Definitions – what is and is not AI

In 1950, a mathematician and world war II war hero Alan Turing asked a simple question in his paper Computing Machinery and Intelligence – Can machines think?. Today, we’re still grappling with that same question. Depending on who you ask, AI can be many things. Many maps exist out there on the internet, from expert systems used in healthcare and finance to facial recognition to natural language processing to regression models. As we continue with this chapter, we will cover many of the facets of AI that apply to products emerging in the market.

For the purposes of applied AI in products across industries, in this book, we will focus primarily on ML and deep learning (DL) models used in various capacities because these are often used in production anywhere AI is referenced in any marketing capacity. We will use AI/ML as a blanket term covering a span of ML applications and we will cover the major areas most people would consider ML, such as DL, computer vision, natural language processing, and facial recognition. These are the methods of applied AI that most people will come across in the industry, and familiarity with these applications will serve any product manager looking to break into AI. If anything, we’d like to help anyone who’s looking to expand into the field from another product management background to choose which area of AI appeals to them most.

We’d also like to cover what is and what isn’t ML. The best way for us to express it as simply as we can is: if a machine is learning from some past behavior and if its success rate is improving as a result of this learning, it is ML! Learning is the active element. No models are perfect but we do learn a lot from employing models. Every model will have some element of hyperparameter tuning, and the use of each model will yield certain results in performance. Data scientists and ML engineers working with these models will be able to benchmark performance and see how performance is improving. If there are fixed, hardcoded rules that don’t change, it’s not ML.

AI is a subset of computer science, and all programmers are effectively doing just that: giving computers a set of instructions to fire away on. If your current program doesn’t learn from the past in any way, if it simply executes on directives it was hardcoded with, we can’t call this ML. You may have heard the terms rules-based engine or expert system thrown around in other programs. They are considered forms of AI, but they're not ML because although they are a form of AI, the rules are effectively replicating the work of a person, and the system itself is not learning or changing on its own.

We find ourselves in a tricky time in AI adoption where it can be very difficult to find information online about what makes a product AI. Marketing is eager to add the AI label to their products but there still isn’t a baseline of explainability with what that means out in the market. This further confuses the term AI for consumers and technologists alike. If you’re confused by the terms, particularly when they’re applied to products you see promoted online, you’re very much not alone.

Another area of confusion is the general term that is AI. For most people, the concept of AI brings to mind the Terminator franchise from the 1980s and other futurist depictions of inescapable technological destruction. While there certainly can be a lot of harm to come from AI, this depiction represents what’s referred to as strong AI or artificial general intelligence (AGI). We still have ways to go for something such as AGI but we’ve got plenty of what’s referred to as artificial narrow intelligence or narrow AI (ANI).

ANI is also commonly expressed as weak AI and is what’s generally meant when you see AI plastered all over products you find online. ANI is exactly what it sounds like: a narrow application of AI. Maybe it’s good at talking to you, at predicting some future value, or at organizing things; maybe it’s an expert at that, but its expertise won’t bleed into other areas. If it could, it would stop being ANI. These major areas of AI are referred to as strong and weak in comparison to human intelligence. Even the most convincing conversational AIs out there, and they are quite convincing, are demonstrating an illusionary intelligence. Effectively, all AI that exists at the moment is weak or ANI. Our Terminator days are still firmly in our future, perhaps never to be realized.

For every person out there that’s come across Reddit threads about AI being sentient or somehow having ill will toward us, we want to make the following statement very clear. AGI does not exist and there is no such thing as sentient AI. This does not mean AI doesn’t actively and routinely cause humans harm, even in its current form. The major caveat here is that unethical, haphazard applications of AI already actively cause us both minor inconveniences and major upsets. Building AI ethically and responsibly is still a work in progress. While AI systems may not be sentiently plotting the downfall of humanity, when they’re left untested, improperly managed, and inadequately vetted for bias, the applications of ANI that are deployed already have the capacity to do real damage in our lives.

For now, can machines think like us? No, they don’t think like us. Will they someday? We hope not. It’s my personal opinion that the insufferable aspects of the human condition end with us. But we do very much believe that we will experience some of our greatest ails, as well as our wildest curiosities, to be impacted considerably by the benevolence of AI and ML.