Ever tried explaining AI to your grandma? Join Jon Krohn as he breaks down data science, AI, and AGI to his 94-year-old grandmother, Annie. Tune in for a refreshingly simple take on complex topics.
Jon Krohn sits down with his 94-year-old grandmother, Annie, for a truly special episode inspired by a listener’s suggestion. Annie, fondly remembered from her previous mentions on the show, is now the star of the conversation as Jon takes on the challenge of explaining his career in data science to her. With patience, humor, and love, Jon breaks down the meaning of data, what data scientists do, and introduces the concepts of artificial intelligence (AI) and artificial general intelligence (AGI). This episode is more than just a tech talk—it’s a touching exploration of how to bridge generations through understanding, curiosity, and storytelling. Join Jon and Annie for a heartwarming dialogue that brings complex ideas to life in the most relatable way, showing that even the most advanced technologies can be made meaningful for everyone, no matter their age or experience.
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Podcast Transcript
Jon: 00:05
This is episode number 816 on explaining artificial general intelligence to a 94-year-old.
00:27 Welcome back to the Super Data Science Podcast. I’m your host, Jon Krohn. If you’ve been listening to this show for a long time, then you will have come across my now 94-year-old grandmother, Annie a few times, including most recently in episode number 800 when she recounted the all changing technological transformations she lived through over the past century.
00:49 Well, a dedicated and thoughtful listener, Dr. John Lewis commented on social media that he’d like to have an episode of this podcast, wherein I explained to Annie what I do for a living. I hadn’t actually done that in real life before, so I thought this was a cool idea, and now you have it. In today’s episode, I explained to a 94-year-old what data are, what data scientists do, what artificial intelligence is, and how artificial general intelligence, AGI could dramatically transform society around the world for the better.
01:20 Today’s episode may be of interest to you if you’re not already familiar with these concepts or if you’d like some ideas on how you yourself might explain some of these concepts to a lay person. Be forewarned that in today’s episode, since I’m the one doing the explaining, I do hog the mic a fair bit in this conversation. If you’d like more loquaciousness from charming Annie, refer back to episode number 800 in which she was doing all the explaining. All right, you ready? Let’s jump right into our conversation.
01:49 So when we had your episode last year, there was someone on social media on LinkedIn, there was someone named John Lewis, Dr. John Lewis, and so he’s something called the chief story thinker at a company called Explanation Age, as well as sitting on advisory boards. And so Dr. John Lewis, a year ago when your episode number 716 came out, he said, “We are usually asked if we can explain what we do.” So as professionals, a lot of people listen to my show. They have jobs similar to what I do, and so Dr. John Lewis says, “We are usually asked if we can explain what we do to a sixth grader, but it would be a great episode of your show to hear you talk with your grandmother about what you do. I bet she will surprise us.”
Annie: 02:47
Good.
Jon: 02:48
So that’s why we’re here today. So thank you for the great idea, Dr. Lewis. We’ve been together, you and me now in the Muskokas, a couple hours drive north of Toronto in Canada.
Annie: 03:00
Toronto. Yes.
Jon: 03:02
Beautiful cottage country.
Annie: 03:04
Oh, beautiful.
Jon: 03:05
I think we might have a good photo for the social media thumbnails that come out from this day of us together-
Annie: 03:11
Together, yeah.
Jon: 03:13
… at this great cottage country, we call it here in Canada. I wanted to talk to you a bit about some things related to what my listeners usually hear about. So have you ever heard of data?
Annie: 03:29
No.
Jon: 03:29
No.
Annie: 03:31
No.
Jon: 03:32
So data are… It’s like little pieces of information. They’re individual data points. And so data, it can be a small amount of information. You could have, what was the temperature today? So that’s like a piece of information, and then you could track that. You could say, okay, what was the temperature in Toronto every day for the last year? So each one of those pieces of information, we would say that’s like a data point.
Annie: 04:06
Oh, it’s a data point.
Jon: 04:08
So when in our job as data scientists, we try to collect for some kind of problem that we’re trying to solve. So at my company, something that we try to do is we try to suggest the best people out of everyone in the United States, everyone who has any working information online, we try to use data that are available in the world. So there’s a lot of data on the internet, which that you know because you have an Instagram account. Even something like the security cameras in your house that you can watch on your computer, all of that, when it records the information about the mailman walks by or a dog walks by and it tracks that information, that’s data being stored.
