Daily Archives: May 26, 2017

Artificial intelligence (AI) is changing the face of business. No longer a futuristic concept, its impact is real. From tech giants like Google, Apple and Amazon to user-centric behemoths like Uber and Starbucks, everyone seems to be using AI technology to transform the customer experience (CX). But, its not just corporate giants that are deploying AI. Smaller organizations are following suit.

Across industries, small businesses are investigating how AI can help them drive down costs, better accommodate customers and remain competitive with their increasingly tech-driven peers. For small businesses unfamiliar with AI, the prospect of rolling it out can be daunting. But, by adopting a strategic approach, small businesses can get a head start on harnessing AI and reaping the benefits it offers.

Related: The Big Trends from Google I/O 2017

Starbucks is one notable example of an enterprise thats embracing artificial intelligence to optimize its CX. At the end of January, the company announced that it would offer voice ordering capabilities via Alexa, Amazons cloud-based virtual assistant with a continuously expanding base of skills. Linking up with Amazon, the coffee giant created a new Alexa skill specifically geared toward ordering at Starbucks. The skill --called Starbucks Reorder -- provides users with a voice-activated way to place their typical order (Alexa, tellStarbucksto start my usual order.) or check their card balance.

Currently in the beta phase, Starbucks evolving use of Alexa points to the experimental approach companies are willing to take when it comes to deploying AI. While big business use of AI is generating the most publicity, small businesses should also consider how they can leverage the technology to meet customer needs.

Related: 8 Companies Changing How Machine Learning Is Used

As customers become accustomed to AI-powered solutions like Starbucks Alexa offering, theyll expect the same from their local businesses. Here are some proactive steps small organizations can take to lay the groundwork for business-applied AI:

Before deciding if AI is appropriate for their companies, small business owners should put on their consumer hats and use the technology outside of work. Test out Amazon Alexa or Google Home to determine what you like and dislike about the devices, and how your own customers might use similar technology to interact with your business. Brainstorm ways you could potentially weave AI into your company and weigh the pros and cons of implementing emerging tech.

Small businesses shouldnt approach AI without a set plan -- its crucial to prioritize specific applications for artificial intelligence technology. For instance, if you run a clothing store, perhaps youll look into predictive analytics technology to reduce staffing inefficiencies. Alternately, if you own a restaurant, you may focus more on the potential of autonomous delivery vehicles. And if youre a company with complicated accounting, you might look to ease the process with AI. By setting specific and highly focused goals, small businesses can more easily lay out a game plan to fulfill them.

Related: How to Learn Anything in the Age of AI

As a November 2016 study on the mounting adoption of AI pointed out, difficulty tracking and making sense of data is one of the key roadblocks enterprises face when deploying artificial intelligence. Small businesses cannot benefit from AI if they lack the IT infrastructure to support it. Therefore, smaller organizations should begin their journey toward AI by adopting a modernized approach to IT -- one that moves away from legacy on-premises solutions and toward cloud-powered resources that will be able to scale up as artificial intelligence technology is implemented.

Actively following the evolution of AI now will pay dividends in the long run. Even if your small business isnt currently ready to deploy emerging AI tools, its important to keep a close eye on the market. That way, youll see when technologies emerge that may benefit your business --and youll be able to track the AI moves your competitors are making so you dont fall behind.

As the examples set by companies like Starbucks illustrate, businesses are pursuing AI to optimize operations and improve the customer experience. By actively making an effort to learn about and embrace artificial intelligence, small businesses can prepare for a future powered by AI solutions.

John Swanciger is a seasoned technology executive with vast experience building highly-scalable platforms and marketplaces. As CEO, Swanciger leads Manta in strengthening its current offerings while expanding products and services for Manta...

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Why Small Business Should Be Paying Attention to Artificial Intelligence - Entrepreneur

You enter your password incorrectly too many times and get locked out of your account; your colleague sets up access to her work email on a new device; someone in your company clicks on an emailed Google Doc that is actually aphishing link initially thought to be how the recent spread of the WannaCry computer worm began.

