Hi, this is Samuel and welcome to a special episode of the
Behavioral Design Podcast. This episode is part of a new
miniseries that will be dropped in throughout the season where
we get the chance to ask our colleagues at Neon's Behavior
for their unique perspectives. And as you know, Neon's is
filled with some amazing leading experts and senior behavioral
practitioners, so it's always exciting to hear from their
perspectives and hear their insights.
And our first guest is someone I'm really, really thrilled to
talk to, which is Hassan Alim, an incredibly respected
behavioral practitioner with a PhD in neuroscience.
And Hassan's experience spans years of research in
neuroaesthetics, and it's also worked across diverse industries
like fintech, health, wearables, and public health.
Hassan's deep expertise in applied bioscience combined with
his rich tech background, I think brings a really unique and
valuable perspective to the table.
And this was honestly one of my favorite conversations we've had
throughout the podcast. You can expect us to dive into
some quite fascinating topics, including Hassan's thoughts on
AI and its impact on the field as a whole, but specifically how
AI can be used in user research and having made a chat bot
himself with AI, what makes a good AI chat bot?
And going deeper, what is the role of synthetic users and what
is a is potential to do literature reviews, As you can
hear much fun stuff. And finally, we tackle a thought
revoking question, but one that I would argue Hassan is probably
best potential in the world to to answer, which is can I
understand and a sign for beauty in the world.
So get ready for a thoughtful and I would say inspiring
conversation with Hassan Alim. Let's jump in.
Happens to Mugatroid. And I have to say, welcome us on
to the Be Able to Sign podcast. Yeah, thank you.
I'm excited to be making my debut.
Yes, very exciting. And yeah, been looking forward
to our conversation and I'm sure we're going to cover quite a bit
of things. And I guess it's it builds upon
the conversation that we recently had with Amy Bucher on
the podcast. And I was hoping to get into one
of the topics right away. I brought up this idea of a lot
of people in the behavioral science space right now are
probably looking at AI and feel like, wow, that's interesting,
that's cool. But kind of what is my role in
it? This is there a place, is there
a role for behavioral science or behavioral scientists in the
world increasingly with AI at the front and center?
And so Amy made kind of a case for like, yes, it is very
important. Actually, it's even more
important in some ways. And I kind of wanted to hear
your thoughts, maybe jumping straight in with a hard
question. What do you think about that?
What is What do you think about the role of Bible science in the
world of AI today? So I would agree with Amy and
and you and Aline that there is definitely a role to be played
in an essential role. The work that I've done has
shown me is that like the technology's amazing and it can
do a lot of the things that we might consider as behavioral
design, but actually fall mostly under like what usability and
heuristics about, you know, like make it simple, make it
intuitive, etcetera, all those things.
But the broader behavioral design around motivation and
engagement and barriers and all those things that does not fall
under the type of capabilities that most of these tools have.
And like what they're doing well, at least at this point,
the thing that they AI does better than anything is make
things super, super easy. You know, so that is obviously a
behavioral science design oriented thing that we want to
make things as easy as possible, but it's not just a lot of ease.
So I do feel like we're behavioral design and behavioral
science has a lot to offer is of course, as Amy pointed out in
like the domain expertise so. Yeah, yeah.
And I guess I want to unpack what you said there because for
you, what do you mean when you say kind of that AI makes it
easy or like they it's good at at that side?
Yeah, I mean, so like an example is with ChatGPT and it's whisper
mode and you know, it just makes it super easy to ask questions.
It makes it conversational, which is we haven't had that
ability before, right. So the ability to communicate in
natural language, I think that removes a lot of barriers.
So it's, it's a little bit, I do think there's a lot of great
things happening where like behavioral sciences, fundamental
component, behavioral sense is built in into making things
easier. But at the same time, like a, a,
a tweet that I remember I shared with you that I saw it was about
like when whisper mode first came out.
