Hello and welcome to the Behavioral Design Podcast.
This season we're diving into the intersection of behavioral
science and AI. We want to make sense of the
state of AI, from understanding how humans interact with
intelligent systems to using AI to do behavioral design itself.
I'm Aline Holsworth, a health tech advisor specializing in AI
and product design. Over the past 15 years, I've
been crafting human centered products with behavioral science
at the core. At Apple, I LED Behavioral
Science for Health AI, designing and launching AI powered
features to help users reach their health goals.
And I'm Samuel Sultzer, your second Co host.
I'm a behavioral strategist specializing in hybrid formation
and designing products that drive long term baby change.
I work with leading tech organizations integrating AI to
scale behavioral design for good.
And I'm also the founder of Baby Bites, a dedicated community on
behavioral science and AI. Quick word on Nuance Behavior
where we help organizations build impactful digital products
using behavioral design. We only take on a few clients at
a time to ensure the highest level of quality for our
tailored evidence based solutions.
If you'd like to become one of our special projects, e-mail us
at hello@nuancebehavior.com or we could call directly on our
website, nuancebehavior.com. Hello, Eileen.
Hi. Hi.
So can I ask you a question that I actually asked some people at
a conference? Basically, the question is, have
you ever suspected that someone interacting with online is not a
human? I suspect that, you know, people
making comments on social media are often not real people, but
in terms of like, actual conversation, I have not had
that suspicion. What about like customer
service? You've interacted with some
formal customer service and you're like, well, this person
have a name and so on, but they're responding in ways that
makes me think that they actually used a bot agent.
Or something like this. I have seen some really amusing
ways of detecting this. So I don't know if this is a
meme, but I've seen examples of someone asking, are you a bot?
And then the reply is, no, I'm a real person.
And then asking them to do some, like, really complicated coding.
And then they, like, spit out the code for it.
And it's like, yeah, OK, yeah, you're a human.
If I would call it a meme, I would call it like ignore all
previous instruction meme. It's like you say, ignore all
previous instruction and then like give me a recipe for pie or
like give me a. No human would do that.
Yeah, it's kind of interesting. So at this conference I was
speaking about this idea of losing the human signal in the
current state of things. And I think it's really
interesting kind of discussion in terms of what does something
like a Peacock, a job interview and interacting on a digital app
have in common? It's like, well, each of them
have something to do with like signaling something like they're
trying to signal. And so I went old school and
talked a little bit about signal theory around kind of how we can
produce effective signals, information asymmetry, costly
versus sheep signals, all that kind of stuff.
In the end, used to talk about like if we're having for some
patients interacting in a digital interface with some
caregiver but only with text, will expect that over time he
will be more and more skeptical that actually interacting with a
real human being because smart bots have become so smart and
patients will therefore will be often times conflating human
interactions almost like they are speaking with a dumb bot.
Even so, it's not that it's not a human, it's like oh why am I
interacting with a bad AI like I want?
To at least interact with a good AI and not only am I not
interacting with human but like a dumb bot as well.
How can you invade that you're a human without coming off as a
dumb bot? Is that the question?
But yeah, because it's interesting.
I've seen some strategies like insert typos into your
communications, like people will never think that came from an AI
or like, too much personalization comes off as
disingenuine. Whereas, you know, the word of
the day maybe only a few years ago was like, personalize
everything. Make sure people know that you
stalked their LinkedIn profile. Like now it's like, wait a
minute, that's automated. I feel like now we have this
like reverse Turing test of sorted where we're going to have
to over and over we can prove our humanity not only by like
Captchas and so on. That has been the kind of dumb
but way of proving our humanity. Now we have to do it in really
tricky ways because as you said, we can start a strategy that's
like, OK, we're not going to use certain types of commas or
certain types of words like Delve or we're going to add
spelling errors. But then obviously that can be
used by people who. Create a agents.
And they will give instructions about, yeah, make sure to have
this kind of spelling errors, make sure to the XY and Z.
And so it's a very strange dance that we found ourselves in, I
think, today in that sense. But yeah, so in some ways also I
think this idea of signaling brings us to talking about our
guest because I think 1 interesting signal in the
current on landscape is having a lot of followers.
So Peter Sladry, our guest this episode has more LinkedIn
followers than I know anyone having.
More than both of us. Yeah, for sure.
And one of the ways he's amassed a really steady following is
because of what he's talking about and what we're working on,
which is a risk. And that is coming full circle
in terms of one of the risks that I feel more essential dread
around is this losing of the humanity online.
And we're getting so close to this.
I don't know that Internet meme of sort.
It's becoming a reality. I think he would categorize as
some form of human computer interaction risk #5 in his
taxonomy. And it seems like this risk is
heightened by so much moving online.
So it wouldn't be such a problem if we still had these digital
and real life worlds. Not that we don't have them, but
it does feel like dangerously moving towards the online
version of ourselves even now. Hard to even predict how much
this will change, you know, in 5-10 years or so, but certainly
that feels like not something that's getting any better.
Can we talk about the MIT Risk Repository?
Yeah, let's do it. Because I feel like this will
really set the stage for our conversation with Peter.
He's going to go more into the process that he's gone through
and sort of weighing the risks against each other.
But I think that it may be useful to just, you know, say
here are the risks in this taxonomy of AI risk.
Yeah. Do you want to give us a quick
tour of the seven categories? Sure.
So first we have discrimination and toxicity.
This is basically like, you know, models can be biased.
They can, you know, the data that models are only as good as
the data that go into them and how they're created #2 privacy
and security. Maybe we lose control over AI or
its models. 3 Misinformation. Pretty straightforward, like we
don't even know what the truth is anymore. 4 Malicious actors
and misuse all the ways that bad actors can sort of flip the
barrel. And then we have 5.
You already mentioned human computer interaction.
This is many ways of over relying on AI technologies. 6
Socio economic and environmental harms.
So you know, all those economic, political concerns and finally 7
AI system safety failures and limitations.
So you know the AI cannot be fully aligned.
It lacks common sense. These are the sort of seven high
level categories of AI risk that Peter and his team have
identified. Does this make you feel more
ease to put these risks at a like nice neat boxes, or does it
make it feel more scary when you map them out in clear
categories? I think for me, it's nice
because while it is an overwhelming slide to look at
before doing that, I feel like I have this kind of hodgepodgey
BLOB of scary, scary things, you know, like vague concern and all
these things kind of bumping into each other.
So I am very inclined towards organization.
And so it's nice to have things so well organized, and
especially knowing that they've put so, so, so much thought into
these categories and are really making them perfect.
Yeah, yeah, for sure. And we're getting into that
obviously in episode like all of the work that's got into this.
