One of the things they have to do is write down, here's the rules of the game,
and here's what, doing a certain good thing pays you, right?
They call it a payoff function.
And so if you, you know, if you take the, the opponent's pawn little piece,
here's the payoff.
Or if you take their queen, it's a bigger payoff.
And so, if you can define that in chess, you can actually define that.
You can make a system that that does very well.
But think about a dating relationship or a marriage like.
And we pretty quickly realize the our attempts
to quantify and define value.
It's we're in a different layer of abstraction.
We you can't the these don't go together.
So today in Anabaptist perspectives,
I'm joined by Ben Harris, and we're going to be diving into
the ontological limits of artificial intelligence.
Ben, you want to start with a
little introduction, and we'll jump into the topic after that.
Sure, Marlin.
Good to be with you. So, my name is Ben Harris.
Hi, everyone.
So I'm a professor up at Sattler College in Boston, Massachusetts.
I coordinate the business program up here,
but my my background comes out of the engineering world.
I spent more than a decade working in machine learning, artificial intelligence,
just many of the classical engineering disciplines.
So, I would.
I think it's hard to define what an expert in the field is, but,
I spent a long time thinking about it and continue to do so because it affects
not only back in, in the technical world, but also in academia.
We contend with AI every day.
So, Marlin, it's good to be with you.
Yes. Thanks for coming on, Ben.
Excited to have the kind of engineering, computer background,
that you bring to it.
So to
start with some of the the drama around AI,
a little over a year ago, May of 2023,
there's this famous statement on AI risk, signed by,
you know, a bunch of the big names, and their short version was mitigating
the risk of extinction from AI should be a global priority
alongside other societal scale risks such as pandemics and nuclear war.
And so that got a bunch of press.
I found it interesting.
A few weeks later, there was a CNN report on a,
a survey from a number of CEOs.
they got 119 responses.
And out of those, 50 of them said,
yeah, AI could potentially destroy humanity in the next 5 to 10 years.
And the other 69 were unconcerned.
So I guess we
can start with, where are you at on that question?
Your view of artificial intelligence and that kind of.
Yeah, that kind of dramatic fear statements.
Oh, that's a that's a classic question for anybody who thinks about AI.
I, I am not on the what we call an AI doomer.
Those are those are probably what you put those,
you know, the Geoffrey Hinton's of the world
that would you know, he's very, he’s had a much longer background here.
This is his view.
I do not see AI as within that short period of time, 5 to 10 years,
as being an existential threat to humanity.
Both for a theological reason, I think, like God has defined
the end of humanity for us, but also that we
we look at the technology and what we assume in the forecast of,
you know, AI improving or growing to a certain level assumes
no major hurdles that we encounter on the way.
I and I just think we're starting to see the hurdles.
Right?
We we at the moment cannot produce
data fast enough in order to support these bigger and bigger AI models.
We don't.
We're running out of,
raw materials for creating training chips and systems that do this.
So there so we're starting to see the hurdles.
And I think that that's going to
that's going to skew the forecast fairly dramatically.
I, I am not particularly concerned about, existential issues.
partly
because what we're going to talk about today is the is the ontology of AI, right?
The movie versions of AI being a threat to humans,
in the in the narrative tend to revolve around a moment
when I realized what it was and what it could be, and it has this self
defensive response or reaction to humanity trying to shut it down.
I don't yet think that AI has the
has an existential understanding of what it is yet.
You know, it probably can give you a text response.
Yes, I system, system system am an AI system,
but that does that does not yet imbue value in a self defensive reaction.
Right? The that we naturally experience as humans.
So I think there's still a gap ontologically and as well as what
we would call artificial generalized intelligence or AGI, right.
Systems that can think and reason for themselves.
I mean, it's still an ongoing open
research area in, in what they call argument mining, right?
You can present a bit of text to an engine and say, is this a good argument?
And it has a really difficult time determining
yes or no, whereas we as humans read that and say, oh, that's
a terrible argument or a great argument, and I find it very persuasive.
And so the, there's the that's one I think about a lot that particular gap.
But there are a number of them that that in my view,
set they set a
fairly big chasm today between where AI is
and anything that we would need to worry about from an existential standpoint.
So you're setting a chasm.
And you're saying those time frames are way over inflated or sorry,
not over inflated.
Opposite of over inflated.
Way too ambitious in a sense.
