I’m embarrassed. I feel a little bit like Frankenstein’s father. The “monster” I built is somewhat more mundane than the big guy of fiction. On the other hand, my creature is real-; it’s alive!-; and taken on a life of its own, morphing into something that’s just as evil and mendacious. Worse yet, my creation is spawning a whole new generation of artificial intelligence impostors and other simple macros masquerading as intelligent machines.

 I wrote briefly about this problem and the rampant confusion in a  recent post, but I think it needs some further explanation so we can all try to get on the same page and set some basic ground rules about this A.I. stuff.

 About 40 years ago, I built a relatively simple system that I named the “consultant in a box.” The system linked specific numerical scores and behavioral rankings with phrases and texts, which were then combined by a word processor into what appeared to be evaluative paragraphs prepared by a sociologist or psychologist. We sometimes jokingly called this glorified Wang program our “shrink on a stick” because it created frighteningly convincing formulations that could fool most readers and reviewers into thinking the reports were the product of thorough research and thoughtful analysis. They were instead pro forma pap being poured out of a production line.

 On its very best days, my little monster machine would put out dozens of slick little synopses that ranked and rated sales people and jobseekers. These rankings were no better than, and about halfway between, horoscopes and fortune cookies. But they were completely convincing because we had figured out how to quickly, easily and inexpensively tell a whole lot of people in a hurry just what they thought they wanted to hear.

 In the years that followed, I incorporated my evaluation engine into a variety of different technical and mechanical environments. It even eventually directed the way various characters reacted and responded to choices made by each player in one of my more successful CD-ROM computer games, called “ERASER TURNABOUT” and published by Warner Bros. Interactive. The way the player initially responded to a detailed “interactive” video conversation with the psychiatrist in the game determined the ways in which the game progressed and the player’s journey as well as his or her likely success. The system essentially produced a different variation of the game every time it was played. These days-; literally decades later-; companies like Narrative Science turn baseball box scores into newspaper stories and stock stats into portfolio analyses, albeit with a touch more science and a little less schmaltz.

 But my shabby past came painfully back to haunt me just recently during a board meeting, as we sat reviewing reports on prospective candidates while trying to find a great new head of sales for one of my portfolio companies. One of the participants started trying to parse and analyze a couple of the boilerplate comments buried in a bogus report that was put together by the company’s HR team. She might just as well have been reading tea leaves. Turns out the HR guys used some outside consulting/recruiting firm that was in turn using a system just like my old one to crank out this crap and try to convince some clients that what they were reading had even the slightest connection to reality.

 If it hadn’t been so ludicrous (no offense to Ludacris), it would have just been pitiful to see such a waste of time and money. I wouldn’t rely on a program like this to pick a horse in the fifth race at Pimlico much less to select the person you were trying to hire to help you build your business. But there we were watching someone trying to make sense out of sentences arranged by a software program that had about as much substance as the server aligning the silverware brings to the task of setting the table. The server knows exactly where to place the spoon, but hasn’t the slightest idea of whether you’re going to use it to eat your soup or your spumoni. It’s all a matter of placement and proximity-;location and language-;and not about performance or personality. Just because you know where to stick a fork doesn’t mean that you understand what to do with it.

 And that’s what got me thinking again about what’s so wrong about the way too many people are talking about artificial intelligence. As I’ve said before, true A.I.-; when it arrives-; won’t be about business process automation. This is the easy stuff that bots ought to be doing already in a bunch of big businesses. A.I. is not as simple as predetermined pattern recognition (or tagging a million pix for future matching), which is really all about accessing memory. Simply asking a machine to find and match text in a database that aligns with the content of queries initiated by a user isn’t moving the needle forward. It’s certainly not to be confused with “reading” or as exhibiting any actual intelligence. And finally, there’s nothing to get all excited about regarding accelerated data sourcing, which is nothing more than rapid recall and retrieval. So, what’s a simple test for the real A.I. of the future?

 I think A.I. comes down to two simple words: Extraction and Extrapolation. A true A.I. system will perform both functions without supervision or ongoing direction. It will have procedural rules, some data management protocols, and guard rails, but no a priori restrictions or limitations.

Extraction in this context means that the system will continuously review the flow of data (which is basically unstructured) and from the data flow it will derive and identify behaviors, frequencies and trends-; not by comparing them to pre-existing models or patterns, but instead by finding new ones that were previously unknown, unbounded or otherwise unidentified. The determination of the ranges and boundaries of these new “objects” will be among the most critical chores of the new systems, which will need to ascertain the extent, perimeters and parameters of the new patterns and objects by applying new measurements of power, density and frequency to the data flows. As the power and presence of the new objects diminishes at the margins, the boundaries of the new phenomena will be ascertained and locked in.  

 Extrapolation in this context means that the system will have the independent capacity to capture these new patterns and objects. And beyond that, to rationally build upon them, expand them and-; most particularly-;generalize their patterns and behavior into other areas, both adjacent and remote. Critically, the fundamental activities will not be the incremental expansion of prior experiences and analytical results, but instead create and develop new projections and anticipatory expectations of future behaviors and activities.

 The bottom line: if you already know why, it ain’t A.I.