CASE ONE: In a class I teach on Transmedia Storytelling, I assign students to do a case study of a transmedia project. I have a list of possible projects to choose from. One of the students picking toward the end had the case she wanted chosen by someone else, so she asked me if there was another similar example of a nonprofit project created by a woman. Reasonable request, but I didn't have one off the top of my head, so I told her I'd get back to her.
Aha! A job for ChatGPT! I put in the parameters of what I was looking for and it gave me back a list of five - two of which were on my syllabus already, one that was inappropriate for unrelated reasons, and two promising candidates I had not been aware of. One looked especially interesting - it was a project on sex trafficking in conflict zones done by an NGO in Central African Republic in 2016. I was intrigued and tried to look it up.
There was no sign of it online, although the organization was real. I tried the Wayback Machine in case they'd taken it down from their site. Nope. I asked ChatGPT who the creators were and it gave me several names, including an artist from CAR I knew because I'd interviewed him recently. I checked his website and portfolio but saw no mention of this project, so I emailed him. He replied that he had no idea what I was talking about.
Turns out ChatGPT had made the whole thing up, but had done so in such a convincing way that almost anyone would have taken it as true. It had links, citations, used plausible information. On this technical subject, I may be one of a handful of people in the world who is an "expert" but it nearly fooled me, and would have if I had not put in more time verifying the work than it would have taken me, in the end, to do my own research the old fashioned way.
CASE TWO: I'm developing a client presentation on basic marketing strategy for a nonprofit I'm doing some consulting for. Since Microsoft has "upgraded" my Office 365 to include Copilot, I thought I'd try to get some value out of it. I ask PowerPoint to generate an infographic of the customer journey in four steps: attention, engagement, conversion and loyalty. This is a pretty routine visualization and I figure that if it were trained on image data like charts and business graphics, it wouldn't have any trouble creating an original image.
Nope. No amount of prompting could get it to generate anything that wasn't incoherent gibberish: not only fundamentally incorrect conceptually, but also ugly, full of weird and unintelligible text, and garishly designed.
After wasting 45 minutes trying to get the AI to amplify my productivity and execute the details of my strategic vision, I gave up and built the whole thing using clip art in about 10 minutes.
CASE THREE: I'm working on a report for a client analyzing some survey data. Unfortunately the person who conducted the survey doesn't know how or why to use spreadsheets, so the Excel files they gave me were nearly impossible to do any kind of analysis on. The data was spread across 4 different sheets, all formatted differently. After spending a little time trying to fix it manually, I dumped them all into ChatGPT to see if it could help me spot the patterns in the data that I needed to use in the report.
Things start out promising, as it did seem able to correlate the different sheets and track the kind of stuff I was trying to look for. But eventually, it starts giving me contradictory answers, citing information that was plainly wrong, giving me rankings of 26 states when I asked for 50, and other weirdness.
I start wondering if any of its answers are reliable or authoritative. I worry about using any of this as the basis for my analysis and discussion of the findings - that is, my own insights based on my experience and interdisciplinary understanding. If the data isn't correct, then nothing I add to it will be true or meaningful. Indeed I may be misleading people or drawing false implications.
All of these strike me as completely ordinary scenarios for knowledge workers seeking tools to enhance their existing expertise, and in all of them, using advanced AI tools for the purposes they are apparently intended for was useless or worse. Most actual AI technical folks I've talked to about this tell me these kinds of problems are fundamental to the way the models are built and can't be easily fixed. In fact, the latest OpenAI models are more prone to hallucination than previous ones.
I do see value in AI's ability to spot patterns in large data sets for things like pharmaceutical research or improving the performance of complex systems at scale (like optimizing delivery routes for packages or reducing waste in manufacturing processes). But in real world cases of high-end knowledge work, if these tools can't handle the simple, routine problems, I don't see how companies are going to recoup the trillions they are investing.
This is exactly my point in many ways. AI is here to stay. It is not going away. We all need to learn how to live with it and how to harness its power to propel us forward.
The LLMs are certainly nowhere near perfect today but they get exponentially better every six months or so. You are actually one of the first people I knew using ChatGPT in 2022/2023. Think about how much better ChatGPT is today vs. 2 or 3 years ago.
My argument is that Interdisciplinary Thinking, which includes critical thinking, discernment, leadership, EQ - these are all the key skills that human beings will need to succeed, and in fact, to excel in the age of AI.
These AI hallucinations you recounted are big problems. But they will get fixed and there will be fewer and fewer of them in the future. For now, the key to not being duped by AI hallucinations is to use the most human elements of reasoning, discernment, critical thinking, etc.
Thanks... just saying that, as you know, I'm an interdisciplinary thinker and not averse to being out front on new technologies, even controversial ones, if their value proposition pans out.