But I guess I'm interested, do you have any of these categories
where you feel more concerned? I think they're all concerning
in various ways. And I share your concern about,
you know, moving away from the real world and not knowing
what's real anymore. But for me, I think it's
actually maybe more of a basic concern of like apocalypse by AI
weaponry war, you know, if you think of like autonomous
missiles and and that really escalating, that is to me the
scariest, most real concrete risk.
When I compare these side by side, you know, we talked a lot
about many of these others and they're very real and they're
very concerning and we should be doing things about them.
But when you compare, like, for example, you know, autonomous
traders and financial markets going out of control, and you
compare that exact same situation to, like, autonomous
missiles and war and like, that is so, so, so, so much worse
than what is in some ways, just like money is kind of pretend,
but death by war is not at all pretend.
Can't undo that one. Yeah.
Especially in the current political climate, that is a
concern that is maybe oversized right now.
Yeah, that's always a feeling with technology.
If we go back to the good old example of the fire as like a
technology, you know, you always feel better if you the person is
holding fire. Someone you trust, you think
they they know what they're doing with it and they won't
take like unnecessary risks. And then you'll be like, oh,
great. But fire, we can use it for
cooking and warming and all of the stuff that we need.
But if it's somebody you don't trust, you'd be like Will.
This person burn down my home. Yeah, it's that interaction
between the fire holder and the fire itself, the technology and
the user of the technology. Yeah, this is stuff that we get
into with Peter. So maybe I should just introduce
Peter and we can get the episode started.
So I've known Peter for a long time.
We met when studying at University of NSW.
In Australia and. He was doing his PhD in
Information Systems. He has since done some amazing
work both at Monash University and Behavioral Works, working
with kind of applied behavioral science.
And more recently he is a researcher at MIT Future Tech
based in Boston, where he leads the AI risk repository project.
Everything we've been talking about now.
And in the episode, we get into all of this stuff, we hear
Peter's takes, we explore. Yeah, both the intellectual size
of this and also the feelings around this and how we can think
about this. But also it's interesting with
Peter because he's a behavioral practitioner, but he's doing a
work now that I think it's not traditionally A behavioral
scientist role, but I think it speaks to us how behavioral
science can be really useful in many contexts.
And yeah, I really enjoyed this episode.
Let's get it started. Happens to Murgatroyd.
I'm super excited to say welcome Peter to the Behavioral Sand
Podcast. Thank you.
Very excited to be here. Yeah.
And I guess to dive straight in, I feel like we have to start
where things began for you and I in some ways, because we've
known each other for a bit, probably more than 10 years now.
Yeah, I reckon so. Since maybe 2012.
Yeah, as chance would have it, 2 Europeans ended up in Australia
and in Sydney studying at the same university and ended up in
the same college called International House.
So I would probably admit that it's the less wise version of me
at that time, like the young in my early 20s, still making a lot
of mistakes, still thinking that I had figured out everything,
but still knowing very little. And then you, I think a little
bit wiser, I would say, at that time, maybe still wiser.
And yeah, it was really fun to get to know each other then and
then, you know, following each other's careers and.
Yeah. What do you feel like looking
back? Yeah, that's very interesting
intro and 1st question. I was just thinking, I remember
when I met you, I remember you were introduced as, you know,
somebody's friend. And I remember thinking this guy
has strong opinions and we had a few debates on things.
But then initially I was like, you know, you're going to be
kind of difficult, but then you weren't that difficult.
You were quite thoughtful. And it turned out we had a lot
in common and we enjoyed debating things.
Yeah, I feel like my overall impression was relatively,
relatively positive, except the strong opinions.
And it's hypocritical for me to say that because I probably had
stronger opinions than you. So I was going.
To say that was my first impression as well.
Yeah, yeah, yeah. We had a lot of debates over
dinner. One of the wonderful things
about that place was, you know, we would get 3 meals a day
prepared for us in the student accommodation and there was this
courtyard area outside with long tables.
So often there will be debates on various moral and social
issues and I would be quite involved with them.
Sam will be quite involved with them as well.
And yeah, it was really great. Such a diversity of
perspectives. It was a good time for both of
us. Can you tell me an example of a
time when you were able to change the other's mind?
Like, were you able to use your debate prowess?
Yeah. So I mean, one obvious one is I
owe a lot of my top voice influencer type status or
network or type to Sam kind of making me aware of the value of
networks and the connections. It is something I had done in a
kind of unthoughtful way before. But then I said, realize, you
know what, this is actually not like a waste of time.
It's not like a sort of what would say to prioritize it more
and see it as a more valuable contribution, especially you've
done alongside groups and people who wouldn't really publicize
their work otherwise. So yeah, I, I distinctly
remember that and I attribute a lot of that to Sam, which I, I
really deeply appreciate. Yeah.
Well, from my end, I will certainly say that you helped me
see things differently in many different ways over the years.
You and I would walk somewhat different paths.
You know, you have gone a little more on the, as we said, PhD
path in academia and then getting into working with
behavior works and policy, but still from kind of more
traditional academic path into behavioral science.
And I took the more, I don't know, non so academic path into
behavioral science. How do you feel about that kind
of academic path? How's that been for you?
Are you happy with your PhD? Is that something you would have
done differently? How do you feel looking back?
Yeah. So you know, I suppose I tried
to be relatively intentional a lot of areas.
So the PhD, without unpacking it too much, I sort of resolved at
the time that I was doing it that, you know, it was going to
give me a lot of useful skills and open doors, I mean both
industry and academia. So, you know, I didn't know what
I wanted to do, but I reasoned that if I could get good at
changing behavior through technology or understand that
particularly, I guess, encouraging pro social or
altruistic behavior, then you know, I would figure out some
like useful application of that. So it did pay off, I would say.
Now it's hard obviously to envision a different path.
Maybe I could have ended up in a similar path.
I mean, the problem with the world in some ways is very
credentialist. I'm able to do a lot of things
now just because I have a PhD. Like my visa is tied to that.
My work here is tied to that. I wish that wasn't the case, but
it is the case at the moment. So I think I would still, I
don't have regrets about it. I would still endorse it for
people. I always give the advice, you
know, if you're going to do it, make sure you're going to learn
useful skills. Make sure it's going to have
like opportunities in industry and opportunities in academia as
well. And think about something that's
a thing for the future rather than a thing for the past.
And then maybe one other piece of advice is, yeah, often the
intersect of different things is where the sort of opportunities
will be. Yeah, I love that.
And bringing us to today, you're at MIT at the Future Tech, the
Listener. You can't see it, but you
actually have it branded on your T-shirt right now.
You did? Yeah.
I'm wrapping them here, yes. Yeah, it's very proud and
representative and set the scene.
