To be AI-first, do AI last
Artificial intelligence has become a buzzword in the tech industry. Companies are eager to present themselves as “AI-first” and use the terms “AI,” “machine learning,” and “deep learning” abundantly in their web and marketing copy.
What are the effects of the current hype surrounding AI? Is it just misleading consumers and end-users or is it also affecting investors and regulators? How is it shaping the mindset for creating products and services? How is the merging of scientific research and commercial product development feeding into the hype?
These are some of the questions that Richard Heimann, Chief AI Officer at Cybraics, answers in his new book Doing AI. Heimann’s main message is that when AI itself becomes our goal, we lose sight of all the important problems we must solve. And by extension, we draw the wrong conclusions and make the wrong decisions.
Machine learning, deep learning, and all other technologies that fit under the umbrella term “AI” should be considered only after you have well-defined goals and problems, Heimann argues. And this is why being AI-first means doing AI last.
One of the themes that Heimann returns to in the book is having the wrong focus. When companies talk about being “AI-first,” their goal becomes to somehow integrate the latest and greatest advances in AI research into their products (or at least pretend to do so). When this happens, the company starts with the solution and then tries to find a problem to solve with it.
Perhaps a stark example is the trend surrounding large language models, which are making a lot of noise in mainstream media and are being presented as general problem-solvers in natural language processing. While these models are truly impressive, they are not a silver bullet. In fact, in many cases, when you have a well-defined problem, a simpler model or even a regular expression or rule-based program can be more reliable than GPT-3.
“We interpret AI-first as though we ought to literally become solution-first without knowing why. What’s more is that we conceptualize an abstract, idealized solution that we place before problems and customers without fully considering whether it is wise to do so, whether the hype is true, or how solution-centricity impacts ourbusiness,” Heimann writes in Doing AI.
This is a pain point that I’ve encountered time and again in how companies try to pitch their products. I often read through a bunch of (sometimes self-contradicting) AI jargon, trying hard to find out what kind of a problem the company solves. Sometimes, I find nothing impressive.
The AI hype has attracted interest and funding to the field, providing startups and research labs with plenty of money to chase their dreams. But it has also had adverse effects. For one thing, using the ambiguous, anthropomorphic, and vaguely defined term “AI” sets high expectations in clients and users and causes confusion. It can also drive companies into overlooking more affordable solutions and waste resources on unnecessary technology.
“What is important to remember is that AI is not some monolith. It means different things to different people,” Heimann said. “It cannot be said without confusing everyone. If you’re a manager and say ‘AI,’ you have created external goals for problem-solvers. If you say ‘AI’ without a connection to a problem, you will create misalignments because staff will find problems suitable for some arbitrary solution.”
Academic AI research is focused on pushing the boundaries of science. Scientists study cognition, brain, and behavior in animals and humans to find hints about creating artificial intelligence. They use ImageNet, COCO, GLUE, Winograd, ARC, board games, video games, and other benchmarks to measure progress on AI.
