We shouldn’t have to remind you, but since too many people are falling for, and buying into, the hype and selecting tech that does not, and can not, ever,work, we are going to remind you yet again.

Computers do NOT think!

To think is to direct one’s mind … where one is an intelligent being, not a dumb box. Computers thunk … they compute using algorithms (which are hopefully advanced and encapsulate expert guidance and knowledge, but that is far from guaranteed).

Computers do NOT learn.

Appropriately selected and implemented probabilistic / statistical / machine learning algorithms will improve their performance over time as more data becomes available, but they do not learn. Learn is to acquire knowledge (or skill), and by definition, knowledge can only be acquired by an intelligent being.

Computer Programs Can Adapt …

but there’s no guarantee the adaption is going to improve their performance under your definition, or even maintain their performance. Their performance could actually decrease over time.

What is critically important is that there are two primary types of algorithms that can be used to create an AI application:

Deterministic and Probabilistic

A deterministic algorithm is one that, by definition, given a particular input will, no matter what, always produce the same output, with the underlying machine always passing through the same sequence of states. As long as you don’t screw up the input, or the retrieval of the output, (and, of course, the hardware doesn’t fail), it is 100% reliable.

A probabilistic algorithm, in comparison, is an algorithm that incorporates randomness or unpredictability into its execution, and may or may not produce the same output given successive iterations of the same input. Nor is there even any guarantee that the algorithm will produce a correct, or even an acceptable, input a given percentage of the time. Well designed, these algorithms may allow for consistently faster computation, better identification of edge cases, or even a lower chance of error, on average, for a certain class of inputs (but with the caveat that other classes of inputs may suffer a higher error rate).

Deterministic algorithms can be relied on to execute certain tasks and functions autonomously with no oversight and no worry. Probabilistic cannot. In other words, you cannot assign a probabilistic algorithm a task for autonomous computation unless you can live with the worst possible outcome of the algorithm getting it wrong. And this is what Gen-AI, and most of today’s “AI” tech, is based on.

This is the critical problem with today’s AI-tech and AI-Hype. Especially when a probabilistic system can, by definition, use any method it likes to determine a probability (which may or may not be at all appropriate, since a model is only valid if it accurately captures the “population” dynamics) and may, or may not, be accurate. For some of these situations, it will be the case that neither the company nor the provider of the system will have enough historical data (market situation and outcome) to even attempt to make a reasonable prediction, and there definitely won’t be enough data to know the accuracy, because standard measures of model accuracy (like the Brier Score), tend to require a lot of data, especially if you have a situation where you need to accurately identify rare events as this could require 1,000 or more “data points” (which, in a typical market scenario, would require enough data to identify the market condition and then the unexpected change”).

(And this is exacerbated by the reality that, for many of these situations, one could likely employ more traditional “statistical techniques” like trend analysis, clustering, classical machine learning, etc. to solve much of the problem at hand.)

It’s important to remember that Gen-AI LLMs, which power most of the new (fake) agentic tech, are all probabilistic based (and designed in such a way that hallucinations are a core function that CAN NOT be eliminated), and much of it is complete and utter garbage for what it was designed for, and even worse for tasks it wasn’t defined for (like math and complex analyses). (Everyday we see a new example of complete and utter failure, often due to hallucinations, of this tech. For example, you can’t even get a list of real books out of it — as per a recent contribution to the Chicago Sun Times which which published its Summer Reading List of 15 books, of which only 5 of which actually exist. And then there are numerous examples of lazy lawyers getting raked over the coals by judges for using ChatGPT to do their homework and quoting fake cases!)

While we do need to augment purely deterministic tech with more adaptive tech that uses the best “statistical techniques” to more quickly adapt to situations, we need to spell out the techniques and restrict ourselves to what is now “classic machine learning” where the algorithms have been well researched and stress tested over decades (not modern Gen-AI powered agentic tech that has worse odds than your local casino). At least then we’ll have confidence and can enforce bounds on what the solution can actually do (to limit any potential damage).

Especially now that we finally have the computing power we need to effectively use tried-and-true “classic” ML/AI techniques that require large data stores and huge processing power for highly accurate predictions. The reality is that even though this tech has existed for at least 25 years, the computing power required made it totally impractical for all but the most critical situations. Twenty-five years ago, a large Strategic Sourcing Decision Optimization (SSDO) model would run all weekend. Today you can solve it in a few seconds on a large rack server (with 64 cores, GB of cache, and high-speed access to TB of storage). The fact that we finally have (near) real time capability means that this tech is not only finally usable in all situations, but finally effective.

[And if vendors actually hired real computer scientists, applied mathematicians, and engineers and built more of this tech, instead of script kiddies cobbling together LLMs they don’t understand, we would be a decade ahead of where we are today.]