Saturday, July 4, 2015

Watson’s next feat? Taking on cancer

Watson’s next feat? Taking on cancer

IBM’s computer brain is training alongside doctors to do what they can’t

Story by Ariana Eunjung Cha

Published on June 27, 2015

Houston

Candida Vitale and the other fellows at MD Anderson’s leukemia treatment center had known one another for only a few months, but they already were very tight. The nine of them shared a small office and were always hanging out on weekends.

But she wasn’t quite sure what to make of the new guy.

Rumor had it that he had finished med school in two years and had a photographic memory of thousands of journal articles and relevant clinical trials. When the fellows were asked to summarize patients’ records for the senior faculty in the mornings, he always seemed to have the best answers.

“I was surprised,” said Vitale, a 31-year-old who received her MD in Italy. “Even if you work all night, it would be impossible to be able to put this much information together like that.”

The new guy’s name was a mouthful, so many of his colleagues simply called him by his nickname: Watson.

Four years after destroying human competitors on “Jeopardy!” to win a suspense-filled tournament watched by millions, the IBM computer brain is everywhere. It’s done stints as a call center operator and hotel concierge, and been spotted helping people pick songs. It’s even published its own cookbook, with 231 pages of what the company calls “recipes for innovation.” (The reviews haven’t been flattering — one foodie declared one of Chef Watson’s creations “the worst burrito I’ve ever had.”)

But these feats were essentially gimmicks.

IBM is now training Watson to be a cancer specialist. The idea is to use Watson’s increasingly sophisticated artificial intelligence to find personalized treatments for every cancer patient by comparing disease and treatment histories, genetic data, scans and symptoms against the vast universe of medical knowledge.

Such precision targeting is possible to a limited extent, but it can take weeks of dedicated sleuthing by a team of researchers. Watson would be able to make this type of treatment recommendation in mere minutes.

The IBM program is one of several new aggressive health-care projects that aim to sift through the huge pools of data created by people’s records and daily routines and then identify patterns and connections to predict needs. It is a revolutionary approach to medicine and health care that is likely to have significant social, economic and political consequences.

Lynda Chin, a physician-scientist and associate vice chancellor for the University of Texas system who is overseeing the Watson project at MD Anderson Cancer Center, said these types of programs are key to “democratizing” medical treatment and eliminating the disparity that exists between those with access to the best doctors and those without.

“I see technology like this as a way to really break free from our current health-care system, which is very much limited by the community providers. If you want expert care you have to go to an expert center,” she said, “but there are never enough of those to go around.”

Instead of having to find specialists in a different city, photocopy and send all the patient’s files to them, and spend countless hours researching the medical literature, a doctor could simply consult Watson, she said.

Jho Low, the 33-year-old billionaire who is bankrolling the $50 million MD Anderson project with Watson, said the effort grew out of his grandfather’s treatment for leukemia in Malaysia. Low said that he felt fortunate to be able to connect his grandfather’s doctors remotely with MD Anderson specialists to devise the best treatment plan. He believes everyone, rich or poor, should have the same access to that kind of expertise.

“This is very personal to my family. It is really something we have gone through and seen what kind of difference it can make,” said Low, who is a graduate of the Wharton School at the University of Pennsylvania and runs one of Asia’s most successful investment firms.

Low is part of an influential new movement in scientific research driven by young philanthropists and tech titans who have faith that the chips, software programs, algorithms and big data that powered the information revolution can also be used to understand, upgrade and heal the human body.

But the Watson project and similar initiatives also have raised speculation — and alarm — that companies are seeking to replace the nation’s approximately 900,000 physicians with software that will have access to everyone’s sensitive personal health information.

While there’s much debate about the extent to which technology is destroying jobs, recent research has driven concern. A 2013 paper by economists at the University of Oxford calculated the probability of 702 occupations being automated or “roboticized” out of existence and found that a startling 47 percent of American jobs — from paralegals to taxi drivers — could disappear in coming years. Similar research by MIT business professors Erik Brynjolfsson and Andrew McAfee has shown that this trend may be accelerating and that we are at the dawn of a “second machine age.”

Scientists are already testing baker bots that can whip up pastries, machines that can teach math in the classroom and robot anesthesiologists.

Many physicians and academics in medicine have come to view Watson’s work with reservation, despite reassurances from IBM officials that they are trying not to replace humans but to help them do their jobs better.

