Part 2
I have split this blog into two parts, as it is over 2000
words in its entirety. Here’s a link to
Part 1:
Varieties of AI
The analytics problem solving stream goes something like
this:
- Description
- Diagnostics
- Prediction
- Prescription
It is something like what a medical doctor does: look at
symptoms, diagnose disease, predict course of disease, prescribe a
treatment. That general process works
for a lot of problems in business (and life in general). I think most people would place different
types of AI at different points on this stream, with a heavy emphasis on
Prediction.
A big division point in the spectrum of data science and AI
methods is between Unsupervised and Supervised methods.
Unsupervised methods
Unsupervised methods include methods such as cluster
analysis and social graphs. Essentially,
you have a lot of data and you are trying to see what patterns or commonalities
exist in that data. You might have a lot
of data on customers, and want to group them into meaningful sets, to aid in
marketing and such (e.g. early adapters, mainstream tech users, tech phobics,
etc.). Or you might have Facebook like
data, and want to see who connects with whom in your network, who is
influential, and so forth. In scientific
fields, that might help in categorizing galaxies or life forms.
I have done some work with these methods, especially K-means
clustering, on such things as student surveys.
They can be interesting, but it can be difficult to persuade decision
makers that they are all that useful.
Supervised methods
Supervised methods include methods most of those based on
neural nets, commonly called machine learning, though Decision Trees would also
fall into this category. The idea here
is that you have a lot of data as input, which is fed through various layers,
using an algorithm rather similar to what neurons do in the brain, tweaking
coefficients that weight inputs, so that the output comes closest to matching
some “ground truth” that you have also fed into the algorithm.
Input layer =>
hidden layer =>
output layer
It is interesting to see the code for a simple feed-forward
network. These implementations can be
done with surprisingly short programs.
However, there are several extensions and refinements of this basic
technique, such as reinforcement learning, transfer learning and deep
learning. They obviously can become
extremely complicated, using additional layers and various feedback mechanisms
to increase the system’s problem solving ability.
A good example is the Alpha Go program, which beat the
world’s best at an immensely difficult game of strategy. As a computer, the program had the advantage
of being fed much historical data on games that humans have played, then
augmented that with millions of games that it played against itself. This reinforcement learning could far
outstrip what any human could learn, limited as we are by our inability to play
more than a few tens of thousands of games in a human lifetime.
Such methods don’t always scale well or transfer well to
different problems, though. To do some
might require a lot of programming maintenance and tweaking, which is expensive
and requires human intervention, which defeats the purpose of AI, at least to
some extent.
An Example, and the Notion of Explainable AI
A fairly simple example that I have used is a multilevel
perceptron neural net, to predict the likelihood of graduate students
completing their degrees. A number of
data elements pertaining to graduate student characteristics were fed into the
system (age, gender, grades, subject, financing, etc.), which then came up with
a prediction model that most closely fitted the actual graduation outcomes that
were given the system, as a sort of training set. That model could then be used to predict the
likelihood of a student graduating, who was not in the training set.
This exploratory analysis was much like a logistic
regression model that I had written to solve the same problem. In my example, the two methods had almost
exactly the same prediction success. The
machine learning model might have done better if I had fed it a lot more
variables, and/or had a lot more cases. It’s
hard to say for sure, as I simply didn’t have more data to experiment with. It was interesting that neither model
actually improved very much on the decisions actually made by admissions
people, overall. In a sense,
crowd-sourcing the decisions seemed to be about as effective as statistical or
machine learning modelling.
Perhaps the most important difference between the machine
learning technique and the older statistical technique (logistic regression) was
that the machine learning method wasn’t very useful for explanation. It could make accurate predictions, but it
wasn’t possible to say which variables that were fed into the model were the
important ones. Old-style logistic
regression, on the other hand, did give estimates of the relative importance of
the variables. That’s important for
understanding a problem and therefore, understanding how to apply a solution to
that problem.
This brings up the concept of “explainable AI”, that some
governments and corporations are insisting upon. In a sense, machine learning can be thought
of as a form of pattern matching, or profiling.
This has obvious political and ethical implications. A machine learning model might be excellent
in predicting credit-worthiness, for example, but part of that excellence might
be based on what would be considered illegal discrimination, if done by a human
being. Obviously, that’s a serious
problem.
For example, in the graduate student problem, my logistic
regression might show students from certain backgrounds do less well than
students from other backgrounds. That
knowledge might be used to filter out those students, or alternatively to
assist them to succeed (with extra funding, for example). But, with a pure machine learning model, one
would never know just why the system is making the predictions that it is, so
these students would be filtered out, without anyone even knowing about it
(unless humans noticed a problem, via their own pattern matching abilities).
Even in something as seemingly objective as self-driving
vehicles, these problems could sneak in.
For example, a self-driving automobile AI might discover that young
women were much less likely to j-walk as young men, and therefore give them
less of a safety margin.
Work is being done on explainable AI. In fact, my SPSS multi-level perceptron
routine had a “relevance factor” option, that seemed to go some way towards
this, for my graduate student model.
But, this could take a long time, and never be perfect. After all, we don’t really know why our own
human neural nets give us the usually reliable goal directed behaviours that
they do.
Conclusion
Some final comments:
- Machine learning will become more and more common.
- Actually implementing it in a business or industry is always a form of Research and Development.
- It takes time and money to:
- o define your problem.
- o know your data, and prepare it to help solve the problem.
- o define your solution.
- You will need to maintain the system.
- AI should be thought of as helping human experts, not replacing them.
- Unless,
of course, Skynet becomes self-aware, in which case all bets are off.
----------------------------------------------------------------------------------------------------------------
Now that you have read of some cutting edge science, you
should consider reading some Science Fiction.
How about a short story, set in the Arctic, with some alien and/or
paranormal aspects. Only 99 cents on
Amazon.
The Magnetic Anomaly: A Science Fiction Story
“A geophysical crew went into the Canadian north. There were
some regrettable accidents among a few ex-military who had become geophysical
contractors after their service in the forces. A young man and young woman went
temporarily mad from the stress of seeing that. They imagined things, terrible
things. But both are known to have vivid imaginations; we have childhood
records to verify that. It was all very sad. That’s the official story.”
Amazon U.S.: https://www.amazon.com/dp/B0176H22B4
Amazon U.K. https://www.amazon.co.uk/dp/B0176H22B4
Amazon Canada. https://www.amazon.ca/dp/B0176H22B4
“The Zoo Hypothesis”, an Alien Invasion Story
Basically, Enrico Fermi argued (quite convincingly, to many observers), that there had been ample time for an alien intelligence to colonize the galaxy since its formation, so where are they? The Zoo Hypotheses says that they are out there, but have cordoned off the Earth from contact, until we are sufficiently evolved or culturally advanced to handle the impact of alien contact.
This story takes a humorous tongue in cheek approach to that explanation. It also features dogs and sly references to Star Trek. Talk about man’s Best Friend.
Amazon U.S.: https://www.amazon.com/dp/B076RR1PGD
Amazon U.K.: https://www.amazon.co.uk/dp/B076RR1PGD
Amazon Canada: https://www.amazon.ca/dp/B076RR1PGD
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