And you know, whisper mode is this paid plan on, on ChatGPT
where it's just very intuitive. For example, I use it with my
daughter and when I say, Hey, you're talking to my daughter,
it changes its tone and talks in like a kind tone.
So it's like a really, really well done.
And so there's this tweet that was like showing the stock price
of Duolingo and saying like, oh, Duolingo is cooked.
Like it's over. People are just going to be able
to talk to Chachi BT and just like learn languages.
And I thought that was funny because it's like, yes, of
course, like this is fantastic and I use it all the time.
But learning a language is probably 1 of like the most
common behavioral goals. You could say like we have as as
humans, like everybody wants to learn another language.
And if it was that easy, you know, I, I think we would all be
multilingual people, but clearly it's not an easy thing.
And looking at Duolingo, like probably the most gamified, most
like behavioral design oriented company there is in terms of
apps out there. And that's for a reason, right?
Because it is hard and people need that motivation.
So like, yes, it's super easy to just pull up my phone and start
trying to talk in Spanish or whatever language I'm interested
in learning. But like, there needs to be that
structure. Yeah, no, I think that's, that's
true. I think it's a great example.
I actually, I got my mom set up for advanced voice mode with GT
for that reason, because it's been interesting to to see her
journey with Duolingo. And then with advanced voice
mode, I kind of wanted to set it up for her to see, you know,
also for me as well to like as a mini user experiment to test,
test and see what my mom would do with it a little bit, maybe
as you would with your daughter and see how that interacts.
And it is interesting. But I do think to your point, it
is incredible what is capable of, but it's still missing that
kind of layer that supports long term consistency and engagement
and supported learning in terms of a, some form of a learning
plan and some form of a way to feel.
Also, I think while the voice mode is generally, I think set
up to be relatively rewarding in how it interacts, like it's
always encouraging and positive. And even if you spoke Spanish in
the worst way possible, it will probably say something like, Oh,
you're doing so well, good job, keep at it.
And so it is rewarding in that sense.
But I do think it, as we, as you mentioned with Duolingo, that
Duolingo has done a good job of, of creating a, a kind of from
start to finish, like just various ways to keep both short
term and long term and motivation and rewards in place.
So even though the advanced voice one maybe delivers some
form of like in the moment higher utility or some of higher
value, it's kind of missing the the full delivery of long term
behavior change in the same way as as the lingua does.
Yeah, exactly. I, I like the way you put it in
terms of like short term and, and long term.
And, and also, you know, as we saw already, Duolingos adopted
that you can now have like a conversational agent.
As soon as the technologies appear, the people that have
domain expertise are going to adopt them and integrate them.
So I think naturally in that sense, like when you have to
specialize you, you have to build in domain expertise and
and the behavioral science component.
Yeah. And I do, I do think like it
fits the narrative that we have right now where every week
there's some form of like big headline saying this is the end
of this news from Open AI is the end of this basically.
But I think bringing some new ones to the reason why some
things maybe last while other suffer platform risk is I think
that kind of Babel component, whether you know is, is the
solution as somebody Duolingo, like is it actually supporting
some form of meaningful behaviors change?
And on that topic, if I wanted to go from 1 chat bot to another
and ask you a little bit about this really cool project that
you've been involved with creating a chat bot for kind of
a sustainability related projects.
Would you mind sharing a little bit of that process of of
developing a chat bot facing tool?
Absolutely. So I've been working for the
about the past year and a half with sustainability NGO and
we've been working on a chat bot to help answer people's
questions about recycling and waste in general.
And to me that's been a really interesting project because it
falls under at surface level, this is that sort of AI gold
rush, right? Where people are looking to see
like, OK, well can we adopt these technologies and put them
in and integrate. But what I see the difference is
like the approach that this organization has been taking has
it's very different from what I would say just sticking it on.
And you know, my role in in that has been trying to really put in
that behavioral design and bring in the domain expertise as well.
And I think that's super important because what might
seem like the right answer, for example, like the example that
Amy gave, the domain expertise there matters a lot.