Each of these events leaves a trace in the form of information flowing through a computer network. But which ones should the security systems protecting your business against cyber attacks pay attention to and which should they ignore? And how do analysts tell the difference in a world that is awash with digital information?

The answer could lie in human researchers tapping into artificial intelligence and machine learning, harnessing both the cognitive power of the human mind and the tireless capacity of a machine. Not only will the combination of person and device build stronger defences, their ability to protect networks should also improve over time.

A large company sifts through 200,000 so-called security events every day to figure out which present real threats, according to Caleb Barlow, vice-president of threat intelligence for IBM Security. These include anything from staff forgetting their passwords and being locked out of the system, to the signatures of devices used to access networks changing, to malware attempting to gain entry to corporate infrastructure. A level of rapid-fire triage is desperately needed in the security industry, Mr Barlow says.

The stakes for businesses are high. Last year, 4.2bn records were reported to have been exposed globally in more than 4,000 security breaches, revealing email addresses, passwords, social security numbers, credit card and bank accounts, and medical data, according to analysis by Risk Based Security, a consultancy.

International Data Corporation, a US market research company, forecasts businesses will spend more than $100bn by 2020 protecting themselves from hacking, up from about $74bn in 2016.

Artificial intelligence can improve threat detection, shorten defence response times and refine techniques for differentiating between real efforts to breach security and incidents that can safely be ignored.

Speed matters a lot. [Executing an attack] is an investment for the bad guys, Mr Barlow says. Theyre spending money. If your system is harder to get into than someone elses, they are going to move on to something thats easier.

Daniel Driver of Chemring Technology Solutions, part of the UK defence group, says: Before artificial intelligence, wed have to assume that a lot of the data say 90 per cent is fine. We only would have bandwidth to analyse this 10 per cent.

The AI mimics what an analyst would do, how they look at data, how and why they make decisions...Its doing a huge amount of legwork upfront, which means we can focus our analysts time. That saves human labour, which is far more expensive than computing time.

IBM is also applying AI to security in the form of its Watson cognitive computing platform. The company has taught Watson to read through vast quantities of security research. Some 60,000 security-related blog posts are published every month and 10,000 reports come out every year, IBM estimates. The juicy information is in human-readable form, not machine data, Mr Barlow says.

The company has about 50 customers using Watson as part of its security intelligence and analytics platform. The program learns from every piece of information it takes in.

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What to expect, who to tell and how to limit the damage

Friday, 26 May, 2017

It went from literally being a grade-school kid. We had to teach it that a bug is not an insect, its a software defect. A back door doesnt go into a house, it's a vulnerability. Now its providing really detailed insights on particular [threats] and how their campaigns are evolving. And thats just in a matter of months, Mr Barlow says. The more it learns, the faster it gets smarter.

IBM says Watson performs 60 times faster than a human investigator and can reduce the time spent on complex analysis of an incident from an hour to less than a minute.

Another even more futuristic technology could make Watson look as slow as humans: quantum computing. While machine learning and AI speed up the laborious process of sorting through data, the aim is that quantum computing will eventually be able to look at every data permutation simultaneously. Computers represent data as ones or zeros. But Mr Driver says that in a quantum computer these can be: both [zeros and ones] and neither at the same time. It can have super positions. It means we can look through everything and get information back incredibly quickly.

The analogy we like to use is that of a needle in a haystack. A machine can be specially made to look for a needle in a haystack, but it still has to look under every piece of hay. Quantum computing means, Im going to look under every piece of hay simultaneously and find the needle immediately.

He estimates that quantum computing for specific tasks will be more widely available over the next three to five years. On this scale, the technology is still a way off, but there are companies that are developing it.

One company pushing to make quantum computing commercially viable is Canada-based D-Wave, whose customers include Nasa, Lockheed Martin and Google. In January the company sold its newest, most powerful machine to a cyber security company called Temporal Defense Systems, which is using it to work on complex cyber security problems.

But there are risks to using AI technology in security systems. After all, machines that can be taught to think like humans can also be tricked.

