Drama-Free Artificial Intelligence

Depending on who’s listening, the current discussion involving the growing role of Artificial Intelligence in business inspires a range of dramatically divergent emotions. There’s often fear, because of what some believe to be AI’s vaguely sci-fi vibe and dystopian possibilities. Among business people, there is also confusion, on account of the inability of most laypeople to separate AI hype from AI fact. Apprehension also looms large, usually from managers who sense that a great wave of technology disruption is about to hit them, but who feel utterly unprepared for it.  
But from our experience with Fortune 500 companies, we’ve come to believe that the proper response by business leaders to AI should be more benign: appreciation. Whatever anxieties it might produce, the fact is that AI is ready today to bring a trio of new efficiencies to the enterprise. Specifically, scores of companies have learned how AI technologies can transform how they process transactions, how they deal with data and how they interact with customers.
Better still, they have been able to take advantage of this AI triad without turning themselves into an Internet Giant and hiring huge new teams of hard-to-find, and not to mention expensive, data scientists. AI products are available now in nearly turnkey form from a growing list of enterprise vendors. True, you and your IT staff will need to do a certain amount of homework to be able to evaluate vendors, and to make sure product implementations map on to your precise business needs. But doing so isn’t a heavy lift, and the effort will likely be rewarded by the new efficiencies AI makes possible.
Companies are benefiting from AI right now, in ways that are making a difference on both the top and bottom line.
“Robotic and Cognitive Automation” is the name we at Deloitte give to AI’s ability to automate a huge swath of work that formerly required hands-on attention from human beings. The most popular form of R&CA involves gathering data from disparate sources and bringing them together in a single document. An invoice, for example, usually cites a number of sources, each of which stores relevant information in slightly different formats. An R&CA system has the intelligence necessary to transcend the usual literal-mindedness of computer systems, and process the information it needs despite the fact that it might have different representations in different places.
As AI techniques have become more robust in recent years, so too have the capabilities of R&CA packages. Now, instead of simply pulling spreadsheet-type data from sundry sources, they can process whole passages of text. Not as well as a human being can, for sure, but enough to get a general sense of the topics that are being covered. As a result, there are now R&CA systems that can “read” through emails and flag those that might be relevant to a particular issue. Such systems are now commonly found, for example, at large law practices, which use them to search through huge email libraries to discover which materials might need to be produced in connection with a particular bit of litigation. This is the sort of routine work that previously required paralegals.
Another cluster of AI applications involves the ability to make better use of a company’s data; these go by the name of “Cognitive Insights.” These tools allow companies to manage the flood of information they collect every day, from business reporting tools to social media accounts. More importantly, it gives businesses the ability to use that information to generate real-time insights and actions.
Consider just one area in which these new AI capabilities can be useful: digital marketing. Staffers running email campaigns can now improve click-through rates by using their AI-acquired knowledge of each customer’s personality to determine which words or phrases in the subject line might be more likely to get the person to read the email. Small changes can make a big difference; reports of double-digit increases in opened emails are common with AI.
Finally, AI is fundamentally changing the way companies work with their customers. This is occurring everywhere, but is most common with interactions with millennials. This cohort grew up with texting on their mobile phones, and is often more comfortable interacting with an app than with a human being.
As a result, millennials are extremely receptive to a new breed of automated customer service applications that AI is making possible. (These are vastly superior to the rudimentary “chatbots” that some companies used in the early days of the Web.) With advances in the AI field known as Natural Language Processing, computers are now able to deal with the sorts of real-world questions that customers are likely to ask, such as “Why is this charge on my credit card statement?” Deploying computers for these types of routine inquiries allow companies to deliver a uniform, high-quality customer experience while simultaneously improving the value of your brand.
You’ve probably noticed that while AI is often described as the equivalent of “thinking machines,” all of the tasks described above are relatively discrete and well-defined. That’s because for all the progress that’s been made in AI, the technology that still doesn’t come close to being able to match human intelligence. AI products perform specific tasks just fine, but don’t expect them (yet) to handle everyday human skills like professional judgment and common sense.
What’s more, AI can’t be used to paper over inefficiencies in a business, whether they be strategic or operational. If the processes you’re using AI for aren’t fundamentally sound to begin with, the new technology won’t be of any help, and may exacerbate problems by hiding them behind added layers of software. You’ll need to use some old-fashioned intelligence to take a good, hard look at your organization before trying to take advantage of the new, artificial variety. It will, though, be well worth the effort.

Jeff Loucks is the executive director of the Deloitte Center for Technology, Media and TelecommunicationsIn his role, he conducts research and writes on topics that help companies capitalize on technological change. An award-winning thought leader in digital business model transformation, Jeff is especially interested in the strategies organizations use to adapt to accelerating change. Jeff’s academic background complements his technology expertise. Jeff has a Bachelor of Arts in political science from The Ohio State University, and a Master of Arts and PhD in political science from the University of Toronto.

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Mic Locker is a director with Deloitte Consulting LLP and leader of its Enterprise Model Design practice. With more than 15 years of consulting experience, and more than three years of operations experience, she specializes in leading organizations through transformational changes ranging from business model redesign and capability alignment, process reinvention, operational cost reduction, and new business/product launches.

The Analytics of Language, Behavior, and Personality

Computational linguists and computer scientists, among them University of Texas professor Jason Baldridge, have been working for over fifty years toward algorithmic understanding of human language. They’re not there yet. They are, however, doing a pretty good job with important tasks such as entity recognition, relation extraction, topic modeling, and summarization. These tasks are accomplished via natural language processing (NLP) technologies, implementing linguistic, statistical, and machine learning methods.