To push on it a little bit.
I still hear you using words like.
Not yet, implying that the time is coming.
It's just not yet.
and I guess even to push on that a little bit more,
you know, we talk about things in computing, like
Moore's Law, which I think is a fairly specific technical definition for that.
But in a general sense,
you know, computing power changes very quickly.
I mean, you even noted in an email to me, it seems like there's new
AI applications coming out every couple of weeks.
so I guess
how would you respond to an argument like that that says, well,
you know, computing power has been doubling
frequently for the last number of years, and we're just going to see,
you know, we can't see what's around the corner.
You know, it's a fair question.
You know why? Why the “Not yet”?
I think the the one of the reasons is if you, you know, say you
you project out some number of years and you,
you allow Moore's law, which is beginning to show asymptotic behavior.
Right?
You if you were not doubling the you know our our speeds are getting quicker,
but we're running into computation issues with these kind of things.
It's largely why GPUs have become
the standard architecture for doing this kind of work.
one, the not yet is an admission that I don't that we don't know.
Right.
We you know, can can you create a technical system
that that is a version of intelligence?
That's a bigger question than I think AI can answer.
That's a, you know, what is what has God defined intelligence as.
And can you create a system that has that?
you know,
we sort of think of it in a, in a narrow band in that,
you know, intelligence is the ability to collect and synthesize
information, to answer a question or to develop something.
but there's there's other types. Right?
We have a sense of asthetics.
Right? Humans look at what is beautiful.
We have a sense of awe, that is uniquely human, right?
And you, you know, if you ask, a large language model,
you know, if a given painting is, is beautiful or if a piece of artwork
is just abhorrent to you, like, it's likely does not have an opinion.
And if it does, it has to create it based on some quantitative
aspect of the artwork.
It has to look at, you know, the okay, the color contrast is within this range.
And most people think that that means it's not a very attractive painting.
It has.
That's the way it has to think because of its computational nature.
we don't think that way. Right?
We we look at it. And what is it?
What does it do to our to our spirit, to our soul, to our mind?
And we have a response. And so,
you know, I think that the not yet is a,
you know, is a is a fair question.
I think we're, we're also running
into some mathematical challenges in that the, the number of,
I'll call them synapses or nodes that we'd have to do to simulate
actual human thought and cognition is still many orders of magnitude
above what the most powerful systems can handle right now.
So even if Moore's Law holds, which is question whether it will,
we have a long time to go before we exponentially reach that point.
We would need to be and who and who knows?
On the way, we may encounter a whole nother obstacle we didn't anticipate.
So, the the challenge is that we you don't.
When you make a forecast, you have to acknowledge
what could cause the forecast to fail.
Right?
Things don't always continue as they were.
You look at, you know, population explosion in the 1970s, right.
There was this huge concern
that it was going to lead to global famine and billions of deaths.
But that never happened. And so,
so we want to look back
soberly at those examples, and I think with a bit of wisdom,
and just encourage ourselves that, like, the Lord has this in hand, right?
He's not he's not going to let AI ruin
his creation.
Okay, so a theological answer there.
Confidence in God.
Yeah.
And the title Ontological Limits kind of highlights what I wanted to the question.
I kind of want to press here.
if we talk about limits of AI,
maybe the first limit we think about is technological.
Can we build it?
What can we build?
What kind of
computers can we build, what kinds of neural networks and so on?
or epistemological what do we know how to do?
Knowledge.
when we say ontological limits, we're getting into.
Some ways, the strongest of those terms because we're trying to say what
what is the limit by the very,
by the very nature of what this thing is,
that produces the limit,
and yeah, I think maybe you've hinted at some of that,
but.
Yeah, kind of at its core,
what do you see is that biggest core limit
or way of talking about that core limit.
I think probably the the, the most stark ontological limit
of AI is is just what we've created it out of, right?
This is a this is a creation of,
Well, it's it's a, sounds funny, a
creation of creation.
Right.
We've our ingenuity has developed this and it's immensely powerful in some areas.
Right.
I don't want to to minimize its effectiveness in its aid in some
things, but there
it is
a it does not involve the supernatural, right.
It does not.
You know, it is a it is a, by definition, natural development.
It is limited by the laws that the that God has put in place
in terms of physics and computation and, and information,
in mathematics. Right.