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Friday, 26 May, 2017

The AI itself is now becoming a target, says Roman Yampolskiy, a professor of computer engineering and computer science at the University of Louisville in the US, who studies artificial intelligence and security.

Hackers may exploit machine learning by gradually teaching a security system that unusual behaviour is normal, known as behavioural drift, he says.

AI can also be used by attackers to fake human voices and create video images that could let criminals into your network. If you get a call from someone whose voice you recognise and they say, I dont have time to talk, give me your password, you will give it to them, Prof Yampolskiy says.

Despite these advances in technology, the core challenge of providing security has not changed, says Mr Driver of Chemring. Its always a cat-and-mouse thing. As soon as you put the gate up higher, then the people will jump higher to get over it."

1. On Friday May 12 2017, mobile operator Telefnica was among the first large organisations to report infection by WannaCry

2. By late morning, hospitals and clinics across the UK began reporting problems to the national cyber incident response centre

3. In Europe, French carmaker Renault was hit; in Germany, Deutsche Bahn became another high-profile victim

4. In Russia, the ministry of the interior, mobile phone provider MegaFon, and Sberbank became infected.

5. Although WannaCrys spread had already been checked, the US was not entirely spared, with FedEx being the highest-profile victim

Read more from the original source:

Artificial intelligence and quantum computing aid cyber crime fight - Financial Times

This is the first episode of a special Speaking With podcast series titled No Problem Too Big, where a panel of artists and researchers speculate on the end of the world as though it has already happened.

Its not the world we grew up in. Not since artificial intelligence. The machines have taken control.

Three fearless researchers gather in the post-apocalyptic twilight: a computer scientist, a mechanical engineer and a sci-fi author.

Together, they consider the implications of military robots and autonomous everything, and discover that the most horrifying post-apocalyptic scenario might look something like unrequited robot love.

Joanne Anderton is an award-winning author of speculative fiction stories for anyone who likes their worlds a little different. More information about Joanne and her novels can be found here.

No Problem Too Big is created and hosted by Adam Hulbert, who lectures in media and sonic arts at the University of New South Wales. It is produced with the support of The Conversation and University of New South Wales.

Sound design by Adam Hulbert.

Theme music by Phonkubot.

Additional music:

Beast/Decay/Mist by Haunted Me (via Free Music Archive)

Humming Ghost by Haunted Me (via Free Music Archive)

Additional audio:

Stephen Hawking interview, BBC News

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No problem too big #1: Artificial intelligence and killer robots - The Conversation AU

Picture a crew of engineers building a dam. Theres an anthill in the way, but the engineers dont care or even notice; they flood the area anyway, and too bad for the ants.

Just as we now have power to dictate the fate of less intelligent beings, so might such computers someday exert life-and-death power over us.

Now replace the ants with humans, happily going about their own business, and the engineers with a race of superintelligent computers that happen to have other priorities. Just as we now have power to dictate the fate of less intelligent beings, so might such computers someday exert life-and-death power over us.

Thats the analogy the superstar physicist Stephen Hawking used in 2015 to describe the mounting perils he sees in the current explosion of artificial intelligence. And lately the alarms have been sounding louder than ever. Allan Dafoe of Yale and Stuart Russell of Berkeley wrote an essay in MIT Technology Review titled Yes, We Are Worried About the Existential Risk of Artificial Intelligence. The computing giants Bill Gates and Elon Musk have issued similar warnings online.

Should we be worried?

Perhaps the most influential case that we should be was made by the Oxford philosopher Nick Bostrom, whose 2014 book, Superintelligence: Paths, Dangers, Strategies, was a New York Times best seller. The book catapulted the term superintelligence into popular consciousness and bestowed authority on an idea many had viewed as science fiction.

Bostrom defined superintelligence as any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest, with the hypothetical power to vastly outmaneuver us, just like Hawkings engineers.

And it could have very good reasons for doing so. In the title of his eighth chapter, Bostrom asks, Is the default outcome doom?, and he suggests that the unnerving answer might be yes. He points to a number of goals that superintelligent machines might adopt, including resource acquisition, self-preservation, and cognitive improvements, with potentially disastrous consequences for us and the planet.

