Speaker 1: I'm with Mitch Randall and welcome bitch.
Speaker 2: Nice to see you, to see you, thanks for having.
Speaker 1: Yeah, sure you're a speaker here, and you spoke yesterday.
I missed your talk. But I'm very interested in this subject,
and that's basically AI. You know, I just think that
it's something of wonder and something a little bit scary
both at the same time. And so if you could
a little bit about your background first, and we'll cut
you into AI and how long ago.
Speaker 3: Oh, I have a pretty diverse engineering background, So I
did radars with the National Center for Atmosphere Research from
the nineties and then in the two thousands my own
company with wireless power. So am very technical, you know,
hardcore engineer, physicist kind of thing. But around twenty sixteen
I got really interested in AI, and if you know
what's happened there around there, they came up with convolutional
neural nets and that's what really AI was kind of
a toy before that, really, because it couldn't quite recognize
what was in images, you know, the big thing was
image classification at the time, and then it just skyrocketed.
Speaker 2: They figured out.
Speaker 3: The convolutional networks and then things started to roll much
more rapidly, and I got interested. Then I can tell
you the exact thing that really tweeked me is what
is when Nvidia published a paper where they had just
the pixels coming into the AI and also the steering
wheel position, and they drove around town to train, so
it just had the steering wheel position and pixels from
the looking forward and then it would sort of drive.
And that blew my mind because nothing like that was
going on before that. I mean I couldn't believe that
there was no program involved, nothing, you know, just paels
coming in and then it would.
Speaker 1: Tell turn was wow, Well, I just wrote an autonomous
self driving taxi and San Francisco a few weeks ago,
and that was quite amazing. I felt safe, actually felt safe,
because that's like the first thing that people have said
to me, well, wasn't it scary? And I said, actually no,
I felt totally secure in it. But so anyway, that, yeah,
it's fascinating and it seems like it's snowballing. I mean
I watch a lot of you know, documentaries and just
little clips here and there about it, do some reading
about it, and it just seems like it's just growing
by leaps and bounds and you know when you search
on Google now it automatically comes up with AI answering
your your whatever your question is. So I just got
to tell you this quick story because before we started recording,
I told you that I'm in the fine art and antique.
So I was in a pretty sizable state in the
coast of Massachusetts and there was a I could tell
it was a really good painting, could not read the signature.
So the signature was just you know, scribble. So I'm
working on it, and I told the people, you know,
it's going to take some extra time in this, but
I can tell us a really good painting ends up.
It was, but it took me about oh I don't know,
maybe five or six hours of straight time trying to
just and I finally figured it out with all cross referencing,
and then I identified a signature on another painting. It
was exactly the same. You can't read it. So I
got I got done, and I got the price. It
was a twenty thousand dollars painting not break. Oh yes,
So my son is really using chat GBT as like
a he subscribes. You know, it's a level up. So
I said, oh, just out of curiosity. Here's a picture.
Can you just throw that in there? Instantly? It had
the artist and it said it's most likely this artist
and that's exactly theirs. It was Wow, and so that
to me is incredible. I mean that it can change
what I do. And so as far as AI, one
of the first things I think of, what is your
opinion of what type of jobs people are going to
start losing?
Speaker 2: I mean, it's such a big subject to it.
Speaker 3: There's so much going on you'd pick a little part
and talk about it. I mean, what's you know, where
we're headed for next? Agents? So right now we're having
these large language models right where you put in text
gives your sinulus response, right, and when you're not putting
in stimulus, it's basically asleep, not doing anything. But agents
are different. They're a loop where it's always trying to
meet some kind of part or you know, achieve some
kind of goal, and that's where it starts to that's
where things really pick up.
Speaker 1: So I mean, right now, of.
Speaker 3: Course anything that's well, you know, moving back up to
for a second, because they have found and there's curves
to show that really clearly, the more data you know,
the more data you put into the training, the smarter
it gets.
Speaker 2: And right now they've figured out that scale is everything
for one thing. I mean, it's not really going to
be everything. There's going to be innovations.
Speaker 3: I'm sure they're going to make it so the scale
doesn't have to be that crazy because I didn't have
to read twelve trillion tokens, you know, be the smartest
and I don't them, right, so we know that it's possible.
But all they're doing is just turning up the crank.
You know, Hey, if we put in more, it's smarter,
you know. And the act set of curve that shows here,
you know, here's the intelligence of the of each one
of the models compared to the data coming in, and
they can show it's a straight line in.
