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
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)
Where can people learn more about Peter? (49:00)
Book reading / listening recommendations? (49:00)
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?
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.
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.
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.
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 andyan 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
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00:52:39 BAILeY
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00:52:41 BAILeY
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00:53:01 BAILeY
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About the author, Frank
Frank La Vigne is a software engineer and UX geek who saw the light about Data Science at an internal Microsoft Data Science Summit in 2016. Now, he wants to share his passion for the Data Arts with the world.
He blogs regularly at FranksWorld.com and has a YouTube channel called Frank's World TV. (www.FranksWorld.TV). Frank has extensive experience in web and application development. He is also an expert in mobile and tablet engineering. You can find him on Twitter at @tableteer.