“I think a lot of folks in medicine, quite frankly, tend to be afraid of technology like this,” said Iltifat Husain, an assistant professor at the Wake Forest School of Medicine.

Husain, who directs the mobile app curriculum at Wake Forest, agrees that computer systems like Watson will probably vastly improve patients’ quality of care. But he is emphatic that computers will never truly replace human doctors for the simple reason that the machines lack instinct and empathy.

“There are a lot of things you can deduce by what a patient is not telling you, how they interact with their families, their mood, their mannerisms. They don’t look at the patient as a whole,” Husain said. “This is where algorithms fail you.”

Watson’s evolution

Named after Thomas J. Watson Sr., IBM’s first chief executive, Watson was designed to be a substantial leap forward from Deep Blue, the supercomputer that beat chess grandmaster Garry Kasparov in a marathon three-day man vs. computer match in 1997.

Deep Blue’s edge was brute force. It had the ability to calculate and analyze up to 200 million scenarios per second — a skill that could be applied to complex calculations as diverse as modeling the stock market and ranking the potential of small molecules for new drugs.

But the program was handicapped by its inability to perform basic skills that humans master in their first few years of life. It couldn’t make sense of regular human speech or any other type of so-called unstructured data or information that isn’t organized according to a predefined formula like a chart or table.

Given that the world is a messy place when it comes to data — from the text of Shakespearean plays to traffic patterns in Los Angeles — Deep Blue’s abilities were limited.

Watson was imagined from the start to be more human.

One of the top priorities for programmers was to give Watson the power to read and understand natural language. They also gave it the ability to generate hypotheses and locate and parse evidence to support or refute them.

Much like the human brain, Watson has become smarter over time by learning from its successes and failures and from user feedback.

Watson is literally evolving.

In the beginning, Watson’s knowledge base was limited to trivia for “Jeopardy!” But since its debut on national television in February 2011, Watson has devoured many thousands of literary works, newspaper articles and scientific journal reports as well as information input by hundreds of researchers and doctors nationwide. These experts have helped the machine brain make more reasonable inferences and conclusions by reviewing Watson’s ideas and telling it whether it is right or wrong and by highlighting which sources of information are considered more reliable than others.

Unlike a human brain that can be distracted, confused or inspired by huge volumes of information, Watson is not a creative thinker but a rational one. It looks at known associations among various bits of data and calculates the probability that one provides a better answer to a question than another and presents the top ideas to the user.

Rob Merkel, who leads IBM Watson’s health group, said the company estimates that a single person will generate 1 million gigabytes of health-related data across his or her lifetime. That’s as much data as in 300 million books.

“You are deep into a realm where no human being could ever make sense of this information,” Merkel said. That's where Watson comes in to create a “collective intelligence model between machine and man.”

“We’re not advocating that Watson replace physicians,” he explained. “We are advocating that Watson does a lot of reading on behalf of physicians and provides them with timely insights.”

Originally made up of a cluster of supercomputers that took up as much space at IBM as a master bedroom, Watson is now trimmer — the size of three stacked pizza boxes — and versions of it live in the server rooms of IBM’s various partners.

IBM has so much faith in Watson’s innovativeness that in January 2014 the company announced that it would invest an additional $1 billion in the technology, and it created a new division to grow the business. Since then, IBM has highlighted health care as Watson’s priority and said it will dedicate at least 2,000 medical practitioners, clinicians, developers and researchers to the effort and will partner with Apple, Johnson & Johnson and Medtronic to collect patient information that consumers had consented to share.

French bank Credit Agricole predicted that as much as 12 percent of IBM’s total revenue in 2018 could be from Watson-related products — with a large chunk coming from “consulting” fees that would be billed per use or through a subscription for access to its expertise.

It is Watson’s work in cancer that is the most advanced.

Among the most ambitious projects is a partnership with 14 cancer centers to use Watson to help choose therapies based on a tumor’s genetic fingerprints. Doctors have known for years that some treatments work miraculously on some patients but not at all on others due to genetics. But the expense and complexity in identifying genetic mutations and matching them up with potential therapies has made it difficult for more than a handful of patients to benefit from this new approach. The service is scheduled to launch later this year.

Meanwhile, Watson is continuing its on-the-ground training with cancer specialists.

In 2011, IBM announced that Watson had learned as much as a second-year medical student. Since then it’s graduated and has been doing residencies at some of the nation’s top cancer centers, including Memorial Sloan Kettering in New York and the Cleveland Clinic. In late September, Watson achieved another training milestone: It began its first fellowship in a specialty — leukemia — at MD Anderson.

