Monthly Archives: August 2017

Inside a red-bricked building on the north side of Washington DC, internist Shantanu Nundy rushes from one examining room to the next, trying to see all 30 patients on his schedule. Most days, five of them will need to follow up with some kind of specialist. And odds are, they never will. Year-long waits, hundred-mile drives, and huge out-of-pocket costs mean 90 percent of Americas most needy citizens cant follow through on a specialist referral from their primary care doc .

But Nundys patients are different. They have access to something most people dont: a digital braintrust of more than 6,000 doctors, with expert insights neatly collected, curated, and delivered back to Nundy through an artificial intelligence platform. The online system, known as the Human Diagnosis Project, allows primary care doctors to plug into a collective medical superintelligence, helping them order tests or prescribe medications theyd otherwise have to outsource. Which means most of the time, Nundys patients wait days, not months, to get answers and get on with their lives.

In the not-too-distant future, that could be the standard of care for all 30 million people currently uninsured or on Medicaid. On Thursday, Human Dx announced a partnership with seven of the countrys top medical institutions to scale up the project, aiming to recruit 100,000 specialistsand their expert assessmentsin the next five years. Their goal: Close the specialty care gap for three million Americans by 2022.

In January, a single mom in her thirties came to see Nundy about pain and joint stiffness in her hands. It had gotten so bad that she had to stop working as a housekeeper, and she was growing desperate. When Nundy pulled up her chart, he realized she had seen another doctor at his clinic a few months prior, who referred her to a specialist. But once the patient realized shed have to pay a few hundred dollars out of pocket for the visit, she didnt go. Instead, she tried get on a wait list at the public hospital, where she couldnt navigate the paperworkEnglish wasnt her first language.

Now, back where she started, Nundy examined the patients hands, which were angrily inflamed. He thought it was probably rheumatoid arthritis, but because the standard treatment can be pretty toxic, he was hesitant to prescribe drugs on his own. So he opened up the Human Dx portal and created a new case description: 35F with pain and joint stiffness in L/R hands x 6 months, suspected AR. Then he uploaded a picture of her hands and sent out the query.

Within a few hours a few rheumatologists had weighed in, and by the next day theyd confirmed his diagnosis. Theyd even suggested a few follow-up tests just to be sure, and advice about a course of treatment. I wouldnt have had the expertise or confidence to be able to do that on my own, he says.

Nundy joined Human Dx in 2015, after founder Jayanth Komarneni recruited him to pilot the platforms core technologies. But the goal was always to go big. Komarneni likens the network to Wikipedia and Linux, but instead of contributors donating encyclopedia entries or code, they donate medical expertise. When a primary care doc gets a perplexing patient, they describe their background, medical history, and presenting symptomsmaybe adding an image of an X-ray, a photo of a rash, or an audio recording of lung sounds. Human Dxs natural language processing algorithms will mine each case entry for keywords to funnel it to specialists who can create a list of likely diagnoses and recommend treatment.

Now, getting back 10 or 20 different doctors takes on a single patient is about as useful as having 20 friends respond individually via email to a potluck invitation. So Human Dxs machine learning algorithms comb through all the responses to check them against all the projects previously stored case reports. The network uses them to validate each specialist's finding, weight each one according to confidence level, and combine it with others into a single suggested diagnosis. And with every solved case, Human Dx gets a little bit smarter. With other online tools if you help one patient you help one patient, says Komarneni. Whats different here is that the insights gained for one patient can help so many others. Instead of using AI to replace jobs or make things cheaper were using it to provide capacity where none exists.

Komarneni estimates that those electronic consults can handle 35 to 40 percent of of specialist visits, leaving more time for people who really need to get into the office. Thats based on other models implemented around the country at places like San Francisco General Hospital, UCLA Health System, and Brigham and Womens Hospital. SFGHs eReferral system cut the average waiting time for an initial consult from 112 days to 49 within its first year.

That system, which is now the default for every SFGH specialty, relies on dedicated reviewers who get paid to respond to cases in a timely way. But Human Dx doesnt have those financial incentivesits service is free. Today, though, by partnering with the American Board of Medical Specialities, Human Dx can now offer continuing education and improvement credits to satisfy at least some of the 200 hours doctors are required to complete every four years. And the American Medical Association, the nations largest physician group, has committed to getting its members to volunteer, as well as supporting program integrity by verifying physicians on the platform.

