Peter Voss on Artificial General Intelligence, Personalizing Personal Assistants, and Motorcycles

In this episode of Data Driven, Frank and Andy speak with Peter Voss about Artificial General Intelligence, Personalizing Personal Assistants, and Motorcycles

Sponsor

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Guest Bio

Peter Voss is the world’s foremost authority in Artificial General Intelligence.

His company Aigo (https://www.aigo.ai/) has created the world’s first intelligent cognitive assistant.

Aigo was funded with a personal investment of $10 million dollars. They currently manage millions of personalized customer service inquiries for household name-brands

Notable Quotes

Aigo is Peter's company. BAILeY's Introduction (00:00)

The east coast has been blanketed with snow. (01:30)

The Expanse books (03:00)

Coding for curiosity? - Frank (11:50)

"Models don't dynamically learn." - Peter (13:00)

Three waves: Logic programming, Deep learning / neural networks, cognitive architecture / intelligence (14:00)

Intelligence v. sentience? - Frank (15:50)

What about bots being "led astray?" - Andy (18:30)

On programming morality... (21:30)

AI Safety is a better description - Peter (22:30)

Asimov's three laws of robotics - Frank (23:15)

On delimmas - Peter (24:15)

"Morality should be about human flourishing." - Peter (25:15)

Are we using digital means to do something analog? - Andy (27:55)

Peter is trained as an electronics engineer. (28:05)

"Context is always super-important." - Peter (28:30)

"You need a feedback system." - Peter (30:00)

AIGO is Peter's company. (31:00)

The three meanings of personal. (34:00)

"Exo-cortex" (33:50)

On context switches (38:30)

Did you find AI or did AI find you? (41:00)

"I took five years off to study..." - Peter (43:00)

What's your favorite part of your current gig? (44:10)

When I'm not working, I enjoy ___. (45:00)

I think the coolest thing in technology today is ___. (45:30)

I look forward to the day when I can use technology to ___. (46:25)

Something interesting or different about yourself (47:00)

How Not to Die (48:00)

Where can people learn more about Peter? (49:00)

Book reading / listening recommendations? (49:00)

The Mind's I (50:00)

Peter's articles on Medium (52:00)

Get a free audio book and support DataDriven - visit thedatadrivenbook.com! (00:00)

Transcript

The following transcript is AI generated.

00:00:01 BAILeY

Hello and welcome to data driven.

00:00:03 BAILeY

The podcast where we explore the emerging fields of data science, machine learning, and artificial intelligence.

00:00:11 BAILeY

In this episode, Frank and Andy speak with Peter Voss, peterboat.

00:00:15 BAILeY

Peter Voss is the world's foremost authority, an artificial general intelligence or AGI.

00:00:21 BAILeY

In fact, he is the one who coined the term in 2001 and published a book on the topic in 2002.

00:00:28 BAILeY

He is a serial.

00:00:29 BAILeY

AI entrepreneur technology innovator who has for the past 20 years, then dedicated to advancing artificial general intelligence.

00:00:38 BAILeY

Today he is focused on his company, IGO, which is developing and selling increasingly advanced AGI systems for large enterprise customers.

00:00:47 BAILeY

Peter also has a keen interest in the interrelationship between philosophy, psychology, ethics, futurism and computer science.

00:00:56 BAILeY

I think you will find this interview a fascinating look at the future of AI.

00:01:01 BAILeY

Now on with the show.

00:01:05 Frank

Hello and welcome to data driven, the podcast where we explore the emerging fields of data science, machine learning and artificial intelligence.

00:01:13 Frank

If you like to think of data as the new oil, then you can think of us like well.

00:01:18 Frank

Car Talk because we focus on where the rubber meets the road and with me on this epic virtual road trip down the information highway because we're still locked in quarantine.

00:01:29 Frank

As always, Andy Leonard.

00:01:30 Frank

How's it going and?

00:01:31 Andy

Good Frank, how are you?

00:01:33 Frank

I'm doing well.

00:01:34 Frank

We had a bit of snow.

00:01:36 Frank

We're recording this on Monday, February 1st and the East Coast has been blanketed in some snow.

00:01:37 Peter Voss

Yes.