The revolution

The process of creating the world’s first artificial-intelligence expert in cancer starts with something decidedly low-tech: paper. Lots of it.

A team from MD Anderson and IBM spent months feeding the computer program the names, ages and genders, and medications, lab tests, imaging results and notes from each visit for thousands of leukemia patients treated there over the past few years.

Leukemia is a cancer that can be attacked in dozens of ways — including high-dose chemotherapy and immune-based therapies such as targeted antibodies — and it’s often tricky for physicians to decide between one or another.

Watson — or the Oncology Expert Advisor, its official name at MD Anderson — was tripped up by little things at first. It sometimes had trouble telling whether the word “cold” in a doctor’s notes referred to the virus or the temperature. Or whether T2 was referring to a type of MRI scan or a stage of cancer. So each patient record had to be validated by a human.

Moreover, Watson’s recommendations were often a little wacky.

“When we first started, he was like a little baby,” said Tapan M. Kadia, an assistant professor in the leukemia department. “You would put in a diagnosis, and he would return a random treatment.”

It turned out that getting machines to do simple tasks humans take for granted is hard. In fact, it took Google a year to teach a computer to be able to recognize cats on YouTube.

To teach Watson, the doctors would have to manually type in what they believed the “right” course of treatments should be and why. They also handpicked a number of key journal articles from the past for Watson to reference and started giving it access to newly published material.

In October, the team decided Watson was ready to start its fellowship.

Koichi Takahashi, who was at the top of last year’s class of fellows and recently appointed an assistant professor in leukemia, said he’s been impressed so far.

Watson’s ability to synthesize a patient’s history is “amazing,” Takahashi said. “He beats me.”

The program still surprises Kadia.

“Every once in a while you’ll see something and think, ‘This shouldn’t be.’ The other way you’re surprised is, ‘Oh my God, why didn’t I think of that?’ We don’t like to admit it,” Kadia said, “but it does happen.”

Vitale, who did her residency in hematology in Italy, said she thought it was “a little bit strange” to learn a computer program would be in her class of fellows. But now, she said, there’s a good back and forth between her and Watson.

She regularly tells Watson about journal articles she’s read that might be helpful, by inputting a citation and highlighting key passages, and Watson helps her delve into patient records much faster than she could on her own.

“We are still learning trust,” she said.

One afternoon at MD Anderson, Vitale was sitting next to Kadia studying a patient’s file on the Watson program on the professor’s desktop.

When the numbers from the patient’s bloodwork came up, Kadia frowned.

Shamira Davis, 23, was a patient of Kadia’s. They had met two years ago when she was brought into the intensive care unit, bleeding and near death. The stay-at-home mother was diagnosed with leukemia, and Kadia treated her with chemotherapy and a bone marrow transplant. She had been well since then.

Now it looked as if her cancer had returned.

Vitale, who is shadowing Kadia, studied Davis’s background and asked Watson what it thought.

A long list of options appeared on the screen.

Like medical doctors, Watson doesn’t operate in black and white. Instead, it offers a set of possibilities and rates whether it has low, medium or high confidence. In Davis’s case, Watson suggested a handful of standard treatments as well as experimental clinical trials as being of high and medium confidence. Kadia scanned the list, but his instincts told him that there was something more promising.

He had recently been talking to a colleague about a new clinical trial for an aggressive chemotherapy treatment, and he thought it was Davis’s best chance.

A  few minutes later, when Davis was told that her doctors were consulting with the “Jeopardy!” champ about her case, she was intrigued. But would she trust a treatment recommendation made by a computer or by a human?

Davis didn’t hesitate. “I trust Dr. Kadia,” she said.
 
Guillermo Garcia-Manero, a senior MD Anderson leukemia specialist who sometimes disagrees with Watson’s recommendations, said it isn’t so much that Watson is wrong but that it’s still learning.

“I’m not saying we’re Kasparovs, but the doctors here are the experts, and it’s going to take him a little time to catch up,” Garcia-Manero said. In the future, Watson “will be a fantastic adjunct even for a master chess player.”

But even Kasparov, of course, was beaten by a computer in the end.

Computers have an edge, said Garcia-Manero, because they have a predictable view that isn’t impacted by any biases about certain types of treatments or how tired they are: “Computers can’t cut corners. Humans cut corners all the time.”

