New DataDriven Episode with Peter Voss

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

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Sponsor: Audible.com – Get a free audio book and support DataDriven – visit thedatadrivenbook.com!

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 problem.

00:13:39 Peter Voss

So you really need knowledge representation.

00:13:43 Peter Voss

That’s also understandable, scrutable.

00:13:46 Peter Voss

You know that it can say, well, why do you say that?

00:13:48 Peter Voss

Well, you know Jane told me or I read it here or you know I figured it out.

00:13:55 Peter Voss

I thought about it, you know.

00:13:57 Frank

Right and so.

00:13:58 Frank

The question I have then would be.

00:14:01 Frank

You mentioned deep learning doesn’t work, and to solve this problem, what sorts of models do like what sorts of what is it?

00:14:09 Frank

What is it you know?

00:14:10 Frank

What do you think would solve this problem and you know, is it reinforcement learning based?

00:14:15 Frank

Is it some variant of existing kind of other models?

00:14:19 Peter Voss

Right, so the number of different ways of looking at it.

00:14:24 Peter Voss

One of the one of the ways of looking at it that I found quite useful is a few years ago, DARPA gave some presentations about the 3rd wave of AI, or the three waves of AI and how they categorized it is the first wave.

00:14:42 Peter Voss

Was basically logic programming and this is what AI was all about for the first few decades.

00:14:48 Peter Voss

It was very much logic based, you know formal formal logic and we still see a lot of that that today, but that would be like flowcharts and decision trees and and and things like that.

00:15:00 Peter Voss

That’s the 1st way the 2nd wave.

00:15:02 Peter Voss

Is is basically deep learning, machine learning or neural networks?

00:15:08 Peter Voss

Statistical models, that’s the second wave, and that’s really where we in now what they called the 3rd wave is essentially a cognitive architecture, something that’s inherently designed to have all of the features required for intelligence.

00:15:26 Peter Voss

So it’s you know, the things that I just rattled off.

00:15:29 Peter Voss

That you could learn immediately that you have deep understanding that you will ask for clarification.

00:15:35 Peter Voss

You have a knowledge representation that allows that’s not opaque.

00:15:38 Peter Voss

That’s not a black box.

00:15:40 Peter Voss

So you start off with an architecture, cognitive architecture, and there’s been a few around over the years.

00:15:47 Peter Voss

A few cognitive architectures, but they’ve never really taken off for various reasons that we’re going to.

00:15:55 Frank

So you keep saying intelligence, and I think this is important, at least for me.

00:16:00 Frank

What?

00:16:01 Frank

Is there a distinction?

00:16:03 Frank

I suspect there is between intelligence and sentience.

00:16:09 Peter Voss

Yes and no.

00:16:10 Peter Voss

I mean, in terms you know what we’re talking about.

00:16:13 Peter Voss

Here is of course, human type intelligence, human level intelligence, and you really have to be aware you have to be conscious.

00:16:23 Peter Voss

I mean, conscious is such a loaded term, but if we just use the synonym of.

00:16:28 Peter Voss

Of aware awareness, you have to be aware of your surroundings.

00:16:32 Peter Voss

You have to be aware of who are you communicating with.

00:16:36 Peter Voss

You have to be aware of yourself as an entity that has to be self aware.

00:16:40 Peter Voss

Yes, because you have to be able to tell the difference between whether you cause something in the world or somebody else, or something else.

00:16:49 Peter Voss

’cause something in the world.

00:16:50 Peter Voss

So the there has to be a self concept, self awareness.

00:16:54 Peter Voss

So yes, when is essential for the kind of intelligence we’re talking about.

00:17:00 Frank

Interesting, but I mean that that would.

00:17:02 Frank

That would start kicking open other kind of ethical concerns, like what and again, this is.

00:17:08 Frank

We’re show about data science and AI.

00:17:09 Frank

Not necessarily philosophy, but I mean there there’s also kind of that notion of, you know, awareness, sentience, an I guess, for lack of a better term, personhood.

00:17:22 Peter Voss

Yes, absolutely one.

00:17:24 Peter Voss

I mean, once you talk about a machine having human level intelligence, you know even setting aside the whole debate of is it really conscious?

00:17:32 Peter Voss

You know what?

00:17:33 Frank

Right?

00:17:33 Peter Voss

What about the hard problem of consciousness?

00:17:35 Peter Voss

And I mean, if you just ignore that.

00:17:38 Peter Voss

If you are interacting with a machine that.

00:17:41 Peter Voss

You know, by all accounts is aware of itself as an entity, which is, as I say, it has to be to to be at human or at a useful level of intelligence.

00:17:51 Peter Voss

It has to be aware of itself as an acting end.

00:17:54 Peter Voss

T.

00:17:56 Peter Voss

Yeah, it makes it makes us feel very uncomfortable because the only other experience we have of self aware beings are other humans.

00:18:05 Frank

Right?

00:18:05 Peter Voss

So it’s it’s.

00:18:07 Peter Voss

It’s something we basically have to to learn to adapt with that.

00:18:12 Peter Voss

No, this is a machine and it’ll tell you that hey, I’m a machine.

00:18:16 Peter Voss

Not a human right, right?

00:18:19 Andy

Well, Peter, what are your thoughts about? I guess boundaries on how these machines would learn the the a GIS versus how some of the AI’s have been taught in the past. We’ve seen when they interact publicly, when they, you know have a conversation similar to what you described.

