Debunking AI myths

Note: Transcripts are automatically generated from episode audio, and are not fully corrected for spelling, grammar, and formatting.

JEREMY KAHN: I have been covering AI for eight years, but for a long time I was covering it alongside other technologies. I was just very fascinated by the subject, but it didn’t seem like something you could cover — quite make an entire beat out of.

RANA EL KALIOUBY: That’s Jeremy Kahn, long-time tech journalist and Fortune’s AI editor.

KAHN: And then with ChatGPT’s debut in November 2022, the whole space has exploded so tremendously that it is now a full time beat.

In fact, it’s not just a full time beat for me, but it’s a full time beat for myself and two other reporters at Fortune. And there’s quite a number of reporters at other publications who just devote all of themselves to the subject of AI.

EL KALIOUBY: But ChatGPT didn’t just create new opportunities for journalists like Jeremy Kahn. It also opened Pandora’s Box. And people started asking big questions about the future of AI:

KAHN: What is AI? What does it all mean? And where is this all heading?

EL KALIOUBY: Exactly. These ARE the questions we need to be asking. But I have to say, when it comes to the answers — there are a lot of misfires.

There’s the misconception that AI is destined to take our jobs. Or that AI will reach super-human intelligence and take over the world. And there’s the idea that AI will replace our human relationships. And while this AI doomerism is prevalent in our society, it isn’t necessarily the reality.

I’m a big believer that if we put people at the center of our decisions when developing new AI technologies, we’ll create an AI future that benefits all of us.

So on today’s show, we’re dispelling some of these AI misconceptions. Jeremy will talk about his new book Mastering AI, which gives us a birds eye view on what’s in store for our future with AI. He’ll also share his honest concerns and how we can use AI smartly moving forward.

I’m Rana el Kaliouby. And this — is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

[THEME MUSIC]

So Jeremy, thank you so much for being on the show.

KAHN: Oh, thank you so much for having me, Rana.

EL KALIOUBY: I love that you’re in the interviewee seat today.

KAHN: Yes, it’s unusual for me. I’m usually the one asking the questions.

Copy LinkWhy this AI moment feels fundamentally different

EL KALIOUBY: Yeah, you’re usually the one asking all the tough questions. So what was the impetus for writing Mastering AI?

KAHN: After ChatGPT debuted, Fortune assigned me a large cover story about OpenAI and the process that had led to the creation of ChatGPT. It came out in January 2023, and after that story I got a lot of interest from people saying, you should write a book on this.

I had always wanted to write a book on AI. I thought it was a fascinating subject and it just seemed like sort of the right time to do it. But I wanted to write a broad book. I wanted to talk about all the impacts I thought AI was going to have on both business, but also broader than business — on society and on ourselves and on democracy and on culture.

I saw all these potential implications for the technology.

EL KALIOUBY: And I love that about the book. So you write about how this moment in AI is different. AI is what we call a GPT, a General Purpose Technology — which is, by the way, not the GPT in ChatGPT. The GPT in ChatGPT stands for Generative Pre-trained Transformers. But yeah, we’re talking about AI as a general purpose technology like electricity.

What makes AI different from all these other earlier innovations?

KAHN: So I think there’s several things. One is it is a general purpose technology, and those don’t come along all that often. It’s much more like the invention of electricity, which is also a general purpose technology, or steam power, or computers writ large, than it is the mouse or the graphical user interface or something, which were much more specific technologies. So that’s one reason it’s different — it impacts a whole breadth of things.

But I think the other thing that’s really different about AI is the speed at which it’s developing. The capabilities are being pushed extremely quickly, and we’ve already seen the capabilities of models expanding very rapidly. It’s a technology that’s going to be adopted very quickly, much faster than we saw with something like electricity, which took quite a long time to be adopted by business and required quite a lot of physical rewiring of houses and all this sort of thing. So that’s another difference. And the speed of adoption by business is very rapid. So that’s the other thing that I think is going to really make this different.