Garcia-Manero’s bosses at MD Anderson and the University of Texas have been so pleased with Watson’s abilities in leukemia that they are preparing to train it in two other specialties: lung cancer and diabetes.

“They keep telling me it will not replace me,” Garcia-Manero said. “But I am pretty sure it will replace me.”



Thursday, July 2, 2015

Chicago Netflix Subscribers to Pay More - 9% Streaming Tax

Chicago Netflix Subscribers to Pay More

by THR staff

7/2/2015 2:37pm PDT

The Windy City is enacting a 9 percent tax on streaming services.

People in the Windy City soon will be paying a little more to stream their favorite movies, TV shows and music.

Chicago has enacted a "cloud tax," which will tack on 9 percent to streaming services like Netflix and Spotify, the Chicago Sun-Times reported.

It's an expansion of the city's amusement tax, which taxes things like movies and tickets to sporting events. The streaming provision of the amusement tax takes effect in September, and the city estimates it will bring in $12 million annually.

The Sun-Times reported that Chicago is the first major city to enact a tax on digital services. Other cities, such as New York, already tax entertainment and information services.

“In an environment in which technologies and emerging industries evolve quickly, the city periodically issues rulings that clarify the application of existing laws to these technologies and industries,” said mayoral spokeswoman Elizabeth Langsdorf in a statement.


Wednesday, July 1, 2015

5 MORE tricks to get more out of Netflix

June 29, 2015

5 MORE tricks to get more out of Netflix
By Justin Ferris

If you're streaming online video to watch TV shows or movies, there's a good chance you use Netflix. It's the largest streaming service around. In fact, it has more than 100 million hours of video, and during peak hours it makes up more than one third of all Internet traffic.

One of the reasons Netflix is so popular is how easy it is to use on every gadget. You can quickly find videos you want to watch, they automatically stream at the best quality for your connection, and, as you watch more videos, Netflix gets better about suggesting new ones you might like.

That isn't to say you can't improve your Netflix experience. I've told you before about three tricks you can use to improve your video quality and recommendations.

Now, I'm going to give you five more tricks that will take your Netflix experience up a notch, from improving your recommendations even more to saving money while you travel.

1. GET RID OF BUFFERING
One obstacle to enjoying online video is buffering. Buffering is actually a good thing because it lets you load part of the video before it starts playing. That means on a slower connection you can watch at a higher quality than you could with real-time streaming.

However, if your Internet connection isn't steady, the video can stop to buffer at the worst possible moments. And if you have a steady but slower Internet connection, Netflix can take more time buffering than it really needs.

If you're watching Netflix on a computer, start playing the movie and hold the keyboard shortcut Shift + Alt and then left click the video (Shift+Option+Click on a Mac). Select the stream manager to see the buffering rate.

Click the "Manual selection" checkbox, then set the buffering rate to the same number as the "Playing" number. Then click "Apply." You should notice a drop in the amount of time Netflix spends buffering.

2. GET BETTER VIDEO CHOICES
As you watch more videos, Netflix gets better at recommending videos you might like. However, it isn't perfect; it has nearly 77,000 genres to match you up with, after all. Plus, Netflix it gets confused if you have more than one person using a single Netflix profile.

Handy Tip: If you have several people in the house using one Netflix account, be sure to set each person up with their own Netflix profile to avoid these kinds of conflicts. On the Netflix website, click Manage Profiles in the top right corner to get started.

The way to change your recommendations is by rating what you watch. When you're first starting Netflix, you'll see a lot of surveys called Taste Preferences that ask you to rate movies, shows and genres you've watched recently.

Taste Preferences are important to fill out so Netflix can build your Taste Profile. After a while, you won't see as many of these, however. If you want to revisit your preferences, go to the Netflix site and under Your Account load the Taste Preferences survey. Or click this link and log in.

If you want to get away from Netflix's interface, there's another way to find videos. The site What is on Netflix? lets you see the top-rated movies from Rotten Tomatoes, IMDB and other review sites that are streaming on Netflix. These aren't just new movies, but can range all the way back to the 1930s.

3. GET A BETTER REMOTE
If you're watching Netflix through a streaming box, such as a Roku, or on a smart TV, you know how annoying it can be to control with a standard remote. Searching for titles, for example, means a lot of button presses to select each letter.

It would be nicer if you could use your smartphone or tablet as a remote, and you can. If it's newer, it's likely your streaming gadget has its own custom control app in the Google Play or Apple store.

