Big Data, Analytics, AI, etc.

As All Analytics was removed from the internet, UBM moved most of the articles I had written for it in the past few years to IW at hhttps://www.informationweek.com/author-bio.asp?author_id=4902 But I also have PDFs of it posted on my Contently portfolio at https://ariellabrown.contently.com/

Facial Recognition Features on Facebook Find Your Face

Facebook doesn't need tags to know that the person in the picture is you. Is that a good thing? It depends on your point of view.

Given the proliferation of faces that gets uploaded in the form of photos and videos on Facebook, a person may not even know that his/her face appears in a particular context.

photo from https://upload.wikimedia.org/wikipedia/commons/e/ef/Face_detection.jpg

To address that problem and to increase access for recognition among the visually impaired, at the end of 2017 Facebook rolled out three new facial recognition features:

• For the visually impaired, the feature provides a verbal description of people the AI recognizes in the photos.
• You can be notified whenever the AI recognizes your face in an uploaded image, even when it is not tagged with your name.
• Working off this link of your name and face, the system can alert you if others put your face in for their profile.

As the company explained in its announcement about it, Facebook's new features are based on the same technology the social media platform uses to bring people's attention to faces in images before they are tagged with a name.

While no one would likely object to applying AI to assisting the visually impaired, there are some questions about the effect of the alert feature, which delivers a "Photo Review" message to the user whose face it identifies. The company puts a very positive spin on it, saying, "You're in control of your image on Facebook and can make choices such as whether to tag yourself, leave yourself untagged, or reach out to the person who posted the photo if you have concerns about it."

Read more in 

Facebook AI Finds Your Face, Enabling New Features

AI and shopping: the perfect match

As visual AI advances, it's becoming a useful tool for marketing fashion both online and on premises. Alibaba recently demonstrated the difference it could make with record sales for this year's Single's Day in China. This marriage of fashion and AI signals possibilities for shoppers.

The volume of sales for this year's Single's Day through Alibaba's sites amounted to $9.3 billion this year, compared to $5.9 billion last year. Technology use played a major role in that surge of sales, as nearly half the orders this year came through smartphones; over double the 2016 number.

However, another form of technology was also involved: AI. Using deep learning, Alibaba researchers developed FashionAI to offer in-store shoppers a familiar kind of screen interface that can make recommendations to customers based on its huge volumes of data.

Read more in AI and Fashion Marketing: A Perfect Match 

Related post: http://writewaypro.blogspot.com/2017/12/visual-ai-what-you-see-is-what-you-shop.html

Can Facebook Prevent Suicide? Ethical Questions Arising from AI

In today’s hyperconnected world, we are generating and collecting so much data that it is beyond human capability to sift through it all. Indeed, one application of artificial intelligence is identifying patterns and deviations that signal intent on posts. Facebook is using AI in this way to extract value from its own Big Data trove. While that may be applied to a good purpose, it also raises ethical concerns.

Where might one get insight into this issue? In my own search, I found an organization called PERVADE (Pervasive Data Ethics for Computational Research). With the cooperation of six universities and the funding it received this September, it is working to frame the questions and move toward the answers.

I reached out to the organization for some expert views on the ethical questions related to Facebook’s announcement that it was incorporating AI in its expanded suicide-signal detection effort. That led to a call with one of the group’s members, Matthew Bietz.

Bietz told me the people involved in PERVADE are researching the ramifications of pervasive data, which encompasses continuous data collection — not just from what we post to social media, but also from the “digital traces that we leave behind anytime we’re online,” such as when we Google or email. New connections from the Internet of Things (IoT) and wearables further contribute to the growing body of “data about spaces we’re in,” he said. As this phenomenon is “relatively new,” it opens up new questions to explore with respect to “data ethics.”

Read more in 

The Ethics of AI for Suicide Prevention

AI Raises Awareness of Fake News

The proliferation of fake news couldn't happen without technology. The internet allows anyone, anywhere to spread information -- whether or not it is true. But technology could also help serve as a tool that makes people more aware of which stories are not trustworthy.

(Image: Mega Pixel/Shutterstock)

True story: one of my social media connections asked for recommendations for reliable new sources and got a few outlets named, though some of us -- myself included -- said that you simply cannot rely wholly on any single source and have to check through multiple sources to be sure you get the full picture of the facts in context to find where the truth lies.

But not everyone is sophisticated enough to be aware that reports they see -- even from outlets with solid reputations -- need to be taken with a grain of salt. That's why Valentinos Tzekas founded FightHoax. Its AI-powered algorithm that empowers anyone to ascertain if an article is fake or not in just seconds without Googling the story.

Read more in 

How AI Can Help You Decide What to Trust in Online News

Beyond Games: The Future of AI and AlphGo Zero

Intel's Bob Rogers explains the possibilities that emerge as AI progresses beyond standard machine learning. DeepMind's self-taught Go champion is just the beginning.