Computational linguist Jason Baldridge, co-founder and chief scientist of start-up People Pattern

Computational linguist Jason Baldridge, co-founder and chief scientist of start-up People Pattern


NLP touches our daily lives, in many ways. Voice response and personal assistants — Siri, Google Now, Microsoft Cortana, Amazon Alexa — rely on NLP to interpret requests and formulate appropriate responses. Search and recommendation engines apply NLP, as do applications ranging from pharmaceutical drug discovery to national security counter-terrorism systems.
NLP, part of text and speech analytics solutions, is widely applied for market research, consumer insights, and customer experience management. The more consumer-facing systems know about people — individuals and groups — their profiles, preferences, habits, and needs — the more accurate, personalized, and timely their responses. That form of understanding — pulling clues from social postings, behaviors, and connections — is the business Jason’s company, People Pattern, is in.
I think all this is cool stuff so I asked two favors of Jason. #1 was to speak at a conference I organize, the up-coming Sentiment Analysis Symposium. He agreed. #2 was to respond to a series of questions — responses relayed in this article — exploring approaches to —
The Analytics of Language, Behavior, and Personality
Seth Grimes> People Pattern seeks to infer human characteristics via language and behavioral analyses, generating profiles that can be used to predict consumer responses. What are the most telling, the most revealing sorts of thing people say or do that, for business purposes, tells you who they are?
Jason Baldridge> People explicitly declare a portion of their interests in topics like sports, music, and politics in their bios and posts. This is part of their outward presentation of their selves: how they wish to be perceived by others and which content they believe will be of greatest interest to their audience. Other aspects are less immediately obvious, such as interests revealed through the social graph. This includes not just which accounts they follow, but the interests of the people they are most highly connected to (which may have been expressed in their posts and their own graph connections).
A person’s social activity can also reveal many other aspects, including demographics (e.g. gender, age, racial identity, location, and income) and psychographics (e.g. personality and status). Demographics are a core set of attributes used by most marketers. The ability to predict these (rather than using explicit declarations or surveys) enables many standard market research questions to be answered quickly and at a scale previously unattainable.
Seth> And what can one learn from these analyses?
People Pattern Audience Page