EL KALIOUBY: You also talk about how AI is kind of encroaching on what makes us uniquely human, and that’s another way where it’s kind of really powerful, but also scary in a way.

KAHN: Absolutely. Yeah, I think that’s another differentiator between other general purpose technologies and AI. I think AI challenges our intellectual abilities and this idea that we are the smartest species, the most intelligent species on the planet. Suddenly we have this thing that we’ve created that is potentially as smart as we are. And I think that is a real challenge to people. It’s a real philosophical challenge to people, I think creates a lot of anxiety. I think some of the fears around AI really stem from that sort of existential dread of something else out there that is as intelligent as we are, but that is not human — people find that scary.

And they worry about, well, what’s my place going to be if there’s this software out there that can do everything I can do?

EL KALIOUBY: Yeah, I think for a lot of people, when they hear artificial intelligence, they think about sci fi movies, so they associate AI with Terminator.

KAHN: Yeah. Terminator, 2001. There’s lots of them. Yeah.

Copy LinkHow AI is starting to blur the boundaries of human connection

EL KALIOUBY: Yeah. So I want to talk about another sci fi movie, which is one of my favorites, the movie Her.

SPEAKER: Oh yeah. That’s a good one.

EL KALIOUBY: Her is one of my favorite movies. The protagonist, Theodore, is played by Joaquin Phoenix. He develops a romance with his super smart, emotionally intelligent operating system, Samantha, voiced by Scarlett Johansson.

It is a modern love story in its own right. And the movie explores themes of heartbreak and loneliness. It also paints a portrait of what our future relationships with AI could look like as they become more emotionally intelligent.

Jeremy doesn’t think we have this kind of AI yet. But he thinks that we’re close.

KAHN: We’re sort of right on the cusp of that. We have systems — if you looked at the demo for GPT-4o, which was one of OpenAI’s latest models, it was very much, clearly, they tried to design a system to be a little bit like Samantha in the movie.

And there’s this whole controversy about whether they deliberately tried to mimic Scarlett Johansson’s voice or not. Whether they did or not, the point is I think they were very much influenced by sort of the vision of that movie when they were thinking about the interface with which people would interact with GPT-4.

And we are moving towards a vision, I think, pretty soon — certainly within the next couple of years — where we’re probably going to have these kind of personal assistants on our phone. They’ll be useful to us, but we’re going to have to give them a lot of information about ourselves in order for them to be useful. And I think we’re already at the point where people are developing emotional attachments to AI chatbots. They’re doing so actually kind of deliberately. If you look at what’s happened with one called Replika that’s out there — it’s a service where you can create chatbots with various different personalities — but a lot of people have chosen to create ones that kind of serve as a romantic companion or even a kind of erotic companion, and people have done this deliberately.

They’ve chosen to do this. It’s not that anyone’s sort of forced them to do it. They’ve done it very deliberately, but they have developed a real bond with these AI chatbots, so much so that when Replika at one point turned off the ability of these chatbots to have sort of spicier conversations, or had them engage in erotic role play with users, a lot of users were really up in arms.

They were really upset. They felt bereft, as if they’d had a lover that died. And I think that reaction is really interesting. And I think it shows that we’re already to some extent in the world that the movie Her envisioned, which is the idea that people would develop these really emotional attachments to these inanimate pieces of software.

And I think it’s slightly troubling. I write in the book, one of my big concerns is that people will find relationships with an AI chatbot just so much easier than a relationship with a real person.

The chatbot has no actual needs which you have to fulfill. It has no wants. It has no real desires. I mean, it can pretend to have these things, but it doesn’t actually have them. And so there’s no real consequence if you don’t fulfill those. You can always reset the chatbot if it gets annoyed with you or whatever.

And it’s totally different than a relationship with a real person. And I worry that people will use this as a crutch. I worry that people will allow their real social skills to degrade, and they will choose the kind of easy, slightly addictive conversations with AI chatbots over the hard work of real human relationships.