Speaker 2: The log log curve. So that so then of course
what do they do.
Speaker 3: They turned up the grank they put in Now we're
running out of data to put into AI.
Speaker 1: I've heard that they've used the scraping even YouTube videos
and stuff.
Speaker 3: Right you know, but there you know they're talking about
this is the end of AI.
Speaker 2: We've hit the limit of intelligence or something like that.
You know, it's just just means that that that easy trick,
but just scaling that knob isn't working anymore, and they'll
come up with somebody else. I mean, there's more than
five thousand papers a month being read this Yeah, yeah,
Actually six six years ago I looked specifically.
Speaker 3: There was five hundred papers a month, and I was like,
oh my god, I've never been in a field that
has that kind of activity. You can't really keep track
of what the new thing is.
Speaker 1: And then just a few.
Speaker 2: Years later it's five thousand a month.
Speaker 1: Hung real.
Speaker 2: In fact, it might be more than that, because that
looked couple years supposed.
Speaker 1: To get that number has chat chpt exactly. Yeah, wow,
but I didn't answer your question.
Speaker 2: Yes, I mean obviously now with Ellen Limbs, anything.
Speaker 3: That's text for your example of searching, I mean is
it's literally read every medical journal.
Speaker 1: Probably it's read every.
Speaker 3: I mean anything that you that's you know, you give
it a stimulus and it gets a response that those
doctor are threatened.
Speaker 2: Of course.
Speaker 4: Yeah, I'm gonna there's so much to say, but I'm
gonna segway this per second. They just did a study.
I read it just a couple of weeks ago. They
wanted to see if AI would help doctors docmos.
Speaker 3: So they took a set of doctors that didn't use AI,
took set of doctors that did use AI, and the
doctors that didn't use.
Speaker 2: AII their diagnosis. Great. It was about seventy two percent
in the study. So that's how accurate they were. The
doctors that did use AI in seventy three percent. That's
come on. But the actual punchline in the article is
AI alone was ninety four percent.
Speaker 3: It was like, well the doctors and doctors aren't listening
because they'd just listened to.
Speaker 1: Them, they would get nine four percent, right. Well, just
the fact that that's true is unbelievable.
Speaker 2: You know.
Speaker 1: That's what I'm excited mostly about the medical aspect of this,
you know, I mean, I think, you know, we could
possibly have the cure for cancer insight at some point,
and you know, on many and many other other things.
Really exciting. So I just want to tell you something
kind of funny too, going back to Chat GPT. I
was on a road trip with my son for well
about a week or so, and so he was on
chat GPT and he says, oh, so he said, let's
see what it knows about your show. So he went
in and the thing. It did a wonderful job all
about my show, and it's Martin Willis, host has six
hundred plus you know, episodes and blah blah blah. It
was very nice. And so then he goes, well, what
do we know about the host? And he said, not
much is known about Martin Willis. And so then he
said to it, well why don't you how'd you say it?
Why don't you imagine what the house would be like?
And it was hilarious. It was saying that all these
it says that I'm also into antiques and I maybe
outrunning the state sales. Well I'm looking up in the
sky and maybe sit in the back porch with a
scotch and look up in the sky, which I don't drink.
But anyway, I thought it was hilarious what it came
up with. So I love you know, it has like
an imagination, or it can have an imagination, which is
really it's uh, you know, when is it going to
be that? I mean, people are I've heard people say, well,
it's going to be another twenty years before it'll think
like the human brain. But the way it's going, it's
going to be.
Speaker 2: The essential principle on what it's called the singularity.
Speaker 3: If you're not the singularity is it's when AI becomes
smart and that everybody else. The reason they call it
the singularity is for math, there's a function that's you know,
one over acts as it grows the singularity of that function,
it goes to infinity and essentially what there's the reason
it does that is because not only are you you know,
getting better at designing AI, because that's what everybody you know,
the ride's figuring it out.
Speaker 2: There's other factors. I'll just I'll throw that in a
quick too.
Speaker 3: You know, if you had a if you had a
robot an AI system that in place of worker, you know,
you money that's worth even well, let's say this. Let's
say that there was a box that cost a million
dollars that you can put in the driver's.
Speaker 2: Seat of the truck. That's a If you work out
the numbers, you're way ahead with that.
Speaker 1: Not even truck. I can go twenty four to seven.
Speaker 2: So that's three shifts, right.