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Its a big deal to have the AMA on board. Physicians have historically been wary of attempts to supplant or complement their jobs with AI-enabled tools. But its important to not mistake the organizations participation in the alliance for a formal pro-artificial intelligence stance. The AMA doesnt yet have an official AI policy, and it doesnt endorse any specific companies, products, or technologies, including Human Dxs proprietary algorithms. The medical AI field is still young, with plenty of potential for unintended consequences.

Like discrepancies in quality of care. Alice Chen, the chief medical officer for the San Francisco Health Network and co-director of SFGHs Center for Innovation in Access and Quality, worries that something like Human Dx might create a two-tiered medical system, where some people get to actually see specialists and some people just get a computerized composite of specialist opinions. This is the edge of medicine right now, says Chen. You just have to find the sweet spot where you can leverage expertise and experience beyond traditional channels and at the same time ensure quality care.

Researchers at Johns Hopkins, Harvard, and UCSF have been assessing the platform for accuracy, and recently submitted results for peer-review. The next big hurdle is money. The project is currently one of eight organizations in contention for a $100 million John D. and Catherine T. MacArthur Foundation grant. If Human Dx wins, theyll spend the money to roll out nationwide. The alliance isnt contingent on the $100 million award, but it would certainly be a nice way to kickstart the processespecially with specialty visits accounting for more than half of all trips to the doctors office.

So its possible that the next time you go in for something that stumps your regular physician, instead of seeing a specialist across town, youll see five or 10 from around the country. All it takes is a few minutes over lunch or in an elevator to put on a Sherlock Holmes hat, hop into the cloud, and sleuth through your case.

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Want a Diagnosis Tomorrow, Not Next Year? Turn to AI - WIRED

Use your brain! Seattle-based language analytics company Textio says the term AI has lost its appeal. (BigStock Photo)

Leave it to the experts in machine learning to figure out that machine learning has lost its oomph. When it comes to terms in job postings which help fill those jobs more quickly, artificial intelligence and AI are also waning, according to Textio, the Seattle startup that analyzes the words that work.

In a new blog post on Wednesday, Textio co-founder and CEO Kieran Snyder takes a shot at the many job listings which rely on those terms to attract new talent. Its not the jobs that are going away, Snyder insists AI and machine learning are obviously still a huge deal for tech companies. Its just that the same-same nature of all those listings waters down the effect and slows the time it takes to fill an opening.

Snyder says AI and ML are going the way of big data, which she calls once cool, then cliche, now beginning to feel hopelessly dated.

Using its own data analysis to test this theory, Textio found that terms like AI and artificial intelligence were hot a couple years ago and did indeed pay off, with jobs advertised with those terms filling nine days or 28-percent faster than average engineering jobs.

But in 2017, job listings with any of these phrases are filling between one and two days faster than average. The increase in investment in these areas has led to an increase in the number of jobs, and thus the number of listings using these phrases has jumped significantly.

A quick search of Amazon jobs because they have more than 7,200 openings in Seattle using the term AI returned 106 listings, and 123 when artificial intelligence was searched.

So, Textio says, if you want to stand out, dont sound like everyone else. The company looked at terms that are on the rise right now but you better hurry. It clearly wont take long for the now-hot deep learning and chatbot to lose their appeal.

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Trying to fill a job in AI? Stop using the term AI, advises listings language experts Textio - GeekWire

A company can have the best technology in the world. It can have the strongest talent. It can have the coolest product ideas. But to train the algorithms that will deliver the intelligence to transform our cities, it needs data. To wit: The company with the most data wins.

Jessi Hempel is Backchannel's editorial director.

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Thats why earlier this year, after leaving Microsoft the previous fall, legendary engineer Qi Lu headed to Beijing to become Baidu's chief operating officer. At his former job, he was, among other things, CEO Satya Nadellas top deputy in helping to lead the companys AI strategy. Clearly, he saw more opportunity across the Pacific: In China, 731 million peoplenearly twice the entire population of the United Statesare online. Says Lu: China has the structural advantage.

On July 26, while Lu was visiting Silicon Valley, we sat down for an exclusive interview. Lu offered up an eye-opening explanation of how Baidu stands to dominate AI in China. And most places in the world, Lu notes, have much more in common with the tiny homes of the Chinese than the sprawling North American McMansions. He believes that could be Chinas biggest advantage in rolling out AI to global markets. Sure, Americas tech giants may have the lead in talentfor nowbut Lu believes that Baidu has what it will take to conquer the world.

Jessi Hempel: In the time since youve arrived at Baidu, theres been a reorganization. As COO, whats your role at the company?