Copy LinkWhy AI companions need guardrails especially for kids

EL KALIOUBY: Yeah, I worry about that too. We both have teenagers. Would you let your kids have AI companions or AI friends?

KAHN: I feel a little worried about it. I think it was okay to have an assistant, but I think we should limit the extent to which we’re using them. We need to be kind of cautious. Definitely some guardrails around how often you let your kid interact with an AI chatbot and what the nature of that conversation is.

As I write in the book, a lot of the studies have compared having an AI chatbot versus nothing, and the data does seem to suggest that if you really are a lonely person, if you have no one in your life to talk to, having the AI chatbot is potentially a positive — that it does provide some emotional uplift and perhaps some mental health benefit. The problem is they haven’t really compared it in any controlled study to having human friends.

But if you already have human friends and then you add this AI companion, I wonder what happens. And my concern is that maybe you let your human relationships atrophy a bit or you don’t work too hard at them. And I don’t think that’s been studied yet.

EL KALIOUBY: And I see a lot of these companies kind of being in a race to capture that intimacy, build an intimate relationship with the users, and there are no guardrails today.

KAHN: Yeah, absolutely. And I think we’ve already been through this experience with social media where we have basically inadvertently allowed these companies to gain a tremendous amount of power over not just what we do online, but actually over our minds — they capture a lot of our mind share as well as our market share.

And it’s particularly had this pernicious effect on children. I don’t think we should wait, with this next generation, with this platform shift to AI assistance. I don’t think we should wait to see what the ill effects are.

I think we should assume, particularly when it comes to children, that there will be some ill effects of creating products that are particularly addictive, like an AI chatbot. And I think we’re going to need some rules. I think we’re going to need some regulations around this to try to safeguard from the overuse of an AI system.

Copy LinkWhat AGI really means and why it is not here yet

EL KALIOUBY: Yeah. So let me switch gears a bit. So the holy grail in AI is this idea of an artificial general intelligence or AGI. And of course, companies like Google’s DeepMind and OpenAI were founded with this goal in mind. Can you talk a little bit about what is AGI? And I don’t know if you want to make a prediction on how close we are.

KAHN: Yeah. So AGI, or artificial general intelligence, is the idea of an AI system that would have all of the cognitive abilities, sort of across the board, of a human being. And it has been kind of the holy grail of the field since the very founding of the field, but we’ve never actually been that close.

I think we’re closer now than we have ever been before. But I don’t think that means that AGI is imminent. There are still a lot of things these systems can’t do that we haven’t quite figured out. And I think it’s actually going to take some algorithmic innovation to probably get there.

EL KALIOUBY: Algorithmic innovation.

Meaning that we will likely need a new kind of machine learning algorithm or a different approach altogether to reach human-like intelligence. For example, AI today doesn’t have much in the way of perceptual abilities or even memory.

Right now, a lot of AI models are based on transformers — a powerful architecture critical in developing models like ChatGPT.

But if we want to level up our models, we probably need to level up the algorithmic architectures behind them.

KAHN: If you go back a few years, there was a definite school of thought among a certain group of AI researchers that all you needed was to keep scaling up the current large language models, which are what underpins ChatGPT. The idea is that you could just keep growing these things, get them bigger and bigger and they would gain more capability, and then complex reasoning, planning, a lot of common sense things that they don’t really have — that those abilities would somehow magically appear as a function of scale. I think very few people think that’s the case anymore. Most people think we will need some sort of further algorithmic innovation. I think it’s very hard to predict when that innovation would happen, but I do not see it imminently on the horizon in the next three to five years. If you went out 10 years, perhaps, or certainly if you went out 20 years, it’s possible we would have systems that reach that capability.

EL KALIOUBY: We are not in the year of AGI yet. But there still is A LOT of innovation happening when it comes to artificial intelligence. So what’s the next big AI innovation coming down the pipeline?