00:18:39 Andy

You know about a sister in Seattle, we’ve seen AI’s lead astray.

00:18:47 Peter Voss

Yes, certainly in the early days.

00:18:50 Peter Voss

I mean it’s a bit like having a child growing up.

00:18:54 Peter Voss

You know if it’s if it’s an environment where everybody swears and child will swear you know and one think anything of it it doesn’t.

00:19:03 Peter Voss

Doesn’t know whether it’s appropriate or inappropriate it you know.

00:19:06 Peter Voss

Obviously, appropriate in that environment, so you clearly need to give it the right kind of grounding or sort of kernel of knowledge and behavior that that you expected to.

00:19:07 Andy

Right?

00:19:19 Peter Voss

Have and so that that sort of in a you know supervised learning and and I’m not.

00:19:24 Peter Voss

I don’t mean supervised in the sort of technical sense, but yeah, human in the loop, give it the right background knowledge, but once it gets to a point where you can actually communicate with it where you can talk to it and here we are talking about.

00:19:39 Peter Voss

Irrational hyper rational being unlike humans you know we are.

00:19:44 Peter Voss

Rationality came very very late in in the evolutionary.

00:19:49 Peter Voss

And so we as an AI inherently doesn’t have the reptile brain you know, to start off, right?

00:19:56

Right?

00:19:57 Peter Voss

So if you explain to it, well, no, that’s not what you say to a customer.

00:20:02 Peter Voss

Or that’s not what you say in this under these circumstances.

00:20:06 Peter Voss

And oh OK, fine, I want you know, I get it.

00:20:10 Frank

So the learning switch remains on.

00:20:10 Frank

So as long as the learning.

00:20:12 Peter Voss

Correct, yeah, as long as it’s open to learn it has, it doesn’t have its own ego.

00:20:13

Correct, it could be.

00:20:17 Peter Voss

Oh thing that has to prove itself will be like, hey, I’m going to be really bad, you know so or a tantrum you know you’re not going to get that so OK fine now I understand.

00:20:23 Frank

Right?

00:20:28 Peter Voss

So yeah, I won’t do that again.

00:20:30 Frank

I’m thinking of the movie Chappie about kind of an AI that kind of learns from a bad environment.

00:20:36 Peter Voss

Yeah, yeah that that was fantastic movie actually yeah.

00:20:39 Frank

Yeah, that was it’s kind of a.

00:20:43 Frank

It’s it’s just interesting.

00:20:44 Frank

Thing is, if you if folks haven’t seen the movie, it’s about a artificially intelligent kind of police robot that gets in the hands of.

00:20:51 Frank

Criminals.

00:20:53 Frank

An I don’t want to spoil anything, but I mean it’s an interesting kind of thought experiment of like you know who trains the.

00:21:00 Frank

In that case, I guess who trains the AI?

00:21:03 Frank

It is.

00:21:05 Frank

You know can change the outcome of.

00:21:09 Frank

Of what the AI learns.

00:21:11 Peter Voss

Yeah, that that movie was filmed made in South Africa.

00:21:11 Andy

Yeah.

00:21:15 Peter Voss

I lived there for a long time so it was quite cool.

00:21:19 Peter Voss

Kind of to see places I recognized.

00:21:21 Frank

Nice nice.

00:21:22 Peter Voss

Yeah, yeah.

00:21:24 Peter Voss

So well made movie actually low budget movie but very well made.

00:21:28 Frank

Yeah, Neill Blomkamp is that the director?

00:21:32 Peter Voss

I don’t recall.

00:21:33 Frank

OK, oh, he’s made a bunch of movie.

00:21:35 Frank

I think he was also behind District 9 which was also relatively low budget but really high quality.

00:21:40 Peter Voss

Correct, yeah yes yes.

00:21:41 Frank

Yes yes yeah.

00:21:42 Andy

Definitely thought provoking there and I I hear you, it just doesn’t sound easy or simple.

00:21:49 Andy

To program I’m trying, I guess the right word I’m looking for is morality or something that’s given a higher priority than you know that that overarches the operating parameters of the learning that takes place or even the acceptance of learning something that maybe filters.

00:22:08 Andy

You know the learning that makes it into the code and is.

00:22:12 Andy

You know, then and then applied to how the AI responds.

00:22:17 Andy

Does that make sense?

00:22:19 Peter Voss

Yeah, I think there’s a big misunderstanding in when people talk about AI safety, and you know, morality built into built into the system, the kind of system we’re talking about is.

00:22:33 Peter Voss

There’s actually going to be relatively little in code.

00:22:37 Peter Voss

Most of its knowledge and abilities and values and things.

00:22:41 Peter Voss

Are really going to be in the Knowledge graph and are therefore adaptable.

00:22:46 Peter Voss

Now you know the sort of scary part of that is that yes, in a way the system will can change its own code.

00:22:53 Peter Voss

It can change its knowledge.

00:22:55 Peter Voss

It’s you know skills and and so on on the fly.

00:22:59 Peter Voss

But you also, you’re not going to be shoehorned by, you know, or crippled by whatever.

00:23:07 Peter Voss

Mistakes you may have made in encoding because you’re not going to code and ethics.

00:23:11 Peter Voss

I mean, that’s absurd.