We look at, you know, questions like there's a famous question called
the halting problem in Computer science that basically is a
we now know is a unsolvable question for a computer system.
And so, you know, it's not without
computing is not a finished business, right?
That we, you know, we know how to do everything
as long as we have enough power.
Enough, enough systems, enough electricity.
we don't we and we can prove that we don't.
Not only that, but never will.
Right.
There's you know, mathematicians have worked on that.
That there are some fundamental, fundamentally
unknowable things in computation in the technical fields.
and so I think those are you're going to encounter
some of those questions in the growth of AI based on its ontology, where you can't
there is not a, it is bound by the laws
that that all physical,
digital systems are bound by.
Right?
It can only go so fast to go and get so hot before it breaks down.
It's limited by the laws of physics.
It it is also limited by the ingenuity in which humans can insert into it.
Right?
We create it's steps, but
we are limited and finite.
So I, I have a difficult time
imagining how a system is going to,
you know, by its own volition, exceed that based on what it's created on.
Well, is part of the the argument there.
Especially for the.
I don't know, either AI optimist or AI pessimist.
Whichever one it is that, you know, has these very high expectations for AI.
I suppose that it's more a question
of your outlook, whether that makes you an AI optimist or pessimist.
but it's part of the
argument that, well, these are neural networks.
They're functioning the same way as the brain.
We don't have to give it a precise algorithm because
it has more capacities for self-learning and self adjustment.
And is the
is the expectation there that something about
that architecture is going to let it get past
what normally applies to other computer systems or...
It's a good question.
There's a, I remember there was a there was a French institute
some years ago that had begun to try and create neural networks with the scale
that would attempt to approximate some of the cognition of the human brain.
And, you know, they had the the resources of the French government.
They had an immense amount of computational power behind them.
And even with all that, they were able to to roughly get to,
if I remember the number correctly.
So don't hold me on the on the citation.
Something like 2 to 3% of brain function.
You know, this is immensely complex
because network complexity is does not grow linearly.
It grows exponentially. Right?
To gain, to grow a system that can get strong enough,
to be a big enough neural network,
even even that is,
is not going to give you human cognition,
because we think about the the use cases that we use neural networks for.
There's, there's really there's two major ones in machine learning.
One is classification.
Basically telling, you know, you look at if you take as input
something and your brain tells you what the thing is, right?
Our brains are that naturally you have image recognition software
or other neural networks that do the same thing.
They take in inputs and they identify something.
the other one is regression.
It's making a, a relationship between two quantities.
So, and allowing you to do a sort of what we call an in-sample prediction.
Right.
If you're if, you know, you know, house size A
and house size B and you see something in the middle,
what should the cost of the home be, right?
That's a that's a regressive, I'm sorry, it's an example of regression.
It's not regressive, but the,
and those are the two main neural network applications.
And there's now, there's variations on them
now, those the base layers of those are what's been built up
to create these transformers that LMS are built on.
So, they exist and they've been extended.
But you
I would say we are we are too far away from,
actual
human brain function to predict that it will it will continue along
that road, even even on any, uninterrupted path.
And finally get to how the human brain works,
there's going to be breaks or discontinuities in the progress
and that and who knows, some of those may scupper the whole thing.
Right?
You may only be able to get so far with the applications of AI.
You just can't go further.
They estimate that the cost to train training is what's most expensive
right now for these systems, to go beyond GPT four.
so for little 040,
to GPT five, GPT 5,6,7 is trillions of dollars per iteration
to accumulate all the data to do the training, the electricity cost
to run the models.
at some point we're going to we're going to run out of money.
We just, you know, humanity will either have to decide,
okay, we're invested in this or we're not.
We just can't keep spending that
those amount of resources to develop a system like this.
so I don't, you know, fortunately, I don't have to make.
I'm glad I'm not in charge of an
AI company that I'd have to make that choice, but,
there's,
you know, whether it's the actual technology, whether it's
the mathematics behind it or the funding to generate these things,
any, any combination of those can cause this thing to fail.
So we have to almost have the perfect storm to go up the
the the graph of progress towards human
cognition, at least in my view.
Yeah. Those are helpful.
One the point you made there at the end, those
literal physical constraints.
I mean, we have all kinds of energy available, but
that as you try to exponentially scale
up the amount of energy you're literally running into.
I don't know what the scale is, but it's registering on
things like grid capacity and electricity generation capacity
and that kind of thing. At some point.