Bostrom illustrates his point with a colorful thought experiment. Suppose we develop an AI tasked with building as many paper clips as possible. This paper clip maximizer might simply convert everything, humanity included, into paper clips. Ousting humans would also facilitate self-preservation, eliminating our unfortunate knack for switching off machines. Theres also the possibility of an intelligence explosion, where even a modestly capable general AI might undergo a rapid period of self-improvement in order to better achieve its goals, swiftly bypassing humanity in the process.

Many critics are skeptical of this line of argument, seeing a fundamental disconnect between the kinds of AI that might result in an intelligence explosion and the state of the field today. Contemporary AI, they note, is effective only at specific tasks, like driving and winning at Jeopardy!

Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence, writes that many researchers place superintelligence beyond the foreseeable horizon, and the philosopher Luciano Floridi argues in Aeon that we should not lose sleep over the possible appearance of some ultraintelligence we have no idea how we might begin to engineer it. Roboticist Rodney Brooks sums up these critiques well, likening fears over superintelligence today to seeing more efficient internal combustion engines appearing and jumping to the conclusion that warp drives are just around the corner.

To these and other critics, superintelligence is not just a waste of time but, in Floridis words, irresponsibly distracting, diverting attention from more pressing problems. One such problem is inequality: AI software used to assess the risks of recidivism, for example, shows clear racial bias, being twice as likely to flag black individuals incorrectly. Women searching Google are less likely than men to be shown ads for high-paying jobs. Add to this a host of emerging issues, including driverless cars, autonomous weapons, and the automation of jobs, and it is clear there are many areas needing immediate attention.

To the Microsoft researcher Kate Crawford, the hand-wringing over superintelligence is symptomatic of AIs white guy problem, an endemic lack of diversity in the field. Writing in The New York Times, she opines that while the rise of an artificially intelligent apex predator may be the biggest risk for the affluent white men who dominate public discourse on AI, for those who already face marginalization or bias, the threats are here.

But these arguments, however valid, do not go to the heart of what Bostrom and like-minded thinkers are worried about. Critics who emphasize the low probability of an intelligence explosion neglect a core component of Bostroms thesis. In the preface of Superintelligence, he writes that it is no part of the argument in this book that we are on the threshold of a big breakthrough in artificial intelligence, or that we can predict with any precision when such a development might occur. Instead, his argument hinges on the logical possibility of an intelligence explosion something few deny and the need to consider the problem in advance, given the consequences.

That superintelligence might distract us from addressing existing problems is a legitimate concern, but aside from an (admittedly successful) appeal to intuition, no evidence is actually offered in support of this claim.

There is room to consider both long-term and short-term consequences of AI.

Its more likely Bostrom and company have had the opposite impact, with the problems of contemporary AI benefiting from increased political, media, and public attention, as well as the accompanying injection of funds into the field. A case in point is the new Leverhulme Center for the Future of Intelligence. Based at the University of Cambridge, the center was founded with $13 million secured largely through the work of its sister organization, the Center for the Study of Existential Risk, known for its work on advanced AI risks.

This is not an either/or debate, nor do we need to neglect existing problems in order to pay attention to the risks of superintelligence. It is important not to allow concerns for short-term exigencies to overwhelm concern for the future (and vice versa) something at which humanity has a very poor track record. There is room to consider both long-term and short-term consequences of AI, and given the enormous opportunities and risks it is imperative we do so.

Robert Hart is a researcher and writer on the politics of science and technology, with special interests in biotechnology, animal behavior, and artificial intelligence. He can be reached on Twitter @Rob_Hart17.

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When Artificial Intelligence Gets Too Clever by Half - Undark Magazine

Will artificial intelligence (AI) replace radiologists? During a session on AI and imaging yesterday at theBig Data in Biomedicine conference, panelists preempted this question (which keeps some radiologists up at night) by clarifying how, at least for now, AI isnt a replacement for doctors, but a tool to help them.