That — and more — after a short break. Stay with us.

[AD BREAK]

Copy LinkHow copilots and agents could change everyday work

So 2023 was dubbed the year of AI chatbots and 2024 is poised to be the year of AI copilots and AI agents. So I just want to take a moment to define those terms and create a distinction between them. I’m guessing a lot of our listeners have experience conversing with an AI chatbot.

The way I like to think of an AI copilot is kind of a chatbot that can collaborate with you and be a thought partner and kind of help you ideate and get stuff done. And then an AI agent is something that you can delegate a full task to and it can go act on your behalf and get it done. Would you agree with that distinction?

KAHN: Yeah. And I think of copilots particularly as something that’s been trained to help you perform a particular professional task. You know, you have a copilot for lawyers, and you might have a copilot that’s different for accountants, and you might have a copilot that’s slightly different for people who work in finance or medicine.

They can give you advice. They can critique your work. They can do sort of first drafts of things for you, potentially act as a bit of a mentor and coach in that professional role. Whereas yes, I think the idea of AI agents — these are systems, software systems that would actually go out and perform tasks for you across the internet, and you could give it a goal and it would potentially come up with sub goals needed to achieve that primary goal, and it would then kind of figure out what tasks do I need to do in order to accomplish the goal I’ve been given, and it would go out and start to actually perform those tasks using other pieces of software or using an internet browser. We know that both these things are pretty close. Lots of copilots are being designed right now and being rolled out right now to businesses. AI agents, we don’t really have yet, but everybody says they’re working on them, and there’s some indication that they may be debuting even before the end of this year.

I think certainly into the early part of next year, we may see a lot of these things getting rolled out. It’s unclear to me exactly what capabilities they will have initially, but I do think they will be able to do some purchasing for us. It’s very likely that they’ll be able to go out and book a restaurant reservation for us or book some travel for us or do some calendar scheduling for us.

I think we’re right on the cusp of agents that will have at least those abilities. The problem is, anyone who’s interacted with a chatbot knows they are good maybe 80 percent of the time, and then 20 percent of the time they provide answers that are really not so great, or they seem very confident but they’re wrong. And I think when we move into the world of AI agency, this becomes even more problematic because if it’s going to take actions with consequences across the internet or using other pieces of software, we’re going to have to have a way of checking that what it’s about to do is really the right step.

And exactly how often it’s going to come back to us and ask, okay, now I’m going to do the following — is this correct? I mean, if it does that too often, that sort of defeats the purpose of an AI agent. If it doesn’t do it at all, I think there’s a real danger it will run amok and do things improperly, and you’ll end up spending money you probably shouldn’t spend, or performing some action with terrible consequences you don’t want it to perform.

I talk a bit about that in the book, and I think companies are going to have to think very hard as they deploy these copilots about what systems they put in place to make sure that the people using these copilots remain vigilant and can perform that kind of supervisory role.

One of the things I say in the book — and actually in the EU’s AI Act that is now, as of a few months ago, in full force — is that if you have a human interacting with an AI system, the human should always know that they’re interacting with an AI system.

And I think that’s right. I think we’ll all get used to dealing with these AI systems the same way we’re already used to dealing with the very primitive systems that were voice based, where you used robo callers and you would hit one on your keypad if you wanted to do something.

And I think we’re already kind of used to that. And I think we’ll get used to this interaction with AI agents as well.

EL KALIOUBY: Yeah, I agree that AIs should always disclose that they are AIs. The AI agent I want — and this is a true story — I need to schedule my colonoscopy, and I’m just dreading having to sit on a call, an hour-long call, waiting for somebody to answer me and schedule. I’m like, can we just have an AI agent that has access to my calendar, my medical insurance data, and can just get it done. When will we have that?