The next iteration of AI is the step of generating its own examples with which it builds the models to extract rules. That’s what AlphaGo Zero did in generating a million examples of different rounds of Go to improve its own play. That was achieved through reinforcement learning, which relies on “feedback — positive reinforcement for what’s right and penalties for what’s gone wrong,” Rogers said.
While that ability opens up great possibilities for systems to learn to answer the questions we want answered, the thing to remember is that the systems “are very much unitaskers,” Rogers said. AlphaGo Zero may be an unparalleled Go player, but playing Go is the only thing “the program is designed to do.” Through transfer learning, AI systems can shift to apply the same kind of deep learning to another domain. Still, they would not do so on their own; someone would have to set them up for that.

Read more in

What AlphaGo Zero Means for the Future of AI

Got sarcasm?

I'm being sarcastic." We've all had at least one exchange in which we either had to explain or had someone else explain that what was said was not intended to be taken straight. Generally, you need to know something about both the context and the speaker to grasp when to take a statement at face value or interpret it as sarcastic.

That's why it's particularly challenging to get handle on intent when attempting sentiment analytics on social media. For artificial intelligence to truly understand what humans mean, it needs emotional intelligence, as well. Iyad Rahwan, an associate professor the MIT Media lab and one of his students, who developed the algorithm with one of, Bjarke Felbo worked on just that.

The results are what they call Deep Moji. Described as "artificial emotional intelligence," Deep Moji was trained on millions of emojis "to understand emotions and sarcasm." Rahwan explained to MIT's Technology Review that in the context of online communication emojis take on the function of body language or tone in offering nonverbal cues for meaning.

Read more in Emojis Train AI to Recognize Sarcasm

AI for Customized Consumer Communication

As AI advances, it's reshaping marketing with new ways of pulling in data and insights to reshape and customize consumer communication. Its impact on marketing was explored in a recent NYC Media Lab white paper, "How AI Is Changing Media Economics."

 Now it's possible to apply real time analytics to a campaign that is tested, tweaked, and tailored to an audience. 
Read more in 

AI: The Ultimate Game-Changer for Media

AI: a fundamental risk to the existence of human civilization according to Elon Musk

photohttps://upload.wikimedia.org/wikipedia/commons/d/d1/
Comic-Con_2004_-_Terminator_statue.jpg

No longer a sci-fi novelty, artificial intelligence is a reality with great potential. While most of the news has

focused on AI’s potential for good, some pundits are now pointing out its potential for harm. They include none other than Elon Musk.

As the founder, CEO, and CTO of SpaceX and the CEO of Tesla, a key player in the emergence of the self-driving car, Musk is certainly no Luddite. When he talks about AI, he is talking about technology that is absolutely integral to his business model. Nonetheless, Musk believes it is imperative that society regulate the advance of AI, as he noted in an interview before an audience at the National Governors Association Summer Meeting this month.

In the course of the interview, Musk referred to his “exposure to the most cutting-edge AI” and warned, “I think people should be really concerned.” The point is not to live in dread of the potential repercussions of AI and respond reactively to them, he said, but to plan proactively for them.

Read more in 

Elon Musk Sounds Alarm on AI

When you're happy, you do show it

 Targeted marketing works when it is done in the right place and at the right time. Getting the place right is increasingly feasible with today's location-aware technology. But getting the time right depends on knowing when a person is in the mood to receive the message.

That's the goal behind the Centiment, a machine learning startup that applies AI to analyzing the expression of emotion “to make advertising more ethical and efficient for everyone.” It does that by creating what it calls “thought-driven AI and Google for feelings.”

In a phone interview, Micah Brown, the company's CEO and founder, explained how this approach is a game-changer that makes ads more effective by taking cues from “moods rather than price and brand.” The ads are designed to fit with how the individuals in the target market “are feeling at the current time rather than what you think is best from them.” 

The mood assessment offers two strategic advantages for marketers. One is that it allows them to deliver ads to people while they are in that receptive mood. The other is that they can see the feelings particular ads generate in their target market.

 Here's a video of Centiment in Action:

Read more in In the Mood for Marketing

It's not just about self-driving cars

Robert Bosch GmbH made headlines this past month as various newsoutlets echoed Bloomberg’s headline: “Bosch to Build $1.1 Billion Chip Plant for Self-Driving Cars.” While it may not quite stray into alternative fact territory, the headlines is somewhat misleading because the chips are not reserved for self-driving cars. Buried within the article is the acknowledgement that Bosch says they will also be used for “smart homes and Internet-linked city infrastructure.” By all measures, it's clear the company intends to be a major player in the global supply chain for connected electronics. 