I work very, very closely with Robin [Li, Baidu CEO]. We make sure he and I are fully in sync. I run R&D, sales, and marketing, because I want to make sure that our overall strategy is fully, fully in sync. Thats number one. Number two, I feel that were now much more clear and focused, in terms of strategy. Its really two battles. One is strengthening our mobile foundations. The other is leading the AI era.

How do you describe your AI strategy?

We believe the best way to commercialize AI technology is to build ecosystems. Essentially, to enable our partners to better accelerate their pace of innovation, using healthy, stable economic models to build strong, long-term win-wins for our developers and partners. The baseline is Baidu Brain [the term Baidu uses for all of its AI assets]. Its broader and more extensive than what Microsoft and Google offer today in the United States, because its a platform. We have 60 different types of AI services in our suite we call Baidu Brain.

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And were the first major company to clearly separate the perceptual and the cognitive layer. Perceptive capability and the cognitive are related, but they are quite different. Most of the [other] AI platforms bundle them together.

What is Baidus equivalent of Siri or Cortana?

We are focusing on two platforms to bring our customers and partners together. The first platform we call DuerOS. DuerOS is a natural language-based, conversation-based, human computing platform. Very much like Alexa, Google Now, Siri, or Cortana in the United States. The only difference is DuerOS is so far ahead of anybody else. DuerOS in China has accumulated more conversation-based skill sets than anybody else. We have 10 major domains [and] over 100 sub-domains of conversational skills that we developed. Were also building up an emerging partner ecosystem. So our partners are building more and more skill sets. Amazon, perhaps, has more than Baidu right now, because they have a larger partner ecosystem in the United States. But compared to most companies, in China, were clearly leading.

Number two, we are also clear leaders in partners. DuerOS today is in over 100 brands of private home appliances, whether its refrigerators, air conditioners, TVs, storytelling machines, or speakers.

How does the US market for voice technology compare to the Chinese market?

The home environment is very different. Because were talking about voice interactions. The acoustic environment, the pattern of noises, will be very different. Alexa, Echo, and Cortana are optimized for American homes. In my view, this only works in North America and maybe a portion of Europe. Essentially, the assumption is that you have spacious homes; you have several rooms. In China, thats not the case at all. For our target, even for the young generation with high incomes, typically they have 60 square meters [645 square feet], sometimes 90 square meters [970 square feet].

We have better opportunities to globalize DuerOS, because guess what? A home in Japan, a home in India, or a home in Brazil, is a lot closer to a home in China than a home in North America.

Bloomberg/Getty Images

So, thats different. Whats similar?

The similarity part is the technology. The core technology is still speech recognition, signal processing, natural language understanding, and the platform. Our platform architecture, in many ways, is very similar to Amazon. In my view, Amazon is doing a very great job. Even though I worked at Microsoft. Im always gonna be rooting for Microsoft. But honestly, Amazon is leading.

But dont you think that Amazons handicap is on its back end, in that it cant keep up on the technology side with Google and Microsoft?

I worked on Cortana four and a half years ago. At the time we all were like, Amazon, yeah, that technology is so far behind. But one thing I learned is that in this race to AI, its actually more about having the right application scenarios and the right ecosystems. Google and Microsoft, technologically, were ahead of Amazon by a wide margin. But look at the AI race today. The Amazon Alexa ecosystem is far ahead of anybody else in the United States. Its because they got the scenario right. They got the device right. Essentially, Alexa is an AI-first device.

Microsoft and Google made the same mistake. We focused on Cortana on the phone and PC, particularly the phone. The phone, in my view, is going to be, for the foreseeable future, a finger-first, mobile-first device. You need an AI-first device to solidify an emerging base of ecosystems.

Its become so much clearer, living in China, what AI-first really means. It means you interact with the technology differently from the start. It has to be voice or image recognition, facial recognition, in the first interactions. You can use a screen or touch, but thats secondary.

At Baidu [headquarters], its all face recognition-based. At the vending machine at Baidu, you can buy stuff with voice and a face. And were also working on a cafeteria project. Our goal is, when you go to a cafeteria, you walk away with food.

Technically, thats possible now in a lot of places, but that doesnt mean people are receptive to it.

Its not all technology. Its about the structure of the environmentthe culture, the policy regime. This is why AI plus China, to me, is such an interesting opportunity. Its just different cultures, different policy regimes, and a different environment.

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So how about the ethical consequences of the tools that were creating? Do people have the same types of conversations at Baidu as they do at Microsoft?