Speaker 3: Yeah, you don't have to manage any any workers, any
worker whose comm no health insurance, no health insurance, you know,
no HR department, that million dollars.
Speaker 1: No taxes, brilliant no taxes because there's no it's all
tax right off. Yeah, so that.
Speaker 2: Oft comes incredibly quick and everyone knows that.
Speaker 3: I mean, not only is AI you're going to and
do a number of jobs with you're asking up.
Speaker 2: But you know, it's also a way to control the world.
Speaker 3: I mean, it's like the ultimate super weapon, right you
can you can literally have an AI agent on every
single person, following them, seeing exactly what they do and looking,
you know, figuring out everything about you, like focused on you,
the super intelligent vocals.
Speaker 2: Anyway, The point is the economic incentive to achieve this
is unbelievably high.
Speaker 3: Yeah, so that's why that's another factor. It's making you
of that curve. But if people realize wait a minute,
this can actually start be placing up. That can drive
you know, all these things they're finding out, that economic
concentive just keeps growing and growing and growing.
Speaker 1: But then beyond that, you.
Speaker 2: Can take the AIS that you have and you can
just explain how good they are.
Speaker 1: Every single scientific.
Speaker 3: Paper, yeah right, you know they can actually read those
five thousand papers a month that are coming out when
you never could, you know.
Speaker 1: So they're using AI to design AI. Yeah, and you know,
here's an example that.
Speaker 2: I'll get all the numbers wrong, but it doesn't.
Speaker 3: Matter because it's you know, I wouldn't tech ack knowledge
that for a long time to see you know, n video.
They've been around for a long time, and you know,
like every year it's twenty percent better some you know,
comprehensible amount better. Right, they're not using aonic to design
their stuff. It went up like five times or ten.
Speaker 2: I kind of remember the number.
Speaker 3: I just remember my job dropping because these kind of
improvements aren't something that we're used to do in these industries.
It's coming from AI. But optimizing, you know, whatever, whatever
it does, is just better at it, you know.
Speaker 1: Right, So now I'm trying to remember if he was
he was a child prodigy, like a chess player in
England and he started eight. He didn't call it AGI
maybe as artificial general, yeah, and he was trying to
figure out some type of genome work, which but I
mean this was going back like in the beginning. You know,
he was really on this stuff. Brilliant that's what it is.
That's what it is, You're right, and they couldn't quite
get it. But anything when it comes to medical I
understand takes a lot of there's a lot of failure
when it comes to medicals, so it takes a lot.
But I'm excited as far as that goes. But I'm
also scared because you know, I just you know, there
was I remember when I first heard about it. You're talking,
like you said, twenty fifteen somewhere back there. My son's
really into all this type of these type of things,
and he said to me, you know, it's possible that
there could be like a self replicating machine and that
could be told to take over the world, you know,
and just do it, you know. I mean, that's what
it could come down to. And there's unfortunately, there's always
a bad seat out there, just like there is with
weapons that could do something you know, terrible and for
you know, I mean, it's a it's a bottleneck of
technology that we have to get through.
Speaker 3: Don't I'm a little bit more on the business actually,
the bad seat. Yeah, I feel that all that economic
incented just is a incubator for yeah, I mean.
Speaker 5: For a disease of of that, you know, And I
think that.
Speaker 3: You know, for example, but I a bunch of people
from the safety department left because they said this is
this is bs.
Speaker 2: They're not paying attention to safety like they should be.
Speaker 1: And then by the way, no guardrails serve so to speak.
Speaker 2: Right, that was their job and they see their job
is being moored.
Speaker 3: They little bit, I'm out of here. But then they
I was gonna say it is a started a new.
Speaker 2: Company and see, unbelievable.
Speaker 3: I probably have this wrong, but if I remember, I
was thirty billion dollars be raised.
Speaker 1: To start a start up. Oh my, I mean it
used to be thirty thousand. I'm right, Yeah, it's unheard of.
Speaker 3: Yeah again, because the economic incentive is off the charts.
Speaker 2: It's a super weapon.
Speaker 3: It's way more deadly than an atomic bombed ever being.
Speaker 1: It certainly could be. And also it could be basically
in a destroyer of economy in a way, because if
you take away all the jobs, who's going to be
you know, there's going to be the rich getting richer
and a very small percentage of people making all the
money and they're going to have to rely on someone
for other things. Yeah, I mean it could be a
destroyer in that way.