Not a small thing.
no. The other thing that was really helpful, for me
and what you said there was AI is really only doing at the base
the two operations,
classification, classifying objects.
And it's gotten
sophisticated at that by by training
humans, coding examples.
and then it going off of those examples
and regression problems, which I'm not
not familiar with, the mathematics of those, but
again, those are fairly simple operations.
I mean, takes a lot of power to carry them out.
fairly simple compared to
the human mind and living a human life.
so, yeah,
I think just breaking it down and saying it does these two things
and builds on them and puts them to good use, to me really helps
to see the a little bit of see the limits or demystify things a little bit.
Yeah.
And I, one thing I would add in to
is that we you see on,
maybe on social media at points people
some very like AI negative people who like AI is not any good at anything.
And they'll bring up an example like I saw one on LinkedIn the other day
that there was a there was like a stone pillar in a field.
And on one side of it had like the rear half of a cow.
And then the stone pillar was maybe ten feet wide.
And on the other end of the stone pillar was like the head of a cow poking out.
And now we would say like, okay, there's two cows in the picture,
but that like an AI system, might put a box around it and say,
here's one cow and its length is 15ft,
right?
And so they would look at an example like that.
And we know, humans know the error.
We we see it and we're like, oh, okay.
a computer system doesn't.
And so I think those, those examples get brought up to say,
like AI is of no, like it's never going to be a threat.
It's, but I think what it points to is there are,
there are there are limits and easy mistakes that AI makes,
especially early ChatGPT days made tons of mistakes.
And so, but humans
will will continue to try and sort of plug the holes in the dam.
Right.
They'll we'll say, okay, here's a, here's an error
that we're getting ridiculed for this thing that the system can't do.
We'll fix the thing.
and I'm, I'm led to believe we're going to keep finding more of those mistakes
that humans have to fix.
And it'll it may get better and better and better, but it, again,
you're you're going to keep finding,
reasons, reasons to make fun of it ultimately.
Now, that doesn't mean it's not powerful.
It's not something to think about, but it's, you know, those
I think the, the, the satirical view of AI is becoming more and more prevalent.
And I don't know what that's going to do to people's view of it.
probably very little.
But, we want I just want to I want to try and think clearly about
is this a like what is the truth here about AI? Yes.
That's a silly mistake that it made.
But does that invalidate the whole project?
Well, of course not. Right.
That's you know, amongst the billions of things it can do.
Here's one that you thought was silly. All right. So move on.
Yeah, exactly.
I think just to pursue that a little bit, though,
and. Yeah, I like the case you've made, kind of,
limits and it's limited.
but would you agree that we can get
we can get a lot better at using some of the tools even without
even without really fundamental changes in capacity.
like, for example, I was,
listening to an accountant
and his statement was, there's
going to come a time sooner or later where
you're basic bookkeeping, categorizing transactions, and so on.
Somebody is going to come up with a tool that does that well enough
that it's barely worth your time to have a human bookkeeper go through
in detail, because it's going to get it
close enough, especially for the purposes of small business.
It's going to get it close enough that
it's not going to matter.
And I don't know, that kind of strikes me as plausible.
It also doesn't strike me as requiring anything
radically new from AI, maybe just some human ingenuity
and how to how to apply it right to the process.
I don't know.
Does that sounds like a fair estimate.
Oh, I think so.
I think there's, like, accounting as an application of AI.
Sure, you can do the.
The mechanics are not tremendously complicated.
You know, they take care and some background knowledge, but the,
nothing about the mathematics is incredibly difficult.
But the, I think, like with that example, one of the constraints we're going to
encounter is at the end of it, you, you make a legally binding declaration.
Right. These are the these are the truth of the accounts.
And so who who is going to to put their legal weight behind that.
Is it going to be the, you know, is it going to be the individual
who's used the system like I certify this is correct,
or are we going to try and pass that off to the AI system?
So like, well no, the AI did it.
So if there's a mistake it isn't my fault.
It's the AI’s fault.
And if we do that which AI companies are going to shoulder the legal burden
for that of you know, that's I think it's, it's going to sort of
be the, the, the fingers pointed at one another issue
with why hasn't this become, why hasn't this use case become widespread?
I think it's because no one is confident enough yet.
That and I'll say yet because I think it it may come a day that people will be.
And this will the dam will break loose.