People Pattern Audience Page


Personas and associated language use.
As a whole, this kind of analysis allows us to standardize large populations (e.g. millions of people) on a common set of demographic variables and interests (possibly derived from people speaking different languages) and then support exploratory data analysis via unsupervised learning algorithms. For example, we use sparse factor analysis to find the correlated interests in an audience and furthermore group the individuals who are best fits for those factors. We call these discovered personas because they reveal clusters of individuals with related interests that distinguish them from other groups in the audience, and they have associated aggregate demographics—the usual things that go into building a persona segment by hand.
We can then show the words, phrases, entities, and accounts that the individuals in each persona discuss with respect to each of the interests. For example, one segment might discuss Christian themes with respect to religion, while others might discuss Muslim or New Age ones. Marketers can then use these to create tailored content for ads that are delivered directly to the individuals in a given persona, using our audience dashboard. There are of course other uses, such as social science questions. I’ve personally used it to look into audiences related to Black Lives Matter and understand how different groups of people talk about politics
Our audience dashboard is backed by Elastic Search, so you can also use search terms to find segments via self-declared allegiances for such polarizing topics.
A shout-out —
Personality and status are generally revealed through subtle linguistic indicators that my University of Texas Austin colleague James Pennebaker has studied for the past three decades and is now commercializing with his start-up company Receptiviti. These include detecting and counting different types of words, such as function words (e.g. determiners and prepositions) or cognitive terms (such as “because” and “therefore”), and seeing how a given individual’s rates of use of those word classes compares to known profiles of the different personality types.
So personas, language use, topics. How do behavioral analyses contribute to overall understanding?
Many behaviors reveal important aspects about an account that a human would struggle to infer. For example, the times at which an account regularly posts is a strong indicator of whether they are a person, organization or spam account. Organization accounts often automate their sharing, and they tend to post at regular intervals or common times, usually on the hour or half hour. Spam accounts often post at a regular frequency — perhaps every 8 minutes, plus or minus one minute. An actual person posts in accordance with sleep, work, and play activities, with greater variance — including sporadic bursts of activity and long periods of inactivity.
Any other elements?
Graph connections are especially useful for bespoke, super-specific interests and questions. For example, we used graph connections to build a pro-life/pro-choice classifier for one client to rank over 200,000 individuals in greater Texas on a scale from most likely to be pro-life to most-likely to be pro-choice. By using known pro-life and pro-choice accounts, it was straightforward to gather examples of individuals with a strong affiliation to one side or the other and learn a classifier based on their graph connections that was then applied to the graph connections of individuals who follow none of those accounts.
Could you say a bit about how People Pattern identifies salient data and makes sense of it, the algorithms?
The starting point is to identify an audience. Often this is simply the people who follow a brand and/or its competitors, or who comment on their products or use certain hashtags. We can also connect the individuals in a CRM to their corresponding social accounts. This process, which we refer to as stitching, uses identity resolution algorithms that make predictions based on names, locations, email addresses and how well they match corresponding fields in the social profiles. After identifying high confidence matches, we can then append their profile analysis to their CRM data. This can inform an email campaign, or be the start for lead generation, and more.
Making sense of data — let’s look at three aspects — demographics, interests, and location —
Our demographics classifiers are based on supervised training from millions of annotated examples. We use logistic regression for attributes like gender, race, and account type. For age, we use linear regression techniques that allow us characterize the model’s confidence in its predictions — this allows us to provide more accurate aggregate estimates for arbitrary sets of social profiles. This is especially important for alcohol brands that need to ensure they are engaging with age-appropriate audiences. All of these classifiers are backed by rules that detect self-declared information when it is available (e.g. many people state their age in their bio).
We capture explicit interests with text classifiers. We use a proprietary semi-supervised algorithm for building classifiers from small amounts of human supervision and large amounts of unlabeled texts. Importantly, this allows us to support new languages quickly and at lower cost, compared to fully supervised models. We can also use classifiers built this way to generate features for other tasks. For example, we are able to learn classifiers that identify language associated with people of different age groups, and this produces an array of features used by our age classifiers. They are also great inputs for deep learning for NLP and they are different from the usual unsupervised word vectors people commonly use.
For location, we use our internally developed adaptation of spatial label propagation. With this technique, you start with a set of accounts that have explicitly declared their location (in their bio or through geo tags), and then these locations are spread through graph connections to infer locations for accounts that have not stated their location explicitly. This method can resolve over half of individuals to within 10 kilometers of their true location. Determining this information is important for many marketing questions (e.g. how does my audience in Dallas differ from my audience in Seattle?) It obviously also brings up privacy concerns. We use these determinations for aggregate analyses but don’t show them at the individual profile level. However, people should be aware that variations of these algorithms are published and there are open source implementations, so leaving their location field blank is by no means sufficient to ensure your home location isn’t discoverable by others.
My impression is that People Pattern, with an interplay of multiple algorithms and data types and multi-stage analysis processes, is a level more complex than most new-to-the-market systems. How do you excel while avoiding over-engineering that leads to a brittle solution?
It’s on ongoing process, with plenty of bumps and bruises along the way. I’m very fortunate that my co-founder, Ken Cho, has deep experience in enterprise social media applications. Ken co-founded Spredfast [an enterprise social media marketing platform]. He has strong intuitions on what kind of data will be useful to marketers, and we work together to figure out whether it is possible to extract and/or predict the data.
We’ve struck on a number of things that work really well, such as predicting core demographics and interests and doing clustering based on those. Other things have worked well, but didn’t provide enough value or were too confusing to users. For example, we used to support both interest-level keyword analysis (which words does this audience use with respect to “music”) and topic modeling, which produces clusters of semantically related words given all the posts by people in the audience, in (almost) real-time. The topics were interesting because they showed groupings of interests that weren’t captured by our interest hierarchy (such as music events), but it was expensive to support topic model analysis given our RESTful architecture and we chose to deprecate that capability. We have since reworked our infrastructure so that we can support some of those analyses in batch (rather than streaming) mode for deeper audience analyses. This is also important for supporting multiple influence scores computed with respect to a fixed audience rather than generic overall influence scores.
Ultimately, I’ve learned to think about approaching a new kind of analysis not just with respect to the modeling, but as importantly to consider whether we can get the data needed at the time that the user wants the analysis, how costly the infrastructure to support it will be, and how valuable it is likely to be. We’ve done some post-hoc reconsiderations along these lines, which has led to streamlining capabilities.
Other factors?
Another key part of this is having the right engineering team to plan and implement the necessary infrastructure. Steve Blackmon joined us a year ago, and his deep experience in big data and machine learning problems has allowed us to build our people database in a scalable, repeatable manner. This means we now have 200+ million profiles that have demographics, interests and more already pre-computed. More importantly, we now have recipes and infrastructure for developing further classifiers and analyses. This allows us to get them into our product more quickly. Another important recent hire was our product manager Omid Sedaghatian. Omid is doing a fantastic job of figuring out what aspects of our application are excelling, which aren’t delivering expected value, and how we can streamline and simplify everything we do.
Excuse the flattery, but it’s clear your enthusiasm and your willingness to share your knowledge are huge assets for People Pattern. Not coincidentally, your other job is teaching. Regarding teaching — to conclude this interview — Sentiment Analysis Symposium in New York, and pre-conference you’ll present a tutorial, Computing Sentiment, Emotion, and Personality. Could you give us the gist of the material you’ll be covering?
Actually, I just did. Well, almost.
I’ll start the tutorial with a natural language processing overview and then cover sentiment analysis basics — rules, annotation, machine learning, and evaluation. Then I’ll get into author modeling, which seeks to understand demographic and psychographic attributes based on what someone says and how they say it. This is in the tutorial description: We’ll look at additional information that might be determined from non-explicit components of linguistic expression, as well as non-textual aspects of the input, such as geography, social networks, and images, things I’ve described in this interview. But with an extended, live session you get depth and interaction, and an opportunity to explore.
Thanks Jason. I’m looking forward to your session.


People Pattern uses data science methodologies to bring clarity to the vast amounts of social data available and helps you discover quality customers and create innovative people-based datasets.

Why you can’t program intelligent robots, but you can train them

If it feels like we’re in the midst of robot renaissance right now, perhaps it’s because we are. There is a new crop of robots under development that we’ll soon be able to buy and install in our factories or interact with in our homes. And while they might look like robots past on the outside, their brains are actually much different.