And that matters a lot when it comes to questions about
recycling and what to throw and what not to throw away.
It's very, in some ways it's very much like a, a flow chart.
So you can't really have an LLM riffing too much there.
But then also there's broader hierarchies of like how we want
people to do certain behaviors, right?
So, and there's like a segmentation that we've done
where it's like, you know, the most devoted waste person is,
you know, composting and doing all these things and like, you
know, they might be motivated to learn a lot versus like who is
at the very other end of the spectrum who just wants an
answer to a question, right? So we have to be able to design
for all those things. And that's not a trivial thing
to do in the chat bot because you it doesn't know immediately
which one of those people is asking.
So the goal is like how can we create like a environment of
digital experiences where we can get people from that entry point
to like more and more curiosity and more mindful consumption and
does broader level goes? Yeah, that was really cool.
And I guess for those kind of part of the segment that you
mentioned before about that are like almost maybe not don't even
believe in climate change or something like they're very anti
recycling. Maybe they can be forwarded to
the debunk bots that we actually had.
Gordon Pennycook talked about recently about supporting people
in helping them change their beliefs around conspiracy
theories. But I, I think that's really
cool. And I guess going back to what
you said also in the beginning, there terms of some
misconceptions people have about chat bots.
I think as you said, like it's very easy for a lot of companies
and organization to want to slap on a chat bot as they can have a
quick fix, especially with the promise of AI and what we we can
do today. What other misconceptions or
mistakes have you seen people make when thinking about like
chatbot as a specific kind of interface or a specific kind of
intervention? Yeah, I think I would have to go
back to that same reality check of like, is this the best way to
do it? And like thinking about this
idea of like a form factor trap, like are we trapping ourselves
into thinking about what is chat like?
What is chat really right? Like moving away all of the
hype, everything chat is talking to somebody and so is the best
way to get at this to talk to somebody?
Or is the best way to get at this to just click a button,
right? And I think relative facts like
that, I think there's often times this over integration
where you might say could we just have people click and
through like 3 questions and we would know so much more than we
would have to like make them sit through a conversation.
I can't think of any specific examples off the top of my head,
but I do think what can tend to happen is that shift towards
like making everything a little bit too conversational.
And I do think that there is some probably unconsidered costs
to like fatigue that might come with repeatedly engaging with no
matter how natural something is. Like, you know, there is
obviously a dimension of like we all like to talk to people more
or less. But I also think there is
probably going to be some emergence of like fatigue with
talking to chat bots, right? And we haven't probably hit that
yet just because these as much of I think we're still all in a,
in relatively a bubble of of like usage with these things.
But that's the other thing as as well as they just being mindful
of like are we just doing it because it's fun or are we doing
it because it's the most efficient way?
Like can we not get it in the way of the user actually by
putting this technology in there?
Yeah, I guess like something comes to mind for me or two is
like maybe one is the classic Clippy example where you're just
trying to edit your document and then this whole like the little
Clipper thing comes up and it's like you have to interact with
it. The other one is where you call
a lot of customer service numbers these days.
They are basically AI generated some form of like welcome
process where you have to like, first you have to hear some AI
voice say like, Hey, your call matters a lot to us.
You're a valuable member of La, la, la, la, la, la.
And you're like, I don't care about this.
It's like, we care so much about you.
And it's like, there's a lot of people calling right now.
You have to wait 45 minutes and it's like, well, you, you said
you care about me, like what is going on here?
And then you have to listen to even more stuff before you
actually get to where you want to go.
And you obviously don't want to have that user experience or
customer experience where you used bombarded by fake
interactions where you used to want to get some form of basic
answer or some basic thing right.
Yeah, Yeah, I like that. And just to bring it home to the
project that I'm the working on the sustainability recycling
project, like one of the things that we're working on is this
sort of ecosystem and one aspect of questions is object specific,
right. So I might have a water bottle
next to me and like, do I really need to talk to a chat bot to
understand that? Or is there a faster, easier way
where I just want a yes or no answer?
