I completely agree with this. Let me add a non-US perspective that may surprise some people.
I think a big part of what is holding back many companies from making effective, genuine, real-world use of AI is that a significant majority of the individuals involved are bad at their jobs.
On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decisions than businesspeople. In my view, this includes things that would traditionally be considered business decisions. The business is unwilling to transition from "working on the solution to the problem" to "working on the AI system that solves the problem" (assuming that they even understand what "the problem" is).
On the technology side, I see a massive oversupply of badly underqualified and generally ignorant "data scientists" and "ML engineers" who have just enough bootstrapped understanding and familiarity with the plethora of (absolutely fantastic) open-source tools to fake an entry into the area, but do not have the genuine depth of background that is needed to actually plan, design, and deliver a high quality solution.
Yes, I have met people without "credentialed" backgrounds who are very good indeed, and yes I have met forward-thinking business people, but the number of people who are in these categories is a tiny fraction of the number of people who think they are in these categories.
And that's why businesses are getting less keen on AI.
>On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decisions than businesspeople.
Maybe acknwledging this would mean losing their jobs. Buisness people have mortgages and children and they probably don't like the idea of having their job replaced by a robot. It is important to remember that the admin/money-side people are not playing the game that they tell the operations people they are playing. They want to keep their jobs, accrue power and money, they don't really care about AI or sales, those are just tools for their own game.
They said technical people, not robots. Perhaps a subtle distinction ;)
Agree totally with the latter part though. In vast swathes of the business world, technology is not perceived as a way to gain a competitive advantage but rather something between an interesting novelty and an annoyance. The problem is there's a cultural expectation that you be "innovative" which tends to be treated as a synonym for deployment of computer-related technology upgrades (although of course it doesn't have to mean this).
But to people who have no intrinsic interest in technology, there's no real way for them to actually innovate. They just don't know where to start. So they latch on to trends they read about in the Economist or NYT on a truly massive scale. They spend days or weeks making PowerPoints about the transformative potential of blockchain, IoT or AI. They take a few programmers who are kicking around but seem bored and allocate them to an "innovation lab" where they putter around making prototypes and having fun but never impacting the business in any way.
This ticks the box labelled "we are innovative" without requiring anyone to think too hard, learn anything new or take any risks, all things that the generic unskilled graduate managerial class in our society hate doing. And of course technology upgrades are risky. When run by non-technical people they tend to go wrong in spectacularly expensive and mysterious ways, so a lot of business executives would rather pull their own teeth out than plan a major IT upgrade. This is partly why tech firms exist as a concept.
As for AI, you can't replace most of these jobs with AI because their outputs are undefined to begin with. And to be frank, my observations over the years has been that a lot of these jobs appear to be undefined in order to enable a form of cultural stuffing. If you're a non-technical executive you wouldn't want your nice business to end up filled with geeks playing board games and aggressively proving other people wrong now, would you? Better keep them diluted by hiring lots of PMs, "operations specialists", "customer analysts", "business insight teams" and so on. That way you can meet your diversity numbers and be surrounded by like-minded people.
Your perspective is not just 'non-us'. The thing is since the early 80s computers can generate huge swaths of data. ML gives you a way to filter that data in a particular way. The same was true of data warehouses, smart systems, etc. The issue is not the business people vs technical people. It is an understanding of what do you want it to do. A few years ago I had a system that could generate 2k in data samples every few seconds (switches, voltages, temps, etc). What are you even looking for in that pile of stuff? You can not just feed that into a ML network and hope for the best. You have to describe what you are looking for. I had this same conversation over and over when working with data warehouse projects. A good BA on a project like that is amazing. Someone who is just kinda meh on it will kill the project dead. I do not understand your business, you do. I can apply what I have learned from other companies but only to a point. After that point I basically have to become a BA in that company just to understand what to write.
I think you've identified the basic, classic difference between artists/thinkers/scientists and everyone else: just as a musician can be given a piano and in time they create complex compositions, those that can see and operate in abstract conceptual spaces will see and use AI to create wonderfully complex compositions everyone else will be slackjawed in awe. This is human nature. AI is not an intelligence player, it's an instrument to find and tune like steroids one's own intelligence. Just like a piano, AI is useless to most people, because they do not have the artists/thinkers/scientists drive and comfort with abstract problems that are easier with even the most basic AI.
Your argument boils down to “AI won’t be useful unless humans are twice as smart as they are” (your examples of the businesspeople and researchers), and thus doesn’t really say anything.
I am an underqualified and generally ignorant data scientist.
I'm really good at understanding technology and talking to people though, so all the true experts who I know enough to talk shop with love me for keeping management off their backs.
I think a big part of what is holding back many companies from making effective, genuine, real-world use of AI is that a significant majority of the individuals involved are bad at their jobs.
On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decisions than businesspeople. In my view, this includes things that would traditionally be considered business decisions. The business is unwilling to transition from "working on the solution to the problem" to "working on the AI system that solves the problem" (assuming that they even understand what "the problem" is).
On the technology side, I see a massive oversupply of badly underqualified and generally ignorant "data scientists" and "ML engineers" who have just enough bootstrapped understanding and familiarity with the plethora of (absolutely fantastic) open-source tools to fake an entry into the area, but do not have the genuine depth of background that is needed to actually plan, design, and deliver a high quality solution.
Yes, I have met people without "credentialed" backgrounds who are very good indeed, and yes I have met forward-thinking business people, but the number of people who are in these categories is a tiny fraction of the number of people who think they are in these categories.
And that's why businesses are getting less keen on AI.
EDIT: Grammar.