more text-driven," he said. "I'm worried about layering AI on a broken delivery process."

But machine learning is starting to change the came in areas large and small throughout healthcare. Kleinberg pointed to the area of imaging recognition. IBM, for instance, made headlines when it acquired Merge Healthcare for $1 billion in 2015, allowing Watson to "see" medical images the largest data source in healthcare.

Then there are the various iPhone apps that say they can help diagnose skin cancer with photos users take of their own moles. Kleinberg said he mentioned the apps to a dermatologist friend of his.

"I want to quote him very carefully: He said, 'Naaaaahhhhhh.'"

But Parkinson took a different view: "About 25 percent of our cases have photos attached," he said. "Right now, if it's a weird mole we're sending people out to see a dermatologist. But I would totally love to replace that (doctor) with a robot. And I don't think that's too far off."

In the near term, however, "you would be amazed at the image quality that people taking photographs think are good photographs," he said. "So there's a lot of education for the patient about how to take a picture."

The patient's view If artificial intelligence is having promising if controversial impact so far on the clinical side, one of the most important aspects of this evolution also still has some questions to answer. Most notably: What do the patient think?

One one hand, Kleinberg pointed to AI pilots where patients paired with humanoid robots "felt a sense of loss" after the test ended. "One woman followed the robot out and waved goodbye to it."

On the other, "some people are horrified that we would be letting machines play a part in a role that should be played by humans," he said.

The big question, then: "Do we have place now for society and a system such as this?" he asked.

"The first time I put something like this in a patient's home was 10 years ago now," said Kidd. "We've seen, with the various versions of AI and robots, that people can develop an attachment to them. At the same time, typical conversation is two or three minutes. It's not like people spend all day talking with these."

It's essential, he argued, to be up front with patients about just what the technology can and should do.

"How you introduce this, and how you couch the terminology around this technology and what it can and can't do is actually very important in making it effective for patients," said Kidd. "We don't try to convince anyone that this is a doctor or a nurse. As long as we set up the relationship in the right way so people understand how it works and what it can do, it can be very effective.

"There is this cultural conception that AI and robotics can be scary," he conceded. "But what I've seen, putting this in front of patients is that this is a tool that can do something and be very effective, and people like it a lot."

HIMSS17runs from Feb. 19-23, 2017 at the Orange County Convention Center.

This article is part of our ongoing coverage of HIMSS17. VisitDestination HIMSS17for previews, reporting live from the show floor and after the conference.

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Artificial intelligence set to transform the patient experience, but many questions still to be answered - Healthcare IT News

By Shirley Siluk / CIO Today. Updated January 26, 2017.

On Friday, Apple filed a lawsuit in Federal District Court for the Southern District of California alleging that Qualcomm's practices have cost Apple nearly $1 billion in damages. That complaint came on the heels of two other lawsuits -- one seeking more than $145 million in damages -- against Qualcomm that Apple filed last week in China.

Qualcomm is also facing allegations of unfair and anticompetitive practices by the U.S. Federal Trade Commission. The FTC filed a complaint against the chipmaker in the U.S. District Court for the Northern District of California on Jan. 17.

'Dominance through Excessive Royalties'

The complaints center on Qualcomm's pricing and patent royalty arrangements for its broadband processors that enable mobile phones to connect with cellular networks. Qualcomm's modem chip design was used to help establish standards for the telecom industry. In return for that standardization, Qualcomm committed to licensing those technologies to other companies on FRAND (fair, reasonable and non-discriminatory) terms.

However, Apple's complaints contend that Qualcomm is charging it royalties for "technologies they have nothing to do with." That statement, contained in an email Apple sent to a number of press outlets last week, continued, "The more Apple innovates with unique features such as TouchID, advanced displays, and cameras, to name just a few, the more money Qualcomm collects for no reason and the more expensive it becomes for Apple to fund these innovations."

Apple said in the email, "Qualcomm built its business on older, legacy, standards but reinforces its dominance through exclusionary tactics and excessive royalties. Despite being just one of over a dozen companies who contributed to basic cellular standards, Qualcomm insists on charging Apple at least five times more in payments than all the other cellular patent licensors we have agreements with combined."

Most recently, Qualcomm has gone even further, withholding "nearly $1B in payments from Apple as retaliation for responding truthfully to law enforcement agencies investigating them," Apple added.

Apple's two complaints in China make similar allegations, according to a report yesterday by Reuters. Those lawsuits accuse Qualcomm of abusing its dominant position in the global chip industry and failing to license its technologies fairly as it had agreed to do.

'A Commercial Dispute over Price of IP'

In a statement issued yesterday, Qualcomm said it had not yet seen the two lawsuits filed in China. The statement included comments from Qualcomm executive vice president and general counsel Don Rosenberg.

"These filings by Apple's Chinese subsidiary are just part of Apple's efforts to find ways to pay less for Qualcomm's technology. Apple was offered terms consistent with terms accepted by more than one hundred other Chinese companies and refused to even consider them," Rosenberg said. "These terms were consistent with our NDRC Rectification plan. Qualcomm is prepared to defend its business model anywhere in the world."

During yesterday's Q1 2017 earnings call, in which Qualcomm reported year-over-year revenue growth of 4 percent and Q1 revenues of $6 billion, CEO Steven Mollenkopf noted, "Apple's complaint contains a lot of assertions. But in the end, this is a commercial dispute over the price of intellectual property. They want to pay less than the fair value that Qualcomm has established in the marketplace for our technology, even though Apple has generated billions in profits from using that technology."

In December, the Korea Fair Trade Commission imposed a fine of 1.03 trillion won ($880 million) on Qualcomm for what it called an "unfair business model" for licensing technology at the handset rather than chip level. In 2015, the European Commission also launched two antitrust investigations into "possible abusive [behavior]" by Qualcomm in connection with its pricing and licensing practices for modem chipsets.

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Apple Joining Google, Amazon, Facebook on Artificial Intelligence - CIO Today