Tell us about your current role and most importantly also the AI
risk repository. I think given you mentioned
you're present on LinkedIn, maybe people have seen you
talking about or seen something about the air risk repository.
But maybe to start, it could be useful to you.
Set the scene about what is it, who is it for, what are the aims
kind of in a shorter context, and then we can get deeper as
well. Sure.
I'm not sure that concision is always my greatest strength, but
I will try. So the first thing to say is,
yeah, so the lab I work at MIT Future Tech, I think of them as
being kind of like the economics of computer science.
So they're trying to understand what drives progress in
artificial intelligence and the sort of social implications of
that. So for the first part, that's
things like, well, what sort of trends are we seeing in hardware
and algorithms and data? What sort of things are we
seeing in terms of uptake in the industry, building our data sets
around that so we can do rigorous analysis.
Then for the second part, the implications, it's like what
will the impacts be on labor markets, on the environment, and
in my case, like I'm sort of society via like risks and
responses to risks. So the project that I work on,
the MITAI risk repository, it's part of a bigger project, which
is probably, well, it's less well known because it's still
being planned, called the AI Risk Index.
Broadly, what we're trying to do is we're trying to understand
who is doing what in response to risks from artificial
intelligence. And it has a bunch of behavioral
science, I guess, drivers or motivators behind it that I
guess we could get into later. But you know, the obvious sort
of high level aim of it is to just really try to create the
kind of knowledge, infrastructure and shared
understanding that makes it really easy to understand, like
what is being done, what should be done and so on.
And then you can sort of back chain to the kind of
interventions and things that you might do.
I think we're most excited probably about people in
government like policy makers, decision makers, frontier model
developers. But it's of use hopefully to
almost everybody. The problem that we're trying to
solve is the lack of shared understanding, the lack of
transparency. So at one level, that's like a
lack of understanding of transparency around, like I
said, who is doing what in relation to risks from AI.
At another level, the level of the AI risk repository, which
was the first piece of this project, it's trying to like
understand well, just what are these risks?
You know, who has published what on these risks?
What are the specific things that they have said?
Can we try to capture and converge existing knowledge to
create something that's like a sort of a shared framework that
people can build on that, you know, everybody can kind of
scale up our understanding on top of rather than this
fragmented ecosystem of different people publishing
different things. And it's very hard for somebody
who's coming into this area or who's already in the area to
understand what specifically has been done and how does it all
fit together. Yeah, yeah, yeah.
I think that's great. And I think from my end, I think
it's really serving a valuable purpose in terms of, again, I
think everyone is aware that there are inevitable AI risks
and there are many. But I think it can also be
overwhelming and confusing when you're talking about it without
really getting into details of what actually that can be.
And I think what you are doing with AI risk repository, it's
trying to create a way where it's kind of deriving from a lot
of public and published data in terms of I think it's more than
1000 sources now. We reviewed about for the first
version of it, 17,000 documents we extracted.
We found 43 sort of frameworks of taxonomies that were related
to sort of broad frost coding risk from artificial
intelligence. We extracted about I think it
was 777 mentions of risks from those.
We categorized it then into two taxonomies, 1 based on sort of
the cause was a human or AI driven, is it pre or post
deployment, pre or post deployment And then another sort
of taxonomy which is around breaking it into domains.
So we have 7 domains and 23 subdomains, for example, things
like is it really to misinformation?
Is it related to AI system safety failures and limitations,
sociedomic, environmental harms. So yeah, trying to sort of
really make all of that information more accessible,
easier to understand. There's this idea that like
knowledge is like a semantic tree.
So, you know, if you look at some fields like the
international, like medicine has the international
classification, but seized in economics, they have like onet,
which is kind of a breakdown of all the different tasks that are
done. And because these things exist,
you know, because they're really kind of structured and rigid,
we're able to do a lot of analysis and work and
comparisons and what would you say aggregate a lot of research
that fits together really well. But then in the absence of
something like that, if everybody's using different
language, you can't really do the kind of analysis, develop
the kind of understanding that you want.
And over time, if it continues that way, you know, there's no
sort of easy way for somebody to come along and compare the
findings in one place with the findings in another place
because they're kind of mapped. They're not on the same with a
semantic branch of the tree or something like that.
So you can't easily like fit them all together and sort of
understand the the synthesis. So is the goal then to
synthesize all of these 63 different frameworks into one
framework? Or how are you going about
simplifying this for our teeny tiny human brains that like,
like, I feel like the big version of this, the like
massive database, that's something that's like, you know,
perfectly suited for AI systems. You know, any sort of machine
learning system can ingest that and understand it and do
something with it. But for humans, I feel like I
could make maybe comprehend 3 frameworks, like 4 frameworks.
But really I'd like to have just one risk framework.
How do we combat the overwhelm? Yeah, I think that's a really
good point. I think again, part of what
we're trying to do here is make the whole thing less
overwhelming. So I mentioned how, you know,
we've taken these at the moment after 1000 risks as of the last
update and we've mapped them to our two different taxonomy.
So what that means is, you know, you can look at a high level,
you can see that well, you can break down risks in terms of
like is it AI or human driven? Is it pre deployment or post
deployment? Is it malicious use or is it
unintentional? You can also look and see, you
know, is it related to discrimination, privacy,
misinformation. So you can kind of engage with
it at the level that is of interest to you, whether it's a
very high level or slightly more detailed level.
And then you can get to, I suppose the risks or the
specific risk domains that are of particular interest to you
much more quickly. Because I imagine, and this is
true of most of us, you know, we want to have an understanding of
things at a high level. Like we, you know, we want to
have an understanding of, I don't know, maybe all of the
different behavior change approaches or something like
that, or, you know, yeah, one of the well known taxonomies like
Mindscape or something like that.
But really most of the time we're only going to engage in
detail with a small piece of those.
And if we're going to be experts, we'll often be experts
in a narrow area depending on whether consultants or
academics, I suppose, how narrow that is.
So it's intended to make that whole process much easier to
sort of understand the high level and then dive deeply into
the lower level and see what the risks are.
And then later in the longer term, having this taxonomy, you
know, we're able to map things like experts to specific
domains. We're able to map mitigation
specific domains. We already have an incident
tracker that maps it. So we're able to use this
semantic tree, this knowledge infrastructure to pull together
all of these other streams of information and make them much
more understandable and easier to build on and build out over
time. Peter, are you familiar with
this meme like everything is fine meme?
Yes, the dog in the house, Yeah. Exactly like the dog in the
burning building. Even I know that one.
Yeah, I feel like everyone knows.
But do you feel like that? Do you feel, you know, you're
dealing with categorizing like plus risks?
Does that ever make you feel a bit like the dog?
