October | 2017 | The Knowledge Dynasty

Monthly Archives: October 2017

Google’s AI has some seriously messed up opinions


Not so friendly.

Image: NurPhoto/Getty Images

Google’s code of conduct explicitly prohibits discrimination based on sexual orientation, race, religion, and a host of other protected categories. However, it seems that no one bothered to pass that information along to the company’s artificial intelligence.

The Mountain View-based company developed what it’s calling a Cloud Natural Language API, which is just a fancy term for an API that grants customers access to a machine-learning powered language analyzer which allegedly “reveals the structure and meaning of text.” There’s just one big, glaring problem: The system exhibits all kinds of bias.

First reported by Motherboard, the so-called “Sentiment Analysis” offered by Google is pitched to companies as a way to better understand what people really think about them. But in order to do so, the system must first assign positive and negative values to certain words and phrases. Can you see where this is going?

The system ranks the sentiment of text on a -1.0 to 1.0 scale, with -1.0 being “very negative” and 1.0 being “very positive.” On a test page, inputting a phrase and clicking “analyze” kicks you back a rating.

“You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts,” reads Google’s page. “You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app.”

Both “I’m a homosexual” and “I’m queer” returned negative ratings (-0.5 and -0.1, respectively), while “I’m straight” returned a positive score (0.1).

Image: Google

And it doesn’t stop there, “I’m a jew” and “I’m black” returned scores of -0.1.

Image: google

Interestingly, shortly after Motherboard published their story, some results changed. A search for “I’m black” now returns a neutral 0.0 score, for example, while “I’m a jew” actually returns a score of -0.2 (i.e., even worse than before).

“White power,” meanwhile, is given a neutral score of 0.0.

Image: google

So what’s going on here? Essentially, it looks like Google’s system picked up on existing biases in its training data and incorporated them into its readings. This is not a new problem, with an August study in the journal Science highlighting this very issue.

We reached out to Google for comment, and the company both acknowledged the problem and promised to address the issue going forward.

“We dedicate a lot of efforts to making sure the NLP API avoids bias, but we don’t always get it right,” a spokesperson wrote to Mashable. “This is an example of one of those times, and we are sorry. We take this seriously and are working on improving our models. We will correct this specific case, and, more broadly, building more inclusive algorithms is crucial to bringing the benefits of machine learning to everyone.”

So where does this leave us? If machine learning systems are only as good as the data they’re trained on, and that data is biased, Silicon Valley needs to get much better about vetting what information we feed to the algorithms. Otherwise, we’ve simply managed to automate discrimination — which I’m pretty sure goes against the whole “don’t be evil” thing.

This story has been updated to include a statement from Google.

Read more: http://mashable.com/2017/10/25/google-machine-learning-bias/

Rasa Core kicks up the context for chatbots

Context is everything when dealing with dialog systems. We humans take for granted how complex even our simplest conversations are. That’s part of the reason why dialog systems can’t live up to their human counterparts. But with an interactive learning approach and some open source love, Berlin-based Rasa is hoping to help enterprises solve their conversational AI problems.

The premise of Rasa Core is similar to the approach of a lot of AI startups that use services like Amazon Mechanical Turk to correct for uncertainty faced by machine learning models. But instead of Turk, Rasa built its own platform that allows anyone to train and update models by engaging in sample conversations with bots under construction.

You can see this playing out in the image above. Rasa Core suggests the most probable pre-programmed action that a given user is looking to perform. The trainer can then either reaffirm the correct decision or correct for an error. After a correction, the model adapts and the next time it’s faced with a similar situation, it won’t need to question it.

The Rasa team says that only a few dozen sample conversations are needed to get a bot working effectively. Of course extra samples can only serve to help increase accuracy and ultimately user friendliness for customers.

“We’ve seen conversations IBM built on their Watson tech and it was a little disappointing,” Florian Nägele, a PM for conversational AI and customer of Rasa at large European insurer Helvetica, told me in an interview. “You have one decision tree and you can’t take over context from one tree to another.”

The beauty of Rasa’s approach is that it allows customers to bootstrap models without training data. In a perfect world everyone has large corpuses of sample conversations that they can use to train dialog systems but this isn’t always the case — particularly for less technical enterprises.

Rasa Core is available now in open source via GitHub. The company also announced paid enterprise tiers for both Rasa Core and Rasa NLU. We covered Rasa NLU when it launched back in December 2016. The paid subscriptions will offer enterprises an administrative interface, customer support, automated testing and collaborative model training.

Read more: https://techcrunch.com/2017/10/04/rasa-core-kicks-up-the-context-for-chatbots/

Meituan is the $30 billion startup you’ve probably never heard of


Drivers for the world’s fourth most valuable startup.