KAHN: I’m kind of hopeful we’ll have that certainly within two years, that we’ll have that kind of ability. And if you think about scheduling as a lower risk potential — I mean, you can imagine scenarios where scheduling could go very wrong and have some very negative consequences, but in general, if you put something in the wrong calendar spot for somebody, it’s usually not the end of the world.

Copy LinkWhy the future of work depends on augmentation not replacement

EL KALIOUBY: It’s like, how much autonomy do you want to give these agents, especially if they have access to a lot of your personal information? All right. So let’s transition to what this all means for the future of work. As AI becomes more and more capable, what are the implications on various industries and various jobs? Just to kind of frame this, I’m a big believer that AI has the opportunity to augment and amplify and unlock human potential if we do it right.

KAHN: First of all, I totally agree. And one of the big pleas in the book is to think of AI as something that’s going to augment human potential and think of it as a complement to human labor and not as a replacement, not as a substitute. And I actually think if we can do that, and if we can convince business to do that, we actually take a big step towards mitigating a lot of the near term risks from the system.

Because if you think about a lot of the things that could go very wrong in the near term with this technology, given the capabilities we have today, they almost all come from the idea of taking the human out of the loop. They come from the idea of substituting human labor one for one with an AI system.

It can be a tremendous complement, but you’re still going to need a person working right alongside it most of the time. And it can automate certain tasks, but it cannot automate full jobs right now.

And so it’s wrong to think about it that way. I hope businesses can kind of get away from that temptation.

Instead, think about how you can make the person more productive. And if you make the human employee more productive, what else can they do? What are the things that the human can uniquely do really well that the AI system can’t do?

And can we get this person doing more of that now? Or can we expand what they do into some other area that’s adjacent to their current job, again where the human is going to have a real advantage. A lot of businesses are going to be able to run themselves more efficiently. They’re going to be able to grow faster. Labor productivity has been a huge problem — in most of the developed world, in most of the developed economies, labor productivity has not been growing fast enough.

And I think AI technology has the potential to finally get that back on a very healthy upward growth trajectory. And exactly how it’s going to impact different businesses or different professions will depend a little bit on the specifics of that profession or that business.

EL KALIOUBY: So at a personal level, I love Jensen, the CEO of NVIDIA’s quote. I think he said it’s not going to be an AI that takes your job, it’s going to be a person who knows how to leverage and harness AI that will. And in your book, you talk about how we spend so much time thinking about how we’re training these AI models, but not as much time thinking about how do we train and upskill people to work effectively alongside AI?

KAHN: Absolutely. And it’s one of the main points of the book. I feel like we spend so much time talking about model training and almost none talking about training people. We should try very hard not to just have the human in a completely vigilant role where all they’re doing is babysitting the AI system’s output. I talk in the book about time I spent with this woman, Jessica Marquez at NASA, who does a lot of their research on what they call human factor engineering.

Like, how do humans work with automated systems? And it turns out if you put a human in a babysitting role, they almost always perform very badly — they can’t remain vigilant for that long. So you really need to give the human something to do. And it has to be a collaborative process — the human still has to be doing something. And as long as the human’s doing something meaningful, they can kind of remain alert. Also, we may have to do things where we do drills, just like they do in aviation, just like they do with astronauts, where you simulate some kind of failure from the AI system.

You actually introduce an error in some sort of safe environment to kind of train the person about what the error looks like and how you recover from that particular failure mode. And so you don’t also have this de-skilling factor. So people don’t lose essential skills.

I think we don’t want to outsource all our writing all the time. Even though it’s business writing, we still want our salespeople to know how to craft a pitch, even if they’re using the AI to help craft the pitch a lot of the time. Once a quarter or something, we should do a drill where you ask your salespeople, okay, today you’ve got to write this pitch completely on your own, not using the AI.

So they just kind of maintain those skills, because I think otherwise we’re going to be in trouble and we’re going to lose things that we want the human to have. I also think you should look at how you can use these systems as a coach. Often people say you should conceive of them like an intern, but I think it’s a mistake to only look at them in that way.