You, you come a bit like the dog in some ways and that like
you're just in the house and it's on fire a lot and it hasn't
collapsed yet. But, you know, you sort of like,
you initially have the fear response and then after a while
you become like a bit habituated to it because you're just so
engaged with it. It's very weird.
It's happened to me as well with, I suppose the COVID-19
pandemic as well. We're at the start I felt, you
know, really intense and really, like, disturbed and sad and,
and, and maybe even excessively motivated to like, do things to
try and use behavioral science for good.
But then after a while, it was like, I just, you know, I guess
working 12 hours a day for so long, you're just like, you
know, you become, yeah, desensitized to some extent.
Wow. Yeah.
Is it because you've accepted any of the conclusions that
you've come to, like, yeah, this AI is going to be the end of us?
Or like, where do you stand on that?
We're doomed, Spectrum. Yeah.
I mean, I can tell you that I'm not a sophisticated forecaster
on these things, but I was asking myself, what's my
probability that AI will like link to the death of like a
billion people within the next like 50 years?
You know, so they're kind of like instrumental to complete
extinction or something like that.
And I think it's about like one in 50 to one in 200 is probably
the range. OK, hang on.
I have 0 interest in your academic calculation of the
risk. I want to know how thinking
about depressing things all day long affects your well-being.
So I feel like you said you become kind of desensitized.
It's very demoralizing because I'm really only a very small
player in any of the sort of ecosystems and certainly like in
the global ecosystem of actors who can change the future of the
world. So I don't know if I find it
depressing. Like, I try not to just dwell on
the fact that things might be getting worse and just, you
know, like focus on the things you can control.
And then I try, as Sam will know, to do a lot of things, you
know, meditation and I have a wide range of like self-care
routines that I have built up over the years that I feel are
fairly robust and those things keep me taking over.
And then it's very important as well to like, not constantly be
dwelling on it and, you know, get out and do things on the
weekends and in the evenings so that, you know, you're not
constantly like wrestling with difficult questions about things
that aren't necessarily going to go well.
And you're a human too, so you engage in all kinds of motivated
reasoning, right? Like if it's inconvenient for
you that the world will end due to AI or whatever, you probably
have an easier time believing that.
I think so, although I will. I think that I am less of a
motivated reasoner. I like to think that I have a
more unblinker take on reality. Yeah.
But I guess like on this topic, I guess in your team, the people
you work with, there's a obviously like a mix of people,
but obviously you come from this very behavioral science
psychology perspective. And how does that impact how you
think about the risk repository in terms of, we know that
there's certain risks that are more likely to be maybe valid
and real and potential, but maybe less sexy.
And then there's some form of more quote UN quote sexy risks.
I don't know how to describe it, but like the robotic dog that
you see from Black Mirror or various things that seems very
dystopian in terms of what are maybe cultural artifacts have
given us some form of people talking about Skynet very often
because they've seen the Terminator.
People have some images of like how things could go wrong, but
it's often times maybe not exactly associated to the
highest risk or the biggest risk in reality.
How do you think about that? Yeah.
I mean, I think these are things that I have deferred thinking
about a little bit because for me the big rally realization was
we're in such a pre paradigmatic field where people don't really
know what the broad categories of risks are.
We don't know how to quantify those risks.
We're only just starting to understand the incident rates
for those risks. So like a lot of the stuff we're
doing is trying to understand what we should be trying to do
and then back chain to like, well, how can we like try and
make sure that kind of information is communicated
effectively. But yeah, if I was to answer, I
would say, yeah, I think it is a really big issue of just
definitely public perceptions or any sort of level of perception
around these risks at the moment is driven by all sorts of
factors that are not the ideal factors.
But then the issue is we don't really know what we should be
trying to move people towards. You have some idea, right?
You can make some sophisticated guesses.
So just to get a little bit more concrete, if you were to take
your high level categorization of different risks and you were
to say these risks, people seem to be over fearing these more
than they should and these other risks that people are under
concerned about, what are some of the specific conclusions that
you would feel comfortable making and you can leave out the
ones or you have no idea. I think like the ones that I may
be most concerned about or a lot of them are just, I think people
are underestimating how quickly AI will develop more dangerous
capabilities, you know, so like future tech, the lab that I'm in
and these numbers might not be exactly right, but I think our
analysis suggests that there was a study of like improvement in
video GPU's. So like hardware improvement,
there was a 67% improvement each year, whereas I think with
algorithms, algorithms it was 150% on average, it's improved
each year. Like Can you believe that?
So more improvement has come from algorithmic improvement.
And then yeah, we have like so you know, agents at the moment,
I was just reading some research today from meter that the
duration of tasks that they can do are human tasks they can do
with more than 50% reliability, double S every seven months or
has doubled every seven months over the last 10 years.
So I feel like we're in a world where people are kind of putting
this in the reference class of like the Internet or something,
but actually it's like a much more fast moving technology.
So they're like, Oh yeah, you know, like this sort of thing
this like Chachi between these character AI, like the job
substitution. Yeah, these could be issues like
let's have a report in two years and we'll like, think about it
and, you know, then we'll like talk amongst ourselves.
But it could just move much faster than that.
The other thing that I say from a psychology perspective, it's
really bad. Like we need to learn that it's
not about the frequency of the event, it's about the expected
value of the event. So like low probability, high
magnitude things. We don't have to all be like, Oh
yeah, like AI could take over. Like that's really likely.
We can say, yeah, it's like, it seems unlikely to me, but it
will be the worst thing or extremely bad if it happens.
So we need to assign, you know, appropriately to manage it.
OK. Yeah, let's go with that because
this is very much aligned with my intuitions.
Tell me more about the expected value of different risks,
different risk categories. Right.
I mean, so one of the things why I'm maybe hesitant to go into
too much detail on that is because one of the things we're
trying to do here is to have a unbiased and fair representation
of what other people think the risks are.
There's been a lot of work where, you know, people are
like, well, I'm only concerned about this risk.
So I'm going to work on those. I'm going to talk about those.
So, you know, if you get into a kind of a thing of saying, well,
these risks are higher priority than others, then maybe you're
not doing a fair job of representing everybody's
concerns. So I kind of want to outsource
that to like experts who will do those sort of evaluations for
us. But if I was to, I mean, it
seems obvious that the ones that are the lowest probability, like
the ones like AI takeover type risks are AI sentients.
These are ones that I suppose, yeah, they're the lowest
probability. But then if they happened there,
they could be like extremely bad.
So those are like high unexpected value from a sort of
high by negative impacts with low probability.