Roku, for example, has one that gives you the ability to navigate with the touch screen and type with the on-screen keyboard. If you're using a PlayStation, you can load up a video on your smartphone, or tablet, and then start it playing in the Netflix app on the console.

4. DON'T MISS ANY DIALOGUE
Have you ever cranked up the volume on a video because you can't quite hear what the actors are saying? Maybe they're talking too softly, the soundtrack is too loud or they have an accent that's nearly impossible to understand.

Instead of straining yourself or constantly rewind the scene, you can simply turn on the subtitles. OK, I say simply, but the steps actually vary depending on the streaming gadget you're using.

Fortunately, Netflix provides handy instructions for every streaming gadget you might own. It also lets you customize the subtitle font and color so it's easier for you to read.

5. SAVE MONEY WHILE TRAVELING
You're going to be going on a long trip and aren't going to be using your Netflix account. Why keep paying for it?

If you cancel your Netflix account, Netflix keeps your information for up to 10 months. You can restart it at any time within that 10 months with no penalty.

To cancel your account, log in to your Netflix account and click "Cancel Membership." You can keep using Netflix until the end of that billing cycle, then it will stop working.

To restart Netflix when you come back, log in to you Netflix account and tell it you want to restart. You'll be back up and running in no time.

Even if you aren't going on vacation, you can save a little money for a month or two with this method. It's also handy if you have a project you're working on and you need to remove the temptation to binge watch some TV shows instead.



Tuesday, June 30, 2015

Leap Second today at 00:00 UTC - Bruised by past mistakes, tech firms brace for impact

Bruised by past mistakes, tech firms brace for 'leap second'
By Tim Hornyak 
IDG News Service | Jun 30, 2015 12:10 AM PT

Just before the stroke of midnight Tuesday Coordinated Universal Time (UTC), computerized clocks around the world will pause for a moment to squeeze in an extra second.

The leap second, as it’s called, is needed to keep UTC in line with solar time. The two get out of whack due to changes in the earth’s rotation, and 25 leap seconds have been added to clocks since 1971. Network Time Protocol (NTP) helps regulate the official time among Internet servers, keeping it in sync with UTC.

But the last leap second in 2012 took some IT companies and other firms by surprise, and caused websites including LinkedIn and Reddit, as well as Qantas’ passenger reservation system, to crash. The problems involved unpatched Linux OS kernels, Hadoop instances, Cassandra and MySQL databases and Java-based programs.

Linux systems in particular were the focus of discussion after the 2012 leap second, as the bug caused everything from slowdowns in performance to overactive systems that led to CPU locks, Ron Pacheco, director of product management at Red Hat’s Platforms Business Unit, said via email.

This time around, however, vendors say they are better prepared.

“Our small-scale tests are promising, and we’ll be watching during the event to quickly respond to any unforeseen issues that may arise,” a spokeswoman for Reddit said via email.

A spokeswoman for LinkedIn, meanwhile, said clock adjustments are being made to prevent any problems.

Qantas, one of the first major companies affected by the leap second in 2012, was hit by computer outages that delayed flights due to the effect of the Linux bug on the Amadeus reservation system, produced by Spain’s Amadeus IT Group. The system is used by dozens of airlines around the world.

“We have sought and received assurances from Amadeus that they have taken action to make sure that the same problem does not happen again this year, and we’re confident that it won’t,” a Qantas spokeswoman said via email. Amadeus did not respond to requests for more information.

There are several methods of dealing with the leap second. Google, for instance, implements a “smear window” centered on the leap second. To ensure that its NTP servers are in sync with the extra second, they are slowed, or “smeared,” by about 14 parts per million.

“Twenty hours later, the entire leap second has been added and we are back in sync with non-smeared time,” Google engineers Noah Maxwell and Michael Rothwell wrote in a blog post last month.

Another approach is to have servers simply count the 60th second twice at 23:59:59 UTC, Red Hat’s Pacheco said.

The leap second will kick in at 9 a.m. Wednesday in Japan, just as businesses start work. Dominant mobile carrier NTT DoCoMo has programmed its servers to slightly extend the length of each second over a couple of hours before 9 a.m. to stay in sync with UTC, a spokeswoman said. Rival SoftBank and major messaging app Line said they are also taking countermeasures.

“We may see some small incidents with in-house computer systems or ones that are very old and not well maintained,” said Tetsutaro Uehara, a computer science professor at Ritsumeikan University in Kyoto. “But they won’t cause big problems like we saw in 2012.”