00:23:13 Peter Voss

You you, you can’t do that.

00:23:13 Frank

Right?

00:23:14 Peter Voss

You know it’s it’s.

00:23:16 Peter Voss

It’s a very high level intellectual process.

00:23:20 Peter Voss

And here we’re talking.

00:23:21 Peter Voss

But ethics where somebody actually thinks about it.

00:23:25 Peter Voss

Of course, we often ethical or not so much based on just emotional responses without us really thinking about it, you know?

00:23:32 Andy

Exactly, yeah.

00:23:35 Frank

So in this kind of framework I mean, what would your thoughts be on the?

00:23:38 Frank

Is it the Asimov three laws?

00:23:42 Peter Voss

Yeah, I mean they you know, Asimov wrote a lot of books after that, basically explaining why the three laws actually can’t work, so I mean they.

00:23:55 Peter Voss

They need to be contextualized.

00:23:58 Peter Voss

It’s sort of a starting point.

00:23:59 Peter Voss

I think it’s a good thought experiment to start off with, but it doesn’t.

00:24:04 Peter Voss

It’s not really something you can encode because things are really contextual.

00:24:09 Peter Voss

They’re hierarchical now, are there?

00:24:14 Peter Voss

You know, moral dilemmas.

00:24:16 Peter Voss

Of course there are.

00:24:17 Peter Voss

You know, in in the real world, often in the real world you have to choose between the lesser of two evils and it may not be easy to to do that.

00:24:25 Peter Voss

You know that.

00:24:26 Peter Voss

You know something bad is going to happen and to whatever whatever input you give might change the outcome, but it’s still going to be bad.

00:24:34 Peter Voss

You know, right?

00:24:35 Frank

Right, it’s all subjective too.

00:24:37 Peter Voss

Yeah, right, and but you know that’s not what everyday life is about really.

00:24:43 Peter Voss

I mean, those are kind of emergency type situations.

00:24:44 Frank

Let’s jump.

00:24:47 Peter Voss

I mean everyday life.

00:24:48 Peter Voss

Morality is actually should be much clearer and I’ve actually written quite extensively about about that in my own research.

00:24:57 Peter Voss

On into a I I I stepped into philosophy and figuring out what freewill is and consciousness and and morality was a, you know, kind of an important thing for me to to understand and.

00:25:12 Peter Voss

You know the morality first and foremost should be about human flourishing.

00:25:18 Peter Voss

I mean, this is in the human domain and as we have robots or software that help us that it will be their reason for being is to help.

00:25:29 Peter Voss

Moment right, and now if they can objectively learn what flourishing entails human flourishing.

00:25:40 Peter Voss

Now there are many subtleties, but on in a in a gross way one could say, well, yes, it’s actually pretty obvious what human flourishing involves.

00:25:51 Peter Voss

If you just focus on the negatives.

00:25:54 Peter Voss

I mean you want good physical health.

00:25:57

Right?

00:25:57 Peter Voss

Right, you know, and that entails you have enough to eat.

00:26:00 Peter Voss

You have shelter and you know this sort of math loves, you know, level levels of hierarchy, measure of yeah, levels of well being so you know, being healthy, being mentally healthy and then as you move up that ladder you can say.

00:26:17 Peter Voss

Sort of spiritual well being, and I don’t mean religious.

00:26:21 Peter Voss

By that, I mean, sort of the appreciation of of art or friendship.

00:26:26 Peter Voss

You know family and and and things.

00:26:28 Peter Voss

Things like that sort of things of the mind that are not not directly to do with mental well being or health.

00:26:37 Peter Voss

I mean, those are the things that are human flourishing, and if the robot can or the program, the AI can measure its performance against that, then you know that that’s sort of a fairly simple reference point.

00:26:54 Frank

Interesting so.

00:26:55 Andy

I I love that term.

00:26:57 Andy

You know, human flourishing as the you know, as the measurement.

00:27:01 Andy

I, I think that’s that’s a Noble goal.

00:27:05 Andy

I just an I’m.

00:27:06 Andy

I haven’t read the you know much of your work, Peter, I apologize for that.

00:27:10 Andy

But now I’m going to.

00:27:12 Andy

Hearing you say that because I, I think that is, uh, that’s probably a good path to follow towards achieving what Asimov was after in the early novels where he talked about the three laws, he was definitely trying to solve that problem.

00:27:28 Andy

Yes.

00:27:28 Andy

I think an an I, I concur, and I have read some of his work where later he said.

00:27:34 Andy

This would break down, and in fact some of his novels, his later novels even talk about that.

00:27:39 Andy

That’s in that’s coded into it.

00:27:41 Andy

So very interesting thinking.

00:27:44 Andy

It reminds me a little and feel free to say no.

00:27:48 Andy

Andy, you’re you’re full of stuff.

00:27:51 Andy

It’s nothing like this, but it sounds an awful lot like we’re trying to do very something very analog, using some very digital.

00:28:01 Andy

Means

00:28:04 Peter Voss

Yeah, I think that’s true, but you know my original training wasn’t electronics engineer, so I don’t see such a huge distinction between analog and digital.

00:28:17 Peter Voss

You know, I mean.

00:28:19 Peter Voss

Yes, they are matter of degrees.

00:28:22 Peter Voss

I think that’s important and so context.