00:01:45 Andy

Yeah, we got more than we've gotten, probably since 2018 or so. About four inches here in FarmVille and then almost an inch of ice on top of that, which always makes it fun, right?

00:01:58 Frank

Yeah, the ice is worse than the snow on.

00:02:00 Frank

Basically so I went out, walk the dog today and one of the dogs and it was crunch, crunch, crunch.

00:02:06 Frank

So there's a nice layer of ice over everything which is going to make driving later fun, but I do have.

00:02:13 Frank

I do have the an all wheel drive car which is fantastic.

00:02:17 Frank

I will never not own one of those again.

00:02:19 Andy

Nice.

00:02:21 Frank

Yeah, you've seen it's the CRV.

00:02:23 Andy

Yes, yeah, it's nice you did well.

00:02:26 Frank

I dubbed it the Rocinante.

00:02:31 Andy

In case our listeners are not familiar with that, with what Frank is referring to, it is not the old novel.

00:02:40 Andy

Frank is not tilting at windmills instead.

00:02:44 Andy

And if I got that reference wrong, correct me.

00:02:46 Andy

I'll just edit that out.

00:02:47 Frank

Oh, you are right, it's from this AM Oh my God, I forgot new book on Cody.

00:02:48 Andy

Not sure.

00:02:51 Andy

Donkey Quixoti wasn't.

00:02:53 Frank

Yeah yeah Cervantes I was gonna say from Cervantes book and I'm like oh what was the name of that?

00:02:53 Andy

Yeah so.

00:02:59 Frank

Which is the opposite of how most people think, but that's what I do.

00:02:59 Frank

OK, good.

00:03:02 Andy

There we go, but it is actually a reference to both the books and a series, The expanse of which Frank and I are great fans, so.

00:03:12 Frank

Awesome, but you know who's not covered in snow today.

00:03:13 Andy

I like it.

00:03:15 Andy

Who is not covered in snow their guest.

00:03:16 Andy

Our guest.

00:03:18 Frank

Who lives in?

00:03:18 Frank

Yeah.

00:03:20 Frank

I'm assuming sunny or Smokey I guess depending on the time of year California Peter Voss Peter welcome to the show.

00:03:29 Peter Voss

Thank you, yes, it's we've got snow on the mountains here, but it's very sunny.

00:03:36 Peter Voss

It's it's nice and we have a lot less smog these days.

00:03:41 Andy

Very good.

00:03:41 Frank

Nice so you are the.

00:03:46 Frank

One of the world's, or if not the world's foremost authority in AGI or artificially artificial general intelligence, and I believe you are the one that coined the term.

00:03:58 Peter Voss

Yes, correct and 2001 myself and two other people. We coined the term artificial general intelligence AGI to really distinguish the kind of work we were doing from, you know, specialized narrow AI which is.

00:04:18 Peter Voss

Pretty much what everybody else is doing.

00:04:20 Peter Voss

The original dream of artificial intelligence was of course, to have systems that can think and learn the way humans do, but that turned out to be a lot lot harder than people thought.

00:04:31 Peter Voss

So over the years, AI really turned into narrow AI using human ingenuity to figure out how to solve one particular problem, like playing chess or.

00:04:41 Peter Voss

Container optimization or medical diagnosis and then to write a program or to train data to do that to solve that particular problem.

00:04:51 Peter Voss

But it's really the external intelligence of the program or the data scientists that is then encoded.

00:04:58 Peter Voss

To solve that problem, whereas we wanted to get back to the original dream of having a thinking machine that it can figure out how to do these things and and learn more humans do so.

00:05:09 Peter Voss

That's why we felt we had to.

00:05:12 Peter Voss

You know, coin a separate term to distinguish it from narrow AI.

00:05:16 Frank

Interesting.

00:05:18 Frank

So for years, AGI has been.

00:05:21 Frank

Kind of thought the stuff of science fiction.

00:05:24 Frank

I think there was a lot of optimistic people like you said that thought we would have it by now.

00:05:29 Frank

I know this is kind of a loaded question, but one do you think we'll ever get there and two, what's the sort of time frame we're looking at?