The human-machine system always performs better than either alone, said Curt Langlotz, MD, PhD, a professor of radiology and biomedical informatics at Stanford. And while AI is achieving human-level performance, its not necessarily superseding it yet.

All panelists spoke about AIs capacity to increase efficiency. With deep learning, AI can identify patterns across vast datasets of images, with volumes in the petabytes (1 plus 15 zeros), to achieve computer-aided detection and classification of disease.

As an example of this efficiency in workflow, Langlotz explained how, as a chest radiologist, he has 70 ICU chest x-rays ready for reading every morning. A small fraction will contain an abnormality, but he doesnt know which ones. It would be great if there was an algorithm to flag those, pull them to the top of my list so I could see those first, he said.

A second optimistic theme of the panel was the potential of AIs reach in the developing world, where physicians and specialists are rare and there are important opportunities for early and accurate diagnoses.

Panelist Greg Moore, MD, PhD, VP of healthcare for Google Cloud, described how AI could address scarcity and error in underserved areas of the world. Billions of people live in radiology scarce zones, he pointed out, and more than 43 million people are affected by medical errors annually.

Googles first medical imaging project was a deep learning algorithm to recognize diabetic retinopathy, the fastest growing cause of blindness. In countries like India, where a shortage of specialists meant 45 percent of patients went blind before a diagnosis, AI can help recognize the condition soit can be treated earlier.

Similarly, Justin Ko, MD, medical director and service chief of medical dermatology for Stanford Health Care, spoke about the creation of a deep neural network to analyze and identify precancerous lesions. He asked inspiring questions: Could we eradicate melanoma because we can catch it earlier? Can we extend diagnosis to remote areas of the world?

AI is evolving rapidly, but radiologists have a job for the foreseeable future, the panelists agreed.

Radiologists still need to validate reports, and humans have the advantage of being able to examine the patient holistically. Ko added, Context is everything. We [dermatologists] dont look at a lesion in isolation. We look at the rest of the skin rather than a single artificial task.

Langlotz also reiterated a caution about the capabilities of AI to develop insights that humans have developed for decades.

During his presentation, John Axerio-Cilies, PhD, CTO of Arterys, a medical imaging startup, explained how his company is addressing patient privacy and negotiating regulations, two of the complex and far-from-resolved issues that make AI challenging to scale. Theres a lot of infrastructure required, he noted.

Progress has been made in building large datasets of images, but the panelists pointed out that integrating different types of data and creating consistency standards for the various stakeholders moving around all this data are important next steps. In short, more work needs to be done.

Natalie Pageler, MD, chief medical information officer of Stanford Childrens Health, moderated the panel.

Previously:Big Data in Biomedicine Conference kicks off on Wednesday,Enlisting artificial intelligence to assist radiologistsandArtificial intelligence could help diagnose tuberculosis in remote regions, study finds Photo of panel by Rod Searcey

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AI for imaging: Experts delve into its promise - Scope (blog)

For decades, technology has relentlessly made phones, laptops, apps and entire industries cheaper and betterwhile health care has stubbornly loitered in an alternate universe where tech makes everything more expensive and more complex.

Now startups are applying artificial intelligence (AI), floods of data and automation in ways that promise to dramatically drive down the costs of health care while increasing effectiveness. If this profound trend plays out, within five to 10 years, Congress wont have to fight about the exploding costs of Medicaid and insurance. Instead, it might battle over what to do with a massive windfall. Todays debate over the repeal of Obamacare would come to seem as backward as a discussion about the merits of leeching.

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Hard to believe? One proof point is in the maelstrom of activity around diabetes, the most expensive disease in the world. In the U.S., nearly 10 percent of the population has diabetes, around 30 million people. Within a decade, some experts say, the number of diabetics in China will outnumber the entire U.S. population. Most people who suffer from the disease spend $5,000 to $10,000 a year on medication, and diabetics with complications can spend hundreds of thousands of dollars on doctor and hospital bills. That and the lost wages of diabetics cost the U.S. alone more than $245 billion a year, according to the Centers for Disease Control and Prevention.