00:28:25 Peter Voss

That’s always super important.

00:28:26 Peter Voss

You know something that may be just totally wrong in one context may be absolutely right in another context.

00:28:32 Peter Voss

So I think that.

00:28:34 Peter Voss

Those subtleties that isn’t not just a decision tree, so in in that sense.

00:28:40 Peter Voss

Yeah, I totally agree with you.

00:28:42 Peter Voss

Sort of binary is you got on this pass and then you got on another part.

00:28:46 Peter Voss

You know it’s basically that you can Flowchart everything and I think that’s one of the the very positives of deep learning machine learning.

00:28:54 Peter Voss

Is sort of the return of the of neural networks and and sort of statistical type of approaches.

00:29:04 Peter Voss

But you know by themselves you can’t just work on statistics.

00:29:09 Andy

Totally agree with that.

00:29:11 Andy

My background is also electronics engineering and it it reminds me more of digital signal processing more than just about anything else.

00:29:22 Andy

That and that’s what I see and a lot of these decision support systems, business Intelligence, an even machine learning and AI that are being applied.

00:29:32 Andy

At at least two, say manufacturing an an sales, you know their signals.

00:29:39 Andy

They’re just signals coming in.

00:29:40 Andy

And then there are responses that interpret you know we interpret the signals and then we prescribe responses.

00:29:49 Peter Voss

Right and and I think the other thing that’s that’s missing there.

00:29:53 Peter Voss

I mean, a lot of deep learning machine learning.

00:29:56 Peter Voss

Just have binary outputs.

00:29:58 Peter Voss

Basically they just categorize us.

00:30:00 Peter Voss

You know, they give you a category answer and not sort of a degree of, or at least that isn’t utilized.

00:30:07 Peter Voss

But there’s even something much more important which also relates to signal processing.

00:30:11 Peter Voss

Is you really want a feedback system?

00:30:14 Peter Voss

You want a dynamic system.

00:30:16 Peter Voss

It needs to interact with the world and and sort of find his own equilibrium.

00:30:21 Peter Voss

You know in whatever is trying to do.

00:30:24 Frank

So we talked about human flourishing and helping humans.

00:30:30 Frank

And I think that dovetails nicely into kind of it kind of helps.

00:30:34 Frank

I think paint a background picture of what you’re doing now with I go, you want to talk a little bit about kind of, you know what I goes done?

00:30:45 Frank

You obviously have the talking points, but you know, you know it seems like it seems like I go is a fulfillment of a much larger mission.

00:30:54 Frank

If I if I can kind of infer that.

00:30:57 Peter Voss

Yes, absolutely. So in 2001 I also started my first AI company after several years of doing my own research and it was ready to start building AGI Systems systems that can learn and learn and reason more like humans do. But of course, it’s really, really hard.

00:31:18 Peter Voss

So we had to start with, you know, something simpler for the first few years we just built various prototypes.

00:31:25 Peter Voss

Basically, testing out the ideas theory that I had and and building a framework and then by 2008 we started commercializing this in the call center space, basically having natural language conversations. But you know at at at a very primitive level, but.

00:31:45 Peter Voss

Much more advanced than the kind of things we all hate when you call into a company and you press one for that and three for that. Or you know, you say something. It has kind of understand that so that company smart action launched in 2008 was basically the 1st generation of technology that be commercialized.

00:32:03 Peter Voss

And the company is now about 100 people. And you know, doing a great job at providing these this call automation. But I found myself getting bogged down with you know, HIPAA compliance. You know security, PCI compliance, scalability, redundancy, you know security? All of those kinds of things.

00:32:23 Peter Voss

The nuts and bolts of delivering assess service.

00:32:25 Peter Voss

So I exited the company and started.

00:32:28 Peter Voss

I got a I seven years.

00:32:31 Peter Voss

Though an an for for five years again, we were just an R&D mode. I mean that the reason we did for five years was partly because I found that the company on the funding I got was limited to 12 people. So we had like on average 12 people work on it. Build the second generation of our brain of our AGI type.

00:32:51 Peter Voss

Architecture.

00:32:53 Peter Voss

And so two years ago we then got to a point where we’re happy with the second generation to commercialize it.

00:32:59 Peter Voss

And this is really what I go is today is the 2nd generation of this conversational AI natural language conversational AI engenh that now in our current commercial focuses on chat.

00:33:14 Peter Voss

But so we call it a chatbot with a brain.

00:33:17 Peter Voss

But yes, you can sort of see from the whole history ultimately what we aiming at what the goal of the company is is to have what we call a personal personal assistant.

00:33:29 BAILeY

And.

00:33:29 Peter Voss

And that is sort of my vision and my my dream that ultimately everybody in the world can have their own personal assistant.

00:33:37 Peter Voss

That it’s like a little Angel sitting on your shoulder that you you know, maybe not quite your best friend.

00:33:43 Peter Voss

Hopefully your best friend will still be human, but your your personal assistant.

00:33:49 Peter Voss

Well, it’s it’s really more like an extension of your your your.

00:33:52 Peter Voss

Own brain and like an EXO core.

00:33:54 Peter Voss

Text.

00:33:55 Peter Voss

You know that can help you remember things help you figure out things, and then of course do the kind of things we would love for you.

00:34:03 Peter Voss

Know Siri or Alexa to do if they were really smart.