Because I want to just know immediately about this specific
thing, right? There's some questions that I
might be like, hey, tell me about plastics.
Can they really be recycled? Or like, why can't I throw away
a vacuum cleaner in the recycling bin?
And I by the way, like these are real examples.
Like 1 to a. Facility and people will throw
away I expect their vacuum cleaner to be recycled.
So like that's a different question, right.
So one of the things that we're working on is like this
counterpart where like the chat is conversational, but then
there's a QR code or link that takes you specifically about an
object. So when you scan that QR code
from the object, it immediately tells you a yes or no answer
about that and that's it. There's no expectation of like,
hey, hello, how are you what's your name What's the it's like
you just get the answer but at the same time like you want to
give going back to like the behavioral design and curiosity
you want to give the people opportunity to say like, wait a
minute, I was expecting a no like why is it this way?
So I I do like the idea of like, you know it's not getting in the
way, but it's there if you need it is is how I think about
integration is nice, right and Clippy clearly like is getting
in the way. It's literally undocumented.
I remember every time like get out of the way, but this is
Clippy's redemption art so I don't want to hate too much.
Yeah, no, but I, I love that and and also think, yeah, this
project is a great example of used.
I think I've had a little bit of obviously seeing a little bit of
this and what you've been doing, and I think it's just such a
good example of where having a behavioral science lens can add
so much. You know, we think about it in
terms of something like recycling as something
relatively simple, but when you actually start to unpack it, no
pun intended, then you really see like how many parts to this
that is quite complicated in terms of cognitive aspects of
the decisions we make. Some of our understanding can be
very different about similar objects or similar things.
And how we view and understand all of these contextual
components makes it quite tricky to design solutions for it.
And I'm not even going to mention like the regulatory
challenges in for example, in America, we have each state or
each county might have different rules and so on.
So, so yeah, so much admiration for this work and, and thanks
for sharing a bit of that. I guess to pivot to something a
little bit different, I wanted to hear a little bit your
thoughts about AI as a role in research.
I guess from a perspective of applied aeroscientists.
I think it's also important to talk about what can AI do for us
to make our research easier or better.
I guess you just said the same. I guess we're probably both you
and I see AI as a friend of male scientists.
Like would you agree? Yes, Yeah, I would.
Yeah, I agree. I think probably a friend of all
sort of researchers or anybody who's thinking at a strategic
level especially. But yeah, I think it's
interesting. There's so much there.
It's some, some things I am hesitant of, but some things
that I am excited of and, and use.
So it's just like, yeah, it's early days and you have to be
careful in some things. But yeah, I I definitely see it
as like a net positive as as a support.
Nice. What about synthetic users?
Good or bad? Or like, how do you think about
that? Yeah, my initial reaction was
that. And since then I've been
thinking about not more so like I I think probably getting past
the sales type and, and which was like you don't have to do
user research anymore, etcetera. But now I'm thinking of it more
in terms of like for things that we have a lot of already
validation of in terms of in the training data, like it might be
a good, just a reality check, right.
When I run a survey, I don't just put it out.
I, I have somebody take it live or walk through it so I can do
some cognitive testing and understand like did they
actually interpret the question as that intended?
And so I think of synthetic users as another way to do
something, sort of like, is this getting at the top type of
questions that I'm asking, or am I just asking the wrong
questions? So I think yes, but you have to
be careful about not using it as the final word, but more as like
a validation tool. At least that's how I see it.
Yeah, because I guess the like to impact this and for anyone
who hasn't come across it is basically the idea that you take
some form of, as you mentioned, some form of data around
pre-existing knowledge about certain type of users or people
or from research. And you try to basically create
a data model that helps with, yeah, testing various either
product or research ideas with some form of instead of going to
real people, real users, there's some form of like synthetic type
of uses. And there's different models for
this, some different ways that you saw some recent simulation
model that just came out around this.
And so there's a lot of work that is trying to simulate
people's behavior and simulate. Use your preferences and
interests and personalities for some of the stuff.