But I don't think anyone yet is ready to sign up
and put their legal business life behind this there.
I think people are still waiting for for more and more evidence that it's okay.
Yeah.
And that could apply to.
To a lot of pieces where you have software doing a lot of it.
I mean, it's thinking, you know, friends that build roof trusses
and this isn't necessarily AI, the software is doing basically
the engineering and saying, yes, this, this truss will meet specs or it won't.
but if they're actually doing a job that requires an engineer's stamp,
well, it's still got to be an engineer that does it, which is an extra step.
That liability part.
They're so good.
Yeah.
Okay.
So I'm guessing given the arguments you've made, that,
you know, artificial general intelligence,
actual purpose, purposeful
behavior by AI systems and so on.
I'm taking it you're very skeptical of that.
Or very skeptical that being in the all things.
Yeah, I'd say that's true.
I have a good degree of skepticism about that. The
I think that we have humans
have struggled to define what AGI actually is.
Right. How do you test it. How do you verify it.
How do you.
And so I think that that particular question of what is AGI is going to,
remain in the academic circles for a while.
It's going to, you know, we're going to there's going to be arguments for
and against of various kinds that, may or may not prove fruitful.
Right.
I don't know that they're going to come to any conclusion.
and I think in the background, AI companies
are going to continue to build systems that more and more closely approximate,
you know, human cognition, even though they we might say
you're still light years away, but we're making progress.
And that's probably true.
And so, so I don't
the yeah, I'm
not tremendously worried about the development of AGI.
you know, sometimes the cases of,
chess or go these board games that have had a long and storied history
in computation, I like, companies like DeepMind.
So, which is now owned by Google, but that's out of London.
have done some really neat work, like developing superhuman
chess engines and go engines that play the game at a, at a level that surpasses
human understanding.
which is pretty cool, the way what they had to develop to do that,
but those are still games with a,
a defined feature space and defined rules and defined options.
Right.
You like when you can do that, when you can do that, when you can box
in the system, the universe, you can make something really neat.
But, boy, humanity resists being boxed in like that.
We just don't.
That's not our nature.
And and so I think part of that is the root of my skepticism is,
like if you look into a field called reinforcement
learning, it's a it's a subpart of machine learning.
But one of the to do it.
Well, this is how the, the, the chess engine from DeepMind was built.
One of the things
they have to do is write down, here's the rules of the game, and here's what,
doing a certain good thing pays you, right?
They call it a payoff function.
And so if you, you know, if you take the, the opponent's pawn little piece,
here's the payoff.
Or if you take their queen, it's a bigger payoff.
And so, if you can define that in chess, you can actually define that.
You can make a system that that does very well.
But think about a dating relationship or a marriage like.
And we pretty quickly realize the our attempts
to quantify and define value.
It's we're in a different layer of abstraction.
We you can't the these don't go together.
And so you know those are
some of the, I guess musings to me of why
I think AGI is still some ways off, if we even can define it.
Yeah.
So along the lines of, you know, AGI.
And again, these questions about intelligence,
the Turing test.
I used to use a version of this with students, long before,
you know, generative AI was on the on the horizons
came from my, my days in college, philosophy 101.
And, you know, basically we're asked to I don't have a technical definition
in front of me, but we're asked to consider the question,
you know, if a computer can give the same answers
so that if you're communicating with it through text or whatever, you can't tell
if there's a computer or a person on the other end.
well, then should we ascribe it
the same mental life and intelligence that we ascribe to a person?
yeah. How do you think about that?
And maybe you have a more precise, computer science definition of the test.
No, the.
The Turing test was all the way back in the 1950s.
Alan Turing, when he was working in the early theory of computation.
no, you you had a good working definition.
You know, if you're if, you know, there's two rooms behind you
and you're getting text inputs from questions you ask,
how do you tell if one is a human and one's a computer, and if it's,
you know, a system that is statistically indistinguishable
from a human, we would say, okay, passes the Turing test.
I think we are actually already there with that, with the Turing test,
I think we have systems that you can, you can write
fairly complex questions to, and this is always an interesting, like,
thought experiment of like, what question would you ask that a, artificial
intelligence system that you know is an AI system would fail to answer well.
Right.
that's kind of an interesting sort of a subfield here, but I think we're
I think we already have systems that pass the Turing test.