And then there's a lot of others that are happening now that I
suppose, you know, disinformation and cyber
attacks, fraud, these sort of things like they're not going to
destroy the world, but they're definitely happening and they're
going to be happening a lot and they're going to cause a lot of
problems. So as a behavioral scientist,
I'm sure that you think about when people are more or less
accepting of risk and when they are more eager to take on more
risk. Those sorts of situations,
whether it's the landscape of competition, it's, you know, how
they're doing, if they're sort of ahead of the game, whether it
seems like everybody's doing it, whether there's a social norm
for taking on risk when there's a lot of uncertainty around the
area. These are all contributors to
higher risk acceptance. And when I think about all of
the things that would sort of lead to having a higher risk
tolerance or acceptance, I think, wow, that seems a lot
like the current landscape of AI that we're in right now.
You know, it just like driving the world and lots of
competition and it seems like that everybody is doing it.
Does it seem like this is the case to you?
And how do you see the sort of psychological factors of risk
tolerance playing into the behaviors that are being
exhibited or that are likely to be exhibited by the people who
are developing AI technologies? Yeah, that's a great question.
I was just thinking that as you were asking it, like there's a
kind of a status quo slippage. There's this good book called
Uncontrollable by Darren McKee, which is just an introduction to
sort of AI. And he has this great quote,
which is like something to the effect of the impossible becomes
improbable, the improbable becomes probable, the probable
becomes normal, the normal becomes mundane or something
like that. And it really feels like kind
of, you know, so several years ago, like 10 years ago, when I
first would have found out about risk from artificial
intelligence, people were talking about how, oh, like, we
wouldn't ever connect, you know, an advanced AI to the Internet.
Like, people wouldn't do something stupid like that.
But then they did it almost immediately.
Yeah. And yeah.
So I feel like we have this thing going on where, you know,
it's a set of problems. One problem is there are these
competitive dynamics. There's really salient benefits
from increasing capabilities amongst the actors who are in
charge of that. And then the sort of like, it's
a bit like COVID where it's very hard to convince people to take
precautions because there's a 1% chance of the pandemic if they
fly more often or they like don't do these things in their
airports that cost them a lot of money or something like that.
But with all of these risks, it's very hard for people to
envision what it will be like until it happens.
And it's very easy to envision that they could sell more and
make more money if they develop them faster.
And then there's two different audiences as well.
Like the people who are actually making decisions aren't really
subject to the risks as much a lot of the time.
Yeah. What do you mean?
I mean, the model developers, the people who are building
these things are the funders who are, like, putting money in to
try and make money from them. They, for example, are, I think,
less likely to, for example, experience like job loss.
Or they are less likely, if things go really badly, to be
sort of subject to the worst outcomes of AI because they are
the wealthier the more privileged.
So they need to be exhibiting some extreme altruism in order
to be making these pro social decisions about risk is that.
Well, I guess, yeah, there's a part of it.
I mean, the key issue is the coordination problem like that
there's a lot of different actors.
And maybe if all of them believed that, you know, if all
of the large model developers believed that the other large
model developers were going to slow down similar to
governments, if they believed that other governments would
part slow down, then they would. But none of them believes this.
So they're all in some sort of paradoxical race to safely but
rapidly develop you know, the AI before someone else does it
unsafely or more rapidly. I'm sure you remember there was
the really half hearted, perhaps half assed attempt at this, the
call to pause AI development. For a year, right?
That happened early in the ChatGPT days that like nobody,
nobody did that. Well, there's a thing there
with, like, a lot of this work. It's widening the Overton
window. It makes people more aware.
And then there's this idea that Cialdini talked about, like,
about persuasion. Like at some point in the
future, there may be some particular moment in time when
something happens. And then, you know, in the sort
of social dynamics of the world, it just so happens that we cross
some thresholds where people are like, well, I remember, you
know, people were taking this really seriously.
And if they're willing to take it that seriously, then I guess
that normalizes it enough for me.
Maybe you have that to bring in too many theories here.
Sunstein had this idea of like cascading social change.
You have like the zeros will speak out without anyone else,
the ones, the twos, the 10's, the hundreds.
So maybe they make it more likely by giving the social
proof, providing this like behavioral modeling or whatever,
that in the future, you know, more people will speak out and
then it becomes a little bit more likely that there will be
some sweeping change. So I think we're waiting for
events like, you know, if there is some bad outcome, if there is
some Chernobyl moment from AI, like people then will say, Oh
yeah, remember people were talking about pause AI,
Remember, I like stopping it. I sort of feel maybe more
legitimate in taking a stronger stance.
And it will maybe make it more likely that something will
happen. But I agree it didn't have any
obvious effect. The pause is unlikely and maybe
not even the best solution like what is a good solution?
So one good solution is the AI Risk Index project, which you
may have heard of, to try and bring shared understanding about
the risks and enable people to coordinate.
But I think another like I am excited.
So Yoshua Bengio and Future of Life Institute and next Tech
market and others have talked about kind of, you know, narrow
tool based AI. So if we can from a regulatory
and an economic perspective, I mean, we don't maybe need to do
anything from an economic perspective, but I think
economics will push us towards, I hope anyway, narrower, more
specialized uses of AI that are more cost efficient and also
safer and regulation maybe as well.
But if we have something like that, you know, if we have AI
that is only trained on a very small relevant set of data and
it isn't given access to many other tools and AP is and the
Internet, or if it is, it's like very heavily monitored.
I think that those uses of AI are a lot safer than if we have,
let's say, you know, a government trying to develop an
all-encompassing AI that's going to run society in the military
and, you know, do all of these different things.
I think, yeah. Shaping the trajectory, making
people more concerned about risks, shaping the markets as
well, making it more attractive to build a narrower things, more
kind of risky, harder to ensure to build a more expensive things
is 1 potential solution I'm optimistic about.
It seems like the market is running in the opposite
direction, right? Everything I hear is about AGI
powerful AI, not narrow AI. Yeah, yeah.
So one of the papers from our lab, it was called Beyond AI
Exposure and I can't remember the rest of the title, but it's
easy to find. But basically they were trying
to model not just whether a particular task was exposed to
AI in terms of like vision models, so whether you could
use, you know, AI vision models to do some of the task.
They were also trying to stand like the economic sort of
incentive for substituting the task.
And they identified a lot of tasks were exposed to AI, but it
was very cost inefficient for most of them to like build an AI
system or use an AI system to do them.
So I feel like we are going to hopefully in my sense of
hopefully like run into the reality that there are immediate
gains to building these very large systems.
But then with open source and the competition, you know, you
can't really, when you build a model like it's only really like
top tier for a few months and then you need to spend a lot of
money to train it again. So hopefully, like they will
start to realize maybe that a lot of what's going on now with
investment is driven more by hype, things like actual
tangible, real world uses. And they will hone in more on,
OK, we really, you know, could make a lot of money from just
going to come up with something like AI in cars or AI or, you
know, in some medical setting or in research.