It’s true that the company is invested in the development of autonomous cars. In March of this year, it GmbH  announced a partnership with the American company NVIDA, which produces artificial intelligence (AI) systems for self-driving vehicles” in which it would  build  an “artificial intelligence self-driving systems for mass market cars” based on NVIDA Drive PX line with Xavier architecture.

So it does make sense that it would be considering the automotive customer, especially if it is true that, as the Bloomberg article states, each car purchased last year “contained an average of nine chips made by Bosch.”

However, the story that Bosch itself  tells about its plans for the factory it is building in Dresden, Germany are not that narrowly focused, as revealed by its press release. 

Read more in 

Beyond the Headlines: Bosch’s $1.1 Billion Investment

Expanding comments with AI

The problem with human moderators is that they have human limitations that cannot keep up with a

pic from https://d1avok0lzls2w.cloudfront.net/img_uploads/Blog-Comments.jpg

huge influx of comments. That was the problem the New York Times faced in balancing reader comments demand with an editorial standard of civility for all published comments. Its solution was a partnership with , an incubator owned by Google's parent company, Alphabet.

Back in September, the Times announced the partnership in an article that set out the challenge faced by its 14 moderators tasked with reviewing the comments on the 10% of articles that do allow them. That alone amounted to 11,000 comments a day. As the article invited readers to try their hand at moderating, it was entitled Approve or Reject: Can You Moderate Five New York Times Comments?

I took the test. The official summary of my results were: "You moderated 4 out of 5 comments as the Community desk would have, and it took you 81 seconds. Moderating the 11,000 comments posted to nytimes.com each day would take you 49.5 hours."

That summation was followed by this: "Don't feel too bad; reviewing all of these comments takes us a long time, too." According to my calculations, however, the Times actually allows more time for their moderators. Given 14 people working 8 hours a day, the number of working hours each day would be 112, or more than twice the number of hours they said would be required for my rate of moderation.

That investment of so many hours is not something they regret, as they regard it as a requisite part of building up "one of the best communities on the web." However, they recognize that needs must dictate a new approach. That's where the machine assistance enters into the picture, enabling the same number of humans to effectively moderate a much larger number of comments and reduce the delay for reviewing time.

Flash forward to June 13, 2017, and the Gray Lady herself announces: The Times Sharply Increases Articles Open for Comments, Using Google's Technolog

Read more in How AI Can Moderate Comments, Eliminate Trolls

AI gets a boost from curiosity

As data analytics become increasingly driven by artificial intelligence (AI),

photo credit: https://c1.staticflickr.com/3/2332/2083892100_3e015d810a_b.jpg

researchers search for a way to drive machine learning. The key ingredient its future development may be a dash of curiosity.

There are all kinds of AI systems currently used by various businesses with different names like Alexa and Albert to personalize then. Perhaps it's time for an AI system named George after the monkey whose curiosity propels him into various adventures.

That would be an apt choice for the Intrinsic Curiosity Module (ICM) developed by a group of four researchers at University of California, Berkeley. The attempt to inject curiosity to achieve self-motivated advances in machine learning was the subject of their paper, Curiosity-driven Exploration by Self-supervised Prediction, that was just submitted to the 34th International Conference on Machine Learning (ICML 2017).

Their premise is that external rewards for learning are of necessity limited and actually rather rare in real life. That doesn't mean that people stop exploring or seeking out answers even when there are no prizes for doing. They are motivated by their own human curiosity. Infusing that kind of motivation in a virtual agent gets it to test things out for itself even when not directed to do so. The test of the effect was done in monitoring how far it would proceed in two video games, VizDoom and Super Mario Bros. as you see in the demo video here:

Read more in Machine Learning Taps Power of Curiosity

Getting insight on all the channels with AI

Marketers have access to more data on their customers than ever before. The challenge is getting rapid insights from all the channels used, in time to act effectively. One solution is an  AI-powered customer retargeting platform for omni-channel marketing operations. Abhi Yadav, ZyloTech's  co-founder and CEO, spoke to me about the function of AI in omni-channel marketing.

ZyloTech, formerly DataXylo, launched in 2014 as an MIT spin off. Its advisory teams includes PhDs in AI, data scientists, and other marketing experts from the university. The platform uses machine learning to analyze all customer data continuously, and in near real time, for actionable insights on omnichannel marketing.

In the current environment, marketers really have “no way of knowing whether the individual” targeted by the ad “is a new, lost, inactive, loyal or brand-conscious customer,”  says Yadav.

Read more in An AI Approach to Omnichannel

AI for customer service

AI is changing our everyday interactions. What once required a human rep can now be handled by a

(Image: panuwat phimpha/Shutterstock)

virtual assistant whose programming allows customer problems to be solved more quickly.

A recent Venturebeat article declared, "AI chatbots are the next big shift in customer service." Those of us of a certain generation expect to wait on a line or on the phone for a person to take care of our customer service issues. But the generation that favors texts to calls has come to have different expectations.