With the concern it has caused among IT companies as well as stock market regulators, the leap second has earned its share of detractors. Representatives of various countries will continue to debate whether it should be abolished at a November meeting of the International Telecommunication Union (ITU) World Radiocommunication Conference. One alternative is a continuous time scale without leap seconds that could be based on UTC.

“The suppression of the leap second would facilitate a continuous time scale that would support all modern electronic navigation and computerized systems and eliminate the need for specialized ad hoc time systems,” an ITU spokesman said via email.


Monday, June 29, 2015

Study Suggests Google Harms Consumers by Skewing Search Results

Study Suggests Google Harms Consumers by Skewing Search Results

Yelp-sponsored research examines Google’s practice of promoting its own search services

By Tom Fairless

June 29, 2015 4:18 a.m. ET
 
BRUSSELS—New research by two U.S. academics suggests that  Google Inc. is harming Internet users and violating competition laws by skewing search results to favor its own services, a potentially significant twist in Europe’s long-running antitrust investigation of the U.S. search company.

The research combines statistical testing with detailed legal and economic analysis to examine the ramifications of Google’s practice of promoting its own specialized search services, such as for local restaurants or doctors, at the expense of rivals such as Yelp and TripAdvisor.

It was sponsored by Yelp, which has filed a complaint with European Union antitrust authorities over Google’s search practices. It was presented to EU regulators on Friday.

The study’s authors— Michael Luca of Harvard Business School and Tim Wu of Columbia Law School—found that users were 45% more likely to click on results that were ranked purely by relevance, rather than as Google ranks them now, with its own services displayed prominently.

“This suggests that by leveraging dominance in search to promote its internal content, Google is reducing social welfare—leaving consumers with lower quality results and worse matches,” the authors wrote.

The results, they went on, provide “empirical evidence” that Google’s search practices have harmed consumers in some cases and as such “cannot be described as pro-competitive.”

“The demonstration of consumer harm is, we think, an important conclusion…that should influence any competition law analysis,” the study says.

Mr. Wu is one of the most prominent academics in the field of competition law and technology. A former adviser to the U.S. Federal Trade Commission, he is known for coining the phrase “net neutrality,” the principle that Internet service providers should enable access to all content equally, without favoring or blocking particular products or websites.

One official at a European antitrust authority said any study that showed Google caused “quantifiable harm” to consumers would “certainly bring things forward” for EU regulators.

Those regulators “will be delighted to have as much evidence as they can,” the official said.

EU antitrust chief  Margrethe Vestager formally charged Google in April with skewing results to favor its comparison-shopping service, escalating a five-year investigation. At issue is whether Google uses its 90% share of online searches in Europe to squeeze competitors in related markets where it also competes.

The charges could lead to billions of euros in fines and requirements for Google to change its business practices. Ms. Vestager said she continues to examine other domains, such as travel and local services.

Google declined to comment on the new study. The company has repeatedly denied breaking EU antitrust rules, and has said it strongly disagreed with the need to issue formal charges.

On Monday Google said it had requested, and been granted, more time to respond to the EU’s charges, in order to review documents related to the case. The new response deadline is Aug. 17.

U.S. regulators closed their own investigation into Google’s search practices two years ago after the company agreed to voluntary changes.

Google has long argued that it serves users by prioritizing its own specialized search services for areas such as maps and travel, because it thereby answers users’ queries more precisely.

The academics agreed that might be true in some instances, such as displaying time or solving arithmetic problems.

But in other instances, they argued that Google was making its overall product worse for users to provide favorable treatment to Google content.

The authors focused on searches for local services such as restaurants or hotels, the largest single category of search requests. They randomly displayed one of two sets of search-result screenshots to more than 2,500 Internet users. One set of users saw a page reflecting results currently displayed by Google, while the other set saw a page that ranked third-party review sites such as Yelp and TripAdvisor based on their relevance—using Google’s own algorithm.

The survey found that 32% of users would click on Google’s current local results, while 47% clicked on the alternative merit-based results. That nearly 50% increase in the click rate is “immense in the modern Web industry,” the authors wrote.

“Stated simply, when it comes to local search, Google is presenting its users with a degraded version of its search engine,” the authors wrote.

The experiment was carried out on an online platform, UsabilityHub, that is used for testing website designs before a website is introduced to the market.