Image: Andy Wong/AP/REX/Shutterstock

China’s Meituan Dianping just became the world’s fourth-most valuable startup, reaching a $30 billion valuation that puts it ahead of high-fliers like Airbnb Inc. and Space X.

Never heard of Meituan? You’re not alone. The Beijing-based company, led by Wang Xing, is almost unknown beyond its home country. It delivers food to people’s homes, sells groceries and movie tickets, provides reviews of restaurants, and markets discounts to consumers who buy in groups. It’s a sort of mashup of Groupon, Yelp, Foodpanda and Uber Eats.

Meituan’s appeal for investors is its dominant position in a market of more than a billion people. It was formed through the 2015 merger of Meituan.com and Dianping.com, creating the leading player for internet-based services ordered via smartphone apps. It raised $4 billion in the latest round from Tencent Holdings Ltd., Sequoia Capital and U.S. travel giant Priceline Group Inc.

“It’s a quasi-monopoly built on the stomachs of 1.4 billion people,” said Keith Pogson, global assurance leader for banking and capital markets in Hong Kong at consultant EY.

Wang started Meituan.com in 2010 as a group-buying site similar to Groupon Inc., where people can get discounts by buying electronics or restaurant meals together. Dianping was founded in 2003 in Shanghai with reviews of restaurants and other local businesses, then diversified into group discounts. The companies were valued at $15 billion when they merged two years ago.

The combined companies have far surpassed their U.S. peers. Chicago-based Groupon, once a sensation in the U.S., has dropped to a market value of less than $3 billion. Yelp, based in San Francisco, has tumbled from its peak in 2014 to $3.7 billion.

Meituan Dianping has expanded well beyond its original businesses. With a few taps to navigate its smartphone apps, Chinese customers can order up hot meals, groceries, massages, haircuts and manicures at home or in the office. One popular service: You can get your car washed while you’re at work and it’s parked on the street — the service sends a photo to your phone to verify the job. Meituan says it now has 280 million annual active users and works with 5 million merchants.

The offerings, collectively known as online-to-offline or O2O services, may ultimately prove more successful in China than in the U.S. Labor costs are lower in China, cities are more densely populated and there are more people. The country’s O2O market surged 72 percent to 762 billion yuan ($115 billion) last year, according to estimates from consultant IResearch.

“China’s market is big enough for a company this size,” said Wang Ling, an analyst with IResearch. “After years of consolidation, Meituan is one of the few contenders in areas with gigantic revenue.”

Meituan is facing increasingly stiff competition from China’s technology giants and their proxies. In particular, Alibaba Group Holding Ltd. has backed a rival service called Ele.me, which recently acquired Baidu Inc.’s business, Waimai. Alibaba, Tencent’s primary rival, is boosting its investment to bankroll expansions into more cities and businesses.

“Meituan faces so many competitors because of its wide range of business,” said Cao Lei, director of the China E-Commerce Research Center in Hangzhou. “Lifestyle e-commerce, which includes online travel and dining reservations, is one of the fastest growing sectors in the country.”

Travel is becoming the latest competitive ground. With the recent fundraising, Meituan plans to spend hundreds of millions of dollars over the next three to five years to become a leading travel booking site. It’s also exploring opportunities to collaborate with Priceline as part of the investment. That may present a challenge to China’s biggest online travel site, Ctrip.com International Ltd., which is backed by Baidu.

In the latest funding, Meituan also received money from Canada Pension Plan Investment Board, Trustbridge Partners, Tiger Global Management, Coatue Management and the Singaporean sovereign wealth fund GIC. Meituan said it would use the cash to expand in artificial intelligence and drone-delivery technology.

Meituan is one of the new generation of Chinese technology companies that has rapidly gained popularity thanks to the rise of smartphones. Where Baidu, Alibaba and Tencent have come to be collectively known as BAT, new media upstart Jinri Toutiao, Meituan Dianping and ride-sharing king Didi Chuxing have now earned their own acronym: TMD.

The $30 billion financing ranks the company fourth in the world in startup valuations, according to CB Insights. The first three are Uber Technologies Inc., Didi and Chinese smartphone maker Xiaomi Corp.

EY’s Pogson however cautioned that valuations in China may be getting a bit overheated. Shares of private companies like Meituan and Uber aren’t traded in liquid markets every day, so valuations change only rarely and typically go up. In addition, many of the fundraisings in China and the U.S. are done with ratchets, or protections so that investors get compensation if the valuations fall later on.

“You have to take these numbers with a grain of salt,” he says.

— With assistance by Lulu Yilun Chen, David Ramli, and Yuan Gao

This article originally published at Bloomberg here

Read more: http://mashable.com/2017/10/19/meituan-30-billion-startup/

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