Thats an enormous problem to solve and a pile of potential cash and customers to be wonwhich is why diabetes is attracting entrepreneurs like ants to a dropped ice cream cone. One of those entrepreneurs is SamiInkinen. He was a co-founder of the real estate site Trulia and has long been an endurance athlete, competing seriously in triathlons and Ironman events. In 2014, he and his wife rowed from California to Hawaii. None of this fits the typical profile of a diabetic, yet in 2011, soon after yet another triathlon, Inkinen was diagnosed with Type 2 diabetes. And like many driven, super-smart data geeks, he dove into research to understand everything about his condition.

Soila Solano injects herself with insulin at her home in Las Vegas on April 18. Solano was diagnosed with Type 2 diabetes six years ago. Nearly 10 percent of the U.S. population has diabetes, the most expensive disease in the world. John Locher/AP

That journey led him to Dr. Stephen Phinney, a medical researcher at the University of California, Davis, and Jeff Volek, a scientist at Ohio State. Phinney and Volek wrote two books together about low-carbohydrate diets and published scientific papers describing how constant adjustments to diet and lifestyle can reverse diabetes in many patients. Diabetes is almost never treated that way because the program is too hard for most people to stick to. It requires so much coaching and scrutiny by medical professionals, youd pretty much have to hire a live-in doctor.

Inkinen convinced Phinney and Volek that technology could essentially re-create a live-in doctor and diabetes coach in a smartphone. Together, the three founded Virta Health in 2014. The company stayed in stealth mode until now, launching in March. It felt like a duty to do this, Inkinen tells Newsweek . Here is an epidemic of epic proportion, and nothing is working. We can combine science and technology to solve the problem at much lower cost and do it safely.

Heres how Virta works and why its approach is so important to the future of health care. On the front end, Virta is software on a smartphone. Diabetics who sign up agree to regularly enter data: glucose levels, weight, blood pressure, activity. Some do this by manually entering information; others use devices like a Fitbit or connected scales to automatically send it in. The app also frequently asks multiple-choice questions about mood, energy levels and hunger more data that the AI software crunches to learn about the patient, look for warning signs and symptoms and guide Virtas doctors.

On the back end, Virta hires doctors who get streams of updates from Virtas software and use the data to help them make decisions about how to adjust each patients diet and medications or anything else that might affect that persons health. Any clinical decision is always made by a doctor, Inkinen says. But the software increases productivity by 10-X. (Thats 10 times, in Silicon Valleyspeak.) When all this works and the patient follows the programs strict dietary and medical controls, diabetes can be reversed, clinical trials of Virtas system have shown. Around 87 percent of patients who had been relying on insulin to control their condition either decreased their dose or eliminated their use of insulin completelya success rate that matches that for bariatric surgery, which is an expensive, invasive, last-ditch effort for severe diabetics.

Virta leverages AI software, smartphones and cloud computing to allow its doctors to continually interact with many times more patients than they can in a clinic or hospital, and it gives its diabetic patients a cross between a pocket doctor and a guardian angel. The result is a promising treatment for diabetes that could get many sufferers off medication and keep them out of doctors offices and hospital emergency rooms. And that, in turn, would greatly lower the overall cost of diabetes.

Virta is just one startup of many attacking diabetes. Livongo is a more automated but less doctor-oriented version of Virtas program. The company, which raised $52.5 million in March, makes a wireless glucose-reading device that uploads the diabetics data. AI software learns about the patient and sends a stream of tips and information intended to help the diabetic manage the disease and stay out of hospitals. Yet another new startup, Fractyl, takes a more medical approach. It invented a type of catheter that seems to cause changes in the intestines that result in reversing diabetes.

Startup tracker Crunchbase lists about 130 new tech-oriented companies (the number changes constantly) involved in some aspect of diabetes. While many of these startups will fail, its hard to imagine that some wont have a significant impact.