The, but I think this it's
the current application of the Turing test, I think is a moving,
a bit of a moving standard in that you,
you have a system that seems to pass the Turing test,
and then humans get used to how it communicates.
Right?
You begin to pick up the flavor or the nuance of, like,
this sounds like it was AI generated, right?
I think we all,
if we've read AI generated text, we sort of know what that feels like.
It's very it's very linear, very clear.
There's no halting pausing.
There's it's like it's mechanical in a sense.
And so,
now what an AI company might say is,
okay, I'll adjust my, my generation algorithm
to make it a little more clunky, a little more human like.
Right.
And then it it
passes the new standard of the Turing test that people can't distinguish.
but I whenever you have a test that relies on human perception of something
that, like, we grow, we learn, right, that that the benchmark
for that test is going to change over time.
So, you know, I think we we have systems
that your early GPT is probably passed the Turing test.
Now the standards even higher.
Right.
For a system to be indistinguishable from a human.
and it's not because the systems have changed
so much as the we have changed and learned.
And so, We've learned how to.
How to deal with it.
Yeah, that's a good perspective.
And just.
Yeah, as we're talking about this
and it occurs to me that it'd be very interesting to read what
philosophers wrote about the Turing test and how that has changed as the
computer
computer capacities have gone up, because that was the angle
from which I came, which which I would have encountered.
It was kind of philosophy of mind. And,
what is it about the human mind,
what makes mind and so on.
And it does strike me that those kinds of questions are really
kind of relevant how we're thinking about AI here, because
if you're coming into the whole discussion,
with a philosophy that says.
You know, we can explain this
all physically or we can explain this by the functions of physical things.
The human mind is just
just is the human brain, or it's the functions
and processes that the human brain runs.
Then it seems to be a fair question.
Well, can we duplicate it with something else?
yeah.
If we're coming in as Christians,
we still may have a large variety of philosophy of mind.
We're going to believe that the brain is important.
but generally,
you know, God is a spirit.
God has a mind without having a brain.
generally we believe that our mind is.
In a certain sense, independent of our brain,
although obviously it functions through our brain.
yeah.
I just have to wonder how much that plays into the whole discussion.
if you simply are a materialist,
then it seems like so kind of naturally lead to this
more AI optimistic view of things.
I don't know how that maps into the academic landscape
either, but.
There's.
There's certainly
in the academic world, there's certainly a sense of techno optimism.
And it's,
And, you know, they treat it again if you if you come at it
from a largely materialist worldview, you end up with just,
you know, an understanding that
that all I'm, all I'm creating technically is a smaller approximation to me.
And it's going to get closer and closer and closer over time.
And so there's no there isn't really a connection between soul, spirit
and mind in the, in the secular worldview
because we're just,
you know,
we're just a random collection of atoms that, that have no lasting eternal value.
I think that's the conclusion,
whereas I think like that is from a Christian perspective.
That's where we come at AI with a different view of like, you know.
Yeah.
So you can create a neural network that is that has the trillions of connections
that our brain does.
you're still missing a piece that you can't simulate.
There's a spirit, there's a soul, there's a, humans are,
you know, by a mathematical definition, irrational creatures.
But in
if we because we are we, you know, we look at we do things that are silly,
not in our best interest, but in from a theological perspective,
that makes sense because we, we identify this conflict
of our of our sin nature and our redeemed nature in Christ.
And so, if you don't have theology, this gets leads to a natural techno optimism.
But I think only in the, in the sense that it is self beneficial.
Right.
Like I'm optimistic technically, if it benefits me, right?
if it doesn't, if it's a threat to me, then I'm not as optimistic about it.
And I have no moral qualms either way. Right.
That's the materialist view.
It's not a moral question. It's it's pragmatic.
Yeah.
So actually, while I was getting ready for this episode,
I was sitting in my little,
office at the school I help with, and I looked out the window
and I noticed plants, flowers.
It's a beautiful sunny day.
And butterflies flitting around
on top of there.
And. Yeah, so I'm drawn into the beauty.
Something a lot of people are.
And so just got me thinking, you know, those are the things we paint.
we draw pictures of,
But not only that, like, we really have, we really have dug into that.
What is the life cycle of the flower?
How can we breed the flower to maximize blooms?
You know, we've traced the life cycle of those butterflies
and learned how they function.
learned the biology of how they function, the life cycle, all of that.
and, you know, I even had
to think about a book we're using in our, in our science program.
