CIOs Start To Rethink Artificial Intelligence

Is it possible that artificial intelligence is not all that smart?
Is it possible that artificial intelligence is not all that smart? Image Credit: Alan Levine

Just about every CIO has heard about artificial intelligence (AI) and the wonderful things that it can do. In fact, by now we are all using some form of AI in our mobile phones and in those Alexa speakers that everyone has laying around their homes. What these devices have been able to accomplish using AI technology have been very impressive; however, the future has always been brighter. We’ve been told for a long time that this is just the start of the AI revolution and that we should expect great things to be coming our way in the future. However, should CIOs really believe this? Is the promise of AI all that it’s been made out to be?

The Problem With AI

CIOs need to understand that a funny thing happens among engineers and researchers who build artificial intelligence once they attain a deep level of expertise in their field. It turns out that those that understand what actual, biological intelligences are capable of, conclude that there’s nothing “intelligent” about AI at all. In a certain sense artificial intelligence is a bad name for what it is companies are doing here. When CIOs use the words ‘artificial intelligence’ to another intelligent human being, they start making associations about their own intelligence, about what’s easy and hard for them, and they superimpose those expectations onto these software systems. This might seem like something that is a purely academic debate. Whatever we call it, surely what matters most about “AI” is the way it is transforming what can seem like operations at almost every industry on earth? CIOs have to be careful to not forget the potential it has to displace millions of workers in trades ranging from white to blue collar, from the back office to trucking.

And yet, across the fields it is disrupting or supposed to disrupt, it turns out that AI has fallen short of many of the promises made by some of its advocates. These disappointments range from the disappointment of IBM’s Watson to the forever-moving target date for the arrival of fully self-driving vehicles. CIOs have to face the fact that words have power. And names, in particular, can carry weight. This is true when they describe systems so complicated that, in their particulars at least, they are beyond the comprehension of most people. Inflated expectations for what AI can do have already resulted in setbacks for the field. In both the early 1970s and late 1980s, claims about how human-level AI would soon arise were made about systems that would seem primitive by today’s standards. As silly as these claims were, they didn’t stop extremely smart computer scientists from making them. The disappointing results that followed led to “AI Winters” in which funding and support for projects dried up.

Next Steps For AI

No one is predicting that we’ll be going through another AI Winter anytime soon. Globally, US$37.9 billion has been invested in AI startups in the past year. This is on pace to roughly double last year’s amount. The challenge that CIOs are facing is that the muddle that the term AI creates can fuel a tech-industry drive to claim that every system involving the least bit of machine learning qualifies as AI. These products are then claimed to be potentially revolutionary. Vendors who call these piles of complicated math with narrow and limited utility “intelligent” also contribute to wild claims that “AI” products will soon reach human-level intelligence. These claims can mislead both the public and policy makers who must decide how to prepare national economies for new innovations.

Inside and outside the AI field, people routinely describe AI using terms we typically apply to our minds. That’s probably one of the reasons so many are confused about what the technology can actually do. Claims that AI will soon significantly exceed human abilities in multiple domains have been made. The tendency for CIOs and researchers alike to say that their system “understands” a given input be it gigabytes of text, images or audio, or that it can “think” about those inputs, or that it has any intention at all, are examples of “wishful mnemonics.” CIOs need to understand that AI is somewhat of a misnomer. What we currently call AI doesn’t really fulfill the early dreams of the field’s founders – either to create a system that can reason as a person does, or to create tools that can augment our abilities. Instead, today’s AI systems use massive amounts of data to turn very, very narrow tasks into prediction problems.

When AI researchers say that their algorithms are good at “narrow” tasks, what they really mean is that, with enough data, it’s possible to “train” their algorithms to, say, identify a dog. But unlike a human toddler, these algorithms tend not to be very adaptable. For example, if they haven’t seen dogs in unusual circumstances – say, jumping a bush – they might not be able to identify them in that context. And training an algorithm to identify dogs generally doesn’t also increase its ability to identify any other kind of animal or object. Identifying cows means more or less starting from scratch.

For consumers, practical applications of AI include everything from recognizing their voice and face to targeting ads and filtering hate speech from their social media. For engineers and scientists, the applications are, arguably, even broader – from new drug discovery and treating rare types of diseases to creating new mathematical tools that are broadly useful in much of science and engineering. Anyplace that advanced mathematics is being applied to the real world, machine learning is having an impact. There are realistic applications coming out of the current brand of AI products and those are unlikely to disappear. They are just becoming part of the scientist’s toolbox: You have test tubes, a computer and now your machine learning. Once we are able to liberate ourselves from the mental cage of thinking of AI as akin to ourselves, we can start to recognize that it’s just another pile of math that can transform one kind of input into another – that is, software.

What All Of This Means For You

One of the key roles of being a CIO is staying on top of new innovations in technology. It is our job to make sure that as technology becomes available, we understand what it is, how it can be used, and if it is a good fit for our company. Assuming that it is a good fit, it then becomes our job to create a plan for deploying it within our company. One such new technology is artificial intelligence (AI). CIOs have been hearing a great deal about this new technology lately. Now they are going to have to determine how much of what they have been hearing is actually true.

One of the biggest problems with the field of AI is that it makes us think about our own brains and what they are able to do. This can lead to a great deal of disappointment when AI is unable to live up to our expectations. This disappointment has led to a number of “AI Winters” where funding and interest in AI has dried up. Currently every product that is being created that has even a little bit of AI in it is being touted as the next big thing in AI. What CIOs need to realize is that currently AI systems can be created that are very good at making predictions for very narrow tasks. CIOs need to view AI as being another useful tool in the hands of the right people.

There is no question that AI is here to stay. We are seeing it become built-it to a number of the products that we now use on a daily basis. CIOs need to understand what the current limitations of today’s AI systems are. We have understand what they are currently good at doing. Knowing this we can make good decisions about where AI can be deployed in our companies in order to generate the most return on our investment in it.

– Dr. Jim Anderson Blue Elephant Consulting –
Your Source For Real World IT Department Leadership Skills™

Question For You: Do you think that CIOs should move forward with AI today or wait to do so until later on?

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What We’ll Be Talking About Next Time

Although CIOs would like to spend their time dealing with issues like servers, networking, and how best to move legacy applications into the cloud, all too often we get dragged into more down-to-earth issues. One of the biggest issues that a lot of companies have to deal with is crime. Crimes that can affect a business can range from stealing inventory to assaulting workers. As CIOs we have access to some pretty high-powered technology. Is there any way that we could harness this technology in order to help our companies deal with the crime that they are facing on a daily basis?