Why We Still Can’t Manage Artificial Intelligence

Now that we’re up to our necks in alligators—namely, the conflict between innovation and governance in AI—we’re starting to see why we haven’t gotten it right—or even seem to be far enough along the way. The answer is, we’re not ready. (In all fairness, some of us are, but generally speaking, not so much.)

First, a short – I repeat, short – history of artificial intelligence in one paragraph.

A Brief History of Artificial Intelligence

In ancient times, myths about artificial beings with intelligence or consciousness flourished. (AI, as you can see, is nothing new.) In the 1940s, machines based on the abstract essence of mathematical reasoning were invented. The 1950s saw the birth of AI research; for the rest of the 1920s,^t century, innovation centers were created all over the world; in this century, machine learning and deep learning Generative AI were invented and produced. The world went crazy.

That wraps up everything you need to know about the history of AI – for now. As promised, one paragraph.

The Race Between Innovation and Management

Do you see anything happening recently that didn’t happen in the millennia before? Demand. Through growing awareness and falling costs, AI has become… what?…hot! The market has overtaken the lab, and the race has begun. And in the race between innovation and management, innovation always comes out of the blocks first, and management has to catch up.

So where do we stand? AI innovation and development—both good and bad actors—are causing other well-meaning good actors to grumble about governance. In the meantime, the amount and pace of development are staggering, both beneficial and disruptive, and it is there, as one might expect, that both opportunities and problems lie.

The relationship between problems and opportunities, by the way, is simple. Problems are poorly dressed opportunities. That’s all.

So why is there such a disconnect between AI innovation and AI governance? I think the answer is embarrassingly simple. There is a component that exists—or should exist—between the two: AI regulation. In my opinion, you can’t govern something if you don’t know what you’re governing, and if you look at the definition of governance, you’ll see what I mean.

What is management?

Governance is the overall system or framework of processes, functions, structures, rules, laws, and norms in an organized group of entities. All of these elements—structures, rules, etc.—must be in place to be managed,

And they’re not. We love innovation, but we loathe regulation. Right now, we’re the proverbial kids in a candy store. We know that regulation, to some extent, is good in the long run, but in the here and now, gosh, it’s a pain. That’s not the most sophisticated way to say it, but go ahead and tell me I’m wrong.

Meanwhile, everyone is running around talking about governance in such a self-aggrandizing way, but they don’t get the heart of the matter. When we regulate – and then we know what we are regulating – we will definitely do it well in terms of governance.