It's called The Girl Who Drew Butterflies,
and it tells the story of a girl who was a painter.
Her art, but just was really
was really closely observing,
you know, insects, butterflies came to understand
these stages of metamorphosis and so on.
when a lot of people, at a time when a lot of people didn't understand those,
and so just kind of really hit me like, okay,
this is what humans do with things that we see and pay attention to.
is there any reason to think that AI
models or agents,
would do any of the same thing with have any of that same,
really It's a
relationship, ongoing relationship with reality.
and does that get at any of the difference, this kind of difference
we're talking about between a human mind and an AI model?
yeah.
I'd love to hear some of your thoughts on that.
I love the question. I,
I mean, my my thoughts.
First, go to the the the the genesis of what we would call curiosity.
Right? The the.
You know, I've, I have children at home.
Some of them are very curious.
And what is it that. And we love curiosity.
It's how we we develop an ever expanding knowledge of the world.
you know, so on the one hand, I think AI
could be constrained and that it has to like
it doesn't really have the ability to explore aside from the digital space
it inhabits.
And so, you know, it could but, you know, that could be that could be overcome
if a, you know, a human, we we give it inputs from a world beyond its own
or we give it pictures and soil samples and scientific reports, and we give it
enough information to, discern connections and patterns and systems.
but I
think at the, at the very bottom of a of a contrived system
like that, we have like human volition, we've done it.
We've, we we have to push AI into the world
to begin to collect this information.
so I don't know that,
you know what?
I'll say this in the, in the world AI inhabits,
I think it already is acting as a curious agent within its world.
Right.
It's I think by its incentive structure. Right.
It says like, I want to be the best AI system.
So I'm going to collect everything that I can, and, and look for patterns
and, and begin to create more and more of an approximation to reality.
there's so much more to the world than just the digital space, right?
So we have to we have to somehow connect AI with that space.
That's a more challenging question.
so I don't know that I would,
I wouldn't
I don't think that AI is going to have agency in that way.
I think one of the problems where, like, I think we're already seeing an inflection
point with that kind of pattern in AI,
because they estimate the amount of the amount of data
on the planet doubles every six months, something like that.
So just the amount of data is exploding based on what we're creating
movies, films, text, all these things.
now what we're realizing
is that the some of the data that's being fed back into these models
to learn and remain current is actually AI generated.
Right?
So AI models are consuming AI generated things
and taking it as as new truth sources.
Right?
So they're the they're building up on what they, what they've already created.
And so any, you know, if there's any error or bias
or anything that's built into those generated sets of data
that's now been replicated and virally expanded across the AI space.
And so, so I think, you know, I my one one thought I have
is there's going to be a bit of a pendulum swing when you realize AI
is spitting out nonsense, because that's all it knows.
and the pendulum swing will say, well, don't
don't let AI have access to anything new yet.
Right?
We need to curate what it's learning from, in order to make this truthful.
And so, you know, we have this curious humans have this beautiful
curiosity to know, you know what, why do things work the way that they do?
Right.
It's led to advances in cosmology into in biology, in mathematics.
You know, we do this naturally.
you know, I
loved I loved seeing recently there was a paper that came out of,
I think it might have been Google
DeepMind as well, but they actually had developed a
AI generated mathematical theorem proving system.
They could give it a mathematical question.
It could prove something mathematically.
I said, that's fascinating because that's that's an area that I had thought
that humans like that was human like creativity and intuition.
That was not an accessible region.
but I had but the more I am
reading about it, the more I'm thinking, okay,
they've not done human things, but they've they've made progress, right?
They've done some things that I thought they couldn't do.
And so, so there but I think that's how progress oftentimes goes in
AI is they, they, it appears they can't do anything.
And then there's this discontinuity and jump into now what they can do.
So I think we've seen those now do I think that
that AI is ever going to have the, the beautiful,
observation of the butterflies, like the girl who drew butterflies
to to understand the biological cycles of these things.
again, I probably hold a healthy skepticism, just like AGI.
I'm not.
I don't think that AI has, because we haven't given it the reason to yet.
We've not incentivized it.
you know, we we learn because it's beautiful to learn
and AI learns because either we tell it to
or we threaten its extinction if it doesn't.
So there's a, you know, I don't think
we've it doesn't have the natural curiosity.
We do. And I think that makes us human.