Read more in  Why Machine Learning Can Improve Customer Service

The eyes have it with AI

The camera is already starting to replace the keyboard,” asserts Netra CEO, Richard Lee. The content that will dominate digital information flow will be visual, and for that reason image recognition is becoming a key component of marketing.

His company derives insight from visual data, fostering understanding of how consumers engage with brands through engagement with images. Netra  is a leader in visual intelligence and search that uses machine learning to help marketers make sense of imagery on social media.

Some brands are already using image recognition to connect with and effectively market to their customers. They include Neiman Marcus. The upscale retailer offers its customers the Snap. Find. Shop  app that enables them to use their phones to snap pictures of styles they like and find similar styles carried by the store.  The app is demonstrated here:

The app allows customers to bypass typing in description of the clothing and rely on the image alone to convey what they seek. That kind of search is what we will be seeing more of in future, according to Lee. 

Read more in AI Makes Visual Intelligence Possible

AI Transforms Business Data Models

(Image: pixone/iStockphoto)

As big data continues to grow, extracting value from it calls for new tools.

Increasingly, businesses that rely on data to drive decisions are applying AI to surface actionable insight quickly and accurately.

Finding innovative solutions to the problems raised in data analytics, particularly with respect to adapting machine learning to credit scores, is what they've been working on for the past six years at Experian's DataLabs. The EVP and Global Head of the labs, Eric Haller, spoke to All Analytics about the new direction for predictive modeling.

Read more in

AI, Machine Learning Power Transformation

Car analytics and AI

(Image: chombosan/Shutterstock)

Today's cars are all about mobility -- not just the kind that transports people and things, but also data mobility. Today's cars are more connected, and they are generating a lot more data that car manufacturers are working to collect, process, and apply to AI developments.

When the average person thinks about the connected car -- whether it is fully automated or packed with sensors that alert the driver to possible dangers -- what comes to mind is the experience for the person in that driver's seat. In fact, the information the driver sees represents only a tiny fraction of all the data collected through the sensor system. The amount of data collected is indeed vast, and car makers are now working on ways to ingest and process it effectively.

Read more in 

Car Makers Drive Hard Towards AI Advances

A virtual assistant you can bank on

In early 2016, SEB, one of Sweden's largest banks with a presence in 20 countries around the globe, started integrating Amelia, an artificial intelligence (AI) platform from IPsoft, into its help desk. Amelia is represented by a blond female avatar and is always referred to as "she" rather than "it."

The artificial intelligence platform is built on semantic understanding, which enables Amelia to interact with users through natural language to determine what actions to take in order to answer a question, fulfill a request or solve a problem. She is also designed to learn through observation.

At SEB, Amelia serves as a customer interface with automated interactions that can scale up to meet expanded support needs. "The driver is to find a way to improve the experience for our customers," explains Mikael Andersson, the bank's IT strategy transformation lead.

- See more at: http://www.baselinemag.com/innovation/banking-on-ai-to-offer-better-customer-service.html#sthash.Mk05GLW5.dpuf

The perks of personalized marketing: Starbucks' use of AI

photo from https://c6.staticflickr.com/4/3133/2873861973_995e8a7902_b.jpg

“Would you like fries with that?” That one-size-fits-all recommendation was about as far as upselling at the point of sale used to go.  Starbucks has come a long way from that with marketing powered by data — and AI.  It's marketing that is uniquely tailored to the behaviors, preferences, and context of each customer.

Matt Ryan, Starbucks EVP and global CSO, and Gerri Martin-Flickinger, EVP and global CTO discussed the strategy at the company's Investor Day.  He explained the impact of digital on Starbucks sales, while she explained what their algorithms do, in The Digital Flywheel: Strategy and impact.

Ryan began by saying that for Starbucks digital represents the “core customer experience.”  He sees it as what distinguishes their business approach from “the rest of the retail world,” and what “gives us the advantage we have.” As proof he showed how the "digital flywheel" contributed to Starbucks' growth in the past three years, and the increase in its rewards program from five million to 12 million customers.

The incentives in the rewards program is one of the ways Starbucks personalizes its marketing.   Martin-Flickinger demonstrated the evolution of the personalization by showing how it has progressed in just a single year. Its baseline is the form of marketing that many businesses still use today, rooted in historical data that they view in spreadsheets, which in turn powers algorithms to fit their general customer base.

In January 2016, Starbucks' marketing messages were limited to 30 variants of weekly email messages that were based on data that was already two weeks old. In June last year, Starbucks moved to a to a real-time personalization engine capable of producing of 400,000 variants of hyper-personalized emails each week. The company continued to tweak its AI engine so that in October, it was able to launch real-time 1:1  personalized offers that are uniquely generated based on each individual customer's behaviors and marked preferences.

Read more in A.I. Informs Personalization for Starbucks