By Tim Conkle, CEO | The 20
Full Forbes Article Here
Artificial intelligence (AI) offers the promise of bringing infinite automation at and beyond a level humanity is capable of at present. It also brings forth the promise of the singularity where all technical growth and development collapses into the automation cycle of advanced artificial intelligence. There isn’t an argument on whether this will happen or not (if we can avoid destroying ourselves until then), just a matter of when. The issue is that the “AI” of today isn’t really all that intelligent, but most people think it is.
Most modern AI is glorified machine learning (ML) at best. Even the most advanced lacks any comprehension or understanding of what it is doing. You have a black box; you plug data into it, and you get out some (hopefully) correct results. That isn’t to say ML isn’t impressive and can’t deliver results; you just need to know what you have and what you’ll get from the process.
There are some overblown claims in ML and AI, but if you understand what AI can do, and more importantly what it can’t, you can temper your expectations to fit reality. I’m going to refer to these various technologies as AI for the sake of convenience unless the distinction is pertinent.
The Promise Of AI
It feels like there’s some kind of AI or ML solution strapped into everything and anything. Security and networking solutions throw in AI. Analytic solutions bolt on ML. This isn’t a coincidence either; we’ve reached an awkward spot in the development of technology. We’re past the era of heuristics and human-generated algorithms in many fields.
There’s been an arms race in technology. Hackers use more and more novel techniques to exploit software and people at levels where even the slightest human slip-up can snowball into catastrophe. Modern viruses have become polymorphic messes of novel exploits that defy analysis outside of dedicated technical research. Humans can’t keep up. They need something at least fractionally intelligent for all the minutia — something that doesn’t get tired and doesn’t make mistakes.
This is where the promise of AI comes in. All of the various AI solutions claim to do this and more; they’ve unlocked the magic solution to every problem, and their solution does things better than any of the old-guard solutions. You just need to buy in, and all your problems will disappear like magic.
Limitations Of AI
Unfortunately, that’s all sales talk. The facts are buried in the fantasy, but it’s up to you to figure out what’s what. Even the simplest machine learning can bring something to the table, but you’re going to disappointed if you’re expecting a steak and you get a bowl of chips instead. Current-generation AI solutions are limited in many ways.
There isn’t an AI solution that has any degree of sentience or understanding of what it’s doing. You get your magic black box, which approximates a human by some measurement, but even the most advanced AI doesn’t understand the data, the results, what it’s doing or why it’s doing something. The AI can’t understand any part of the process, so bad data gets bad results. Another flip side to this, if the principle the process was created around was flawed, the entire process will be flawed as well. A person can use their better judgment to know whether something makes sense or not — a machine can’t (yet).
You need to know the right questions to ask to determine whether a product can or will fit your needs. What theoretical principles are behind the implementation? How does it collect or work with training data? Is the process adjusted regularly, or is it static? Are the real-world statistics in line with the theoretical statistics? What do you need to maintain? You need to pull at the thread until you unravel the whole thing to something you can understand. Otherwise, what exactly are you buying? There’s going to be a limit, and it’s up to you to figure out what it is.
Putting These Factors In Perspective
AI offers the promise of boundless improvement to virtually any process when done right, but that hinges on it being done right. What are you trying to solve, and how does the solution target that? You need the right solution for the right problem, or else you’re just wasting time. A good programmer won’t necessarily make a good technician.
If you’re introducing your findings to your company, you need to temper their expectations. A solution doesn’t have to be bad to not be the right choice, but many people treat it as a zero-sum game. This reductionist approach makes sense when you don’t understand all of the factors: Either it works, or it doesn’t.
You’re fighting an outside salesperson familiar with the product, what it can do and all of the smooth talking to sell your superiors on how sleek it is. If you don’t understand it and no one else at your organization does, who can make sense of the claims enough to make the right choice? To top this off, if you don’t understand it enough to relay the information, who will trust your interpretation of the solution for better or worse? It may be worthless, but you can’t just say that; you need to explain and show why it doesn’t fit.
The future is going to be lined with developments in AI, but that doesn’t mean every product that adds AI will be the right choice every time. What are you trying to do, and what does the solution do? Pull the fact out of the fantasy and see what you can actually expect. It’s not magic, but as Clarke’s Third Law states, “Any sufficiently advanced technology is indistinguishable from magic.” Do you want to fall for magic snake oil or see it for what it is and unlock the true potential behind a given technology?