These efforts matter to all of us because diabetes is such an enormous drain on health care resources. Venture capitalist Hemant Taneja, who helped start Livongo, says technology could take $100 billion out of the annual cost of diabetes in the U.S. Imagine if even 20 percent of diabetics could get off medication and have little need for a doctors care. All of those medical resources would get freed up for other patients and other conditions, which should help lower prices of health care for all. If we want to massively lower health care costs, we need to figure out how to address metabolic health issues [like diabetes] at their core, Inkinen says. I would bet my house that in 15 years, the future health care company looks like what were doing todaynot treating diseases at the end of the road but catching them along the way and reversing them.

A user checks blood glucose with the Livongo system. Livongo

Over the past decade, medical records in the U.S.long kept on paper in doctors horrible handwritinghave been digitized and fed into software. That hasnt helped lower health care costs yet, and in fact it is adding to them as systems get installed and medical professionals learn to use software that can be clunky. Epic Systems, the biggest electronic medical records company, handles 54 percent of patients records in the U.S. but gets bad marks for being so hard to use that it eats up doctors and nurses time. One report from Beckers Hospital Review said that almost 30 percent of Epic clients wouldnt recommend it to their peers. A survey by Black Book Market Research found that 30 percent of hospital personnel were dissatisfied with their EMR systems, with Epic getting the strongest dissatisfaction.

But theres a larger gain from the pain of EMRs: Enormous amounts of medical information are now digitized. As more medical interaction happens onlineas with Virta or Livongothe more kinds of data well collect. Internet of Things devices, whether Fitbits or connected glucose meters or potential new devices like Apple AirPods that take biometric readings, will add yet more data. All this data can help AI software learn about diseases in general, and about individual patients, opening up new ways for technology to be applied.

Some of the new applications of AI will simply improve a tragically inefficient health care industry. Qventus is a startup using AI to take all the data flowing through a hospital to learn how to free up doctors and nurses to see more patients and improve outcomes. Were creating efficiency out of seemingly nothing, Qventus CEO Mudit Garg tells me. Two years ago, work like this was so unsexy. But this is where the rubber meets the road.

One of his clients, Mercy Hospital Fort Smith in Fort Smith, Arkansas, has been able to treat 3,000 more patients a year with the same resources, an increase of 18 percent. Here again, technology is increasing the supply of medical services, potentially changing the cost equation that keeps forcing health care prices higher.

AI is also starting to automate some of the work of doctors. IBMs Watson, which uses machine learning and massive computing power to reason its way through questions, is on its way to becoming the best diagnostician on the planet. Its software can soak up all manner of available (and anonymized) patient data, plus the tens of thousands of medical research papers published every year (far more than any human could read). The system can even keep up with the news, learning, for instance, which regions are affected by a certain contagious disease, which might help diagnose someone who recently traveled to one of those areas. By asking patients a series of questions spoken into any kind of computer or connected device, Watson can quickly narrow down the possible causes of a medical problem. Today, IBM works on test projects with major hospitals like the Cleveland Clinic to put Watson in the hands of doctors, who are learning how to use the technology like a brilliant assistant.

But the day will come when Watson or something like it is available to everyone through a smartphone or some other device. Amazon is starting down that path by partnering with HealthTap to offer what it calls Dr. A.I. on Alexa, Amazons voice-activated AI gadget for consumers. Its not nearly as robust as Watson but works on the same idea. Just tell it your medical problem, and it will ask you questions to help narrow down what it might be.

A clerk works in the medical records department at Clinica Sierra Vista's East Bakersfield Community Health Center in Bakersfield, California on October 20, 2009. As medical records increasingly become digitized, their data will help AI learn more about diseases and how to help patients. Phil McCarten/Reuters

As health care AI develops, startups are also creating new kinds of genomics-based medicine. Just 16 years ago, the Human Genome Project and geneticist Craig Venters startup, Celera Genomics, published the results of their human genome sequencing within a day of each other in 2001. Venter said his project took 20,000 hours of processor time on a supercomputer. This year, startup Color Genomics is offering a $249 genetic test that can sequence most of the pertinent genes in the human body. Colors goal is to make genetic sequencing so cheap and easy that every baby born will have it done, and the data will inform his or her health care for life.