00:34:06 Peter Voss

That that’s really that personal assistant.

00:34:08 Peter Voss

The reason we actually call it personal personal assistant, and the reason we do that is.

00:34:15 Peter Voss

They are actually what should be personal, personal personal assistant.

00:34:17 Peter Voss

They are actually three different meanings of the word personal that are all very import.

00:34:23 Peter Voss

So the the one personal that it’s yours, you own it, you control it, not some megacorporation, so it serves your purpose.

00:34:31 Peter Voss

So it’s a one personal second.

00:34:33 Peter Voss

Personal is that it’s personalized to you, so it’s not a one size fits all it’s you know it knows your preferences, your history, what you’re interested in your.

00:34:44 Peter Voss

Understand your context and a third personal is that it’s private.

00:34:50 Peter Voss

That you control what it shares with whom.

00:34:53 Peter Voss

So that’s our vision.

00:34:54 Peter Voss

To have a human level understanding personal personal assistant that can help.

00:35:04 Peter Voss

Optimize individuals lives that can help you cut through fake news, you know, make better decisions in in life and and and so on, and so the commercial path we’re taking an, you know, providing an IVR with a brain, a chat bot with a brain is basically for us to move towards that.

00:35:22 Peter Voss

That goal in the long term.

00:35:24

Yeah.

00:35:25 Frank

Interesting.

00:35:26 Frank

First off, I love the term exocortex.

00:35:28 Frank

I think that sums it up exactly.

00:35:31 Frank

Because I have, I have Google Assistant.

00:35:34 Frank

I have Siri, I have.

00:35:35 Frank

I have one of kind of every device you know, whether it’s if I say her name, she’ll speak up soon, but.

00:35:41 Frank

You know who I’m talking about?

00:35:42 Frank

Starts with an A, but I also have Cortana an you know each one of them.

00:35:47 Frank

Has their own strengths, but none of them really.

00:35:51 Frank

We actually did a live stream on this and you know they don’t really understand context and at least that’s what I call it.

00:35:58 Frank

But I think I know what you would would the term you are using.

00:36:00 Frank

This kind of a knowledge graph.

00:36:02 Frank

You know.

00:36:02 Frank

Like you know, if you know my.

00:36:06 Frank

My in-laws will be in town for the next two weeks, say, right.

00:36:09 Frank

If I told my personal personal assistant that then it would know, you know, not the schedule or maybe 2 schedule.

00:36:15 Frank

You know trips or whatever.

00:36:17 Peter Voss

Right exactly

00:36:18 Frank

But but you know, and then in that kind of humorous example, I love my in-laws.

00:36:23 Frank

If you’re listening, this is not aimed at you, but.

00:36:27 Frank

But you know, we would know like you would want to spend more time with the family or even less time or kind of factor that into whatever scheduling kind of conflicts may come up is that is that a kind of a.

00:36:38 Peter Voss

Yeah, absolutely yeah.

00:36:40 Peter Voss

In fact, on our website we have and I go versus Alexa exactly to compare the kind of conversations you’d like to have.

00:36:50 Peter Voss

And you can have if you have a more intelligence.

00:36:52 Peter Voss

System and the the current technology which is really just a stimulus response.

00:36:57 Peter Voss

You know you say something, it categorize it.

00:37:00 Peter Voss

It you know picks out the intent that you want and then it’ll just execute a response.

00:37:06 Peter Voss

You know there might be a little tiny little flow chart thing you know.

00:37:09 Peter Voss

Like if you say give me Uber, it might ask you where do you want to go to and.

00:37:13 Peter Voss

You know how many people are going and do you want to buy X?

00:37:16 Peter Voss

But you know, it’s just a scripted flow chart, so it’s really all the current chat bots.

00:37:23 Peter Voss

Or sometimes I call them.

00:37:24 Peter Voss

Personal assistants are actually all the combination of wave one and Wave 2 technology.

00:37:30 Peter Voss

It’s basically a wave 2 technology to try and figure out what the intent is, and that will be one of a few 100.

00:37:39 Peter Voss

Item set that the system can do, so the you know intent, recognition and then it triggers basically a response.

00:37:47 Peter Voss

And that responds.

00:37:48 Peter Voss

Somebody sat down and you know, typed it out.

00:37:51 Peter Voss

Or did an API call.

00:37:53 Peter Voss

If it’s a weather Reporter or whatever, but you know, there’s no.

00:37:57 Peter Voss

There’s no deep understanding.

00:37:59 Peter Voss

There’s no context.

00:38:00 Peter Voss

There’s no learning.

00:38:01 Peter Voss

There’s no reasoning.

00:38:02 Peter Voss

There’s no disambiguation.

00:38:05 Peter Voss

Yeah, there’s no brain.

00:38:07 Frank

Right no?

00:38:08 Frank

I mean that makes sense because we’ve all had those experiences when we talk to these devices where they just don’t get it.

00:38:15 Frank

You know it’s kind of.

00:38:16 Frank

Bright

00:38:17 Frank

It’s very easy to anthropomorphize them and think that you know there’s a thought behind it.

00:38:22 Frank

And I see my kids do this like they’ll interact with it.

00:38:24 Frank

And then you know they’ll they’ll hear, you know, I’m sorry I can’t help you with that, you know.

00:38:30 Frank

And it’s kind of like they get frustrated with them.