You can also look at them as kind of a coach. And in fact, one of the ways you can avoid de-skilling and get the advantage of AI is to have the human always do the first draft, instead of outsourcing the first draft to the machine. And then you act as the editor or the critiquer of that draft. Have the humans write the draft first and then ask the AI system to critique what the human’s written — and there you can have the system act as a kind of senior mentor and provide coaching advice. And I think you can uplift human performance in that way and at the same time avoid de-skilling. So I think that’s an interesting way that businesses should maybe encourage their workers to use these copilots.

Copy LinkHow to prevent workers from losing autonomy in an AI-driven world

EL KALIOUBY: Yeah. There’s a quote in this book, and I’m going to read it. “While educated elites will retain a high degree of autonomy at work, where they will be empowered by AI copilots, blue collar and service sector workers could find themselves turned into cyborgs, still human, but with their every moment on the job dictated by a demanding and cruel AI manager.

Increasingly human agency will become a luxury item.” So intriguing. What do you mean by that?

KAHN: Well, I think we’re starting to see this happen already. And it was before generative AI even came along — just with some predictive AI systems that were being used in things like retail. They were deciding which workers worked best with which other workers based on data. And the managers of those retail branches, who used to get to schedule employees however they wished based on who they thought worked best together, who got along with whom — they kind of lost that autonomy. Instead, they were being told, no, the system, the computer’s algorithm is telling you that Mike works best on the graveyard shift, by some fractional amount, better than when he works the day shift. Nevermind that it’s really bad for Mike’s health — the algorithm is saying if he wants to keep this job, he should be assigned the graveyard shift. People don’t want to feel like they’re in some sort of prison system. They want to have a certain amount of freedom.

So I just think we have to be careful.

Be careful about how we implement these things, and really always keep the human and human value at the center and not blindly follow.

Copy LinkWhat schools should teach in the age of AI

EL KALIOUBY: I serve on the board of my kid’s school and, like many academic institutions, we’re grappling with this question: what should we be teaching our kids and what kind of skills are going to be really crucial in this age of AI? What do you think? I certainly have a point of view, but I’d love to hear yours.

KAHN: So I have a couple of thoughts. One is I do not think you should ban the use of the technology, which some schools have — they’ve said you can’t use ChatGPT or you can’t use Meta’s AI system or Anthropic’s Claude. I think that’s a big mistake. I think actually we have to integrate this into the curriculum in some way, but with some clear structures around it and some clear rules about how the students should use it. But I do think in terms of skills that we should be teaching — for instance, even though these systems can do a lot of our writing for us, I think we absolutely should still be teaching kids how to write, because I think writing and thought are not actually separable activities.

And I think one of the big dangers of this technology is that it encourages a kind of belief — I think a false belief — that writing and thought are separable. That you can have these thoughts, you can just put them down in bullet points, or you could just dictate them now with speech-to-text systems.

You could literally just talk to your phone, tell it some random thoughts you had and say, go out and write an essay for me that expresses these points, and it will do it and it might even do a reasonable job. But I think the student’s thinking is probably lazy, and there are still going to be holes in the arguments that the essay is trying to make, and it is not going to be as good an essay as if the student wrote it themselves.

I think we still need to teach critical thinking skills.

I think those are essential. I think we need to teach kids not to believe everything they read and see and to really question what the source of information is. Part of the reason we teach research is to teach critical thinking, to teach people what the source of the information they are receiving is.

And when you’ve gone out and had to do that research yourself, you become very familiar with what some of those sources are and what the pluses and minuses of different sources are. I think those things are essential. I think the STEM skills are still going to be essential. I think we’re still gonna need to teach math.

I think science is more important than ever. We are still going to need human doctors and nurses. Absolutely.

And so we’re still going to need to train those students who are talented in those areas for those jobs. I also think these other skills — emotional intelligence, very important, and social skills, very important. So I don’t think you need to radically change the skill set that you’re trying to teach, but you may have to adjust how you teach them.