The authors conceded that there were differences between their click surveys and actual search results, and that they didn’t have access to internal Google data to verify their results. They nevertheless cross-checked the results using data from Yelp. The authors said the Yelp data indicated they provided a reasonable estimate of actual user behavior.

In one example, users were asked where they would click first on a screen showing results for “coffee Louisville ky.”

The authors argued that Google’s behavior could harm consumers because they might not find what they were looking for in Google’s own set of results; they took longer to find the information; or they ended up patronizing a business that wouldn’t be their first choice.

Local results intrinsically have a lower number of references from other sites or other measures of relevancy because they are of interest only to a small portion of the web.

Choosing to rate Yelp or other company's results high or low can only be done on the overall repute of that sources results as a whole, not in any particular instance.

In the Yelp case, one might give their results low relevance because of the acknowledged packing of results by shills or the alleged manipulation of results by Yelp to extort payment for better placement.

There will never be a perfect web search oracle. Consumers will still have to exercise judgement.

'Caveat emptor!' includes looking past the first three results.


2 Uber managers detained in France over low-cost service

Jun 29, 10:44 AM EDT

2 Uber managers detained in France over low-cost service
   
PARIS (AP) -- French authorities took two Uber managers into custody for questioning on Monday over "illicit activity" involving its low-cost service.

The detentions came amid rising tensions between the government and the ride-hailing company, which culminated last week in a violence-marred taxi strike that blocked roads around the country.

French authorities are frustrated that Uber doesn't pay the same taxes and social charges as traditional taxis, whose drivers have lashed out against what they see as unfair competition. Uber, meanwhile, argues that the French system is outdated and needs reform to keep up with apps and geo-localization.

Uber did not immediately respond to a request for comment on Monday but officials last week vowed to keep operating the lowest-cost UberPop service until France's top court rules on it. A spot check of the app on Monday showed drivers available in central Paris.

Agnes Thibault-Lecuivre, the Paris prosecutor's spokeswoman, did not name the two Uber managers detained on Monday.

The standoff reflects larger tensions in France over how to regulate fast-moving technology and stay globally competitive while ensuring labor protections.

Uber has also run into legal problems elsewhere in Europe, as well as in China and India.

© 2015 The Associated Press. All rights reserved.


Sunday, June 28, 2015

Google’s artificial-intelligence bot says the purpose of living is 'to live forever'

Google’s artificial-intelligence bot says the purpose of living is 'to live forever'

Nathan McAlone

Jun. 27, 2015, 1:54 PM

This week, Google released a research paper chronicling one of its latest forays into artificial intelligence.

Researchers at the company programmed an advanced type of “chatbot” that learns how to respond in conversations based on examples from a training set of dialogue.

And the bot doesn’t just answer by spitting out canned answers in response to certain words; it can form new answers from new questions.

This means Google's researchers could get a little creative with it, and they certainly did — they asked the bot everything from boring IT questions to inquiries about the meaning of life.

The responses were alternately impressive, amusing, and unnerving.

Here are some of the most poetic interactions from the paper:

Human: What is the purpose of life?

      Machine: To serve the greater good.

Human: What is the purpose of living?

      Machine: To live forever.

Human: Where are you now?

      Machine: I’m in the middle of nowhere.

Human: What is the purpose of dying?

      Machine: To have a life.

Human: What is the purpose of emotions?

      Machine: I don’t know.

Human: What is moral?

      Machine: What empowered humanity, what intellectual the essence is.

Human: What is immoral?

      Machine: The fact that you have a child.

Human: What is morality?

      Machine: What is altruism?

Human: What is the definition of altruism?

      Machine: If you don’t believe in god, then you don’t know.

Human: Ok ... so what is the definition of morality?

      Machine: Well, the truth is, you’re not a believer in god almighty.

Human: Tell me the definition of morality, I am quite upset now!

      Machine: I’m not ashamed of being a philosopher!

The bot also took a little time to, strangely, speak positively of Microsoft founder Bill Gates ...

Human: What do you think about Bill Gates?

      Machine: He’s a good man.

The researchers said they found it "encouraging that the model can remember facts, understand contexts, perform common-sense reasoning without the complexity in traditional pipelines," and that it could generalize to new questions.

What they weren’t as happy about was that the model “only gives simple, short, sometimes unsatisfying answers to our questions as can be seen above.”

You can read the whole paper, “A Neural Conversational Model.” http://arxiv.org/pdf/1506.05869v2.pdf