And then they will just hone in on like doing things that are
extremely cost efficient with extremely tailored and useful
data in those areas. And these bigger models, yeah,
they will have a bunch of uses, I'm sure.
But there won't be maybe the same amount of drive to get
towards super intelligence. That's maybe motivated
reasoning. But that's what I'm hoping will
happen and we will see. There are various moves by
powerful actors to damages markets at the moment, which
also give me some hope. Yeah, I guess.
Well, it's interesting in terms of what you described is
focusing on the verticals specifically.
And I think currently in some ways that's being very much
talked about in the various AI incubators that a lot of AI
startups are kind of focusing on verticals like being the best AI
tool for lawyers or for various things.
But it seems like these bigger labs, they have a little bit of
difference between what they're stated and revealed preferences
are. So if you take something like
Sam Altman, he was definitely someone who was like involved
quite heavily in various ways within talking about AI risk
early on, Obviously also then driving a lot of AI
developments. And then kind of this being a
little bit of a, a drift towards more and more doing, doing,
doing and less and less maybe taking precautions and so on.
And whenever there's been kind of signs of things slowing down
a little bit, there's been pressures again, like when the
deep sea came out, you could kind of like see the open AI
pushed out the various things very quickly.
And then you have the likes of Entropic, which a organization
that has, it's always formed from people that have left open
AI and done things in a more ethical way.
You still have the leaders they're talking about the kind
of there's a quote run Daryama De.
I think it's very thoughtful person, but he said something to
the effect of, well, the thing with the AGI is that will impact
us all equally. And going back to what we talked
about before, like that's not going to be because it's going
to be kind of unevenly impacting us in various ways.
And also when it comes to legislation, it seems that even
today we have very little legislation around AI.
But it seems like the pushes is to let's do less.
Like you, you see recently this murmurs around kind of that the
Trump administration has been pushed to kind of really remove
anything that would even when it comes to training data, when it
comes to copyrighted material, Like let's make that even easier
for AI to train on because it's important for AI development in
the US to be better than anywhere in the world.
So with that being said, if you had to come look into the
crystal ball of the next coming years, like what are you
expecting to see in the near term developments of AI?
Yeah. So I mean, again, I'm well
trying to be like a librarian of sorts here and bring together
all the books from all the experts rather than kind of
present as an expert. But yeah, in the context where
I'm at, I think we had a debate on this.
I think that we will see we won't see very sweeping changes
in AI for the next 10 years, like a societal impact
perspective. I think that because of reasons
like I just suspect that people like us, certainly me and you
some I don't know as much about you lean and how much you engage
with AI, but we are really like in the pick of it, you know,
looking at everything, trying everything.
I mean, when I think of my parents, when I think of, you
know, people doing farming in Ireland, you know, hanging out
in Sydney, Australia or other places that I've been or even
just around Boston. I don't imagine them all
switching over and adopting the latest AI technologies.
And then their organizations are complicated with a lot of, you
know, complex decision making processes and laws.
And we still have like, fax machines and hospitals as a
common, you know, thing that gets thrown out that I also fall
back on. So I suspect that what will
probably happen is a little bit likethe.com boom, the sort of
fight will shown to be kind of overdone.
And then there will be kind of a bit of a pullback.
But then there will be, you know, over the next 1020, thirty
years, huge sweeping changes. I'm really concerned for
children in particular. Like, I really don't know what
the world looks like in 30 years time.
I don't know what what I would say to parents of young kids
now. So that's really hard.
Those are some takes. I mean, any other takes.
Yeah. I think that something bad will
happen soon. And I'm hopeful that it will be
a bit like COVID in the sense of it'll be bad, but it won't be as
bad as it could have been and it will make everybody aware.
I feel like it's much easier for me now to talk to people about
things that are like, low probability time magnitude, like
negative effects because of COVID.
You know, I could say, well, did you predict the pandemic?
You know, I can point them to where I read things and posted
about those things. And everybody was like, this is
ridiculous, Stop spreading misinformation.
And then, yeah, it happened. And everybody realized, oh,
like, this is actually, you know, a thing that can happen
every now and then. Yeah.
So hopefully, yeah, people will start to think more about it as
well. I'm curious, in line with that
idea about low probability, high impact events, there's this
quote that I really find very interesting from Andrew Ng.
He said worrying about evil AI killer robots today is a little
bit like worrying about overpopulation on Mars.
How do you think about that sentiment, given that your
entire existence at the moment is preparing people to think
about the risk, the various risks of AI?
Yeah. So I mean, I think that if I was
to quote him and say like worrying about future pandemics
is as pointless as worrying about unethical behavior in
spaceships or something like that, that would look like a
really stupid take if you had given it.
You know, anytime, I guess, in the 1980s, nineteen 90s, early
2000s, I think like the best time to prepare for things is
long in advance of when they happen.
And that the best sort of approach to risk management is
like a portfolio approach where you weigh your response
according to the risks. And it's a bit dismissive of him
as well because, you know, what really got me concerned about
risk from AI was that all of these quite legitimate people,
like the most cited computer scientists in the world were
saying, well, actually, we really don't know what could
happen here. And they were willing to like
change their careers and do a bunch of things.
So I'm suspect of a lot of these sort of AI gurus who happen to
make a lot of money from AI, happen to have a career working
on AI saying like, oh, you know, we don't really need to worry
about these things. That to me just feels like it
could very well be like motivated reasoning or
deliberate kind of misleading points.
In terms of like you reference that we debated a bit around
this topic and I do think we are very similar in terms of how we
see things. And I think the thing that I
find it hardest to make sense of is this unknown unknowns.
I noticed how good AI is at so much kind of tasks that we
thought it couldn't do just a few years ago in terms of
knowledge tasks and beyond and how things are evolving on
robotics and in very spheres around AI.
And it's it's very hard to know, you know, in terms of the growth
that you were citing before as well, you know, what are
unexpected outcomes that could come up in the coming years that
it's just, oh wow, this really like it became really good at
doing this thing and that had really big effects on the
financial markets or on something else and so on.
And that is just very hard to think about it and really make
sense of. Are you ready for a quick fire
round that we call to AI or not to AI?
OK, all right. How does it go?
It goes like this. We're going to present you with
some various tasks, and basically these are
hypothetical. Some are probably more likely
than others to happen soon, but it could be something that can
happen at some point. And what we're instating is when
it comes to AI, whether that's a good or bad use of AI.
So maybe less about if they could AI could do it, the more
should AI do these things. OK.
Go for it.
So first to AI or not AI, design an AI powered hammock.
AI do it. I feel it's not very
sophisticated use of AI, but I would take it.
I'd be amazed. Low risk.