00:05:38 Peter Voss

Yes, it's an interesting question, so absolutely, I believe it's it's.

00:05:42 Peter Voss

Possible, and in fact the reason we got together. We wrote a book called Artificial General Intelligence. As I said in 2001 was because we believe the time is ripe to get back to this original dream that the technology had advanced enough. Both hardware and software technology and cognitive psychology. Cognitive science.

00:06:02 Peter Voss

That we now understood enough and had fundamentally had the tools in place to tackle this problem and to say.

00:06:11 Peter Voss

So I I absolutely believe that it can be solved soon, and in fact we will leave.

00:06:18 Peter Voss

We are on on the way of solving this problem now in terms of time frame.

00:06:24 Peter Voss

Normally the way I answer this question is I don't measure it in time.

00:06:28 Peter Voss

I measured in dollars.

00:06:31 Frank

I like that time is money, so I guess.

00:06:34 Frank

That's a reasonable correlation.

00:06:35 Peter Voss

Yeah, and and the reason I do, I say that is because.

00:06:39 Peter Voss

Still, today almost nobody is working on AGI. You know, 99% of all the effort in artificial intelligence is still on narrow AI, so if this continues, it will take a long long time for us to reach human level AGI. But if that changes.

00:07:00 Peter Voss

And you know the kind of funding that's going into deep learning machine learning suddenly was applied to AGI.

00:07:06 Peter Voss

Then I think it could easily happen at less than 10.

00:07:09 BAILeY

Yes.

00:07:10 Frank

Oh wow.

00:07:11 Andy

Very cool, so I'm curious is there any like lead in does?

00:07:16 Andy

Does time and money invested in deep learning and narrow AI?

00:07:23 Andy

Does any of that help move the cost?

00:07:25 Andy

Say further the cause for AGI?

00:07:29 Peter Voss

Slightly, I believe, you know.

00:07:32 Peter Voss

Obviously, any advances in languages and data collection in hardware development and the general experience.

00:07:42 Peter Voss

In that sense, it does help it.

00:07:44 Peter Voss

But in another sense, it's actually the opposite.

00:07:46 Peter Voss

It's actually hindering it because a whole generation of software engineers and data scientists are now coming into the field, believing that deep learning machine learning is a way to do it.

00:08:00 Peter Voss

And all we need is more data, more horsepower and will solve this problem.

00:08:05 Peter Voss

And that's I think barking up the wrong tree, and it's a it's a dead end.

00:08:10 Peter Voss

So in that sense, what's happening today with deep learning?

00:08:12 Peter Voss

Machine learning is actually counter to achieving.

00:08:16 Andy

GI interesting very interesting.

00:08:20 Frank

Was it always that way or it's just the way the market kind of went frenzied over just narrowed AI?

00:08:26 Peter Voss

Why?

00:08:26 Peter Voss

Well, we've had several windows of AI.

00:08:30 Peter Voss

You know the the disappointments over the decades.

00:08:33 Peter Voss

You know, when we had expert systems, people believe that you know they would really, you know, show real intelligence and then it kind of fizzled out.

00:08:42 Peter Voss

And so we've had.

00:08:43 Peter Voss

We've had various windows, and but of course, deep learning machine learning has been so spectacularly successful in several areas.

00:08:52 Peter Voss

You know, image recognition, improving speech recognition, and you know various other fields that just, you know, it's the only game in town as it has been very, very successful.

00:09:04 Peter Voss

But people are also starting to realize what the limitations are of it.

00:09:11 Peter Voss

So yeah, it's it's kind of at the moment.

00:09:14 Peter Voss

The only game in town, and it has really been successful in many.

00:09:17 Andy

Areas, So what are those limitations?

00:09:20 Andy

And how does AGI addressing?

00:09:23 Peter Voss

Yeah, so fundamentally when you think about intelligence, you know if you think about just common sense.

00:09:32 Peter Voss

If we talk to a person and we judge them to be intelligent or to be totally non intelligent, the kind of things we expect is that they can learn.

00:09:43 Peter Voss

Immediately that when you say something a, they understand what you're saying and they integrate that knowledge with their existing knowledge so you know if you say my sister's moving through Seattle next week or something.