00:38:32 Frank

And you know?

00:38:34 Frank

I I get it like I understand but Microsoft funny story.

00:38:39 Frank

Microsoft actually had a video.

00:38:42 Frank

At one of the trade conference is when we could travel where they showed they called.

00:38:47 Frank

It turns so they show somebody interwest basically having a whole conversation starting from their car.

00:38:57 Frank

Hey, remind me to do this.

00:38:58 Frank

Remind me to do that and the AI was able to keep up.

00:39:01 Frank

Through across.

00:39:03 Frank

Let’s say two or three dozen context switches you know an it’s a miracle. If any of these current day as of you know, state of the art as of 2021, can follow one, let alone two context switches.

00:39:07 Frank

Yeah.

00:39:18 Frank

So it sounds like you’re kind of addressing that by putting intelligence in the back end and not just.

00:39:25 Frank

You know natural language processing, kind of.

00:39:27 Frank

Muscle for lack of a better term, sounds like you’re building something in the back.

00:39:29 Peter Voss

Yeah.

00:39:33 Peter Voss

Yeah, exactly.

00:39:33 Peter Voss

I mean that’s why we call it a chat bot with a brain and it’s a brain.

00:39:38 Peter Voss

That’s the important thing.

00:39:39 Peter Voss

The brain has the ability to, you know it has the knowledge graph that is dynamically updated as it learns.

00:39:45 Peter Voss

Has deep language understanding.

00:39:47 Peter Voss

It has reasoning, test context, short term memory, long term memory.

00:39:52 Peter Voss

All of all of those capabilities are there in the in the background, basically managing the conversation and learning as you go along and making sense of things.

00:40:01 Peter Voss

And you know at the moment that’s nowhere near human level, but it’s having the right architecture to start off with to be able to to do that.

00:40:12 Peter Voss

I mean, over the years we’ve seen many demos like that, you know, in a very constrained environment you can write code something together with a flow chart and and so on.

00:40:22 Peter Voss

That looks very impressive, but you put it out in the wild and it just falls apart immediately.

00:40:27 Andy

Right?

00:40:28 Frank

I delivered a lot of demos showing off kind of the art technology and I’m always very careful about having guardrails on what I talk about how I talk about it for good reason.

00:40:41 Frank

And it’s not just, you know, not just Microsoft, but I think most of the mainstream ones are kind of constrained by that.

00:40:49 Frank

So at this point we asked seven kind of questions just to kind of help the audience kind of know you a little better, but some of these are fill in the blanks.

00:41:00 Frank

None of these are complicated questions or weighty questions, so the first one is how did you find your way into.

00:41:10 Frank

Hey I did or did.

00:41:14 Frank

It wasn’t the other way around.

00:41:15 Frank

did I find you?

00:41:17 Peter Voss

Yeah, so as I mentioned earlier, I started off as an electronics engineer.

00:41:22 Peter Voss

Yeah, and then I I fell in love with programming as chips became programmable, programmable.

00:41:30 Peter Voss

I started programming them and I said this.

00:41:32 Peter Voss

This is so much fun and instant gratification.

00:41:35 Peter Voss

You can write something and you know, see the results immediately.

00:41:39 Peter Voss

Whereas within electronics you have to build a new circuit board and wait.

00:41:42 Peter Voss

Days or weeks for you know to come together.

00:41:46 Peter Voss

So I had an electronics company and so my company turned into a software company.

00:41:53 Peter Voss

And we actually did very well.

00:41:55 Peter Voss

I developed an ERP software system, a comprehensive software system for medium sized businesses, and our company grew very rapidly.

00:42:04 Peter Voss

We actually did an IPO.

00:42:07 Peter Voss

And that was fantastic.

00:42:10 Peter Voss

But the reason I’m telling our story is that a the experience of writing software, writing, ERP software and I wrote three generations of IT architecture and do a lot of coding myself.

00:42:24 Peter Voss

I was very proud of the software that we had, but I also realized.

00:42:27 Peter Voss

How dumb it is?

00:42:29 Peter Voss

You know anything that the programmer doesn’t think of.

00:42:32 Peter Voss

Will just it won’t know what to do at at.

00:42:35 Peter Voss

You know?

00:42:35 Peter Voss

It’ll just throw an error and you know I thought you have to be able to do something more intelligent.

00:42:41 Peter Voss

How can you bring intelligence into software and doing the IPO, the company being successful, you know, gave me enough sort of time and money when I exited the company too.

00:42:52 Peter Voss

To say OK, let me deeply understand what intelligence entails.

00:42:57 Peter Voss

And so I took five years to study intelligence but also related fields.

00:43:04 Peter Voss

Epistemology, you know, theory of knowledge and philosophy.

00:43:08 Peter Voss

Cognitive psychology, psycho metrics.

00:43:12 Peter Voss

How do we measure intelligence?

00:43:14 Peter Voss

You know, IQ tests.

00:43:15 Peter Voss

What do IQ tests measure?

00:43:17 Peter Voss

Are they meaningful?

00:43:18 Peter Voss

And how do children learn?

00:43:20 Peter Voss

How does our intelligence differ from animals and and all of that?

00:43:24 Peter Voss

And then of course I studied a lot of AI.

