This is a clip from a conversation with Bjarne Stroustrup from Nov 2019. New full episodes are released once or twice a week and 1-2 new clips or a new non-podcast video is released on all other days. You can watch the full conversation here: https://www.youtube.com/watch?v=uTxRF5ag27A
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Bjarne Stroustrup is the creator of C++, a programming language that after 34 years is still one of the most popular and powerful languages in the world. Its focus on fast, stable, robust code underlies many of the biggest systems in the world that we have come to rely on as a society. If you're watching this on YouTube, many of the critical back-end component of YouTube are written in C++. Same goes for Google, Facebook, Amazon, Twitter, most Microsoft applications, Adobe applications, most database systems, and most physical systems that operate in the real-world like cars, robots, rockets that launch us into space and one day will land us on Mars.

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So a crazy question, but I work a lot with machine learning, with deep learning.

I'm not sure if you touch that world much, but you could think of programming as a thing that takes some input.

Programming is the task of creating a program, and a program takes some input and produces some output.

So machine learning systems train on data in order to be able to take an input and produce output.

But they're messy, fuzzy things, much like we as children grow up, you know, we take some input, we make some output, but we're noisy, we mess up a lot, we're definitely not reliable, biological system are a giant mess.

So there's a sense in which machine learning is a kind of way of programming, but just fuzzy.

It's very, very, very different than C++.

Because C++ is, just like you said, it's extremely reliable, it's efficient, you can measure, you can test it in a bunch of different ways.

With biological systems or machine learning systems, you can't say much except sort of empirically saying that 99.8% of the time it seems to work.

What do you think about this fuzzy kind of programming?

And in my domain, I don't like fuzziness.

That is, people say things like they want everybody to be able to program.

But I don't want everybody to program my airplane controls or the car controls.

I want that to be done with people that are specifically educated and trained for doing, building things.

Similarly, a language like C++ is not for everybody.

It is generated to be a sharp and effective tool for professionals basically and definitely for people who aim at some kind of precision.

You don't have people doing calculations without understanding math, right?

Counting on your fingers is not going to cut it if you want to fly to the moon.

And so there are areas where an 84% accuracy rate, 16% false positive rate is perfectly acceptable and where people will probably get no more than 70.

What I have seen is more like 84 and by really a lot of blood, sweat, and tears, you can get up to 92 and a half.

So this is fine if it is, say, pre-screening stuff before the human look at it, it is not good enough for life-threatening situations.

And so there's lots of areas where the fuzziness is perfectly acceptable and good and better than humans, cheaper than humans, but it's not the kind of engineering stuff I'm mostly interested in.

I worry a bit about machine learning in the context of cars.

I've read some of the papers, but I've not ever done it.

And The idea that scares me the most is the 1 I have heard, and I don't know how common it is, that you have this AI system, machine learning, all of these trained neural nets.

And when there's something that's too complicated, they ask the human for help.

But the human is reading a book or asleep, And he has 30 seconds or 3 seconds to figure out what the problem was that the AI system couldn't handle and do the right thing.

I mean, how do you do the cutaway between the machine and the human?

And for the designer of 1 of the most reliable, efficient and powerful programming languages, C++, I can understand why that world is actually unappealing.

To me, it's extremely appealing because we don't know how to get that interaction right, but I think it's possible, but it's very, very hard.

I would much rather never rely on a human if you're driving a nuclear reactor if you're Or an autonomous vehicle it's much better to design systems written in C++ that never ask human for

So That's 1 reason I have to keep a weather eye out on what's going on in that field, but I will never become an expert in that area.

But it's a good example of how you separate different areas of applications and you have to have different tools, different principles, and they interact.

No major system today is written in 1 language and there are good reasons for that.


Speaker 0: So a crazy question, but I work a lot with machine learning, with deep learning. I'm not sure if you touch that world much, but you could think of programming as a thing that takes some input. Programming is the task of creating a program, and a program takes some input and produces some output. So machine learning systems train on data in order to be able to take an input and produce output. But they're messy, fuzzy things, much like we as children grow up, you know, we take some input, we make some output, but we're noisy, we mess up a lot, we're definitely not reliable, biological system are a giant mess.

So there's a sense in which machine learning is a kind of way of programming, but just fuzzy. It's very, very, very different than C++. Because C++ is, just like you said, it's extremely reliable, it's efficient, you can measure, you can test it in a bunch of different ways. With biological systems or machine learning systems, you can't say much except sort of empirically saying that 99.8% of the time it seems to work. What do you think about this fuzzy kind of programming?

Do you even see it as programming? Is it totally another kind of world?

Speaker 1: I think it's a different kind of world. And it is fuzzy. And in my domain, I don't like fuzziness. That is, people say things like they want everybody to be able to program. But I don't want everybody to program my airplane controls or the car controls.

I want that to be done by engineers. I want that to be done with people that are specifically educated and trained for doing, building things. And it is not for everybody. Similarly, a language like C++ is not for everybody. It is generated to be a sharp and effective tool for professionals basically and definitely for people who aim at some kind of precision.

You don't have people doing calculations without understanding math, right? Counting on your fingers is not going to cut it if you want to fly to the moon. And so there are areas where an 84% accuracy rate, 16% false positive rate is perfectly acceptable and where people will probably get no more than 70. You said 98%. What I have seen is more like 84 and by really a lot of blood, sweat, and tears, you can get up to 92 and a half.

So this is fine if it is, say, pre-screening stuff before the human look at it, it is not good enough for life-threatening situations. And so there's lots of areas where the fuzziness is perfectly acceptable and good and better than humans, cheaper than humans, but it's not the kind of engineering stuff I'm mostly interested in. I worry a bit about machine learning in the context of cars. You know much more about this than I do. I worry too.

But I'm sort of an amateur here. I've read some of the papers, but I've not ever done it. And The idea that scares me the most is the 1 I have heard, and I don't know how common it is, that you have this AI system, machine learning, all of these trained neural nets. And when there's something that's too complicated, they ask the human for help. But the human is reading a book or asleep, And he has 30 seconds or 3 seconds to figure out what the problem was that the AI system couldn't handle and do the right thing.

This is scary. I mean, how do you do the cutaway between the machine and the human?

Speaker 0: It's very, very difficult. And for the designer of 1 of the most reliable, efficient and powerful programming languages, C++, I can understand why that world is actually unappealing. It is for most engineers. To me, it's extremely appealing because we don't know how to get that interaction right, but I think it's possible, but it's very, very hard.

Speaker 1: It is. And I was stating a problem,

Speaker 0: not a solution. I would much rather never rely on a human if you're driving a nuclear reactor if you're Or an autonomous vehicle it's much better to design systems written in C++ that never ask human for

Speaker 1: help. Let's just get 1 fact in. Yeah. All of this AI stuff is on top of C++. So That's 1 reason I have to keep a weather eye out on what's going on in that field, but I will never become an expert in that area.

But it's a good example of how you separate different areas of applications and you have to have different tools, different principles, and they interact. No major system today is written in 1 language and there are good reasons for that. You

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Bjarne Stroustrup: Deep Learning, Software 2.0, and Fuzzy Programming