Today’s robots aren’t rigid automatons built by a manufacturer solely to perform a single task faster than, cheaper than and, ideally, without much input from humans. Rather, today’s robots can be remarkably adaptable machines that not only learn from their experiences, but can even be designed to work hand in hand with human colleagues. Commercially available (or soon to be) technologies such as Jibo, Baxter and Amazon Echo are three well-known examples of what’s now possible, but they’re also just the beginning.

Different technological advances have spurred the development of smarter robots depending on where you look, although they all boil down to training. “It’s not that difficult to builtd the body of the robot,” said Eugene Izhikevich, founder and CEO of robotics startup Brain Corporation, “but the reason we don’t have that many robots in our homes taking care of us is it’s very difficult to program the robots.”

Essentially, we want robots that can perform more than one function, or perform one function very well. And it’s difficult to program a robot to do multiple things, or at least the things that users might want, and it’s especially difficult to program to do these things in different settings. My house is different than your house, my factory is different than your factory.

A collection of RoboBrain concepts.

A collection of RoboBrain concepts.

“The ability to handle variations is what enables these robots to go out into the world and actually be useful,” said Ashutosh Saxena, a Stanford University visiting professor and head of the RoboBrain project. (Saxena will be presenting on this topic at Gigaom’s Structure Data conference March 18 and 19 in New York, along with Julie Shah of MIT’s Interactive Robotics Group. Our Structure Intelligence conference, which focuses on the cutting edge in artificial intelligence, takes place in September in San Francisco.)

That’s where training comes into play. In some cases, particularly projects residing within universities and research centers, the internet has arguably been a driving force behind advances in creating robots that learn. That’s the case with RoboBrain, a collaboration among Stanford, Cornell and a few other universities that crawls the web with the goal of building a web-accessible knowledge graph for robots. RoboBrain’s researchers aren’t building robots, but rather a database of sorts (technically, more of a representation of concepts — what an egg looks like, how to make coffee or how to speak to humans, for example) that contains information robots might need in order to function within a home, factory or elsewhere.

RoboBrain encompasses a handful of different projects addressing different contexts and different types of knowledge, and the web provides an endless store of pictures, YouTube videos and other content that can teach RoboBrain what’s what and what’s possible. The “brain” is trained with examples of things it should recognize and tasks it should understand, as well as with reinforcement in the form of thumbs up and down when it posits a fact it has learned.

For example, one of its flagship projects, which Saxena started at Cornell, is called Tell Me Dave. In that project, researchers and crowdsourced helpers across the web train a robot to perform certain tasks by walking it through the necessary steps for tasks such as cooking ramen noodles.  In order for it to complete a task, the robot needs to know quite a bit: what each object it sees in the kitchen is, what functions it performs, how it operates and at which step it’s used in any given process. In the real world, it would need to be able to surface this knowledge upon, presumably, a user request spoken in natural language — “Make me ramen noodles.”

The Tell Me Dave workflow.

The Tell Me Dave workflow.

Multiply that by any number of tasks someone might actually want a robot to perform, and it’s easy to see why RoboBrain exists. Tell Me Dave can only learn so much, but because it’s accessing that collective knowledge base or “brain,” it should theoretically know things it hasn’t specifically trained on. Maybe how to paint a wall, for example, or that it should give human beings in the same room at least 18 inches of clearance.

There are now plenty of other examples of robots learning by example, often in lab environments or, in the case of some recent DARPA research using the aforementioned Baxter robot, watching YouTube videos about cooking (pictured above).

Advances in deep learning — the artificial intelligence technique du jour for machine-perception tasks such as computer vision, speech recognition and language understanding — also stand to expedite the training of robots. Deep learning algorithms trained on publicly available images, video and other media content can help robots recognize the objects they’re seeing or the words they’re hearing; Saxena said RoboBrain uses deep learning to train robots on proper techniques for moving and grasping objects.

The Brain Corporation platform.

The Brain Corporation platform.

However, there’s a different school of thought that says robots needn’t necessarily be as smart as RoboBrain wants to make them, so long as they can at least be trained to know right from wrong. That’s what Izhikevich and his aforementioned startup, Brain Corporation, are out to prove. It has built a specialized hardware and software platform, based on the idea of spiking neurons, that Izhikevich says can go inside any robot and “you can train your robot on different behaviors like you can train an animal.”

That is to say, for example, that a vacuum robot powered by the company’s operating system (called BrainOS) won’t be able to recognize that a cat is a cat, but it will be able to learn from its training that that object — whatever it is — is something to avoid while vacuuming. Conceivably, as long as they’re trained well enough on what’s normal in a given situation or what’s off limits, BrainOS-powered robots could be trained to follow certain objects or detect new objects or do lots of other things.

If there’s one big challenge to the notion of training robots versus just programming them, it’s that consumers or companies that use the robots will probably have to do a little work themselves. Izhikevich noted that the easiest model might be for BrainOS robots to be trained in the lab, and then have that knowledge turned into code that’s preinstalled in commercial versions. But if users want to personalize robots for their specific environments or uses, they’re probably going to have to train it.

Part of the training process with Canary. The next step is telling the camera what its seeing.

Part of the training process with Canary. The next step is telling the camera what it’s seeing.

As the internet of things and smart devices, in general, catch on, consumers are already getting used the idea — sometimes begrudgingly. Even when it’s something as simple as pressing a few buttons in an app, like training a Nest thermostat or a Canary security camera, training our devices can get tiresome. Even those of us who understand how the algorithms work can get get annoyed.