Annie: 05:03
Stored.
Jon: 05:05
Remember we used to have the free plan for your security cameras in your house, and then you only had a few hours. And so that was like saying we had a few hours of data.
Annie: 05:15
Data.
Jon: 05:17
And then we went in and we paid with a credit card, and then we had several hours. Or several days, sorry.
Annie: 05:24
Days now, yeah.
Jon: 05:25
Days, yeah. So storing data, it’s not free because we need to have computers somewhere that are storing the information. And so that’s why we had to pay to get days of recorded footage on all of your security cameras. So that’s what data are. And so at my company, for example, at Nebula, we are looking at all of the data that we can find on people that’s available publicly online, on the internet. Then we collect all of those data and we then try to build some kind of computer program that can predict when somebody’s looking to hire someone, let’s say they’re looking to hire a plumber.
Annie: 06:19
Plumber, yeah.
Jon: 06:20
Or they’re looking to hire an electrician. If you look over all of the United States, some people are going to be good for that plumbing job that are completely different from the kinds of people that are going to be good for the electrician job.
Annie: 06:34
Electrician.
Jon: 06:36
But we can build a computer program that can rank everyone from best to worst. So we can say, if we have information on 200 million people in the US-
Annie: 06:49
Wow.
Jon: 06:50
… we can write a computer program to take the data on those 200 million people, and somebody says, “I want to hire a plumber,” we can rank the 200 million people in the US for that job from best to worst, and then they can start looking from the best. So my job is to find the right kinds of data out there and think about a business problem that could be solved, and then create a computer program that can learn from those data and say, okay, if somebody’s a specialist in…
Annie: 07:32
In plumbing.
Jon: 07:35
In plumbing, then they’re going to be a great plumber.
Annie: 07:39
Good. Yeah, that’s right.
Jon: 07:40
If somebody’s great-
Annie: 07:41
At electricity.
Jon: 07:42
At electricity-
Annie: 07:43
So they’re going to be great.
Jon: 07:44
They’re going to be great as an electrician. Exactly.
Annie: 07:47
That makes sense.
Jon: 07:49
Good. And then so something that’s one step more complicated is we are increasingly more and more and more people in my field, people with my job, data scientists, we’re trying to create machine systems, computer programs that are able to do more and more amazing things.
Annie: 08:16
Like?
Jon: 08:17
So the goal, I guess, is all of us together, we’re trying to make something called artificial intelligence, which… So you and me, we have intelligence. Obviously that’s an easy word to understand. Artificial intelligence means that it isn’t like the way that a dog learns or a human learns, it’s a computer learning. And so you could even say that this kind of program that I already described about predicting who is the best plumber or who is the best electrician, that is kind of artificial intelligence because in order for that to work well, it has to kind of understand the meaning of the language like a person would and so that it can say, “Oh, these words, those are great for a plumber. These other words, those are great for an electrician and for any kind of job out there.”
09:13
But what we’re trying to do, generally, a lot of us data scientists all over the world, we’re sharing ideas on the internet. We write papers, we record things like this podcast-
Annie: 09:31
Like now.
Jon: 09:31
Like this. And so we share information, we share conversations, and we’re all building towards creating a computer program that could learn not just something specific. We have computer programs like I just described that could say, find the best plumber, and you have a different computer program that can drive a car automatically. And you have another computer program that you can play chess against. What we’re gradually trying to build towards is having one computer program that can do everything that a person could do.
Annie: 10:09
Wow.
Jon: 10:11
So it could drive-
Annie: 10:11
That’d be special.
Jon: 10:13
Right. So it could drive a car.
Annie: 10:14
Car.
Jon: 10:15
It could beat you at chess.
Annie: 10:16
Yes.
Jon: 10:17
It could answer any kind of questions you have.
Annie: 10:20
Could do the plumbing.
Jon: 10:22
That’s the idea is that eventually to actually even have the computer program be inside a robot and be able to do the plumbing.