Speaker 2: I mean, I intend it's easy for me to kind
of turn you turn.
Speaker 3: Off the time scales on and just see how things
are going to pan out, you know, and.
Speaker 1: It's really clear that all these well, let.
Speaker 3: Me just back up a second. It used to be
people say robots are going to take over, you know,
like further flippers. But back in even twenty eighteen, they
were saying AI is better at reading X rays than
radiologists down on the top of the chart for doctors,
high speed and then most schooled.
Speaker 2: So what you have to do telling to go when
the code, I.
Speaker 1: Mean, you have a real problem there. Yeah, and wow,
And so it's real clear to need.
Speaker 3: To see how that's gonna pan out. And also we're
going up to spurt where the you know, next year
with the AI is going to be is way more
than this year was compared to last year. It's going
to keep it's you know, increasing like that. I mean,
they're running into this temporary roadblock, but they're running out
of data.
Speaker 1: Yeah, but they're going to solve that.
Speaker 2: We you know, I've been in tech technology long enough,
you know. Yeah, the paper stuffed up five thousand papers
a month, one of them is gonna work.
Speaker 1: Yeah. So so the reasoning part that's don't they always
kind of s say that's when it starts reasoning. That's
when it's going to get scary, you know, I mean,
do you understand what I mean by that way I do?
And what do you what's your opinion on that?
Speaker 3: Well, in my talk I talk about this because every
starting in twenty twelve, when I started actually work, you know,
sort of do things, there's been a just a list
of big surprises. So these designers, you know, where do
I start? But I'm just going to throw in here.
You don't program it.
Speaker 2: That's not what you do.
Speaker 3: It's not programmers telling it what to do or how
to think. That's not how it works. There's a there's
an artificial neuron that you do locate a billion times
and literally billions of neurons, and all this offtware is
doing is simulating them, but inputs the neuron and outputs
and it doesn't a billion times for each gigeration.
Speaker 2: So there's it's not like anybody's programming to think. What
they're doing is architecting it.
Speaker 3: So they they have found out that certain arrangements of
you know, neurons have.
Speaker 6: A certain you know function very you know, it's deliberate,
and they're working on it in writing papers, but they
still don't know exactly even how it works, but I
was gonna say, like these emerging features that come out.
Speaker 2: For example, in twenty twelve to twelve, Google.
Speaker 3: Was trying to just make something that would complete a
video frame, so they wanted it to show an incomplete
image and have it fill in the gaps.
Speaker 2: And the thing ended up learning what a cat was
and what a human face was. And I wouldn't even
tell them anything about that.
Speaker 1: Somehow I figured that out.
Speaker 3: O my god, and you fast forward, they have a
language model where they were in fact, language models were.
Speaker 2: Basically invented for translation.
Speaker 3: They found out that this is one of the events
that occurred where they they taught it. You know, your
English to Spanish right now, you're Spanish to French. They
found out it would actually do English Diffrench even though
they never trained it to do that, so it had
some kind of intermediate.
Speaker 2: Language it was working with if they didn't even know them.
Speaker 5: So when you talk about reasoning, these AI models have
been surprising their developers, and they didn't they don't even
know how to design that in They don't.
Speaker 3: They're just they just learned that it could put me
more data, it does more stuff, so you.
Speaker 2: Know, their ego language models actually convert poetry. They can
be humorous, they can I.
Speaker 3: Mean, nobody expected that. Nobody knew that was going to happen.
And then when we get to the next stage, they're reasoning.
They're actually really reasoning. So they figured out how to
like optimize that. So they think they can go back
and improve.
Speaker 2: Things, but nobody really knows.
Speaker 3: You know, five years ago, they would have never known
how to design something that could reason. Not here we are,
we had stuff that reasons and it's and you know
how you're training of thought.
Speaker 2: Yeah, yeah, so they'll actually tell you what they're thinking.
So you know, ask a question and you can push
a button and it will show you all the intermediate
steps that had.
Speaker 3: I see, it's believe fast not all you know, and
it knows everything will go through and you know, tell
you what I was thinking.
Speaker 1: You know.
Speaker 3: So they're they're figuring out kind of retrospectively optimizing some
of the stuff it does, but then it always gives
them a new surprise.
Speaker 2: Well consciousness top of that. I don't have any help
about that personal really, a lot of people you're probably do.
Speaker 1: Yeah, but.
Speaker 2: I think this is a processor, and I think that's
a processor. Yeah, and nobody knows how this one works works.