“For most applications, I don’t think consumers want to do anything,” Izhikevich said. “You want to press the ‘on’ button and the robot does everything autonomously.”

But maybe three years from now, by which time Izhikevich predicts robots powered by Brain Corporation’s platform will be commercially available, consumers will have accepted one inherent tradeoff in this new era of artificial intelligence — that smart machines are, to use Izhikevich’s comparison, kind of like animals. Specifically, dogs: They can all bark and lick, but turning them into seeing eye dogs or K-9 cops, much less Lassie, is going to take a little work.

Watson-powered toy blows past Kickstarter goal in a day

First it was Jeopardy!, then it was cancer, e-commerce and cooking. Now, IBM’s Watson artificial intelligence system is powering a line of connected toys.

And it looks as if people are impressed with the idea: A company called Elemental Path launched a Kickstarter campaign on Monday for a line of toy dinosaurs, called CogniToys, and had surpassed its initial goal as of Tuesday morning. The company was aiming for $50,000 and had raised more than $70,000 as of 11:40 a.m. Tuesday.

Essentially, the dinosaurs are connected toys that speak to IBM’s Watson cloud APIs, which the company began rolling out last year. According to the Kickstarter page, the CogniToys will allow children to engage with them by talking — asking question, telling jokes, sharing stories and the like. In addition, the page states, “The technology allows toys to listen, speak and simultaneously evolve, learn and grow with your child; bringing a new element of personalized, educational play to children.”

cognitoys2

Elemental Path is not the first company focused on building natural language and artificial intelligence into toys. Possibly the best-known example so far is a startup called ToyTalk, which is building natural language iPad apps and was founded by former Pixar CTO Oren Jacob.

The evolution of artificial intelligence, and the ability to easily train toys, robots, apps or anything, really, is going to be a major focus of Gigaom’s Structure Intelligence conference September 22–23 in San Francisco. We’ll also talk a lot about machine learning and AI at our Structure Data conference March 18–19 in New York, where speakers from Facebook, Yahoo, Spotify and elsewhere will discuss how data in the form of images, text, and even sounds are allowing them to build new products and discover new insights about their users.

Here’s more evidence that sports is a goldmine for machine learning

If you really like sports and you’re really skilled at data analysis or machine learning, you might want to make that your profession.

On Thursday, private equity firm Vista announced it has acquired a natural-language processing startup called Automated Insights and will make it a subsidiary of STATS, a sports data company that Vista also owns. It’s just the latest example of how much money there is to be made when you combine sports, data and algorithms.

The most-popular story about Automated Insights is that its machine-learning algorithms are behind the Associated Press’s remarkably successful automated corporate-earnings stories, but there’s much more to the business than that. The company claims its algorithms have a place in all sorts of areas where users might want to interact with information in natural language — fitness apps, health care, business intelligence and, of course, sports.

In fact, someone from Automated Insights recently told me that fantasy sports is a potential cash cow for the company. Because its algorithms can analyze data and the outcomes of individual matchups, it can deliver everything from in-game trash-talk to post-game summaries. The better the algorithms are at mimicking natural language (i.e., not just regurgitating stats with some static nouns and verbs around them), the more engaging the user experience — and the more money the fantasy sports platform, and Automated Insights as a partner, make. Automated Insights already provides some of this experience for Yahoo Sports.

industries_sports_example

So it’s not surprising that STATS would acquire Automated Insights. STATS provides a lot of data products to broadcasters and and folks selling mobile and web applications, ranging from analysis to graphics to its SportVU player-tracking system. At our Structure Data conference next month in New York, STATS Executive Vice President of Pro Analytics Bill Squadron will be on stage along with ESPN’s vice president of data platforms, Krish Dasgupta, to discuss how the two companies are working together the sate an ever-growing sports-fan thirst for data. (We’ll also have experts in machine learning and deep learning from places such as Facebook, Yahoo and Spotify discussing the state of the are in building machines that understand language, images and even music.)

And Automated Insights isn’t even STATS’s first acquisition this week. On Tuesday, the company announced it had acquired The Sports Network, a sports news and data provider. In September, STATS acquired Bloomberg Sports.

More broadly, though, the intersection of sports and data is becoming a big space with the potential to be huge. Every year around this time, people in the United States start going crazy over the NCAA collegiate men’s basketball tournament (aka March Madness) and spend billions of dollars betting on it in office pools and at sports books. And every year for the past several, we have been seeing more and more predictive models and other tools for helping people predict who’ll win and lose each game.

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Statistician superstar Nate Silver might be best known for his ability to predict elections, but he has been applying his trade to sports including baseball and the NCAA tournament for years, too. It’s no wonder ESPN bought him and his FiveThirtyEight blog and turned it into a full-on news outlet that includes a heavy emphasis on sports data.

The National Football League might present the biggest opportunity to cash in on sports data. Aside from the ability to predict games and player performance (gambling on the NFL — including fantasy football — is a huge business), we now see individuals making their livings with football-analysis blogs that turn into consulting gigs. There’s a growing movement to tackle the challenge of predicting play calling by applying machine learning algorithms to in-game data.

Even media companies are getting into the act. The New York Times dedicates resources to analyzing every fourth down in every NFL game and telling the world whether the coach should have punted, kicked a field goal or gone for it. In 2013, Yahoo bought a startup called SkyPhrase (although it folded the personnel into Yahoo Labs) that developed a way to deliver statistics in response to natural language queries. The NFL was one of its first test cases.