Problem is, I've been hearing AI folks talk publicly about the "nuisance" of hallucinations and how it's a minor problem that will eventually be solved, for several years (and trillions of dollars of investment). But when you actually talk to the systems architects and people deep in the plumbing, they'll tell you it is not at all a trivial problem and the only solution is "brute force" - that is, training the model on all its mistakes to avoid it making the same mistakes again. They can't figure out why the bot tells you to put stones on your pizza, but they can flag that case and say "don't say that again." At scale, it's impossible to squash every bug, and computer science does not have a better reasoning model yet. Some respected (human) experts do not consider it a solvable problem at all.
There's a scene in "The Big Short," which I recommend everyone rewatch, where the character played by Steve Carrell has dinner with a smug bond trader who, in very AI style, confidently and authoritatively spins a narrative about the business realities of the financial system. Carrell gets up from the table and tells his team "Short everything that m'fer has touched." We know now who was right.
I think that's a good way to think about AI both in micro and macro. From a personal utility and productivity standpoint, I can't trust it to do tasks I'd assign to an intern. If I had passed along any of the AI-generated "solutions" in the above examples to my paying clients, I'd be fired or laughed out of the room, which is worse because my reputation is my currency. If I actually hired a human assistant, I'd have to watch them every minute to make sure they don't take any of the tempting shortcuts made possible by this tech, which diminishes the trust and value of that relationship. I have no idea where AI has poisoned the data in whatever tranche of information assets I am using for my work. As noted above, some of these are really insidious, and without the very highest levels of discernment, critical thinking, expertise and experience, almost anyone can get fooled.
The makers of the tech - some of whom I have interviewed or talked with personally - are entirely oblivious and dismissive about this, which strikes me as the height of irresponsibility. Imagine a car maker shipping a car whose brakes only worked 90% of the time, then telling the world, "trust us, we'll get it right eventually."
On the business side, so much money is riding on this that the only hope for providers of a flawed product is to push this narrative of inevitability, in part because the players involved are seen as too big to fail. I am personally not a fan of the "might makes right" argument, even if it is sometimes true.
Anyway I definitely appreciate the efforts of thoughtful people like yourself to find an upside here. Maybe there's one to be found. I don't have all the answers. But I do have lots and lots of questions.
You make excellent points, Rob, as always. The big takeaway for me, and what I want to get across to people, is that it never works out well when people fight against emerging technologies. They lose 99% of the time. They become dinosaurs.
AI is not even emerging at this point. It has been in the works for 60+ years. It is very much here now. And it is not going away.
So rather than worry about AI replacing one’s job, I encourage everyone to play with AI tools and agents, to experiment as much as possible with LLMs, and to figure out how to enhance their skills and output using AI so they not only protect themselves from being trampled by AI in the future, but they enable themselves to skyrocket forward.
CASE ONE: In a class I teach on Transmedia Storytelling, I assign students to do a case study of a transmedia project. I have a list of possible projects to choose from. One of the students picking toward the end had the case she wanted chosen by someone else, so she asked me if there was another similar example of a nonprofit project created by a woman. Reasonable request, but I didn't have one off the top of my head, so I told her I'd get back to her.
Aha! A job for ChatGPT! I put in the parameters of what I was looking for and it gave me back a list of five - two of which were on my syllabus already, one that was inappropriate for unrelated reasons, and two promising candidates I had not been aware of. One looked especially interesting - it was a project on sex trafficking in conflict zones done by an NGO in Central African Republic in 2016. I was intrigued and tried to look it up.
There was no sign of it online, although the organization was real. I tried the Wayback Machine in case they'd taken it down from their site. Nope. I asked ChatGPT who the creators were and it gave me several names, including an artist from CAR I knew because I'd interviewed him recently. I checked his website and portfolio but saw no mention of this project, so I emailed him. He replied that he had no idea what I was talking about.
Turns out ChatGPT had made the whole thing up, but had done so in such a convincing way that almost anyone would have taken it as true. It had links, citations, used plausible information. On this technical subject, I may be one of a handful of people in the world who is an "expert" but it nearly fooled me, and would have if I had not put in more time verifying the work than it would have taken me, in the end, to do my own research the old fashioned way.
CASE TWO: I'm developing a client presentation on basic marketing strategy for a nonprofit I'm doing some consulting for. Since Microsoft has "upgraded" my Office 365 to include Copilot, I thought I'd try to get some value out of it. I ask PowerPoint to generate an infographic of the customer journey in four steps: attention, engagement, conversion and loyalty. This is a pretty routine visualization and I figure that if it were trained on image data like charts and business graphics, it wouldn't have any trouble creating an original image.
Nope. No amount of prompting could get it to generate anything that wasn't incoherent gibberish: not only fundamentally incorrect conceptually, but also ugly, full of weird and unintelligible text, and garishly designed.
After wasting 45 minutes trying to get the AI to amplify my productivity and execute the details of my strategic vision, I gave up and built the whole thing using clip art in about 10 minutes.
CASE THREE: I'm working on a report for a client analyzing some survey data. Unfortunately the person who conducted the survey doesn't know how or why to use spreadsheets, so the Excel files they gave me were nearly impossible to do any kind of analysis on. The data was spread across 4 different sheets, all formatted differently. After spending a little time trying to fix it manually, I dumped them all into ChatGPT to see if it could help me spot the patterns in the data that I needed to use in the report.