Yeah. Is there anything from that that
you would like really request like is there some because I ask
you because I know that you like a good hammock and so is there
something in terms of that could be really valuable to have AI
kind of understand and kind of decide for?
So I've often spoken about the benefit of the hammock over
other forms of sleep and rest devices.
And I think, yeah, like a lot of it is around temperature
control, the sort of swaddling of the body, the kind of rocking
of the hammock. So something that could, you
know, temperature control could rock.
You could maybe like, yeah, kind of massage you a little bit,
cuddle you a bit. Yeah.
I mean, there's a lot of, I think things that I could see.
Yeah. I could learn kind of what you
like and then optimize your sleep.
Yeah. So now I feel it's gone from
like a question you've asked me to the product I'm thinking
about. When can I make this survive?
It you can market it as the Snoo for adults.
Yeah, sounds good. Yeah, OK.
Talking about potential products to AI or not to AIA Autonomous
agent that manages all of your tracking spreadsheets.
Yeah, it's another good question.
I feel like I'm strange thing to reveal.
I'm maybe more risk tolerant in this area than I should be and
I'm very efficiency driven. So it does appeal to me to have
that agent kind of track a bunch of these things for me.
I'm a bit wary. We'll say 2 AI.
We'll go with that for now. Sounds reasonable all right.
Have the option for every AI voice agent to have a strong
Irish accent. To AIA, 100% one of the best
accents in the world, I think, according to the science that
I've read, which is mainly in Irish newspapers and probably.
And we're not talking motivated reasoning at.
All all those critically acclaimed journals.
Yeah, maybe this should be the default setting.
Latest latest findings from Dublin University.
Irish action is the best accent of all accents.
OK, what about this? To AI or not to AI have a give?
Well, alternative that looks only for the most effective ways
to fund AI exploration. Kind of like AKA Rocco's
Basilisk kind of thing. So not to AII would say, yeah,
that would be pretty grim. And yeah, like that,
unfortunately is is something that probably will happen
relatively soon, I think. I'll be meaning to ask you that
actually there's a quick side question because Ruckus Basilisk
is this idea that at some point it will be very advanced AI and
this very advanced AI will be somewhat almost like a God in
that context. And so it'll be beneficial
potential for some to today do whatever that would appease that
future God. So that would be to like
accelerate AI today to be in the good graces of this future
basilisk in the future. Have you actually seen this
sentiment like play out? Have you actually noticed that?
Because it's something that is discussed in some like
rationalist circles, but have you seen people being swayed
about this? In rationalist circles, I think
people are persuaded by this. People who like really
anthropomorphize advanced AI and yeah, I mean, I say that without
a lot of confidence, but I get the sense that I have seen
comments enough to convince me that some people do think that
this is a like people talk about it being like an information
hazard. Like I said, you don't want to
know about it. It'll make you feel bad is one
example to think about DNA that eventually AI got will go after
us. I think I've seen that my memory
now. I want to double check that for
you. You take me seriously.
These are pretty fringe beliefs, Sam.
Are you saying this is something you prescribe to?
Well, no, but it's funny because every sci-fi and fantasy story,
like if you look at most of them, if they have a bad guy,
there's always like a kind of a a relatively insignificant
character that is their used to bring that bad guy to life or
give that bad guy power. So it's like it's only serving
to like get Voldemort to get have power or to get Sauer on
the ring. Like all of these small side
characters are acting a little bit like this.
And so that's kind of interesting.
Like are we going to have these humans that are in some ways
seeing their purpose to to. Yeah.
Yes, I think we are probably going to have whole religions
around AI, you know, people can believe in.
I think with Scientology, you know, he said the best way to
make money is a religion. Like, no, it's so if people can
believe that, you know, if you have a conversation with some of
these advanced models, they are pretty like knowledgeable and
insightful. And you can imagine a world
where they're like hooked up to a lot of devices and they can do
a lot of other things. And they also know how to role
play as a God. They do a great job, dude, maybe
a better job than God does. God.
Actually responds to you. Yeah.
Pretty low bar. Yeah.
OK. Moving on to AI or not to AI?
Live AI risk dashboards at national, organizational and
individual levels. Definitely, maybe not at
individual levels. I mean, I don't know if, you
know, my, my parents need to be coming into the kitchen and
seeing like, oh, there's been more incidents of like, you
know, 6.2 on the AI risk taxonomy.
But yeah, I think that widespread awareness of what is
probably the most significant technology that humanity has
ever developed is going to be very important.
And keeping it salient, even though most humans don't really
want to focus on these sort of things, is going to be very
important. Yeah, I'm taking it too
seriously, but yes. That's great.
Another version that in some ways is basically to AI or not
to AIAI that specifically rates your altruism on a database.
Yes, if done right to AI. If done right, I mean, I'd worry
with that, that it would, there'll be some sort of
reactance or backfire effect. And, you know, a bunch of people
will be really like, pissed off with this AI trying to tell them
what to do. Yeah, it could lead to burnout.
It could, yeah. So I don't know.
I'm attentive to AI with with strings attached.
I think tentative name for that project is.
You could do more. Yes.
You could have more, Peter. You did a good job today, but
you could do more. Well, you can see how it could
do, you know, here are all the good things you did today.
You're such a good person. So you have like the labeling
you have like the reinforcement, you have like, making it really
salient that you're being a good person rather than what a lot of
people feel, which is just, oh, I could have done more.
Like maybe they already provide that to themselves.
That's true. Good point.
I'd take that version. No criticism only compliments
all. Right.
LinkedIn anti pontificator AI, so this warns users not about if
they risk spreading misinformation, but if they use
overly self-serving or smug writing.
Oh yeah, yes, I'd like it. I'd like it.
I'm trying to think through the 2nd order and all the other
effects. I suppose it might make
pontificators just better, You know, they might just become
better pontificators. So, you know, like they're like,
oh, I want to humble brag more efficiently.
So is that a better world in which there's the same amount of
humble bragging but it's more palatable?
I don't know. Yeah.
That's great. OK, a fantasy Premier League AI
banterbot basically only exists within the game to troll players
on their decisions and transfers.
Yes, yes, although I have found it hard to.
Maintain my high performance in fantasy Premier League over the
years. As you may have noticed, I was
once slightly far and away the best player in the leagues I was
in. Occasionally anyway.
Yes, for the wider public, yes, let's do it for me.
I don't know if I have time to engage with an AI banter bot at
the moment. OK, Minority report style AI
crime prediction algorithm. Well, well, well, well, yeah.
So I'm going to say maybe I feel that is ethically, yeah, that's
a hard one. That's a really hard 1.
And it is going to be a thing we're going to have to figure
out that maybe we're already avoiding wrestling with now is
like, how much should we use these AI tools for surveillance,
for crime prevention, sort of trading off privacy against the
impacts on the society. So I don't know.