00:43:27 Peter Voss

To understand artificial intelligence on what had been done in the field so over 5 year period, I basically put all of that together and I mean it’s once I got into this idea of understanding intelligence and seeing the potential of building a machine that has intelligence.

00:43:43 Peter Voss

I mean, what could be more exciting.

00:43:45 Peter Voss

So I’ve been on a.

00:43:47 Peter Voss

On a bus for, you know, last 25 years really and and then.

00:43:52 Peter Voss

So yeah, I don’t know if it found me or I found it, but here we are.

00:43:57 Andy

Well, that’s very impressive.

00:44:00 Andy

And I would and and thank you for doing all of that work.

00:44:04 Andy

That’s that’s not easy.

00:44:05 Andy

That’s almost a mission more than you know, any kind of, say, mission statement.

00:44:11 Andy

So I admire that.

00:44:14 Andy

Our next question is, what’s your favorite part of your current gig?

00:44:20 Peter Voss

Yeah, so I mean I I couldn’t think of doing anything else and I actually work seven days a week and but to me it’s not work.

00:44:27 Peter Voss

I mean this is what I love to do and actually I actually love.

00:44:32 Peter Voss

I’m one of these rare animals I like I love both.

00:44:35 Peter Voss

See the theory and the technology.

00:44:37 Peter Voss

On the one hand but also business.

00:44:39 Peter Voss

I’m CEO of the company.

00:44:41 Peter Voss

And I love interacting with customers.

00:44:43 Peter Voss

I like the business aspects of it, so I like to see technology.

00:44:48 Peter Voss

You know, go from theoretical research and understanding.

00:44:52 Peter Voss

Stuff and actually making making a difference in the world and generating revenue.

00:44:57 Peter Voss

Because if it’s of value to somebody, they’ll pay you for it.

00:45:00 Peter Voss

So I really love both aspects of of my work.

00:45:05 Frank

Awesome, so here’s our first complete this sentence when I’m not working, I enjoy blank.

00:45:14 Peter Voss

OK, only one but.

00:45:17 Frank

Well, I mean, as many as you like.

00:45:18 Frank

I mean, as long as you keep it.

00:45:20 Frank

You know G or PG rated.

00:45:21 Peter Voss

Yeah alright yeah I love I love music concerts and we could go to concerts.

00:45:27 Peter Voss

I gotta concerts.

00:45:28 Peter Voss

I love hiking and I love riding my motorbike so I guess at three.

00:45:33 Frank

Cool.

00:45:35 Andy

That’s very cool.

00:45:36 Andy

So we have our second of three fill in the blanks.

00:45:40 Andy

I think the coolest thing in technology today is blank.

00:45:46 Peter Voss

Oh

00:45:48 Peter Voss

That we have so many people that AI is exciting again.

00:45:56 Frank

That’s a great answer.

00:45:57 Frank

I I start when I was in college during the well, my first day I winter and it was pretty pretty dismal.

00:46:05 Frank

I took a prolog course.

00:46:07 Frank

And my professor was just a huge fan of Prolog that was going to change the world.

00:46:11 Peter Voss

Right?

00:46:12 Frank

An I kept waiting for like OK, when’s the big reveal?

00:46:15 Frank

When’s the big reveal?

00:46:18 Andy

Yeah.

00:46:18 Frank

Final project no.

00:46:20 Frank

Still no big.

00:46:20 Andy

Bill all right?

00:46:22 BAILeY

Right?

00:46:24 Frank

Uh.

00:46:26 Frank

Complete, here’s another complete this sentence.

00:46:30 Frank

I look forward to when I can use the to the day when I can use technology to blank.

00:46:37 Peter Voss

To have a personal assistant that can help me in my life.

00:46:43 Frank

Yeah, me too.

00:46:43 Peter Voss

Cool.

00:46:44 Frank

I’m gonna put that above self driving car.

00:46:47 Peter Voss

Yeah, right?

00:46:49 Andy

Very nice, well we ask our next question is to share some.

00:46:55 Andy

Not really a question request.

00:46:57 Andy

Here’s something different about yourself and we put a reminder on here to remember.

00:47:03 Andy

It’s a family podcast.

00:47:05 Peter Voss

Look

00:47:08 Peter Voss

Yeah, what what?

00:47:10 Peter Voss

I think a lot of people when they meet me think is weird is how much I’m into futurism and life extension technology.

00:47:20 Peter Voss

I love life and I’d like to live for as long as I can.

00:47:24 Peter Voss

So I’m very health conscious.

00:47:26 Peter Voss

I’ve actually been on calorie restriction for the last.

00:47:29 Peter Voss

20 years to try and you know, stay as healthy as.

00:47:32 Peter Voss

Possible.

00:47:34 Peter Voss

And you know many other aspects of life extension that I’m I’m interested in.

00:47:39 Peter Voss

Of course, cover doesn’t exactly helping the the general thing of life extension, you know, but again, I I find it so.

00:47:43 Andy

Right?

00:47:48 Peter Voss

Uh, distressing that so few people actually care about, you know, staying healthy and living living a long life and how little money actually goes into life extension research.

00:47:59 Peter Voss

So I’m very much into that.

00:48:00 Peter Voss

But then also, what’s different about me that I also love motorcycles, so there I guess.

00:48:06 Peter Voss

A bit of a contradiction so.

00:48:07 Frank

But you can’t drive.

00:48:08 Peter Voss

Life has to be worth living to.