Things start out promising, as it did seem able to correlate the different sheets and track the kind of stuff I was trying to look for. But eventually, it starts giving me contradictory answers, citing information that was plainly wrong, giving me rankings of 26 states when I asked for 50, and other weirdness.
I start wondering if any of its answers are reliable or authoritative. I worry about using any of this as the basis for my analysis and discussion of the findings - that is, my own insights based on my experience and interdisciplinary understanding. If the data isn't correct, then nothing I add to it will be true or meaningful. Indeed I may be misleading people or drawing false implications.
All of these strike me as completely ordinary scenarios for knowledge workers seeking tools to enhance their existing expertise, and in all of them, using advanced AI tools for the purposes they are apparently intended for was useless or worse. Most actual AI technical folks I've talked to about this tell me these kinds of problems are fundamental to the way the models are built and can't be easily fixed. In fact, the latest OpenAI models are more prone to hallucination than previous ones.
I do see value in AI's ability to spot patterns in large data sets for things like pharmaceutical research or improving the performance of complex systems at scale (like optimizing delivery routes for packages or reducing waste in manufacturing processes). But in real world cases of high-end knowledge work, if these tools can't handle the simple, routine problems, I don't see how companies are going to recoup the trillions they are investing.
Great examples, Rob! Thanks for sharing.
This is exactly my point in many ways. AI is here to stay. It is not going away. We all need to learn how to live with it and how to harness its power to propel us forward.
The LLMs are certainly nowhere near perfect today but they get exponentially better every six months or so. You are actually one of the first people I knew using ChatGPT in 2022/2023. Think about how much better ChatGPT is today vs. 2 or 3 years ago.
My argument is that Interdisciplinary Thinking, which includes critical thinking, discernment, leadership, EQ - these are all the key skills that human beings will need to succeed, and in fact, to excel in the age of AI.
These AI hallucinations you recounted are big problems. But they will get fixed and there will be fewer and fewer of them in the future. For now, the key to not being duped by AI hallucinations is to use the most human elements of reasoning, discernment, critical thinking, etc.
Thanks... just saying that, as you know, I'm an interdisciplinary thinker and not averse to being out front on new technologies, even controversial ones, if their value proposition pans out.
Problem is, I've been hearing AI folks talk publicly about the "nuisance" of hallucinations and how it's a minor problem that will eventually be solved, for several years (and trillions of dollars of investment). But when you actually talk to the systems architects and people deep in the plumbing, they'll tell you it is not at all a trivial problem and the only solution is "brute force" - that is, training the model on all its mistakes to avoid it making the same mistakes again. They can't figure out why the bot tells you to put stones on your pizza, but they can flag that case and say "don't say that again." At scale, it's impossible to squash every bug, and computer science does not have a better reasoning model yet. Some respected (human) experts do not consider it a solvable problem at all.
There's a scene in "The Big Short," which I recommend everyone rewatch, where the character played by Steve Carrell has dinner with a smug bond trader who, in very AI style, confidently and authoritatively spins a narrative about the business realities of the financial system. Carrell gets up from the table and tells his team "Short everything that m'fer has touched." We know now who was right.
I think that's a good way to think about AI both in micro and macro. From a personal utility and productivity standpoint, I can't trust it to do tasks I'd assign to an intern. If I had passed along any of the AI-generated "solutions" in the above examples to my paying clients, I'd be fired or laughed out of the room, which is worse because my reputation is my currency. If I actually hired a human assistant, I'd have to watch them every minute to make sure they don't take any of the tempting shortcuts made possible by this tech, which diminishes the trust and value of that relationship. I have no idea where AI has poisoned the data in whatever tranche of information assets I am using for my work. As noted above, some of these are really insidious, and without the very highest levels of discernment, critical thinking, expertise and experience, almost anyone can get fooled.
The makers of the tech - some of whom I have interviewed or talked with personally - are entirely oblivious and dismissive about this, which strikes me as the height of irresponsibility. Imagine a car maker shipping a car whose brakes only worked 90% of the time, then telling the world, "trust us, we'll get it right eventually."
On the business side, so much money is riding on this that the only hope for providers of a flawed product is to push this narrative of inevitability, in part because the players involved are seen as too big to fail. I am personally not a fan of the "might makes right" argument, even if it is sometimes true.
Anyway I definitely appreciate the efforts of thoughtful people like yourself to find an upside here. Maybe there's one to be found. I don't have all the answers. But I do have lots and lots of questions.
You make excellent points, Rob, as always. The big takeaway for me, and what I want to get across to people, is that it never works out well when people fight against emerging technologies. They lose 99% of the time. They become dinosaurs.
AI is not even emerging at this point. It has been in the works for 60+ years. It is very much here now. And it is not going away.
So rather than worry about AI replacing one’s job, I encourage everyone to play with AI tools and agents, to experiment as much as possible with LLMs, and to figure out how to enhance their skills and output using AI so they not only protect themselves from being trampled by AI in the future, but they enable themselves to skyrocket forward.