Some of the pedagogical methods have to change. There’s a big debate about computer coding, because these systems are very good at coding. But I think they actually serve best as a co-programmer. They don’t write the best code out there, generally. And the best coders say that they can use these systems to become much more efficient, but they wouldn’t rely on them a hundred percent. And I think there are other skills about logic and reasoning that you learn through coding that are useful.

So we should probably still teach this. Plus we are going to need some smart people to keep building these systems for us, and that does involve a certain amount of coding, at least for now.

EL KALIOUBY: Yeah. I’m a computer scientist by background and I do think the field of computer science is facing a lot of disruption, but to your point, we’re still going to need amazing machine learning scientists who are going to think about the next innovations.

KAHN: Yeah, absolutely.

EL KALIOUBY: When it comes to all these technologies, I don’t think computer science is going to become obsolete, but it’ll look different.

Personally, I’m not advising my tech-obsessed 15 year old to dedicate his time to learn how to code. Instead I want Adam to be AI literate — to understand how these AI algorithms are trained and validated, where they are deployed and where things can go wrong.

We’re going to take a short break now, but when we come back, we’ll dive into some of the most promising use cases for AI and get into what we need to be cautious of.

[AD BREAK]

Copy LinkWhere AI could deliver the biggest benefits in education and medicine

So I’m a huge believer that AI can help us tackle some of the biggest challenges facing humanity if we do it right, and some of the areas that you talk about in your book include the potential for AI to transform education.

So let’s kind of dive into that a bit.

KAHN: Yeah. I think it’s going to be one of the most transformational areas, and I think one of the areas where AI actually is going to have the biggest positive impact is on education, because it enables every student to have a kind of personal tutor and it can adjust to their learning style. If a student’s a visual learner, it can provide a visual example. If somebody learns best by listening, it can provide spoken word as opposed to having to read text.

And I think it can do all this with sort of endless patience that a human’s never going to have. And I think that’s fantastic and potentially hugely transformative. Again, I think if we use specific copilot systems that have been designed by educational technology companies — maybe based on one of these large language models underneath, but fine tuned not to allow the student to cheat, not to just give the answer away, but to use Socratic method to help students learn — I think there’s tremendous potential there. And they can also provide the human teacher with very specific feedback on that student.

They can say, you know, Sally is struggling with this particular concept in math right now. And then that teacher can really fine tune their one on one interaction in the classroom.

It can also act as a bit of a coach and mentor to the teacher, potentially suggesting ways to improve the lesson plans, suggesting different pedagogical techniques. And all the concern about cheating — again, I think there are easy ways to kind of avoid that.

I talk a little bit in the book about implementing flip classrooms where essentially the delivery of the material takes place at home, as opposed to through a lecture in class. And then you do the kind of practice work in school where the teacher can provide more individual feedback. And what you can have is actually these AI tutors doing the feedback while the teacher kind of goes around and spends a little bit of each hour with each student.

And I think that would be a great way to maximize things.

EL KALIOUBY: I think what is fascinating about all of this is the changing role of an educator or a teacher, right? It becomes more perhaps of a facilitator, a coach. So I’m kind of excited to see how this unfolds. So another area that we’re both passionate about is how AI is accelerating scientific discovery and transforming medicine and healthcare.

I’m particularly keeping an eye on the intersection of sensor technology with AI and how it’s going to deliver personalized and preventative medicine, hopefully democratizing access to healthcare for everyone. What are you keeping an eye on and what are you excited about?

KAHN: Yeah, that area — personalized medicine — I’m very excited about. I think that’s huge. I do think we’re increasingly going to all be wearing various sensors, and the integration of that with predictive AI, and potentially quite a lot more information also about our genetics that AI will help model.