Yeah. Thanks. Yeah.
So I keep hearing you come back to the, “Not yets” a little bit.
But if there is one theme in your limits that you talk about,
kind of ontological limits of AI is
is it always being downstream of humanity in some way or another?
Yeah. We don't understand exactly what it's doing.
We we set it up and we don't always understand exactly how it works.
The whole black box effect.
but it's downstream from you talking about
the incentives we give it, the way we program it, the parameters,
what we want it to do,
the inputs we curate for it.
so yeah, that is helpful.
yeah.
Any particular concluding thoughts you'd like with,
like to, leave on this topic?
I think just the.
You know, you're you're right point to highlight.
Like. Yeah. So there is a, there is a sense of not yet.
And again part of me I go there because I in candor,
I don't actually know, but I make a forecast that could have a,
you know, there's a margin of error in the forecast
and there's things that could go wrong or right that make it wrong.
And so,
I only forecast out as sort of as far
as I could see, the, but you think about something like quantum computing.
So the new technology that there's engineering
challenges to solve, but say we solve them and we have quantum computers
that can do incredibly quick calculation
in, in some previously inaccessible problems.
all of these developments, you know, AI developments, growth
in neural networks or training or, you know, GPT eight say.
Or quantum computing, right.
They all solve sort of little niche things and they do it very well.
But I guess the analogy I go back to in my own mind is that you
without it, without a unified system or understanding of reality,
all of these things will remain niche solutions, right?
You don't.
It'd be a little bit like, you know, you
you try and fix something wrong with your body and you, you know, you,
you sprain your finger and so you splint it.
Right. You have a solution to the problem.
You've not cured cancer, right?
You've you've solved a problem.
Right.
And you've made progress towards the solution to the whole.
But you there is not a systemic understanding yet.
And so all of these developments,
you know, I'll say not yet.
My my prediction is it's a long way off.
But, I think for all of these things to work in concert to develop anything
that might be, an ontological break for artificial intelligence,
just this to me, there's still too many unanswered questions.
Is they don't none of these things see reality in the same way.
Like quantum and classical computing also don't see reality in the same way.
So like you gotta find a way to bridge that before these are effective together.
And it's
I think that there's
we're finding more questions than we have answers to these days.
And at least in my view.
That's an interesting way maybe to put your.
Not yet.
You said we won't get to an on. You know,
you don't think we'll get
to an ontological breakthrough.
and so in some ways,
what you're saying is, look, the ontology of what we have now is,
is, in reality, nothing like artificial general intelligence.
You can't conclusively rule out that at some point,
we would be able to develop systems
that are really fundamentally different at how they work.
And that's kind of the not yet.
And as part of your emphasis is like it wouldn't just be a development,
it would be something would be a very fundamental breakthrough.
Yeah, I'd say that's a good that's a good assessment of it.
It's it's very hard to prove a negative.
Right. So that's one of the challenges you run into in logic.
And so and it, so yeah, I think we have there's
and it always
seems, it always seems impossible
to solve some of these problems until someone solves it.
Right. There's that's true of mathematics, right?
We have all these unsolved problems until someone,
you know, goes out of mathematics for a long enough
and they come up with a solution.
So, I think, you know, my my skepticism lies in the, in the,
the nature of the questions being asked in that, you know, we're saying,
you know, in order to create things that make us uniquely human, we need to,
you know, more and more silicon chips doesn't get us there, right?
It needs to be something else. Right?
Some other way of understanding reality, processing information,
that we just don't have.
And I, and again, I don't think too long
about what it would take to do that, but the,
When we reach a point in
innovation that every time we innovate, we get more questions.
I'm, I'm waiting for the
I'm not really waiting, but in a sense, if the tide began to turn and the
we began to answer, a lot of these things or major breakthroughs are covering
lots of them that might tilt the needle of optimism versus pessimism.
For me, I might say, okay, I'm maybe I was in error before,
but at the moment we just keep getting more questions and it's yeah,
that's that's sort of the root of my the root of my not yet answer.
Yeah.
Well, thank you, Ben, for, diving into this.
I've enjoyed this and. Yeah.
Thanks for sharing that.
with our audience here.
Thank you to the audience
for tuning in here to Anabaptist Perspectives.
And if you've enjoyed this, you can subscribe to the channel
or share this episode.
And we'll catch you in the next episode.