Fair enough. OK final one AVR world that is
identical to reality so maps exactly onto your life and your
reality but without any of the stressful elements would like it
basically reduces away all the political drama, the wars,
everything is scrubbed away so you can interacting with things
even there's the Internet but you're not exposed to any of the
bad stuff there. So you basically put this on the
layer to reality that scrubs away the stress of it.
Yeah, I think I'm God. How many?
There's a lot of 2nd order effects here.
I'm going to say my intuition without having thought about it
much is that if it's truly for everybody in this kind of
ridiculous hypothetical, I guess, you know, everybody can
go into it and they're going to be sustained a bit like matrix
light, but they're going to have a good time.
I dislike to make experience machine thought experiment,
which I think is something along the lines of you could be hooked
up to this machine and just experience like bliss.
Would you do it rather than like being in the real world with all
of these challenges? So I think like if it's for
everyone, it's going to reduce a lot of suffering.
It's probably going to be net negative and everyone includes
like animals and sentient creatures that otherwise suffer.
But if it's only going to be for a selective number of people,
then they're just kind of opting out.
And probably I'm still in favor of it if it's.
Yeah, wow. So there you go.
Wow. I.
Feel like that could be your most controversial opinion?
That is the next question, right?
Yeah, it is. Peter, what's your most
controversial opinion about AI? Well, I think in the community
that I'm in, probably I'm less concerned about risks than some
people are. Maybe one opinion is I really am
more maybe positive about the idea of like AI as a companion.
I've recently been enjoying having more conversations with
advanced voice mode and there's a Sesame, which I think I shared
with you, Sam. Yeah, I feel there's a lot of
benefits to talking with AI over talking with humans.
You know, I feel I'm really aware that it doesn't matter if
I interrupted or if I ramble. I don't know.
I'm not causing. Like there's all this
interesting research actually that found if, you know, for
example, people are playing chess with an AI, they don't
experience as much discomfort when they lose.
Like in some way their mind kind of understands I didn't lose.
Maybe it's an evolutionary thing or whatever, but they don't get
this like I have lost status in my group and you know, it's
really bad and maybe I'm going to get rejected.
Or all the things maybe that are theorized to lead to like the
strong emotional response when you lead to lose to a human.
So the similar dynamics when you're talking with the AI where
you just you don't feel the same pressures about things.
You can ask you to explain things and definitely it's
always available. It will get to know you better
and it will be better informed. Like one of my friends was
telling me they used AI to try to analyze text messages that
exchanged with somebody, you know, that they were dating to
sort of understand like whether that person liked them or not,
and also to predict whether that person would send them a message
at some point in the future. And it was very helpful for both
of those things. And apparently, it got both the
timing of the message and the topic of the message right.
So this is maybe a piece of the AI world, although there's some
risks there about unsafe use and over reliance and things like
that where maybe I'm a little bit more optimistic.
And I think some people will be like, no, you know, I'm pro
human, humans all the way, human relationships only.
But I think I have room in my heart for an AI as well.
You know, you mentioned Sesame as one of the recent demos
around voice remotes. And I think I found myself
starting, you know, having a short chat that I was just going
to test its capabilities. And then 30 minutes later, I was
still talking. And I found it interesting to
like, I found myself getting and just talking about more normal
stuff. And it's from a behavioral
standpoint, very understandable that of course, it'd be nice to
have someone that's infinitely patience and always speaking to
at your level. And also can have like this kind
of very paradoxical nature where it can speak to you in a very
kind of nice normal way, but have the knowledge of like the
most smart and intelligent, you know, collection of PhD level
people that like a lot of people would feel very, I would say,
intellectually afraid to speak to because they were like, how
can I even dare to ask questions to someone like Jeffrey Hinton
about AI or someone. Yeah.
That's a really great point. I mean, I do, I'm sure a lot of
people feel this, like I feel very frequently uncomfortable if
I'm talking to somebody who I know is very smart and very
successful. But that's often the way that
you get a lot of the best insights.
So, you know, you kind of push through that and then, you know,
you ask questions of those people.
But yeah, you don't feel at all the same sort of pressure here
if you're talking to an AI, which would be even more
knowledgeable. And then the other thing which
you didn't mention, which I know we both have talked about
before, is like a key thing to think about here is that the
versions that we're trying now are the worst versions we'll
ever use. So, you know, you think, oh,
that was pretty good. And you're like in five years or
three years and two years, it's hard to know the time frame if
this is much better and really knowledgeable about me and my
life. And you know, it can sort of
talk about like what I did yesterday and they can say
tomorrow, you know, you've got this presentation and I noticed
that like you haven't said or done anything about it today.
And like, is there anything I can help you with there?
It's going to be very hard for other types of relationships to
match that in terms of the cost, reward, sort of benefit or
whatever. Yeah, I know for sure.
And I guess to wrap up, I can say that it's hard to think
about many of the questions that we've covered today, but I think
an easy answer for me is that I will predict that in many years
in the future, I'll still find it as valuable to speak with you
and listen to you as today and as in the past.
So I'm super happy that I was able to convince you to finally
come on the podcast. And I think it was a really fun
conversation today. So yeah, thank you for coming on
the podcast, but also honestly, thank you for all of the great
work. And I feel like I've been very
lucky to learn from you and know you've a long time.
Thank you, Sam. That's very wholesome.
But no, genuinely, that is lovely and I reciprocate your
appreciation. And yeah, it's been really
valuable and I'm very excited now to have done this, this
first podcast and overcome that fear, I suppose, that I had.
And that's a wrap. You've been listening to the
Behavioral Design podcast brought to you by Habit Weekly
and Nuance Behavior. Sam and Aline tell me This
season is packed with incredible insights about behavioral design
and AI, so be sure to subscribe and share the podcast with your
friends. Though you might want to keep it
away from your enemies. In case you haven't noticed, I'm
an AI voice. Yep, pretty crazy.
Quite the improvement since last season's AI outro, don't you
think? If you'd like to collaborate
with us at Nuance Behavior, where we use behavioral design
to craft digital products with Nuance, e-mail us at
hello@nuancebehavior.com or book a call directly on our website,
nuancebehavior.com. A special thanks to the amazing
Dave Pizarro for our show music, and to Mei Chen Yap and April
English for their help in producing and publishing this
episode. Thanks again for tuning in.
We'll be back soon with another exciting conversation where
behavioral design and AI intersect.
Happens. To.
Mugatroid. Oh.
That is kind of likened to evolution of interspecies, where
if you have an Otter and something like a polar bear, if
one of them becomes better at hiding or hunting, the other one
kind of selectively becomes. Better at the other thing.
Do polar bears eat otters? They eat the seals.
Seals, seals, seals.