Combine genetic data about a person with all the kinds of data Watson can ingest, and were close to being able to build AI software that can at least supplant that first visit to a doctor when youre sickwhich, of course, is when you least want to travel to a doctors office. Instead, people will increasingly speak to a smartphone or to something like Dr. A.I. on Alexa about their health problems and, if necessary, send in photos of that rash or funky toe. If the system has your health care records and genetic data, it can gain more insight into your condition than any doctor operating on an informed hunch.

An early prototype of Watson in Yorktown Heights, NY. The cognitive computing system was originally the size of a master bedroom in 2011. Clockready

On many occasions, the app might tell the user the problem is nothing seriousa robot equivalent of Take two aspirin and call me in the morning. Other times, the app might send the user to a clinic to get a test or X-ray. If thats how it plays out, a large chunk of the traffic into doctors offices and hospitals will fade away.

Add it up, and in these next few years were going to see a parade of tech applications that reduce demand on the health care system while giving all of us more access to care. Doctors should be freed up to do a better job for patients who truly need their attention. Theoretically, all of this will help keep more people healthier. And if were all healthier and using health care less, the laws of supply and demand should kick in, sending the overall cost of health care tumbling.

However, there are bumps ahead because, as our erudite president recently said, nobody knew that health care could be so complicated.

The economics of health care are weird. First of all, the usual forces dont apply to highly regulated industries, and health care is perhaps the most regulated in the U.S. and around the world because lives are at stake. In most countries, regulators prevent AI software from crossing the line into independently offering a diagnosis or clinical advicethats strictly the purview of doctors. New medical devices, like Fractyls, have to get approval from the Food and Drug Administration. Lobbyists often slow regulatory change to maintain the status quo and benefit incumbents charging inflated prices.

Personal health care decisions in the U.S. often get influenced by insurance companies, employers who pay for health benefits, and Medicare. Unlike most industries, consumers in health care dont have much information about pricing or quality, so they cant weigh options and make rational choices. Moreover, we think about health differently from anything else we buy. Many of us are never satiated with health carewe always want more and better health care, if we can afford it. One study published in March showed that telehealth making doctors available by video callprompted people to seek care for minor illnesses they otherwise wouldve ignored. Only 12 percent of telehealth visits replaced in-person visits, and the other 88 percent was new demand.

Until recently, most new medical technology has been high-end products that give doctors and hospitals a reason to charge more for something that couldnt have been done in the past. Think MRI machines or robotic limbs. These improve quality of life but add to costs. In 2008, the Congressional Budget Office concluded, The most important factor driving the long-term growth of health care costs has been the emergence, adoption, and widespread diffusion of new medical technologies and services.

A doctor and patient demonstrate how they use Virta for diabetes monitoring and treatment. Virta Health

The next wave of health care technology is different. The combination of data and AI was not available until the past year or two, and it can lead to the kind of automation that has disrupted so many other industries. Many health care entrepreneurs are focused precisely on the win-win-win prospect of lowering the cost of care while making it better and available to more people. Of course, there will be challenges to address, such as making sure our highly sensitive medical data stays protected and private, even as it flies around various networks and systems.

As startups bring these technologies online, theyre often doing an end run around insurance companies, instead finding demand among consumers or employers who offer health coverage. Livongo, for instance, points out to companies that each diabetic employee costs thousands of dollars a year in care. Pay for the Livongo service, the pitch goes, and your company will save money as those employees better manage their conditions. By last year, Livongo had signed up more than 50 large customers, including Quicken, Office Depot, Office Max and S.C. Johnson & Son.As the thinking goes among health care startups, once employers and consumers embrace new technology, insurance companies, regulators and health care incumbents will have to follow.

As that happens, the technologists promise, economic forces will finally stall or reverse the climbing cost of health care in the U.S. and around the world, a development that would, if were lucky, leave the president and just about every member of Congress speechless.

Jon-Paul Pezzolo

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