00:48:11 Andy

Understood I like that I’ve recently started reading a book called How Not To Die.

00:48:11 Andy

And this is true.

00:48:20 Andy

I don’t know if you’re familiar with with that work.

00:48:22 Peter Voss

Oh, he’s the author.

00:48:24 Andy

Let’s see, it’s right here and it’s Michael Gregor.

00:48:28 Andy

I think is how you pronounce his last name.

00:48:30 Peter Voss

Yeah, yeah yeah, it doesn’t ring a Bell and immediately, yeah, but yeah cool.

00:48:35 Andy

All about.

00:48:36 Andy

Yeah, all about plant based diets.

00:48:38 Andy

I understand he has a fantastic YouTube channel.

00:48:41 Andy

I haven’t yet checked it out so I’m just in the beginning stages of that, but the book is well written.

00:48:47 Andy

It sounds a little like listening to you talk about the things you’re passionate about as well, and he’s been into this for decades an you know, just trying the same goal.

00:48:56 Andy

You know, just.

00:48:57 Andy

Trying to extend life.

00:48:59 Peter Voss

Right, right, you know healthy life, of course.

00:49:01 Peter Voss

Yeah, sure, sure.

00:49:03 Frank

An where can we find out more about what you’re up to and what you’re doing?

00:49:08 Frank

Obviously there’s I go and I believe the URL is.

00:49:11 Frank

I go dot AI.

00:49:13 Peter Voss

Correct, yeah I got a I so yeah we have quite a couple of videos and some links to articles.

00:49:21 Peter Voss

And then most of my articles are on medium.com so just look for my name Peter boss on medium.com and I have quite a number of articles there about philosophy ethics, calorie restriction even of course a lot about AI and all sorts of things.

00:49:40 Peter Voss

Yeah.

00:49:41 Andy

Rational very cool yeah very cool.

00:49:42 Peter Voss

Rational ethics.

00:49:43 Andy

Yeah, very cool. Well Audible is a sponsor of our show. Listeners can get a free audiobook by visiting the data drivenbook.com an we like to ask our guest Peter if they’ve listened to a good book or read a good book. If they don’t listen to audiobook.

00:50:03 Peter Voss

Yeah, you know the thing that jumps to mind is is a book that’s been around for a long time. Is a mind the mind’s eye, which I think is is is just a fantastic book by Doug Hofstadter and who’s the other author anyway? You would find it by that. It’s just.

00:50:23 Peter Voss

A great book about yeah what what the mind is and so on.

00:50:30 Frank

Interesting, that means sounds familiar, but Huffstetler was he.

00:50:30 Frank

Very cool.

00:50:35 Frank

Known for.

00:50:37 Peter Voss

Yeah, he’s he’s one of the AI gurus.

00:50:42 Peter Voss

I think he was in New Mexico at.

00:50:46 BAILeY

OK.

00:50:48 Peter Voss

I skipped my mind now what what it is.

00:50:50 Peter Voss

But yeah, he’s he’s well known figure in AI but not really.

00:50:55 Peter Voss

Not for the last 20 years or so.

00:50:57 Peter Voss

Sort of more like eighties 90s.

00:51:00 Frank

OK.

00:51:02 Frank

Awesome, but definitely record that will definitely put these recommendations in the show notes as well as links to your articles on medium and we just like to remind our listeners that they can pick up a free audiobook because Audible is a sponsor and if you go to the data drivenbook.com.

00:51:21 Frank

You will be taken to an audible page and your first audiobook will be on us.

00:51:27 Frank

And if you subscribe, you get an audible subscription.

00:51:30 Frank

You will get a nice little Pat on the back and maybe enough to buy a latte at Starbucks and helps monetize the show and keep Andy and I caffeinated.

00:51:42 Andy

Absolutely, that’s important.

00:51:43 Andy

You know, very important.

00:51:46 Andy

This was a fantastic show.

00:51:48 Andy

Peter, I can’t thank you enough for taking time out of your day to talk with us.

00:51:53 Andy

I find the work fascinating as we were chatting, I found your articles on medium and.

00:52:00 Andy

It’s a nice list in the list.

00:52:02 Andy

It’s just, uh, categories.

00:52:03 Andy

I see there’s several articles in some of the categories and I’m going to read him.

00:52:08 Andy

I’m going to be reading them in and learning more about AGI, so I appreciate that.

00:52:13 Andy

Sir, thank you.

00:52:15 Peter Voss

Yeah, thank you, thanks for having me on on the show and feel free to send me any questions or ideas that you have was looking to communicate with.

00:52:25 Peter Voss

People were interested in this.

00:52:28 Andy

Awesome.

00:52:28 Frank

Awesome, well thank you very much for your time and we will let the nice British lady and the show.

00:52:33 BAILeY

Thanks for listening to data driven.

00:52:37 BAILeY

We know you’re busy and we appreciate you.

00:52:39 BAILeY

Listening to our podcast.

00:52:41 BAILeY

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00:52:43 BAILeY

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00:52:50 BAILeY

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00:52:53 BAILeY

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00:53:01 BAILeY

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00:53:09 BAILeY

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Andy Leonard

andyleonard.blog

Christian, husband, dad, grandpa, Data Philosopher, Data Engineer, SSIS and Biml guy. I was cloud before cloud was cool. :{>

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