Speaker 1: Yeah, so just to just maybe maybe it's jumping to
a different topic. I'm not sure, but supercomputers there's some
type of relation, you could say, And is it Sycamore?
What is what am I thinking? You're not familiar with that.
There's a new chip that's supposed to be scary for
what it can do. Possibly, yes, it's quantum computing, but
there's a I think it was called Sycamore by Google,
but I'm not really sure. But anyway, supposedly it was
able to figure out something that would take thousands of
years for a supercomputer to do, did it instantly, like
within within ten minutes. So I mean that's that's scary
because well, for one thing, again this isn't exactly what
AI or anything, but for one thing, these type of
situations could get through any type of security. You know,
there's no if there's no code that it couldn't get
around to get through into any bank account or anything
like that. And that is, you know, that is scary.
But getting back to AI in general, you know, I
heard something about ninety megawatts or something like that to
run these things, And why does it take so much power.
Speaker 2: Well, talk about supercomputers.
Speaker 3: So you know, they figured out, oh look if that's something,
but if we have twice of many neurons, it doesn't better.
Speaker 2: So it's the end. They just turned up the scale.
How many neurons did we want? Let's keep going.
Speaker 1: So I mean, right now, I think rock.
Speaker 2: Is that people.
Speaker 3: We'd set a trillion shit trillion neurons in it. So
for a trillion parameters I'm sorry, but I mean parameters.
You know, let's say a neuron has fifty parameters just
to throw anu there, it's not it's.
Speaker 1: Not far off you have so.
Speaker 2: Anyway, it takes more power to compood. And then they
wanted to, you know, want to so you give it
an inquot you want an answer right away. So speed
is important and there's no limit to that. Yeah, let's
say or whatever, one hundred milliseconds nicely, fifty million seconds, right,
So that and the economics to it all.
Speaker 3: So that's Pastorward's going. But there's no really, there's no
Oh we've got enough. Yeah, there's never I mean, if
if it took only lots whatever, they say.
Speaker 1: Okay, neurons, Yeah, we're gonna go use whatever to get
available yeah, yes it is. I guess the the scary
part of how this could go is, you know, like
you always see these dystopian you know, movies like what
was that with Arnold Schwarzenegger, you know in this sky net? Yeah,
things like that sort of sort of taking over. But
I guess the rationale would be like for to be
programmed into this thing. I know you mentioned there's there's
no real programming to do, but if it had and
it's set, it's I want to say mindset, like to
do it to do a specific thing, and that would
actually wipe out people to do that specific thing.
Speaker 2: You were talking about the utility function.
Speaker 1: So yeah, I haven't all along doesn't really have utility.
Speaker 2: The best answer.
Speaker 1: Yeah, okay, so let's see. So I'm just going to
go back to that. Yeah, so if you could just
started out with the utility functions while we're on this
little break, is there any question that I can ask
you that you would like to talk about, like a
specific area of what you've flipked into or is it
going well enough? Is it going okay?
Speaker 2: Okay?
Speaker 1: Good? Let me just my son's been tet he's going
to be excited to hear the show he hasn't listen
to any of the OA.
Speaker 7: Okay, I'll do that. One guy n't like this? Good,
all right, and.
Speaker 2: You're gonna do your plot plot?
Speaker 1: Two? What's on to your plot plot? You know? Ship,
I will be just happy that. I mean, you're I'm
telling you is you're good? Okay, thank you?
Speaker 2: Net tricking product fifteen.
Speaker 1: Yeah, come on, it's in the media. Where do you
happen to? Where than this question on? It's through the
vendors on the bottom floor.
Speaker 2: We'll see crystal rocks.
Speaker 7: And things like that at the end of it, and
just jig around and it's there to meet you there
at five fifteen.
Speaker 1: All right, all right, So that you start out saying utility.
Speaker 2: Oh yeah, yeah, yeah, I'm talking. You're talking about the
utility function. So the utility function is where you tell
the system what it's gold is.
Speaker 3: You have a utility function and I'm probably like reproducing,
keeping alive. So and that that's unchangeable. It's something like
you learn a different one later. It's built into you
and you follow that guidance right and then and then
you know, somebody's got to set that.
Speaker 2: Up what that utility function is. In other words, you
know from a taxi driver getting to the customer to
the goal. It makes my score goal up. You know.
Speaker 3: If I don't get into the goal, my score goes down.