Annie: 10:31
Plumbing,
Jon: 10:32
Be able to be the electrician. It’ll take my job too eventually is the idea.
Annie: 10:39
So, we don’t want that to happen.
Jon: 10:42
Well, that’s a complicated question because we might be able to get to a point where maybe computers working inside of robots, maybe they could do everything.
Annie: 10:52
Yeah, that could be so.
Jon: 10:56
That’s kind of the idea. And so maybe we don’t know how long it’ll take. Some people think to have… Having the robots is something different because you’ve got to build all the robots, and that takes time. But the computer programs that might be able to run the robots and do everything, do plumbing, do electrician, do the ranking of people for jobs, be able to do my job and build other computer programs, even smarter ones-
Annie: 11:28
Even pick cherries, maybe?
Jon: 11:29
Even pick cherries, anything at a farm. Exactly. So being able to get our food and have high quality nutrition without needing farmers to do all the work-
Annie: 11:42
The work.
Jon: 11:43
… so we’re well-fed. So some people think it might even just be a few years before we have computer programs that can at least do all of the thinking that a human could do, but it’ll probably be a while longer after that before you have the robots as well that can use that brain power, that artificial intelligence and be able to do all of the labor.
Annie: 12:06
Oh, that’d be great.
Jon: 12:09
Yeah, because the idea is, there’s all kinds of complexities because if robots can do all the work-
Annie: 12:15
Work.
Jon: 12:15
… and nobody needs to have a job, well, then that creates… It’s something different than we’ve ever had before. Right?
Annie: 12:21
Yeah. That would be different.
Jon: 12:24
It’d be like how you’re in retirement now. It would be kind of like everyone’s in retirement, and so it creates a different kind of society. We need to figure out how does that work in terms of taxation and in terms of people, maybe everyone getting enough income automatically from the government, I guess.
Annie: 12:45
It would have to be.
Jon: 12:47
I think so. But basically-
Annie: 12:50
Or unless you save enough to use your money?
Jon: 12:55
Yeah, exactly. I guess that’s another thing as well. I guess the idea here is that potentially everyone would have their needs met without needing to work, and your kids could get a high quality education for free. You get great healthcare. Maybe the robots and all of these thinking computers, this artificial intelligence, they might be able to come up with lots of ways that people could live longer, healthier lives, happier lives.
Annie: 13:24
To 150 at least.
Jon: 13:25
I hope so, and I hope we can figure it out soon so that we can have you going to 150 feeling very happy and healthy.
Annie: 13:34
Yeah, wouldn’t that be great. Maybe.
Jon: 13:35
Maybe
Annie: 13:36
Yeah, I could surprise.
Jon: 13:38
Exactly. We need you to hold out for at least a few more years.
Annie: 13:41
Years.
Jon: 13:43
But that’s going strong for you right now.
Annie: 13:45
Yeah, so far so good.
Jon: 13:48
Exactly. In the previous conversations we’ve had that we’ve aired on my podcast, on my show, it’s been me asking you questions. This time it’s mostly me, just because we had this person, Dr. John Lewis, asked me to explain what we do for a living. I’ve been basically hogging the mic. And so I hope that this has still been an interesting episode where I think it’s been clear, at least from your responses, that you’ve understood what I’ve been saying and all makes sense.
Annie: 14:19
Oh, yes. Yes.
Jon: 14:20
That’s good. You also did have great points there about robots being able to pick the cherries or whatever other agriculture out there.
Annie: 14:28
Well, that would be great too.
Jon: 14:30
To have everything taken care of.
Annie: 14:32
Yeah. Instead of using a step ladder, that way the robot would do everything.
Jon: 14:37
Exactly. It’d be safer too.
Annie: 14:38
Yeah, safer.
Jon: 14:38
No one can fall off a ladder. And that’s usually work… Not a lot of people… If you want to go pick apples for fun or pick cherries for fun, that’s one thing, going on a Saturday with your kids. But most people, they don’t love having the job of picking cherries or picking grapes. So hopefully we can replace those things and hopefully it ends up being a very positive development for everyone in Canada, in the United States, in the world. And maybe it means it could be like we’re at the cottage all the time. So in conclusion, wouldn’t it be nice if when you’re spending time with your children, your grandchildren, it could be like this week that we had together at the cottage on the lake, going on boats.