I’m watching drug discovery very carefully. I mean, huge potential to discover new medicines. I think the issues will be to see what actually makes it through to market. But there’s a lot of things that are now in phase one and phase two clinical trials. We’ve yet to have the first AI-discovered compound make it all the way to phase three, but we’re not that far off with a couple of the early candidates. So we’ll see, but there’s a tremendous pipeline of these coming. And I think that’s going to be transformational as well. I think we’re going to see much better treatment for chronic conditions, much better treatment for cancer, thanks to AI. I’m also excited about AI’s potential for material science.

Better batteries, new materials that are going to be more sustainable, break down in the environment — I think there’s tremendous potential there, again, using AI to try to uncover these kinds of new chemicals.

I think we’re going to enter a kind of golden age of scientific advancement in part because we’ve given scientists this incredible new tool that’s going to enable them to go out and discover new things.

Copy LinkThe most urgent AI risks from persuasion to disinformation

EL KALIOUBY: So we talked about the incredible potential for good with AI. Let’s cover some of the worries and concerns you see. So what’s top of mind for you when it comes to potential dangers with AI?

KAHN: Yeah. I worry that you see some of the chatbot companies moving towards a business model that might allow people to pay to have the chatbot tell us certain things. Right now it seems kind of innocent because the data deals that have been struck tend to have been with news organizations, where the chatbot company is actually paying the news organization to license their data. But as part of that deal, the news organizations have been negotiating, oh, but we want our news stories displayed more prominently by the chatbot. And I think, although that sounds innocent and good at the moment, it’s really dangerous because we’re on a slippery slope where next it’s not going to just be the news companies.

It’s going to be the big brands out there, and they’re going to want to tell people about the latest Nike shoes and the latest H&M clothing or whatever it is, and there’s going to be some commercial arrangements struck. And I also worry about disinformation and the risk of political disinformation and conspiracy theories. We’re already starting to see the effects of that. There’ve been a number of cases of voice clones and deep fakes that have had influence on elections around the world.

They’ve started to play a little bit of a role in the US — they haven’t played a huge role yet, but I’m worried about that. And there’ve been some very interesting studies out of Cornell University on how persuasive chatbots are. And it turns out they’re extremely persuasive, much more persuasive than human salespeople tend to be. So I think there’s a real danger that if what it’s persuading us of is something political, there’s huge potential for abuse there.

And I think we need to be very attuned to those dangers. Right now we don’t have very good ways of policing some of this. People talk about digital watermarking, there are various proposals for standards, but none of the standards right now are foolproof — they all can kind of be defeated — and there’s also not widespread enough adoption yet of that watermarking.

So I’d say those three are the risks I worry most about.

EL KALIOUBY: You’ve been writing about AI a lot, and you have a front seat to how AI is evolving, but how do you use AI in your everyday life? And one of the questions I have for you is, did you use AI to write your book?

KAHN: Yeah, everybody asks this, and I did not use it to write the book, and people are always really surprised, and they say, why not? And I have to say, at least the AI systems I was using when I started writing this book about a year ago, while they were very good for business writing, I actually found that for the kind of writing I wanted to do for the book, which was very conversational, I just couldn’t get it to write the way that sounded like me.

I occasionally use it for phrase finding. If I felt like I was overusing a particular phrase or a particular metaphor, I would sometimes ask it for advice — what is another metaphor for this? And it would provide some suggestions, and every once in a while I would take those suggestions. But I did not use it to write whole passages of the book.

I use it, of course, to help me figure out how to help my kids with their homework sometimes, because sometimes they get asked a math question I don’t quite understand how to explain. But I’ll ask ChatGPT, can you explain this to me thinking step by step, explain your reasoning?

It’s pretty good at that. And then that enables me to help my daughter with her homework.

EL KALIOUBY: Although I’m thinking she could go straight to ChatGPT.

KAHN: She could, she could skip me. Yeah, she could absolutely do that.

EL KALIOUBY: Jeremy, this was great. Thank you so much for joining us today.

KAHN: Oh, thank you so much, Rana. It’s great to be here.