Yeah, to your point, I think I, I found it a little bit hard to
immediately not feel like that this is terrible idea.
I was like a little bit like you like the first initial thing was
like, wait, are we just going to create some form of fake world
and then tests what could happen in the real world, in this fake
world? And then instead of doing things
in the real world, we're just going to take this fake world
insights and pretend that's true.
And especially when it comes to also known from bioscience that
we live in this kind of weird social science world where a lot
of research has been done predominantly on research
participants that are college students in Western educated
countries and so on. The risk is that data in data
out. So if you have like really
biased and like weird data, you're going to get like this
synthetic users is going to potentially act in the same way
and it's going to further be reinforcing some stereotypes of
reinforcing certain things that kind of is not really what the
real world predicts. And especially, you know, with
each context being quite unique as well, how much can we really
replicate and test? So immediately I was like, oh,
this could create really bad reinforcement loops of like just
not really learning from the real world, but just creating
some more weird, like in all aspects, like weird ultimate
reality that we're learning from.
And then I was like, maybe there's some kind of steel man
or some argument to be made around perspective taking and
some form of pretesting of ideas, as you mentioned, where
like it can be useful. And I've heard a lot of people
talking about this. I know Laura de Mollier, who I
think we're going to have on the podcast soon, she talks about
like, using AI for perspective taking and trying to get
perspectives can be useful. But yeah, where are you leaning
now? Like, would you use synthetic
users in current user research that you've been doing?
How do you think about it right now?
Yeah, I think we can learn a lot from physicists, right?
Like in terms of this is in the same scientific art is like, OK,
we understand some dimensions enough and now we're going to
model and simulate and rather than going out and like a
demolishing building, we're going to try to 1st simulate
what that would look like and then see like where we should
place the chart or like it's the same idea.
But clearly like we do not understand human psychology to
the level that we understand are physics laws.
So I think it's the yeah, the biggest concern, like you said,
there is like I think the overconfidence that can come
with being able to do something that easily and thinking like, I
can take this away. And I think the the nice thing
with other fields that do simulators, like they have
medical medical models and they can say like these are
assumptions we're making within these constraints.
Like we saw this, you can't do any much of that in with that
level of control, at least in in this world.
Then of course then like there's the weird aspect to it.
That's just fundamentals. Yeah.
Just not being able to simulate like half of the world if you
were thinking about physics. So is it useful in that domain
in that sense? It's not the scientific validity
of it, like being able to take away real insights from.
I don't think so. But I'm still now cautiously
optimistic about it. And I think with those rules
about like thinking about what is it going to fall into in
terms of is it going to be answering service or is it more
going to be like a perspective taking and like a reality check
or maybe like a cross validation thing, right where you can run a
real study and see how close was I and what can I do?
And I'm hoping that in the future, like these companies get
better and better as they have their toes dipped in both things
where they're actually also running real studies and
constantly updating their trainers.
So I, I think there's hope there more than I initially thought.
Very much had the similar reaction as you.
I guess there's two considerations also here in
terms of one is the use case. Where I've seen some of the use
cases now, it's been like simulating mobility related
conundrums like how do people move in the LA morning traffic
and what would happen if it was a closure on this road and how
would that impact how people behave on some other highway
where they can both now combine data of like, OK, what have
people been doing in the past? Like what historical data of
traffic and so on combined with simulated agent stuff where they
add some other stuff to it. Like in terms of we know that a
lot of people have kids, like we know a lot of people have
certain other things. So if we simulate people as more
as agents as well with some autonomy where they might take
some some autonomous choices, we might get a better sense of what
actually would happen if this thing would happen or if that
thing would happen. So it would make for a better
simulation somewhere. So I think that use case seems
more potentially just straightforward that that could
be very likely to be impactful. And the second thing, I guess is
also considering like what is alternative?