Similar. Protection of privacy is of paramount importance to us. Ultimately, our users trust in our technology. So, this is something we talk quite a bit about. And we are going to continue to seriously invest in capabilities to make sure that you can trust our services, in terms of privacy. For example, we talked about voice interactions. Were working on technologies that would prevent the unintended activation of smartphones. Its because we know that people dont want their conversations to be shipped to the Cloud. I may have very private conversations in my living room. [But sometimes] the speakers think you are trying to wake them up, and then send those bits to the Cloud.

Do you think that Chinese consumers care as much? Do you think that they expect something different, by virtue of the fact that they live under a different political environment?

Our assumption is that people will care about this. Ultimately, we believe people are rational. If theres a compelling benefit, people will weigh the consequences and then make those choices. I think this is global.

Baidu announced an ambitious self-driving initiative called Apollo this spring, and youve announced 50 partnerships so far. Why are you doubling down on autos?

If you want to truly build digital intelligence to be able to acquire knowledge, make decisions, and adapt to the environment, you need to build autonomous systems. In autonomous systems, the car is the first major commercial application that is going to land.

Its just like the phone ecosystem today. The phone ecosystem is the largest silicon software ecosystem. I believe the same thing will happen for the autonomous system. The car is going to build a larger ecosystem. And the same set of capabilitieshardware, sensors, chip sets, softwarewill be used to build industry robots, home robots. We want to have hundreds of companies and universities all at work on this, building a very large ecosystem. Then we can build robots, build drones, and build all those autonomous systems. So, to me, autonomy is a key.

You were instrumental in developing Apollo, right?

I am the COO of the company, but I run that business directly. For the last three plus months, I probably spent about about 40 percent of my time on the autonomous driving technology producttalking to customers; talking to partners. Essentially, from where things are today, toward the future of being able to be fully autonomous, the fundamental technological path for the self-driving technology is the speed of iterations.

What does that speed depend on?

Essentially, how much data you can get. Because to be able to drive on the road, you have to drive different kinds of roads in different kinds of conditionslighting, weather, whether its wet, how much physical pressure is on your tires. And with Apollo, we will be able to pull together all the resources, particularly the data resources, in a way that enables everybody to be better off.

We wrote a manifesto of Apollo. Essentially, there are four principles. Each is important. One is open capability. At Baidu, we open up our capabilityin code, in services, in datato all partners. This works particularly well in China, because China is highly, highly fragmented. Theres more than 250 car OEMs [original equipment manufacturers], unlike the United States, which is a heavily concentrated industry. None of the OEMs will have the full capabilities to build out deep R&Ds. With our code base that we released on July 5, [we will make it possible for] one person to assemble a vehicle in three days that can do autonomous driving in limited forms and start on R&Ds.

The second is shared resources. Essentially, with the Apollo design, there are two tiers. You are able to use the Apollo code and capability, and some data sets, with no strings attached. The second tier is enables you to use all the data that Baidu providesHD maps, the training databut we ask you to contribute your data. However, theres a key principle. The more you contribute, the more you should be able to get back.

The third principle is the accelerating pace of innovation. Essentially, because were able to put together more data, we are able to achieve more capability in our simulation engines. We enable everybody, collectively, to innovate at a much faster pace.

And the fourth principle is sustained win-win. Baidu is the biggest model. Its going to focus on delivering high-end services, high-value services, HD maps, [and] security services. Were competing against nobody. We enable each OEM, whether its Bosch, Continental, or Nvidia, to be able to do more.

This is the reason I created a subsidiary in the United States, Apollo US. And, also Apollo Singapore. The Singapore government essentially was like, Wow, this isJust come to Singapore. Im ready to invest.

Bloomberg/Getty Images

What needs to happen to enable fully autonomous vehicles in China?

Technology alone is not going to enable self-driving cars for a long time. Ill give you just a simple example. Lets say theres some kind of a road incident in a city, and the police come, and theres no signs. Say, he or she just hand writes a sign on a piece of paper to say, Please slow down to less than five miles an hour. Watch carefully as you proceed. And they hold it up. You need the technology to be able to read handwriting and understand the human language to be able to do that. Thats going to take a long, long time.

To enable full autonomy, you need new rules, new laws. Thats number one. Number two, as part of Apollo, working with all our partners, we actually found out theres so much more commercialization, much, much earlier than full autonomy. The Audi 8 is great example. Essentially, the car automatically follows the flows in heavy jammed traffic. And thats common to Beijing, Shanghai, and in the Bay Area. Now you just let the car drive, and you can read something and do something else. And besides following a car, there are so many other scenarios.

