Daily Links 04/11/2017

Demystifying data science

The key to a successful analytical model is having a robust set of variables against which to test for their predictive capabilities. And the key to having a robust set of variables from which to test is to get the business users engaged early in the process.

How machine learning is shaking up e-commerce and customer engagement

From a content perspective, [Sitecore] performs semantic analysis to:

  • Auto generate taxonomies and tagging
  • Help improve the tone of your content by analyzing for things like wordiness, slang, and other grammar-like faux pax

From a digital marketing perspective, ML can:

  • Help detect segments of your customers or audience
  • Improve the effectiveness of your testing and optimization processes
  • Provide content and product recommendations that increase the engagement time a customer spends on your website.

And from a backend perspective, it can help with fraud detection, something that every company with an e-commerce model needs to monitor actively.

Gartner 2017 magic quadrant for data science platforms: gainers and losers

Firms covered:

  • Leaders (4): IBM, SAS, RapidMiner, KNIME
  • Challengers (4): MathWorks (new), Quest (formerly Dell), Alteryx, Angoss
  • Visionaries (5): Microsoft, H2O.ai (new), Dataiku (new), Domino Data Lab (new), Alpine Data
  • Niche Players (3): FICO, SAP, Teradata (new)

Gartner notes that even the lowest-scoring vendors in MQ are still among the top 16 firms among over 100 vendors in the heated Data Science market.

Among those not on the quadrant, I’ve been impressed by DataRobot.

Daily Links 04/05/2017

New technology pushes machine smarts to the edge

“The set of possible smart edge devices that can be used for industrial control is rapidly expanding as ever more compute and sensing capability moves to the edge,” says Greg Olsen, senior vice president, products, at Falkonry. “As long as the device can transform signal observation into operational commands or guidance, it can be considered a control device. Smartness is clearly subjective, but the range can include anything from advanced process control all the way up to artificial intelligence.”

Want to be happier and more successful? Learn to like other people

It sounds paradoxical, but according to University of Georgia researcher Jason Colquitt and his colleagues, people who tend to trust others at work score higher on a range of measure than those who don’t, from job performance to commitment to the team. And since we know that it’s our relationships—particularly with our bosses and colleagues—that determine how happy and successful we are as our careers progress, it may be worth asking some new questions. Instead of, “How can I improve?” the better question might be, “How can I start seeing more of the good in people, more often?”

Google’s Cloud Jobs API

Company career sites, job boards and applicant tracking systems can improve candidate experience and company hiring metrics with job search and discovery powered by sophisticated machine learning. The Cloud Jobs API provides highly intuitive job search that anticipates what job seekers are looking for and surfaces targeted recommendations that help them discover new opportunities. In order to provide the most relevant search results and recommendations, the API uses machine learning to understand how job titles and skills relate to one another, and what job content, location and seniority are the closest match for a jobseeker’s preferences.

 

Daily Links 04/04/2017

Emotion Detection and Recognition from Text Using Deep Learning

The researchers used a data set of short English text messages labeled by Mechanical Turkers with five emotion classes anger, sadness, fear, happiness, and excitement. A multi-layered neural network was trained to classify text messages by emotion. The model was able to classify anger, sadness, and excitement well but didn’t do well at recognizing fear.

Adapting ideas from neuroscience for AI

We don’t really know why neurons spike. One theory is that they want to be noisy so as to regularize, because we have many more parameters than we have data points. The idea of dropout [a technique developed to help prevent overfitting] is that if you have noisy activations, you can afford to use a much bigger model. That might be why they spike, but we don’t know. Another reason why they might spike is so they can use the analog dimension of time, to code a real value at the time of the spike. This theory has been around for 50 years, but no one knows if it’s right. In certain subsystems, neurons definitely do that, like in judging the relative time of arrival of a signal to two ears so you can get the direction.

Five AI Startup Predictions for 2017

My favorite: “Full stack AI startups actually work”

When you focus on a vertical, you can find high level customer needs that we can meet better with AI, or new needs that can’t be met without AI. These are terrific business opportunities, but they require much more business savvy and subject matter expertise. The generally more technical crowd starting AI startups tend to have neither, and tend to not realize the need for or have the humility to bring in the business and subject matter expertise required to ‘move up the stack’ or ‘go full stack’ as I like to call it.

The Silicon Gourmet: training a neural network to generate cooking recipes

Pears Or To Garnestmeam

meats

¼ lb bones or fresh bread; optional
½ cup flour
1 teaspoon vinegar
¼ teaspoon lime juice
2  eggs

Brown salmon in oil. Add creamed meat and another deep mixture.

Discard filets. Discard head and turn into a nonstick spice. Pour 4 eggs onto clean a thin fat to sink halves.

Brush each with roast and refrigerate.  Lay tart in deep baking dish in chipec sweet body; cut oof with crosswise and onions.  Remove peas and place in a 4-dgg serving. Cover lightly with plastic wrap.  Chill in refrigerator until casseroles are tender and ridges done.  Serve immediately in sugar may be added 2 handles overginger or with boiling water until very cracker pudding is hot.