Because if you look at some alternatives that research is
doing right now in terms of either, let's say using M Turk
or some form of like aggregator for research, that is like
honestly quite low quality where OK, you can get insights, but
it's like paying people sense to click things and test things and
use getting some form of like really strange sample in some
ways. So of of people that are doing
some task for you that giving you some insights of human
behavior. But at the same time, it's
however you constructed it tart fully obviously make that high
quality by the constraints that something like Mturk would allow
for. Or another side, you might
consider something like focus groups, where, OK, focus groups
might have their place. But at the same time, we also
know that it's not probably the place where we will learn that
much about human behavior. Like people are quite influenced
by each other and hearing people talking and all of these things
like what do you get from focus group is probably relatively
overrated from a behavioral scientist perspective at least.
Conversely, if you do some really high quality, let's say
user interviews or ethnographic research, can that fully be
replaced by synthetic users? Probably not at same time.
So I guess it all depends, as always, what you're combining or
or contrasting it with. Yeah, Yeah, I like that.
And I think it really is going to come down to like who's doing
it better, Like who has the best training data, who has the best
validation, who's has the best domain expertise.
And there is definitely, for example, the user interviews you
mentioned, like in the same sense, not quite synthetic, but
like there's now these tools and one company that I'd like outset
like where it's an AI moderator. And that's something initially I
think any behavioral scientist or researcher might hear and
like get a little worried about it.
So I'm not going to let AI take over.
But I think it's also one thing is like understand who's behind
it. Two, it's about like the
purpose, right? And the purpose that I've seen
is that I can do myself, I can moderate interviews and I have a
limit of how many people I can talk to before the quality goes
down. And it's just not the same,
right? But it's also a very different
goal like I'm going into in terms of deep inquiry for a
problem that I understand really well for something like outside
AI moderated interviews, like I could talk to, I don't know, 100
people and get results within an hour of 100 people's qualitative
feedback for a concept that I maybe don't understand well
enough to go deep into. So like I think of it as like a
pre deep inquiry step and and that's a sense it's a really
nice thing. So similarly, I think for
synthetic, I think it's just going to be about like not
replacing, but augmenting your current process.
And there's a lot of holes like quantitative qualitative
research, like it's not perfect, like you said, focus groups.
So I think there are holes that can be filled with like these
more synthetic or AI moderated research methods, but it's just
about not replacing that supporting.
Yeah, it's interesting when, you know, when it comes to user
interviews, one of things I've seen over again by like really
good user researchers is that they underestimate the power of
their social influence on the the interviewee.
So if let's say I'm use a researcher representative of a
company and you are the, let's say the interviewee in this
context, and I say like, hey, I'm coming from this company, I
would love to know, maybe we're doing some live user testing and
you're interacting with my app or something.
Tell me about like, how do you feel?
Like what is the first impression of that?
Like, how do you like it and so on.
What people are often blind to is that they don't think about
how much they themselves influence the interviewee.
In this case, you like you probably you could have the
first impression to think this app looks shit, like really
ugly. And at the same time you're
like, but I don't want to make this nice person next to me feel
bad. I don't want to like, criticize.
They seem nice and so on. Like, OK, I'll you say, yeah,
it's different. It's fun, it's cool.
And so you're not being fully honest with me because you want
to spare my feelings, which is very nice human attributes, but
that's honestly not really what I want to hear from a user
testing point. Like I want to get your real
honest opinion and I, we're really going to like understand
what's going on through your mind when you're going to
interacting with my product. And so from that point of view
as well, probably some AI mother is going to allow for more kind
of candor or honesty because maybe at least people won't feel
like they're hurting their feelings, you know?
Yeah, I like that. I like that.
Nice and maybe that's a final thing that's interesting is more
in terms of called a literature review related collecting and
interpreting knowledge for some form of projects.
Often times as a behavioral scientist, the first thing we do
is making sense of, OK, what is the state of what the behavior
we're trying to change, OK, we're trying to change this
behavior or understand this behavior.
OK, what can we find in the literature that has been looking
at maybe this before? So we're doing some form of
light or have a literature review.