A breakdown of what happens on fourth down.

A breakdown of what happens on fourth down.

Injuries are also a big deal, and there is no shortage of thought, or financial investment, into new ways of analyzing measuring what’s happening with players’ bodies so teams can better diagnose and prevent injuries. Sensors and cameras located near the field or even on players’ uniforms, combined with new data analysis methods, provide a great opportunity for unlocking some real insights into player safety.

All of this probably only skims the surface of what’s being done with sports data today and what companies, teams and researchers are working for tomorrow. So while analyzing sports data might not save the world, it might make you rich. If you’re into that sort of thing.

Luminoso brings its text analysis smarts to streaming data

Luminoso, a sentiment analysis startup with DNA from MIT’s Media Lab, says its new product can take consumer feedback from Twitter, Facebook, Google+ and potentially other feeds, and boil it into one stream to provide a near real-time look at how people feel (or at least talk) about a given topic.

Compass was put through its paces for the Sony One Stadium Live event associated with last year’s World’s Cup and is now coming to market broadly, Luminoso co-founder and CEO Catherine Havasi in an interview.

The company’s technology already let companies collect and analyze data from all those feeds but that’s more of a deep dive into static, archived information — Compass , available as a standalone product, is all about near-real-time analysis of streaming text, newswires, social media, Havasi said.

For the World Cup effort, [company]Sony[/company] wanted to see (or hear) what people were talking about and because of the sheer number of games and locations and number of languages, it wanted to do that quickly, without requiring human moderators.

Typically, many sentiment or text analysis solutions require a human “expert” to keep entering and tweaking keywords, Havasi said. “If you’re a news organization following Ukraine, you add keywords manually so you don’t miss anything. That’s a very slow process and the queries get very large.” Compass automates that process.

Big advertisers — customers include REI, [company]Intel[/company], [company]Autodesk[/company] and Scotts — can use Compass to watch how people are reacting to Super Bowl ads as the broadcast unfolds, to gauge the impact of digital marketing campaigns.

As collection of data — including that included in social media feeds — text analytics has become a vibrant field — other players include Lexalytics, Clarabridge and Digital Reasoning and others.

The company, which garnered $6.5 million in venture funding last July, said one thing that differentiates it from other text analytics vendors is that its technology knows, or can quickly learn, the difference between Shell, as in the oil company and a seashell. Or the husk of a peanut or a spent bullet casing. And it can perform similar context-aware analytics in most of the European languages plus Chinese and Japanese. (Oh yes and Emojis.) It also offers an API so companies can connect it up to internal, private data sources.

Pricing is based on the amount of data processed and number of languages screened.

 

luminoso emoji

InboxVudu uses NLP to help you focus on the emails that matter

A text-analysis startup called Parakweet (whose initial product focused on book recommendations) has launched a new application, called InboxVudu, that’s designed to help users reduce the stress of email by showing them just the messages that need their attention. And while it turns out that no amount of curation can really help ease the email burden of a technology journalist today, the app might work very well for other folks.

InboxVudu works by analyzing the text of a message and figuring out if the sender is asking something from the recipient — “I need an answer to that big question,” or “Please RSVP by Feb. 13” or something along those lines. At the end of the day, users receive an email from InboxVudu showing them the message that need their attention. From that email, as well as an associated web application, users can reply to emails, mark them as “resolved,” flag false positives and even mute the sender.

In Gmail, at least, the messages also find their way to an InboxVudu-labeled folder users can peruse them at their leisure.

A sample screenshot of an InboxVudu "digest."

A sample screenshot of an InboxVudu “digest.”

Parakweet co-founder and CEO Ramesh Haridas explained in an interview that the app works with about 90 percent accuracy today, based on internal testing, and that he hopes it will get even smarter as the company adds more signals into its models. For starters, there’s all the interaction data that users will generate by replying to messages and flagging false positives, which Parakweet can use to train the system both individually and at an aggregate level. Haridas also suggested the application might someday prioritize emails from people to whom users respond very quickly, or consider a sender’s job title or other measures of “global importance.”

I suggested InboxVudu could be really valuable as a way of helping users understand their “email graphs,” if you will, and Haridas agreed. He said the company is considering offering users’ statistics about their activity and which of their email contacts are the most important. However, he made sure to add with regard to privacy, “It’s all being processed by machines, so it’s never seen by a human being.”

Those feature and algorithmic improvements are still a way out though, but I’ve been using the first iteration of InboxVudu for about a week. And although it works as advertised — I’ve noticed few if any false positives — seeing 20 nicely bundled PR pitches doesn’t make it any easier to read them all or reply to them all. I’d consider muting the senders, but the nature of PR is that sometimes pitches are compelling and sometimes they’re not, even from the same person.

inboxvudu_iphone_highlights

One really nice thing about InboxVudu, though, are the “follow-up” messages it displays — those where I’ve asked something from someone else and am still awaiting a response. I’m prone to being disorganized, so any reminders of the various stories or projects I’m working on, especially ones involving other people, are helpful.

If there’s one thing I’m confident about, though, it’s that programs like InboxVudu will continue to get better as the field of artificial intelligence continues to improve. As the speakers at our upcoming Structure Data (March 18-19 in New York) and Structure Intelligence (Sept. 22-23 in San Francisco) conferences demonstrate, advances in AI are happening fast, especially in fields such as language understanding and personal assistant technologies.