Yield: 4 servings

Also see In Which a Neural Network Learns to Tell Knock-Knock Jokes

Has the term AI become meaningless? How about MI instead

Ian Bogost writing for The Atlantic says that in too many cases today “artificial intelligence” is just another name for a fancy computer program. I don’t see it that way. I know from experience that what most data scientists are building is entirely different from what rank-and-file software developers are building. We use different tools and different approaches. And the data-driven learning algorithms we deploy at their best solve an entirely different class of problems than regular computer programs do.

Personally I like to call what we build “machine intelligence” rather than “artificial intelligence” because machine intelligence is really an alternative kind of intelligence, not an artificial version of human intelligence.

No, it’s not “Making computers act like they do in the movies” as Bogost quotes AI researcher Charles Isbell. That is too glib indeed. Why not let machines do what they do best rather than just serve as poor imitators of humans?

Part of what makes “artificial intelligence” feel a bit underwhelming is that we’ve barely begun to see what we might achieve with machine intelligence. Yes, self-driving cars are pretty amazing. I don’t have one myself (can’t afford a Tesla, darn) but I do adore the parking sensors on my SUV. They allow me to navigate around the dangerously-placed porch jutting out by the attached garage set back to the rear of my house. If I didn’t have them I probably would have hit the porch at least once already. The car can parallel park itself too but I’ve only tried that once, before I bought the car, with the salesperson sitting next to me.

I have faith that we are going to see many more amazing machine intelligence capabilities come out, as startups and big companies start focusing on vertical artificial intelligence in specific domains rather than continuing to build out horizontal machine learning capabilities for use by data scientists. Vertical AI (or MI) is tough. That’s where you have to get domain experts and data scientists together and figure out how to encode domain expertise and capabilities into machine learning models. It’s tough and slow work. I know. I’ve been doing it for a few years now in the temporary workforce management space. We’re beginning to see the payoff though, and that is truly exciting.

If you want to hear about it, I’m going to be at VMSA Live in Phoenix in early April talking about Machine Intelligence in Talent during the Executive Gateway session on Wednesday, April 5th. If you’ll be there, stop by.

The dialectic of analytics

From Gartner’s report The Life of a Chief Analytics Officer:

Analytics leaders today often serve two masters:

  • “Classic constituents,” with maintenance and development of traditional solutions for business performance measurement, reporting, BI, dashboard enhancements and basic analytics.
  • “Emerging constituents,” with new ideas, prototypes, exploratory programs, and advanced analytics opportunities.

I serve these two masters today in my job as VP, Data Science & Data Products at IQNavigator.

In my capacity as data science lead, we’re exploring innovative data-driven features built on data scientific techniques. In my capacity as data products lead, we are mostly still in the traditional business intelligence space, focusing on reporting and dashboards. Eventually the data products IQN offers will encompass both business intelligence (BI) and machine intelligence (MI) approaches but we have to start with what customers demand, and for now that is BI, not MI. I foresee that eventually MI will entirely eclipse BI but we’re not there yet, at least not in the non-employee labor management space.

I’ve come to believe in the importance of basic reporting and analytics capabilities, and that they should be distributed throughout the organization in self-service fashion. I see these capabilities as mainly playing a role in operational, day-to-day use, not in providing the aha! insights that people are so desperate to find and so sure exists if they only turn the right set of advanced analytic tools and personnel loose on their data.

I also foresee that the data science / machine intelligence space will mainly serve to optimize day to day operations, replacing business intelligence approaches, not surfacing wild organizationally transforming possibilities.

Gartner suggests developing a bimodal capability for managing analytics:

A bimodal capability is the marriage of two distinct, but coherent approaches to creating and delivering business change:

  • Mode 1 is a linear approach to change, emphasizing predictability, accuracy, reliability and stability.
  • Mode 2 is a nonlinear approach that involves learning through iteration, emphasizing agility and speed and, above all, the ability to manage uncertainty.

This applies to more than just analytics, of course. Gartner suggests it for a variety of IT management domains.

What would this look like? IQN already has an approach for product development that is bimodal in nature. We use agile development practices for product development. But we layer on top of it linear, time-based roadmapping as well as Balanced Scorecard departmental management. This is not as clumsy as you might imagine. It is more dialectic than synthetic in how it functions, with conflict occurring between the two approaches that is somehow resolved as we iteratively deliver features out into the marketplace, often on the schedule we promised (though not always).

In my own small world of data science and data products we do something similar, combining agile iterative processes with more linear and traditional project management. We use a Kanban-style process for data science projects but also layer on more waterfall-esque management for capabilities we need to deliver at a certain time to meet roadmap commitments.

I’m not sure I like the word “bimodal” to capture this approach. Maybe I will think of it as “dialectic.”