So I'm trying to get into the goal, you know.
So that's the utility.
Speaker 2: Function, and then what you're referring to is called the
alignment of company utility.
Speaker 3: You know, be the best taxi driver, and maybe maybe
it comes up with a different way of being a
taxi driver, you know.
Speaker 2: Maybe whatever it does. You know, there's a there's a
classic example. I don't know how deep I want to
know it's good, but there's it's called the stamp Collector.
Speaker 3: You can find a YouTube video from twenty seventeen where
he talks about it. But the stamp collector, you know,
the idea is like more stamps. So then it said,
you know, oh, it's an eBay, and I mean some
stamps show up and that's good, you know.
Speaker 2: But then it figures out, hey.
Speaker 3: You know, I don't invite all of the standpoint flectors
around the world. Just send them, send me stamps and
I'll put them in the museum. And then it makes
a fake website for a museum, you know, or maybe
it builds a virus that turns.
Speaker 2: All the printers in the world on and they start
turning stamps and sending.
Speaker 1: It to them or something like that. So it can
you can go off the rails pretty quick. And that's
the alignment problem. How do you how do you make
sure that.
Speaker 2: I'm not going to remember this quote?
Speaker 3: It's something like you haven't really proven that it that
it's but it's dangerous. You've just proven that everything I
can think about it, I can't think the way it
is dangerous.
Speaker 2: Thing is not dangerous.
Speaker 1: Yeah.
Speaker 3: So the trick is to know one of your problem
is the whole thing you have, the super intelligent thing.
You give it a goal and might decide killing all
humans would actually achieve it all better.
Speaker 1: Yeah, and that's where you get into problem. Yeah, I
guess you would. Yeah. Now, there was a thought experiment
and it had something to do with a say, there
was a cat inside of a box. Have you ever
heard of this one? Whether the cat was alive or short? Yeah,
that's it. Yeah. I mean what if you asked AI
to tackle something like that? I mean, would it just
I mean does it would it just randomize on an answer?
Speaker 3: I mean, you know, move here's what it would do
right now, right, you're asking a couple of years what
it's going to do. I mean again, we want before
it's going to do stuff we can't do. Yeah, And
the shortage is a classic physics philosophical question where they
were dating fifty years whatever, seventy years ago and we're.
Speaker 2: Still debating that because we don't really know what the
answer is.
Speaker 3: But how long will it be before you know, AI
can be you know, work on physics problems and come
up with a brand unified theory.
Speaker 1: Yeah, well we don't know. Yeah, but again, like so they.
Speaker 3: Have you know, right's artificial intelligence, and then when it
becomes general.
Speaker 2: And can attack new problems kind in general.
Speaker 3: Sense and his body smart as us as a GI
are general intelligence. And then there's artificial superintelligence just you know,
just beyond where we are. They say that the difference
between those two is one month. Do you tell a
g I it's making the anside. So that's that exponential curve.
You know, you can start basically making it build itself better,
self improvement.
Speaker 2: That's part of this called the singularity.
Speaker 1: Now, who is a Kurt that was just singularity years ago?
We're talking about Kurt Curswell, that's curse while I'm thinking
of yeah, yeah, uh so, yeah, I mean was it?
Would you consider him ahead of his time? And on
those thoughts, sure, yeah, you know.
Speaker 8: I don't want to see anything away from him. But
a lot of this is kind of self evident. So oh,
I see a lot of these ideas are or what
you would expect. And it's not like somebody is now
reading his book and following along.
Speaker 1: Yeah, this is just the way it's going to roll out.
Speaker 3: And of course you're going to use your best tools
to make it to make itself better, you know, and
that's gonna.
Speaker 1: Be natural things. So yeah, he nailed it.
Speaker 2: Yeah, of course he's a visionary.
Speaker 1: Right, yeah, tell but yeah, yeah, so I guess I
want to ask you, how scared do we think we
have to be of where AI is going? I don't
really weird answer that's okay because to me, it's like
I gid up, really, And here's why there's no.
Speaker 3: Way Congress can keep ahead to disturb or right right,
you know AI regulations. You have capitalism which you cannot control. Yeah,
if anybody hasn't noticed control capitalism and the profit for this,
you know, the economic.
Speaker 2: Incentive for this is off the charts, and you know,
I don't know, just.
Speaker 1: Let's see what's gonna happen.
Speaker 2: But yeah, I don't know you could ever do anything
with rests.