If Parakweet’s Kiam Choo, who studied under deep learning guru Geoff Hinton at the University of Toronto, can figure out a way to make to make email substantially less burdensome even for people like me with 29,000 unread messages, then the more power to him.

Now IBM is teaching Watson Japanese

IBM has struck a deal SoftBank Telecom Corporation to bring the IBM Watson artificial intelligence (or, as IBM calls it, cognitive computing) system to Japan. The was announced on Tuesday.

Watson has already been trained in Japanese, so now it’s matter of getting its capabilities into production via specialized systems, apps or even robots running Watson APIs. As in the United States, early focus areas include education, banking, health care, insurance and retail.

[company]IBM[/company] has had a somewhat difficult time selling Watson, so maybe the Japanese market will help the company figure out why. It could be that the technology doesn’t work as well or as easily as advertised, or it could just be that American companies, developers and consumers aren’t ready to embrace so many natural-language-powered applications.

The deal with SoftBank isn’t the first time IBM has worked to teach a computer Japanese. The company is also part of a project with several Japanese companies and agencies, called the Todai Robot, to build a system that runs on a laptop and can pass the University of Tokyo entrance exam.

We’ll be talking a lot about artificial intelligence and machine that can learn at our Structure Data conference in March, with speakers from Facebook, Spotify, Yahoo and other companies. In September, we’re hosting Gigaom’s inaugural Structure Intelligence conference, which will be all about AI.

Microsoft throws down the gauntlet in business intelligence

[company]Microsoft[/company] is not content to let Excel define the company’s reputation among the world’s data analysts. That’s the message the company sent on Tuesday when it announced that its PowerBI product is now free. According to a company executive, the move could expand Microsoft’s reach in the business intelligence space by 10 times.

If you’re familiar with PowerBI, you might understand why Microsoft is pitching this as such a big deal. It’s a self-service data analysis tool that’s based on natural language queries and advanced visualization options. It already offers live connections to a handful of popular cloud services, such as [company]Salesforce.com[/company], [company]Marketo[/company] and GitHub. It’s delivered as a cloud service, although there’s a downloadable tool that lets users work with data on their laptops and publish the reports to a cloud dashboard.

James Phillips, Microsoft’s general manager for business intelligence, said the company has already had tens of thousands of organizations sign up for PowerBI since it became available in February 2014, and that CEO Satya Nadella opens up a PowerBI dashboard every morning to track certain metrics.

A screenshot from a sample PowerBI dashboard.

A screenshot from a sample PowerBI dashboard.

And Microsoft is giving it away — well, most of it. The preview version of the cloud service now available is free and those features will remain free when it hits general availability status. At that point, however, there will also be a “pro” tier that costs $9.99 per user per month and features more storage, as well as more support for streaming data and collaboration.

But on the whole, Phillips said, “We are eliminating any piece of friction that we can possibly find [between PowerBI and potential users].”

This isn’t free software for the sake of free software, though. Nadella might be making a lot of celebrated, if not surprising, choices around open source software, but he’s not in the business of altruism. No, the rationale behind making PowerBI free almost certainly has something to do with stealing business away from Microsoft’s neighbor on the other side of Lake Washington, Seattle-based [company]Tableau Software[/company].

Phillips said the business intelligence market is presently in its third wave. The first wave was technical and database-centric. The second wave was about self service, defined first by Excel and, over the past few years, by Tableau’s eponymous software. The third wave, he said, takes self service a step further in terms of ease of use and all but eliminates the need for individual employees to track down IT before they can get something done.

The natural language interface, using funding data from Crunchbase.

The natural language interface, using funding data from Crunchbase.

IBM’s Watson Analytics service, Phillips said, is about the only other “third wave” product available. I recently spent some time experimenting with the Watson Analytics preview, and was fairly impressed. Based on a quick test run of a preview version of PowerBI, I would say both products have their advantages over the other.

But IBM — a relative non-entity in the world of self-service software — is not Microsoft’s target. Nor, presumably, is analytics newcomer Salesforce.com. All of these companies, as well as a handful of other vendors that exist to sell business intelligence software, want a piece of the self-service analytics market that Tableau currently owns. Tableau’s revenues have been skyrocketing for the past couple years, and it’s on pace to hit a billion-dollar run rate in just over a year.

“I have never ever met a Tableau user who was not also a Microsoft Excel user,” Phillips said.

That might be true, but it also means Microsoft has been leaving money on the table by not offering anything akin to Tableau’s graphic interface and focus on visualizations. Presumably, it’s those Tableau users, and lots of other folks for whom Tableau (even its free Tableau Public version) is too complex, that Microsoft hopes it can reach with PowerBI. Tableau is trying to reach them, too.

“We think this really does 10x or more the size of the addressable business intelligence market,” Phillips said.

A former Microsoft executive told me that the company initially viewed Tableau as a partner and was careful not to cannibalize its business. Microsoft stuck to selling SharePoint and enterprise-wide SQL Server deals, while Tableau dealt in individual and departmental visualization deals. However, he noted, the new positioning of PowerBI does seem like a change in that strategy.

Analyzing data with more controls.

Analyzing data with more controls.

Ultimately, Microsoft’s vision is to use PowerBI as a gateway to other products within Microsoft’s data business, which Phillips characterized the the company’s fastest-growing segment. PowerBI can already connect to data sources such as Hadoop and SQL Server (and, in the case of the latter, can analyze data without transporting it), and eventually Microsoft wants to incorporate capabilities from its newly launched Azure Machine Learning service and the R statistical computing expertise it’s about to acquire, he said.