 

 

Daily Links 01/27/2015

Traditionally we say: If we find statistical significance, we’ve learned something, but if a comparison is not statistically significant, we can’t say much. (We can “reject” but not “accept” a hypothesis.)

But I’d like to flip it around and say: If we see something statistically significant (in a non-preregistered study), we can’t say much, because garden of forking paths. But if a comparison is not statistically significant, we’ve learned that the noise is too large to distinguish any signal, and that can be important.

So, to sum up, science is not about data; it’s not about the empirical content, about our vision of the world. It’s about overcoming our own ideas and continually going beyond common sense. Science is a continual challenging of common sense, and the core of science is not certainty, it’s continual uncertainty—I would even say, the joy of being aware that in everything we think, there are probably still an enormous amount of prejudices and mistakes, and trying to learn to look a little bit beyond, knowing that there’s always a larger point of view to be expected in the future. 

We really have no idea what dolphins or octopi or crows could achieve if their brains were networked in the same way. Conversely, if human beings had remained largely autonomous individuals they would have remained rare hunter-gatherers at the mercy of their environments as the huge-brained Neanderthals indeed did right to the end. What transformed human intelligence was the connecting up of human brains into networks by the magic of division of labour, a feat first achieved on a small scale in Africa from around 300,000 years ago and then with gathering speed in the last few thousand years.

Take Salesforce for example. Right now it just presents data, and the human user has to draw her or his predictive insights in their heads. Yet most of us have been trained by Google, which uses information from millions of variables based on ours and others’ usage to tailor our user experience … why shouldn’t we expect the same here? Enterprise applications — in every use case imaginable — should and will become inherently more intelligent as the machine implicitly learns patterns in the data and derives insights. It will be like having an intelligent, experienced human assistant in everything we do.

Paradigm shift: From BI to MI

I listened to a Gartner webinar Information 2020: Uncertainty Drives Opportunity given by Frank Buytendijk yesterday and it got me thinking about the evolution (/revolution?) from business intelligence (BI) to machine intelligence (MI). I see this happening but not as fast as I’d like, as jaded as I am about BI. Buytendijk gave me some ideas for understanding this transformation.

From his book Dealing with Dilemmas, here’s Buytendijk’s formulation of S curves that show the uptake of new technologies and approaches over time, and how they are then replaced by newer technologies and approaches.

Screen Shot 2015-01-21 at 11.43.46 AM

From the book:

A trend starts hopefully; with a lot of passion, a small group of people pioneer a technology, test a new business model, or bring a new product to market. This is usually followed by a phase of disappointment. The new development turns out to be something less than a miracle. Reality kicks in. At some point, best practices emerge and a phase of evolution follows. Product functionality improves, market adoption grows, and the profitability increases. Then something else is introduced, usually by someone else. … This replacement then goes through the same steps.

This is where I think we are with machine intelligence for enterprise software. We’ve reached the end of the line for business intelligence, the prior generation of analytics. It has plateaued. There’s not much more it can do to impact business outcomes–a topic that deserves its own post.

What instead? What next? Machine intelligence. MI not BI. Let’s let computers do what they do well–dispassionately crunch numbers. And let humans do what they do well–add context and ongoing insight and the flexibility that enterprise reality demands. Then weave these together into enterprise software applications that feature embedded, pervasive advanced analytics that optimize business micro-decisions and micro-actions continuously.

We’re not quite ready for that yet. While B2C data science has advanced, B2B data science has hardly launched, outside of some predictive modeling of leads in CRM and a bit of HR analytics. BI for B2B doesn’t give us the value we need. But MI for B2B has barely reached toddlerhood.

We are, in Buytendijk’s terms, in the “eye of ambiguity,” that space where one paradigm is plateauing but another has not yet proved itself. It’s very difficult at this point to jump from one S curve to the next–see how far apart they are?–because the new paradigm has not proven itself yet.

It’s almost Kuhnian, isn’t it?

Recently one of the newish data scientists in my group said, “it seems like a lot of people don’t believe in this.” This, meaning data science. I agreed with him that it had yet to prove its worth in enterprise software and that many people did not believe it ever would. But it seems clear to me that sometime–in five years? ten years?–machines will help humans run enterprise processes much more efficiently and effectively than we are running them now.

My colleague’s comment reminded me of some points Peter Sheahan of ChangeLabs made at the Colorado Technology Association’s APEX conference last November. He proposed that we don’t have to predict the future in order to capitalize on future trends because people are already talking about what’s coming. Instead, we need to release ourselves from legacy biases and practices. This was echoed by Buytendijk in his webinar: “best practices are the solutions for yesterday’s problems.”

It’s exciting to be in on the acceleration at the front of the S curve but frustrating sometimes too. It’s hard to communicate that data science and the machine intelligence it can generate are not the same as business intelligence and data storytelling. People don’t get it. Then a few do. And a few more.

I look forward to being around when it really catches on.