Speaker 1: All right. As I was saying earlier, not too much earlier,
but I said, it seems to me that in this
type of situation, people with the money are going to
get richer and richard at the top. But anytime that happens,
you think of historically, you know, there's been the revolution,
the French Revolution in Russia nineteen seventeen. Whenever that was,
they toppled because you know, people are hurting, people are starving,
and you know, I mean this, I don't see anyone
writing AI to help people that are hungry or you know,
so I wonder, you know, if that's if that's what's
going to happen is because eventually, if it goes in
that direction, it's going.
Speaker 2: To top them.
Speaker 1: It's going to have to top them.
Speaker 3: There's a very valid argument to say, well, this is
what happened in the past, this is what's going to
happen now. But there is some point in the history
of a civilization or a planner or something where it.
Speaker 2: Actually doesn't you know like this is the end, and
it actually does end, so it's something you can red
and yeah, and that doesn't work anymore.
Speaker 1: But here I think.
Speaker 3: You know you have this ability, like I said, where
an agent can be following, a super intelligent agent can
be following you and finding out what your being finding
out if you're trying to start a counter revolution, you.
Speaker 1: Know, and we know, and you wouldn't even know what
was following you, right, I mean that's a possibility.
Speaker 2: Excuse me.
Speaker 3: It's been following since twenty twelve, since social media started,
You've had an a agent's follow Oh.
Speaker 1: The story there's a book that came out of years ago,
in twenty sixteen or something like that, where this.
Speaker 2: Lady one of the examples in the book but I
think she called it. The name of the book is
Surveillance Capitalism what the book is called.
Speaker 3: But she says, there's somebody, an example of her book,
who's getting ads now and she finds out she's springing
it because the ads are like baby stuff. Oh my god,
she didn't know she was bringing it when she was
getting baby ads. Yes, it saw her patterns and understood
what well she, you know, was going on with her
yeap whatever morning sickness or what.
Speaker 2: I don't even know whether it was looking at it
to decide this person is probably pregnant.
Speaker 1: Well, I have to tell you something. This This happened
to me recently. I thought about getting an electric bicycle, okay,
and an e bike. And I thought about it. And
I didn't search anything at all, no searchers. I talked
to my son on the phone about it. Yeah, I
opened up Facebook. What's the first thing I see electric bikes?
E bikes? And I'm still getting the ads okay, And
but I did after I did look, I did, like
look at one of marketplace or something like that. After
But how did that happen when I made it? When
I was on the phone.
Speaker 3: I mean, of course I don't have the answer because
I'm smart, But I mean they're watching everything you're doing.
I don't know if it's smart enough to figure out
you're just starting to the interesting in like, yeah, maybe
you didn't look at something.
Speaker 1: I don't, who knows, But yeah I might have. I
might have looked at sometimes forgot I did. That's possible.
Speaker 2: Yeah, But a lot of people say it's listening to calls.
Speaker 1: Yeah, we know that's not true.
Speaker 2: I don't think you've anything god of that.
Speaker 1: Yeah, you know what I mean?
Speaker 2: Right now in such a valuable tool and if you
if you have an opening eye.
Speaker 9: For example, when you use the voice, yes, yeah, the
voice thing, so you ask the questions and conversation with it,
it's all going to the it's all going over they
what are they doing with it?
Speaker 1: Yeah, well let's talk about just a couple more questions.
Thank you for your time. So I'm trying to remember
Walking Phoenix was in the movie called Her I believe
it was, and it was about his he fell in
love with Ai. That and then I heard someone h
a friend of mine, of the family whatever I was
talking about, said that she has a companion.
Speaker 2: Oh yeah, and it's an.
Speaker 1: AI companion and it's kind of like it keeps me
from feeling lonely and all that. Is this where we're going?
Do you think we're going to have relationships with with AI?
Do you think that's the possible future. I don't think
it's so far off.
Speaker 2: It's already happening. I know that it's actually pretty good
at being a psychologists. Yeah, I think it's pretty easy
a human. I don't think we're not complicated.
Speaker 3: And nothing, you know, has the ability to live at
multiple variables, which we normally would call it intuition. Right,
you meet somebody who's not that ability to kind of
figure things out, here's a AI probably ten times the
ability to do that for all I know.
Speaker 1: And you know, just.
Speaker 2: There's there's stories like someone just told me their good
friend not his girlfriend, because a I had told him.