Annie: 15:23
Boats. Like we did on a boat.
Jon: 15:24
Yeah, we did go on a boat this week, and we’ve been just having fun, playing music together.
Annie: 15:31
Together.
Jon: 15:32
We’ve been eating together, cooking meals together, barbecuing. Theoretically, the idea with this artificial intelligence that me and everyone else is trying to build, we could have just it basically be like holidays all the time for everyone.
Annie: 15:46
For everybody. That’d be great.
Jon: 15:49
Yeah, and that’s the idea. So we’ll see.
Annie: 15:51
We’ll see.
Jon: 15:52
We’ll keep working at it.
Annie: 15:53
Well, and too people, if they save enough money, they could do that.
Jon: 15:57
They could do that now. Exactly.
Annie: 15:59
Like I did too. I saved money from since I was little and did hairdressing.
Jon: 16:06
So people, historically, it’s been the case that if you worked hard enough, you could save up enough, you could retire.
Annie: 16:12
Retire.
Jon: 16:13
But I guess the idea is that in the future, not just in Canada, where we’re wealthy, people in general are wealthy. And so a lot of people in Canada, if they work hard like you did, and they save up, they can retire. The idea here is not only would anyone anywhere in the world be able to retire… Because right now in places, there’s lots of countries all over the world where it doesn’t matter how hard you work, you’re going to be working hard your whole life.
Annie: 16:45
Hard, yeah.
Jon: 16:45
You don’t really get to retire.
Annie: 16:46
No.
Jon: 16:48
But with potentially what we’re working towards, not only could anyone anywhere in the world retire, but it would be like you’re basically born retired, and everyone in this society could be born retired. So wouldn’t that be something?
Annie: 17:02
Yeah, that’d be great.
Jon: 17:04
I think so. It’d be a lot of fun. We would certainly have a lot of fun. The way that you and I growing up when I was little, basically you were kind of retired. You had many days in the week that you could spend with me.
Annie: 17:17
Spend, yes.
Jon: 17:18
And then I had to go and spend so many years working so hard. Even now, we live apart from each other. I live in the United States, you’re in Canada.
Annie: 17:26
In Canada.
Jon: 17:28
But in this kind of world in the future, I would never have needed to left. I could potentially leave if I wanted to for fun, or otherwise, I could just spend time around you and my loved ones and just be enjoying life all day, every day that you want.
Annie: 17:44
Yeah, just like we did this week.
Jon: 17:46
Just like we did this week. Exactly. So hopefully that’s something exciting for all of us to look forward to. It certainly keeps me motivated on this show, as well as motivated with my day job, building AI systems and automating things little bit by bit, publishing software so that other people can see it.
Annie: 18:05
See it.
Jon: 18:08
Very exciting times. So John Lewis, hopefully that answered your question or your idea about an episode with my 94-year-old grandmother here. We’ll see what other kind of questions come out of this, and next time we have you on the show.
Annie: 18:25
The show. That’d be great. Thank you for having me, and we’ll be talking again or speaking again.
Jon: 18:33
Yeah, for sure. I mean, you and I definitely will be. And then it probably won’t be long before you’re on the show again, especially if all the episodes you’ve had so far-
Annie: 18:40
So far.
Jon: 18:41
… people have loved them and they keep wanting to hear more.
Annie: 18:44
More. That’s great. Thank you for having me.
Jon: 18:48
Yeah, thank you.
18:48
All right. I hope you found those explanations of data, data science and AI helpful, either for you personally or to frame these concepts effectively to someone you know. You may have noted that I didn’t use the specific term AGI in my conversation with Annie because, well, it seemed to me that that was unnecessarily complicated after all that I’d already thrown at her in our conversation. If you yourself, however, are looking for more detail on AGI, I recommend referring back to episode number 748 of this show.
19:20
To be sure not to miss any of our exciting upcoming episodes, be sure to subscribe to this podcast if you haven’t already, but most importantly, I hope you’ll just keep on listening. Until next time, keep on rocking it out there, and I’m looking forward to enjoying another round of the Super Data Science Podcast with you very soon.
