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How is it that we can both believe that artificial intelligence is going to wipe away every job while at the same time believing that the job market is the strongest it has ever been?
In this episode we present the so-called “Artificial Intelligence Fear Paradox” while diving in to how it affects our work environment as well as how we can easily overcome the problem!
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Music for the show: Foolish Game / God Don’t Work On Commission by spinmeister (c) copyright 2014 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/spinmeister/46822 Ft: Snowflake
Welcome to data couture, the podcast about data culture at work at home. And on the go. I’m your host Jordan bohall. If you like what you hear, be sure to subscribe lows, get the latest episodes wherever you get podcasts. And if you’d like to stay up to date on everything data couture, be sure to follow us on Twitter at data couture pod. Finally, if you’d like to help support this in future episodes, consider becoming a patron of the podcast through our Patreon email@example.com forward slash data couture.
Now on to the show.
Welcome to data couture. I’m your host Jordan and on today’s episode, we’re talking about the artificial intelligence, fear paradox. And the first act will start with finding out what this paradox is all about. Then, the second act, we’ll talk about how this plays out in real life before moving on to the final act where we’ll consider how to defeat this deadly paradox. Stay tuned.
Okay, let’s get on to this first part of the show. Namely, what is the AI fear paradox? Before we get going, maybe it’ll help the reader or the listener I suppose you’re probably not reading this. Understand what paradoxes a paradox simply a situation that combines contradictory features or qualities. How is this associated with artificial intelligence?
Well, I’m going to be quoting someone called Brian bergstein, who wrote for the MIT tech review, in his article, the AI paradox. So the AI fear paradox has two parts. The first part says improvements in computers skills will stack up until machines are far smarter than people, the super intelligence will largely make a human labor unnecessary. Okay, that was the first part.
The second part says the job market is currently at an all time high with industries and professions thoughts be in the crosshairs of automation, filling anywhere near the overwhelming effects of the automation presumed. So let’s unpack this a bit. I’m not going to go full philosophy on you fulfilled African you and get into archaic positioning of various aspects of the artificial intelligence literature.
But instead, let’s consider this fear part of the paradox, if it really is a paradox. So the first part of the paradox, namely that machines slash automation will eventually be capable enough to take all the jobs appears to be a reaction to the increasing complexity, and likely ability of various types of artificial intelligence. Second part of the paradox, namely that the job market is as strong as ever is a realistic and also quantifiable measure of job satisfaction and statistics in the United States, and in many parts of the rest of the world.
So the seemingly paradoxical part is that we can both hold beliefs, we can hold both beliefs for that matter, even though they seem to negate one another. This leads to the so called fear. Now, we’re going to get into what is actually happening in this paradox later in the show. But first blush, this doesn’t seem to be paradoxical at all to me Why? Well, because you can both believe that.
Computers will eventually be clever enough for computationally sophisticated enough to represent human understanding and human cognition, while also believing that in the current state, the job market is rather strong, right. And so even though computers will eventually be able to take all of our jobs away, right now, they’re not doing so much. So and the third act, we’re going to talk about what the differences between actual artificial intelligence, and what’s so often referred to as artificial intelligence, will also get into a little bit of the job statistics, but not too heavy. However, you know, at least we have a little bit better understanding of the paradox, how it’s formatted and what’s going on. So stay tuned.
So how is this playing out in real life at everyone’s jobs? Well, there’s a fairly strong emotional reaction, whether or not the people at your work actually understand what this paradox is, they certainly exhibit behaviors that would suggest that they hold these seemingly paradoxical beliefs. What I’m seeing in my job is a certain level of an emotional reaction to change. And so they understand that automation is going to do away with quite a few of their job functions. While at the same time, they supposedly welcome the sort of automation that my data teams able to provide. Right, and I’m sure all of you have witnessed the same problem.
It’s the it’s this kind of emotion that I don’t quite understand how to quantify or how to specify, but how it plays out is that even though we develop very sophisticated tools and automation tool sets, the end result is people don’t use them, they asked for them, because maybe their bosses told them to ask for them. But nevertheless, they don’t look at them. How do I know that? Well, because of course, like all good data professionals, we track the usage and metrics over time. So what ends up happening is the certain kind of siloed of knowledge and skill sets, either unwilling to work in a cross functional manner.