When we first met, you were at Microsoft. You left several months before you arrived at Baidu. Why?

I broke my leg in October 2016. I needed two surgeries. Bill, Satya, and I are still super close, so when I go to Seattle usually I go see Satya at his house. I visit with Bill. I promised to be their personal advisors.

It seems 2017 is a bellwether year for AI development in China. Whats significant about this year?

Its a combination of the readiness of technology and the number of industry verticals AI can commercialize. And at the global scale, I do feel that theres opportunities for China and the United States to collectively drive the world forward. Im probably influenced by Bill Gates quite a bit. He always talked about how the world economy right now, for practical purposes, is a single engine economy. The United States has five percent of the worlds population, but produces about 24 percent of economic output and 60 percent of innovation. Its just not going to be able to sustain the pace of growth, because the world has seven billion people. Maybe three-plus billion people are living a modern life. We have transportation; we eat processed food; we have refrigerators. But then theres a sharp drop off. The other populations are living in completely different living conditions. Our job is to elevate everybody to living a modern life. How do you do that? By more innovations, better growth. Really, China should become the second innovation engine, and [Gates] genuinely believes a more innovative China, and a more developed China, is a great thing for the world. I believe that, too.

When you began beefing up your AI resources several years ago, you focused on building a lab in Silicon Valley. When American researcher Andrew Ng left Baidu last spring, his replacement to head Baidus AI labs was in China. Has AIs talent in China caught up to the US?

The United States is still overall stronger, no question. But the gap between China and the United States is rapidly closing. Theres no doubt about that. And since I have lived in China for over six months now, honestly, I read more papers, I talk to more AI developers, and you can feel the strength of the talent base.

Baidu will do more and more AI work in China, for sure. But at the same time, were continuing to invest in the United States, in the Bay Area and also Seattle. We just opened a Seattle campus, because we acquired a company called Kitt.ai. For the very top echelon of the talent, the United States is still better, and we want to fully leverage that.

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How Baidu Will Win China's AI raceand, Maybe, the World's - WIRED

Scam artists and others use a method called cloaking to make their links on Facebook (left) look more innocuous than the content they serve (center). They even show Facebook reviewers fake versions of their sites (right). Credit: Facebook

Facebook says it is intensifying its efforts to control scams and fake news by taking a harder line on "cloaking," a tactic that bad actors use across the web to avoid detection.

"We've recently been ramping up our enforcement," says Rob Leathern, Facebook product management director. "We are making it clear: We don't tolerate cloaking."

Cloaking is a longtime but straightforward practice of so-called black hats online. Fraudulent marketers, pornographers and even racists have used it to disguise their true nature in search results and in social feeds.

Facebook was already seeking out links on its platform to landing pages that don't deliver what was promised, serve deceptive ads or have too many ads. Possible penalities included warnings, lower visibility for links and outright bans from the platform.

Some of the more elaborate cloaking efforts, however, are tough to recognize. One method is to show Facebook one version of a site to gain approval, then serve something different when Facebook users arrive.

"For example, they will set up web pages so that when a Facebook reviewer clicks a link to check whether it's consistent with our policies, they are taken to a different web page than when someone using the Facebook app clicks that same link," Leathern wrote in a blog post Wednesday. "Cloaked destination pages, which frequently include diet pills, pornography and muscle building scams, create negative and disruptive experiences for people."

The company says it has beefed up both human reviews and its artificial intelligence algorithms to spot cloaking. "In the past few months these new steps have resulted in us taking down thousands of these offenders and disrupting their economic incentives for misleading people," Leathern wrote.

Widespread phenomenon Jessie Daniels, a professor of sociology at Hunter College-City University of New York, has studied cloaking since the 1990s, and says it's very much mixed in with the problems of false news and racist propaganda propagating on the web.

Cloaking is often done by "someone who is concealing authorship in order to disguise a political agenda," Daniels says.

She points to search results from a query as simple as "Martin Luther King." The first page of Google results includes the site martinlutherking.org, which promises "historical trivia, articles and pictures" in "a valuable resource for teachers and students alike." The site is actually run by the racist group Stormfront.

"The thing I look at are people who are politically motivated and people close behind them who are profit-motivated," Daniels says. "It's always the pornographers and white supremacists."

Stopping cloaking will be difficult for any company, even Facebook, Daniels adds. In the end, it takes a lot of human reviews. Facebook has recently hired 3,000 people to comb the site for objectionable videos, but it wouldn't say how many people are dedicated to cloaking patrol.

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