What you Need to Know Before Buying AI/Machine Learning

Tom H. C. Anderson
May 8th, 2018

7 Things to Know About AI/Machine Learning (Boiled Down to two Cliff Notes that are even more important).

In case you missed our session on Artificial Intelligence and Machine Learning (AI/ML) at the Insights Associations’ NEXT conference last week, I thought I would share a bit on the blog about what you missed. We had a full room, with some great questions both during and after the session. However, 30 minutes wasn’t enough time to cover everything thoroughly. In the end we agreed on four takeaways:

  • AI is part of how research & insights pros will address the ever-increasing demand for fast research results
  • AI Helps focus on the most important data
  • AI can’t compensate for bad data
  • AI isn’t perfect

So today I thought I would share seven additional points about AI/ML that I often get questions on, and then at the end of this post I’m going to share the ‘Cliff Notes’, i.e. I’m going to share just the 2 most important things you really need to know.  So, unless you want to geek out with me a bit, feel free to scroll to the bottom.

OK, first, before we can talk about anything, we need to define what Artificial Intelligence (AI) is and isn’t.

1. AI/ML definition is somewhat fuzzy

AI, and more specifically machine learning (ML) is a term that is abused almost as often as it is used. On the one hand this is because a lot of folks are inaccurately claiming to be using it, but also because not unlike big data, its definitions can be a bit unclear, and don’t always make perfect sense.

Let’s take this common 3-part regression analysis process:

  1. Data Prep (pre-processing including cleaning, feature identification, and dimension reduction)
  2. Regression
  3. Analysis of process & reporting

This process, even if automated would not be considered machine learning. However, switch out regression with a machine learning technique like Neural Nets, SVM, Decision Trees or Random Forests and bang, it’s machine learning. Why?

Regression models are also created to predict something, and they also require training data. If the data is linear, then there is no way any of these other models will beat regression in terms of ROI. So why would regression not be considered machine learning?

Who knows. Probably just because the first writers of the first few academic papers on ML refenced these techniques and not regression as ML. It really doesn’t make much sense.

2. There are basically 2 types of ML

Some ML approaches are binary like SVM (Support Vector Machines), for predicting something like male or female, and others like Decision Trees are multi class classification.

If you are using decision trees to predict an NPS rating on an 11 point scale then that’s a multi class problem. However, you can ‘trick’ binary techniques like SVM to solve the multi class problem by setting it up to run multiple times.

Either way, you are predicting something.

3. ML can be slow

Depending on the approach used, like Neural Nets for instance, training a model can take several days on a normal computer. There are other issues with Neural Nets as well, like the difficulty for humans to understand and control what they are doing.

But let’s focus on speed for now. Of course, if you can apply a previously trained model on very similar data, then results will be very fast indeed. This isn’t always possible though

If your goal is to insert ML into a process to solve a problem which a user is waiting for, then training an algorithm might not be a very good solution. If another technique, ‘machine learning’ or not, can solve the problem much faster with similar accuracy, then that should be the approach to use.

4. Neural Nets are not like the brain

I’ll pick on Neural Nets a bit more, because they are almost a buzz word unto themselves. That’s because a lot of people have claimed they work like the human brain. This isn’t true. If we’re going to be honest, we’re not sure how the human brain works. In fact, what we do know about the human brain makes me think the human brain is quite different.

The human brain contains nearly 90 billion neurons, each with thousands of synapses. Some of these fire and send information for a given task, some will not fire, and yet others fire and do not send any information. The fact is we don’t know exactly why. This is something we are still working on with hopes that new more powerful quantum computers may give us some insight.

We can however map some functions of the brain to robotics to do things like lift arms, without knowing exactly what happens in between.

There is one problematic similarity between the brain and Neural Nets though. That is, we’re not quite sure how Neural Nets work either. When running a Neural Net, we cannot easily control or explain what happens in the intermediary nodes. So, this (along with speed I mentioned earlier) is more of a reason to be cautious about using Neural Nets.

5. Not All Problems are best solved with Machine Learning

Are all problems best solved with ML? No, probably not.

Take pricing as an example. People have solved for this problem for years, and there are many different solutions depending on your unique situation. These solutions can factor in everything from supply and demand, to cost.

Introducing machine learning, or even just a simpler non-ML based automated technique can sometimes cause unexpected problems. As an example, consider the automated real-time pricing model which Uber used to model supply and demand as inputs. When fares skyrocketed to over $1,000 as drunk people were looking for a ride on New Years eve, the model created a lot of angry customers and bad press.