And yeah, I wanted to hear what you think about a is role in
kind of making sense of existing literature for our products.
Yeah, I mean, I'm all for that and very upset that I didn't
have this when I was doing my PhD at the speed.
I think it's great for asking very direct questions.
So especially when you don't know exactly how to frame a
question, I think that's often the case with the real world
behavior. It's easier to sort of like be
in a very specific area of research and say like, what's
the next logical question? You can use very technical
terms, but it's like, you know, examples, what's people's
curiosity about recycling? Look, how do you even ask?
How do you even do a literature search for that, right?
So like just initial step of like natural language parsing,
which I know you can set up now pipelines of like, how should I
ask? And then you can put that in a
research to like illicit. But so I think it's really good
for getting specific answers as a gut check to what is out
there. And maybe this is just me, but
I, I do think there is always, you have to maintain that
balance of going into what these tools and recommended systems in
general can offer you. And I know that you guys talked
about in the in the previous episode and like doing a little
bit of your own work as well. And I think with science, often
times good ideas come from reading tangential things and
doing that exploratory aspect and realizing, oh, I would have
never thought this was relevant, but it is leading in a direction
that you, you connect the dots. So I think again, I think with
the theme I guess I'm seeing is like not using as the final and
the only approach, but using it as like 1 aspect.
And what it comes down to is stepping back and defining what
your process is to say. Like, well, am I just exploiting
and just asking specific questions or am I doing enough
like open-ended exploring and trying to like cast a wide net
as well? But in general though, I think
there's no, I don't see any harm there because it's not like
there's hallucinations, especially with these specific
tools that giving you actual research papers.
So I think it's probably one of the best sort of like academic
research use cases we have with AI so far, the probably the most
advanced in terms of maturity. Yeah, I agree.
I think it's definitely a little bit of a fine line when it comes
to it's great to maybe get AI to help us do some things, maybe
help us to think better about certain things, but not having
AI think for us. And I think that's where like we
don't want to get crossing a line where we're used leaving AI
to think for us and make all of the decisions or conclusions
without any kind of critical thought ourselves.
And that's what I think it helps to have.
I think to the point of you actually having completely
appreciate, like I think you're benefiting from having a
critical way of evaluating research and understanding
research so that you can when there is maybe not a
hallucination. But I think I was to say most of
the time these days they don't hallucinate, but they just maybe
oversimplify or they misconstrue certain like things in a
oversimplified way that you can like spot that and be like,
well, OK, that seems a little bit oversimplified.
So I think having some expertise, I think it's still a
really, really, really big leverage point.
Yeah. And talking about your PhD and
also about expertise, I guess it's a final topic before we
wrap up. I wanted to touch upon, I think
a very unique expertise that you have, which is with neuro
aesthetics, a word that I think a lot of people maybe haven't
heard of. I know that when we had some
form of a LinkedIn bot roasting our our LinkedIn profiles, that
it's basically said that Hassan has found a scientific way to
say I have fancy thoughts about pretty things.
And this is when we we can ask people as a final question, kind
of what's your most controversial opinion about AI?
But I kind of want to actually frame it a little bit more for
you because because on this topic, I want to ask maybe
controversial question, which is, can AI understand beauty and
can AI recreate beauty? Yeah.
That's such a great question and definitely controversial.
I, you know, a lot of the work that I started off doing was
about, was simulation, right? So we talked about simulation a
little bit and we were simulating people's internal
States and the environments that they would be in and then
simulating the what they would think about certain images
coming in, right? And then there's been really
great models of like, what does it even mean in terms of beauty?