And so while they will let data team come in and automate certain aspects, they won’t work maybe very quickly, or they won’t. They won’t allow you to automate the jobs that they can, are fundamental to what their particular role entails. And so what happens is, any automation initiative either fails completely or, and best case scenarios, frankly, are only partially productive and only partially automate pieces. And so they’re still all of these processes that occur day in and day out that easily should be automated, but nevertheless, don’t get ended or don’t end up getting automated because people fear for their jobs, fear fear for their jobs getting taken over by what they consider automation or artificial intelligence.
And so what happens is initiatives for the broader goals of becoming data driven, fail. And what happens is quite a bit of the organization exhibits this ostrich like behavior, even though they know that eventually automation and artificial intelligence will be coming. They try to hide as much as possible. They fear the future. This is really why we’re talking about this today. The artificial intelligence fear paradox. While we’ll talk about while Why might not really be a paradox, in the technical sense, still persists as a significant barrier to doing anything with regard to data driven culture transformations, or automation transformations. are anything that will make your business future proof for the next five to 10 years.
Okay, so what can we do about this paradox? Well, to start, let me get to why I don’t consider this a true paradox. And that’s because there’s a difference between what’s considered artificial intelligence now. So current AI, what is true AI, artificial intelligence, current AI, it’s, well, not to make it too simple, but it’s more or less just a set of statistical methodology is that do quite a bit of pattern searching. And so if there are jobs that all is happening is just matching various patterns to fit a particular schema, then fine, yeah, or we’re doing artificial intelligence, but true artificial intelligence, the the type of thing where, like in the movie iRobot, or any other similar movie, or pop culture reference, where there is a robot that exhibits traits that can no other way be described as human.
That’s what I’m considering true AI type of cognition where it’s indistinguishable, not necessarily by underlying technology, I’m not saying that it has to be, I don’t know biological, or that it has to
have the same fundamental building blocks, humans, but nevertheless exhibits a sort of cognition, that can be emotional, that can reason that can think about the future and ways that it can ask what if questions will also, I don’t know exhibiting features like paranoia, or, or fear, or sadness, or grief, or depression or guilt, anything like that, right? This?
This is what one might consider true artificial intelligence. So really, the paradox comes down to the fear of automation, the fear of automation, taking all of our essential job functions away, and leaving us like a bunch of Neo politeness, hippies sitting around a tree thinking about philosophical matters, which, to be honest, sounds really amazing to me, but is not practical. So what can we do about this? What can we do about this actual fear this fear of automation taking away our jobs? Well, because we’re not talking about true artificial intelligence, we’re not talking about true, artificially created cognitive beings, we’re just talking about statistical patterns, searching methodologies, that leaves a lot of room open, to be human, that leaves a lot of room open for critical thinking and the role that critical thinking can play in our own lives and in the lives of our organizations and in the success of our organization.
So the way to avoid automation, taking away our jobs, and I’m talking about machine learning engineers, and data scientists and data engineers, and DBS, and all the rest, because let’s face it, people like Microsoft, or the companies like Microsoft, are creating automated machine learning tools. So what’s really going to separate us is the ability to think critically think about problems that are affecting our business that are affecting ourselves, and then utilizing these automation tools to enhance our abilities to find new solutions to problems that we otherwise wouldn’t be able to attend to.
Because we’re stuck in the whirlwind of dealing with the autumn, autumn a double, I’m not sure how you would say that the tasks that can be automated, right. The second piece is fostering creativity and fostering creativity as an organizational asset. So while you might be able to be a plus logical reasons, we can’t all be Vulcans, we’re all humans. And so in order to be successful, we have to inject a dose of creativity into our critical thinking roles so that not only are we doing what everybody else what every other organization is doing and thinking in this critical manner.
But we’re injecting this creative framework, this artistic framework, this human framework, into everything we do, so that we know that our jobs, our products, our processes, truly represent our customers, our members, the end result we’re achieving for achieving. Nevertheless, the result were that we’re attempting to achieve any of this kind of thing well, not only allow us to overcome this fear of automation, as it properly should be described, but nevertheless, allow us to take the fear of the unknown with a grain of salt and know that as humans as good, creative, critical thinkers, we can overcome anything that might be sent our way. And this, I truly believe will solve the artificial intelligence fear paradox.
Thank you for listening, and I hope you have a wonderful day.
That’s it for the show. Thank you for listening. And if you liked what you’ve heard, then consider leaving a comment or like down below. Stay up to date on everything data couture, be sure to follow us on Twitter at data couture pod to consider becoming a firstname.lastname@example.org forward slash data couture music for the podcast. It’s called foolish games. God don’t work on commission by the artist spin Meister us under a Creative Commons Attribution 3.0 license,
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by your host Jordan Bohall.