More on dangers of AI/ML in a bit…

6. It’s harder to beat humans than you think

One of the reasons ML is often touted as a solution is because of how much better than humans computers allegedly are. While theoretically there is truth to this, when applied to real world situations we often see a less ideal picture.

Take self driving cars as an example. Until recently they were touted as “safer than humans”. That was until they began crashing and blowing up.

Take the recent Tesla crash as an example. The AI/ML accidentally latched onto an older faded lane line rather than the newly painted correct lane line and proceeded without breaking, at full speed, into a head on collision with a divider. A specific fatal mistake no human would have been likely to make.

The truth is if we remove driving under the influence and falling asleep from the statistics (two things that are illegal anyway), then human accident statistics are incredibly low.

7. ML is Context Specific!

This is an important one. IBM Watson might be able to Google Lady Gaga’s age quickly, but Watson will be completely useless in identifying her in a picture. Machine learning solutions are extremely context specific.

This context specificity also comes into play when training any type of model. The model will only be as good as the training data used to create it, and the similarity to future data it is uses for predictions.

Model validation methods only test the accuracy of the model on the same exact type of data (typically a random portion of the same data), it does not test the quality of the data itself, nor the application of this model on future data other than the training data.

Be wary of anyone who claims their AI does all sorts of things well, or does it with extremely 100% accuracy.

My final point about Machine Learning & two Cliff Notes…

If some of the above points make it sound as if I’m not bullish on machine learning, I want to clarify that in fact I am. At OdinText we are continuously testing and implementing ML when it makes sense. I’m confident that we as an industry will get better and better at machine learning.

In the case of Tesla above, there are numerous ways to make the computers more efficient, including using special paint that would be easier for computer cameras to see, and traffic lights that send signals telling the computer stating “I am red”, “I am Green” etc., rather than having to guess it via color/light sensing. Things will certainly change, and AI/ML will play an important part.

However, immediately after my talk at the Insights Association I had two very interesting conversations on how to “identify the right AI solution”? In both instances, the buyer was evaluating vendors that made a lot of claims. Way too many in my opinion.

If you forget everything else from today’s post, please remember these two simple Cliff Notes on AI:

  1. You Don’t Buy AI, you buy a solution that does a good job solving your need (which may or may not involve AI)
  2. Remember AI is context specific, and not perfect. Stay away from anyone who says anything else. Select vendors you know you can trust.

There’s no way to know whether something is AI or not without looking at the code.

Unlike academics who share everything under peer review, companies protect their IP, Trade Secrets and code, so there will technically be no way for you to evaluate whether something actually is “AI” or not.

However, the good news is, this makes your job easier. Rather than reviewing someone’s code your job is simply still to decide whether the products solves your needs well or not.

In fact, in my opinion it is far more important to choose a vendor who is honest with you about what they can do to solve your problems. If a vendor claims they have AI everywhere that solves all kinds of various needs, and does so with 100% accuracy, run!

@TomHCAnderson

4 thoughts on “What you Need to Know Before Buying AI/Machine Learning”

  1. I’d add one more thing about AI (esp. vs. more traditional academic and applied research) that I think is important to understand at a high level. I am not an expert, but from what I know, the goal of AI is to help draw conclusions, predict, and help make decisions. However, because it is (often) set free to “ransack the data” to some or even a very great extent, AI is NOT necessarily designed to help you understand why it came to those conclusions, decisions, predictions. That means that when you “open the hood” on an AI model, it’s possible that you might get predictive power without being able to explain it clearly. Think about that before you commit: would you give recommendations to a client without being able to explain in detail and with confidence exactly how you came to them? Up to a point, maybe – but not without limit. I may be dwelling on a subtle (academic) distinction without a meaningful difference, not sure – just thinking out loud….

  2. Thanks Ed, yes that is especially the case with Neural Nets as I mentioned above. Another topic I meant should have added is really on the quality and amount of training data. This is an area where a lot of ad-hoc market research data can fall short on. Can’t stress that point enough….

  3. Great blog post and nice follow up for those of us at NEXT! It was interesting to watch Google giving a stunning demo of Assistant making an actual phone call as not only did the AI voice sound very natural but it was able to respond to the person on the other end who was unclear. I saw a presentation of using chatbot with qualitative research and had to wonder how this might make OLBB more efficient and take less professional time (and therefore cost less). You and I have discussed how inefficient text analytics is for say 700 pages of unique text coming out of an OLBB project. No one has cracked that code if you ask me. Thanks being one of the vendors who lays it all out.

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