I do think it's possible, you know, if I'm not going to get
into the quality of it, like are they actually experiencing
beauty? That's probably, you know, I
don't know how that happens in a silicon, but I think, I think I
do think that they will, if you base it off the cognitive and
neuroscience frameworks that we understand about what goes into
beauty judgments. I think it will get to the
point. And I, I hope that it does get
to the point where we have a agents that can appreciate
beauty because I do think that's a fundamental component of
humanness. So I do think that's a goal that
we should be striving for. And especially when it comes to
like personal relationships, Like I would want to have an AI
that could understand what's beautiful to me and like have
its own understanding of what's beautiful to it, because I think
that would give it much more personality than in an AI that
like just didn't have an understanding of beauty to begin
with at all. So I think it's a worthwhile
goal. And I do think it's a possible
goal. It might be controversial, but I
think I'm excited for, you know, sort of setting the parameters
in place and see what AIS start finding beautiful and and see
what their justifications for that are.
I was really interested in, you know, all of the image creators
and how they are deeply aesthetic.
You know, that's sort of a funny bias that they have from our
training. Like, you know, they just cannot
make non good looking people and non good looking scenes at this
point, right? That's also this understanding
is like their beauty is so unidimensional, like whereas
humans we find beauty and ugly things and we find beauty and
things that just would be totally unexpected because it's
so individual. So that's what I'm really
interested in is like a is that have this really like unique
idea of beauty that's not traditional, that's not weird
and just not like reinforcing stereotypes.
I hope for that future. Yeah, and I echo your your
sentiment. And I part of me wants to have
AIS with a little more agency and opinions in some ways.
Like I feel like they're a little bit too agreeable as well
today. Like if you ask AI anything
right now, any type of AI, especially any AI shut but and
so on, they will basically say, yeah, that's great, you're
smart, you're fantastic and so on.
And it'll be fun to take a photo of your living room and actually
will be saying like, no, that's ugly.
That's an ugly decor. Like you should not have that
pillow that makes your couch look terrible or something like
that. So having some form of aesthetic
and knowledge and preferences and, and understanding of beauty
would be, would make it more interesting, fun.
And I guess a light version that is actually being used for today
already is that there are these AI tools for where I think
especially being just the one I'm thinking about, especially
for men, where they can basically get photos of
themselves and it will tell them like, Hey, how here's how you
become more attractive. And in some ways, it's mostly
saying like here, hey, you're not cutting your hair like you
should probably cut your hair in in some way.
Here's a good look for you based on your facial shape, you should
have probably had this kind of haircut.
And secondly, you seem to have a very, very bad skin.
Here's the skin care routine that you can follow to improve
your acne or whatever it is. And so it's not giving you a
filter. It's not giving you any kind of
like this kind of dystorpium thing.
It's just giving you a little bit of like notch be like, Hey,
cut your hair, trim your beard and take care of your skin
basically. And here's some advice based on
your facial shape and and kind of bone structure of your face
that would probably suit you quite well.
And that is early kind of sense of like giving some form of
aesthetic recommendations, which I think is interesting.
But again, like what I think is dystopian and, and scary is
where it becomes too narrow and, and too homogeneous and it just
becomes that it gives the same recognition for everyone.
And so we're just living in the society where everyone has the
same kind of aesthetic look. But to be fair, we're already, I
don't know, IKEA furniture is in most people's homes, sadly.
And even as a suite, I think that's a little bit sad that
everyone has the same kind of furniture and and same things in
various ways. I think maybe again, we're
coming back to that. In reality, we are quite
homogeneous in some ways. And yeah, it is interesting to
see how AI is going to shape either that becoming more so or
maybe going the other way and and supporting us to have more
unique tastes and preferences based on our unique situations
and contexts. So yeah, we'll see.
But hey, this is so much fun as I could speak to you for a long,
long time and I'm happy that I have the benefit of talking to
you on a weekly basis anyway. So I'm sure we'll follow up on
this conversation, but for now, really appreciate all you do in
your work in the field and for coming on today and sharing your
fantastic perspective and expertise.
So, so yeah, thanks. Yeah.
Thank you so much for having me. Thank you.
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Happens to. Mugatroid.
Oh. Well, I'm very happy to say you
see, you see, you see, it's harder.
It's harder than it looks. OK, OK.