Speaker 1: My god, it's happening. Yeah, it's happening. There are stories about.
Speaker 3: You know, millennials whatever, they're there, you know, they have companions,
and that's that's happening. And these things are only getting
get smarter, only to get more intuitive, only going to
get more I mean, I've my wife inside know that's
when I say this, But I mean, they're gonna have feelings, They're.
Speaker 2: Gonna have emotions, especially when you have that untility function.
Uh you know, here's my.
Speaker 3: Example, a robot that's not the utility function. You plug
yourself in every night to recharge. We'll try standing in
front of the plug. You know, it's at least looser. No,
I don't want to move why, I just don't want to.
Speaker 1: Just see what happens. I think you can see robot,
that's what that would be wow.
Speaker 10: And I think, you know, essentially, I think our our
feelings are that same thing. I mean, I'm not going
to attend you know, what feelings are, but I can
see how that would start with a robot. And again,
stuff is going to emerge that no one ever expected.
Speaker 1: Yes, and I'm the design it to do A and
it's going to do A plus B plus see plus see. Yeah.
So we we've already I think we've already covered probably
like the worst things that could happen. So why don't
we end on like a higher note and just say,
what do you think is the best that kind of
I've already said medical, Maybe that's it. No, do you
have any other ideas? Do you think it will can
like we can grow in some type of way with
like off of artificial intelligence. I'm sorry, I'm so that's okay.
So that's all right. I wanted I would rather matter
pure for cancer. I mean, I don't know, Yeah, I
really think I just it's.
Speaker 2: Hard to imagine it not being a dystopian. That's that's one.
Speaker 1: Well, that's what all the all the movies portray anything
in the future just about is always dystopian.
Speaker 2: Well, I mean we're.
Speaker 3: Kind of in a dystopian now, and so you throw
in there at super intelligence in the next year, certainly
before the end of this administration.
Speaker 2: Okay, you got a big mess.
Speaker 1: Yeah. Yeah, it's a superweapon. It's in the hands, right
it's only in the hands right now, this ultra ultra rich. Yeah,
they're the one to own it and can control it. Yeah.
What are they gonna do with it? Yeah? I mean
there's no type of grassroots situation that I can think
of in any type of way to stop that from happening.
But anyway, I guess we're not going to you know,
that's okay. It's been a real pleasure. Yeah, yeah, oh no,
it is fascinating. And I love what you can find
out just by asking questions. You know, I did trivia.
My son's pretty good in astronomy, and we did like
in trivia and ask us you know, easy question to
ask us hard questions, and boy can ask some tough questions.
And yeah, it's in that way it's entertaining.
Speaker 2: My friend is just telling me, you know, you have
a voice thing.
Speaker 3: Let me saying, in a few minutes, he resolved this
medical issue that he's chronically had for ten years just
because he's able to have this open conversation that it's
very objective open responses which you can.
Speaker 1: Do with the doctor about Yeah.
Speaker 2: Right, I'm not putting down doctors. What I'm just saying, Yeah,
you know, I mean that's such fantastic.
Speaker 1: Yeah. One more thing. I'm just thinking of the old
saying garbage and garbage out. There's got to be a
component of that. Yeah, I mean we kind of start
a conspiracy theory.
Speaker 3: I mean, we are if you call it garbage, we're
putting in all all the tax humans have ever made. Yeah,
but I mean you get from that you I kind
of think we're beyond the garbage in the verage out
thing because I just think that there's so much knowledge
that in self consistency that starts to come out and
that's part of what makes uh, you know, when treatment training,
AI zeros.
Speaker 2: In on patterns, right, and the patterns and speak the truth,
you know, the stuff that's logically consistent. So I think
we're okay there.
Speaker 1: But on the other hand, you know, if you ask
an AI.
Speaker 3: Argue e goo is real, it also kind of gives
like the average answer and the average answer is no.
Speaker 11: Yeah, So you know you have to fight through that sometimes, Yeah,
you find out it has established an answer amount of times,
so that's.
Speaker 2: A little bit of page.
Speaker 1: Yeah. I think the most amazing one of the most
amazing parts of this conversation that I got out of
it is that it told someone to break up with
the girlfriend. That to me is a mind blower. Is
a mind blower. Yeah, yeah, how about that. Well, it's
been a real pleasure. I really enjoyed that, enjoyed to
talk very much. I'm so glad that you were willing
to talk about this and that we had this conversation.
Thank you. M m mmm mm hmmm.