“I came to Microsoft largely because Satya convinced me that the company was all in behind data,” Phillips said. For every byte that customers store in a Microsoft product, he added, “we’ll help you wring … every drop of value out of that data.”

Joseph Sirosh, Microsoft’s corporate vice president for machine learning, will be speaking about this broader vision and the promise of easier-to-use machine learning at our Structure Data conference in March.

Microsoft CEO Satya Nadella.

Microsoft CEO Satya Nadella.

Given all of its assets, it’s not too difficult to see how the new, Nadella-led Microsoft could become a leader in an emerging data market that spans such a wide ranges of infrastructure and application software. Reports surfaced earlier this week, in fact, that Microsoft is readying its internal big data system, Cosmos, to be offered as a cloud service. And selling more data products could help Microsoft compete with another Seattle-based rival — [company]Amazon[/company] Web Services — in a cloud computing business where the company has much more at stake than it does selling business intelligence software.

If it were just selling virtual servers and storage on its Azure platform, Microsoft would likely never sniff market leader AWS in terms of users or revenue. But having good data products in place will boost subscription revenues, which count toward the cloud bottom line, and could give users an excuse to rent infrastructure from Microsoft, too.

Update: This post was updated at 10:15 a.m. to include additional information from a former Microsoft employee.

Why data science matters and how technology makes it possible

When Hilary Mason talks about data, it’s a good idea to listen.

She was chief data scientist at Bit.ly, data scientist in residence at venture capital firm Accel Partners, and is now founder and CEO of research company Fast Forward Labs. More than that, she has been a leading voice of the data science movement over the past several years, highlighting what’s possible when you mix the right skills with a little bit of creativity.

Mason came on the Structure Show podcast this week to discuss what she’s excited about and why data science is a legitimate field. Here are some highlights from the interview, but it’s worth listening to the whole thing for her thoughts on everything from the state of the art in natural language processing to the state of data science within corporate America.

And if you want to see Mason, and a lot of other really smart folks, talk about the future of data in person, come to our Structure Data conference that takes place March 18-19 in New York.

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How far big data tech has come, and how fast

“Things that maybe 10 or 15 years ago we could only talk about in a theoretical sense are now commodities that we take completely for granted,” Mason said in response to a question about how the data field has evolved.

When she started at Bit.ly, she explained, the whole product was just shortened links shared across the web. That was it. So she and her colleagues had a lot of freedom rather early on to carry out data science research in an attempt to find new directions to take the company.

Shivon Zilis, VC, Bloomberg Beta; Sven Strohband, Partner and CTO, Khosla Ventures; Hilary Mason, Data Scientist in Residence, Accel Partners; Jalak Jobanputra, Managing Partner, FuturePerfect Ventures.

Hilary Mason (center) at Structure Data 2014.

“That was super fun, and also the first time I realized that the technology we were building and using was actually allowing us to gather more data about natural human behavior than we’ve ever, as a research community, had access to,” Mason said.

“Hadoop existed, but was still extremely hard to use at that point,” she continued. “Now it’s something where I hit a couple buttons and a cloud spins up for me and does my calculations and it’s really lovely.”

Defending data science

It was only a couple years ago that “data scientist” was deemed the sexiest job of the 21st century, but that job title and the field of data science have always been subject to a fair amount of derision. What’s more, there’s now a collection of software vendors claiming they can automate away some of the need for data scientists via their products.

Mason disagrees with the criticism and the idea that you can automate all, or even the most important parts, of a data scientist’s job:

“You have math, you have programming, and then you have what is essentially empathy domain knowledge and the ability to articulate things clearly. So I think the title is relevant because those three things have not been combined in one job before. And the reason we can do that today, even though none of these things is new, is just that the technology has progressed so much that it’s possible for one person to do all these things — not perfectly, but well enough.”

She continued:

“A lot of people seem to think that data science is just a process of adding up a bunch of data and looking at the results, but that’s actually not at all what the process is. To do this well, you’re really trying to understand something nuanced about the real world, you have some incredibly messy data at hand that might be able to inform you about something, and you’re trying to use mathematics to build a model that connects the two. But that understanding of what the data is really telling you is something that is still a purely human capability.”

The next big things: Deep learning, IoT and intelligent operations

As for other technologies that have Mason excited, she said deep learning is high up on the list, as are new approaches to natural language processing and understanding (those two are actually quite connected in some aspects).

“Also, being able to use AI to automate the bounds of engineering problems,” Mason said. “There are a lot of techniques we already understand pretty well that could be well applied in like operations or data center space where we haven’t seen a lot of that.”

Hilary Mason

Hilary Mason (second from right) at Structure Data 2014.

Mason thinks one of the latest data technologies on the path to commoditization is stream processing for real-time data, and Fast Forward Labs is presently investigating probabilistic approaches to stream processing. That is, giving up a little bit of accuracy in the name of speed. However, she said, it’s important to think about the right architecture for the job, especially in an era of cheaper sensors and more-powerful, lower-power processors.

“You don’t actually need that much data to go into your permanent data store, where you’re going to spend a lot of computation resources analyzing it,” Mason explained. “If you know what you’re looking for, you can build a probabilistic system that just models the thing you’re trying to model in a very efficient way. And what this also means is that you can push a lot of that computation from a cloud cluster actually onto the device itself, which I think will open up a lot of cool applications, as well.”