00:10:01 Peter Voss

That knowledge needs to fit in somewhere.

00:10:04 Peter Voss

You know you know the person who's talking.

00:10:06 Peter Voss

You may know who the sister is, or you may not know who the sister is.

00:10:10 Peter Voss

You probably know what Seattle is.

00:10:13 Peter Voss

You may have images of, you know, rain pouring down all the time or whatever, but so you integrate that knowledge.

00:10:21 Peter Voss

And if you're not cleared, my maybe the person has two sisters, so then you would ask her, do you mean your older sister you know your younger sister?

00:10:30 Peter Voss

And so we expect an intelligent human to basically do.

00:10:35 Peter Voss

You know what's technically called one shot?

00:10:37 Peter Voss

Learning?

00:10:38 Peter Voss

You hear something once you see an image.

00:10:40 Peter Voss

Once you learn that and you integrate it into your existing knowledge base.

00:10:46 Peter Voss

And if you're not sure how to interpret it.

00:10:49 Peter Voss

Then you ask clarifying.

00:10:50 Peter Voss

Questions until you know what it what it is.

00:10:54 Peter Voss

So you have deep understanding you have disambiguation.

00:10:59 Peter Voss

You have learning instant learning, one shot learning.

00:11:03 Peter Voss

You have long term memory.

00:11:05 Peter Voss

You remember that next week you you know if you paid attention, you will remember that and you have reasoning about.

00:11:12 Peter Voss

30 now deep learning machine learning as it's done today, really doesn't offer any of those.

00:11:20 Peter Voss

So if you if you had a human and you told them something and they didn't remember it, they didn't understand that they didn't ask for clarification.

00:11:27 Peter Voss

You wouldn't think of them as being very intelligent, would you?

00:11:33 Frank

No, I mean, my kids are smart, but when I tell them to bring the trash cans back from the street, they'll conveniently forget.

00:11:39 Frank

But I, I think I know where you're going with that, yes?

00:11:42 BAILeY

All right?

00:11:44 Frank

But the question I have, it sounds like you're trying to and I know this is going to be not really good analogy.

00:11:50 Frank

Or maybe it is you're trying to code for curiosity.

00:11:54 Peter Voss

That's very much part of it, but you know even deeper is understanding.

00:11:59 Peter Voss

Basically, when you have some input, do you?

00:12:02 Peter Voss

Do you understand you know what the implications are, how it fits in with the rest of the knowledge that you have?

00:12:08 Peter Voss

And you know, even that, that's sort of more even more fundamental than curiosity.

00:12:13 Peter Voss

But yeah, curiosity is then wanting to gather more information, so this is inherently an interactive process.

00:12:22 Peter Voss

You know, an intelligent person would ask follow up questions you know they would want to kind of.

00:12:29 Peter Voss

Fill in the pieces of the puzzle and you know that they can be more.

00:12:33 Peter Voss

In fact effective in their communication on their or their job.

00:12:37 Frank

Right so.

00:12:37 Peter Voss

So yes, that's definitely part of it.

00:12:40 Frank

So calling back to your example of someone's sister moving to Seattle you you would ask, you know, I didn't know you had a sister or how many sisters do you have or how many siblings do you have and.

00:12:51 Frank

Where is she moving to?

00:12:52 Frank

Why?

00:12:53 Frank

I guess that's kind of.

00:12:55 Frank

I guess it's all about building that knowledge map inside.

00:12:58 Frank

Your head or then your head being could be a program I guess.

00:12:58 BAILeY

Exactly.

00:12:59 BAILeY

OK.

00:13:02 Peter Voss

Yeah, and deep learning machine learning really doesn't allow for that at all.

00:13:07 Peter Voss

You know you accumulate masses of data and you train a model, but that model is then static.

00:13:14 Peter Voss

It's a read only model.

00:13:15 Peter Voss

You know, it doesn't dynamically learn, so it may have a sort of a knowledge graph, but even that knowledge graph is.

00:13:23 Peter Voss

Is very opaque, it's.

00:13:26 Peter Voss

Yeah, it's not scrutable you know and and this is this is such a big problem with deep learning machine learning that you don't know why it gives a certain response, which is a huge...