Edtech Unicorns and JIT Training

Udemy went IPO last week, and PitchBook just published a note on the category, so I thought to write about my positive experiences with Coursera.  Online learning is segmented by subject, level, and quality of instruction.  See the research note for a complete rundown.

The edtech boom has not waned now that most schools and universities are again meeting in person. 

Coursera is oriented toward college credit and professional certification.  My instructor for neural nets, Coursera co-founder Andrew Ng, is a professor at Stanford.  They offer online degree programs in conjunction with major universities.  For example, you can earn a Master’s in Data Science through CU Boulder.

I was intrigued by that, but … I have a specific business problem to solve, and I already have grad-level coursework in statistics.  It doesn’t make sense for me to sit through STAT 561 again.  For me, the “all you can eat” plan is a better value at $50 per month.

What I need, today, is to move this code off my laptop and into the cloud.  For that, I can take the cloud deployment class.  If I run into problems with data wrangling, there’s a class for that, too.  This reminds me of that scene in The Matrix, where Trinity learns to fly a helicopter.

People can gain the skills they need, as and when they need them – not as fast as Trinity, but fast enough to keep up with evolving needs on the job.  I think this is the future of education, and 37 million students agree with me.

Looking for Work

I am ready for my next engagement.  This blog, together with my Linked-In profile, gives some indication of what I have accomplished and what I can do for your business.  There are also some case studies on my web site.

I am currently interested in digital retail, digital marketing, and artificial intelligence.  I generally do contract work, but will consider salaried.  If you have a job that requires my particular set of skills, please get in touch.

What is “Real” AI?

Clients ask me this all the time.  They want to know if a proposed new system has the real stuff, or if it’s snake oil.  It’s a tough question, because the answer is complicated.  Even if I dictate some challenge questions, their discussion with the sales rep is likely to be inconclusive.

The bottom line is that we want to use historical data to make predictions.  Here are some things we might want to predict:

  • Is this customer going to buy a car today? (Yes/No)
  • Which protection product is he going to buy? (Choice)
  • What will be my loss ratio? (Number)

In Predictive Selling for F&I, I discussed some ways to predict product sales.  The classic example is to look at LTV and predict whether the customer will want GAP.  High LTV, more likely.  Low LTV, less likely.  With historical data and a little math, you can write a formula to determine the GAP-sale probability.

What is predictive analytics?

If you’re using statistics and one variable, that’s not AI, but it is a handy predictive model just the same.  What if you’re using a bunch of variables, as with linear regression?  Regression is powerful, but it is still an analytical method.

The technical meaning of analytical is that you can solve the problem directly using math, instead of another approach like iteration or heuristics.  Back when I was designing “payment rollback” for MenuVantage, I proved it was possible to algebraically reverse our payment formulas – possible, but not practical.  It made more sense to run the calculations forward, and use iteration to solve the problem.

You can do simple linear regression on a calculator.  In fact, they made us do this in business school.  If you don’t believe me – HP prints the formulas on the back of their HP-12 calculator.  So, while you can make a damned good predictive model using linear regression, it’s still not AI.  It’s predictive analytics.

By the way, “analytics” is a singular noun, like “physics.”  No one ever says “physics are fun.”  Take that, spellcheck!

What is machine learning?

The distinctive feature of AI is that the system generates a predictive model that is not reachable through analysis.  It will trundle through your historical data using iteration to determine, say, the factor weights in a neural network, or the split values in a decision tree.

“Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.”

The model improves with exposure to more data (and tuning) hence Machine Learning.  This is very powerful, and will serve for a working definition of “real” AI.

AI is an umbrella term that includes Machine Learning but also algorithms, like expert systems, that don’t learn from experience.  Analytics includes statistical methods that may make good predictions, but these also do not learn.  There is nothing wrong with these techniques.

Here are some challenge questions:

  • What does your model predict?
  • What variables does it use?
  • What is the predictive model?
  • How accurate is it?

A funny thing I learned reading forums like KD Nuggets is that kids today learn neural nets first, and then they learn about linear regression as the special case that can be solved analytically.

What is a neural network?

Yes, the theory is based on how neurons behave in the brain.  Image recognition, in particular, owes a lot to the dorsal pathway of the visual cortex.  Researchers take this very seriously, and continue to draw inspiration from the brain.  So, this is great if your client happens to be a neuroscientist.

My client is more likely to be a technology leader, so I will explain neural nets by analogy with linear regression.  Linear regression takes a bunch of “X” variables and establishes a linear relationship among them, to predict the value of a single dependent “Y” variable.  Schematically, that looks like this:

Now suppose that instead of one linear equation, you use regression to predict eight intermediate “Z” variables, and then feed those into another linear model that predicts the original “Y.” Every link in the network has a factor weight, just as in linear regression.

Apart from some finer points (like nonlinear activation functions) you can think of a neural net as a stack of interlaced regression models.

You may recall that linear regression works by using partial derivatives to find the minimum of an error function parametrized by the regression coefficients.  Well, that’s exactly what the neural network training process does!

What is deep learning?

This brings us to one final buzzword, Deep Learning.  The more layers in the stack, the smarter the neural net.  There’s no danger of overdoing it, because the model will learn to skip redundant layers.  The popular image recognition model, ResNet152 has – you guessed it – 152 layers.

So, it’s deep.  It also sounds cool, as if the model is learning “deeply” which, technically, I suppose it is.  This is not relevant for our purposes, so ignore it unless it affects accuracy.

Digitally Disrupting Dealer Systems

I hesitated to use the D word here.  So much of digital is normal, healthy evolution, that saying “disruption” is like crying wolf.  So, I will digress briefly into that discussion before presenting my thesis, which is: traditional dealer-system providers are about to be whipsawed bigly by digital retail.

According to Gartner, digital disruption is “an effect that changes the fundamental expectations and behaviors … through digital capabilities.”  This idea of changing expectations is echoed by Aaron Levie, to the effect that businesses “evolve based on assumptions that eventually become outdated.”

If your UI even vaguely resembles an airline cockpit, you’re doing it wrong – John Gruber

Another common theme in studies of digital disruption is that people will come from outside the industry, bringing new attitudes and techniques that incumbents can’t match – something I like to call “advanced alien technology.”

Modal’s Aaron Krane came from online sports betting, and famously wondered why there is no “buy now” button on the Mercedes-Benz web site.  Andy Moss of Roadster came from online fashion retail.  I think I am on solid ground arguing that DR pioneers bring something fundamentally different.

In fact, I can identify the baseline assumption which is now outdated.  In olden times, the user of auto retail software was an auto retail employee.  These were experts, executing an esoteric process, and they could be trained to deal with crappy user experience and disjointed workflow.

Today’s user is, of course, the car buyer.  A few years ago, I wrote that each of the six canonical tasks in DR would need a “buddy” on the dealer side, with which to share information.  For example, the website may disclose prices for protection products, and it would be nice to pull retail markup from the menu system.

It’s hard to believe how quickly DR has evolved.  Roadster had just launched Express Storefront when I wrote that article, and already the buddy system is dead.  If a car buyer can desk her own deal, at home in her pajamas, why use a different system in the dealership?

The advantages to using the same system in store and at home include trust, transparency, cost savings, and reduced demands on the salespeople.  The new generation of in-store DR means that salespeople can be experts in customer service (and cars) instead of complicated software.

This marks the culmination of important trends in auto retail, from “one experience” at Sonic to “single point of contact” at Schomp, and it should serve as a wakeup call to old-school software vendors. Digital retail will drive a gradual shift in dealer process, but a rapid one in software.