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 the 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. It's good to be back, Haley.
Hi, it's very good to be back. Yeah, I can't believe it's been
like 10 months. What happened?
I'm not who's counting What can happen in 10 months?
Yeah, what can happen? What can be born out of that
period? Yes, yes, about 40 weeks or so.
Yeah, it's, it's, it's really good to be back.
I'm honestly super happy to to get back on track with this.
And I'm, yeah, so excited about today as it marks kind of our
return of the podcast, the first episode of Season 4.
And there's so much to get to. We're going to do a little bit
of a season preview. We're going to cover some of the
biggest myths in kind of intersection of AI and
behavioral science. We're going to cover some top AI
terms that any behavioral scientist should know and some
other fun updates and things, but I think we should just jump
straight into it and cover some breaking news.
Yes, it is indeed breaking and and local to you, Sam, Did you
know that very recently, right around the corner from you the
the Nobel Prize in Physics was just awarded?
No, this is actually Full disclosure.
You told me just before the episode that this has happened.
So. So.
Yeah, and we're pretending that I'm telling you now.
I know. And it's funny because yeah,
this is definitely news that maybe I would be expected to
know before because it happened in Stockholm.
But no. Yeah, big news.
Yeah, it's very exciting. And the this year's winners of
the Nobel Prize in Physics are notable for many reasons.
One, because how did they not win the Nobel Prize before now,
but also because it's it's perfectly aligned with our focus
of this season of the podcast. So the winners were John
Hopfield and Jeffrey Hinton, who surely are recognizable names to
all of you, no? Well, maybe, maybe for some.
I think, yeah, for him to, definitely.
Be more. Yeah, he's he's been more on the
pages with many of these things. Is that not always the work that
leads into people being well known, but more of the
controversy. So the fact that he recently
stepped down from his work at Google and talked about stepping
down in order to be more outspoken about the the risks
and ideas of of AII think maybe made him more well known
recently. But he's been known, yeah.
I feel like the the media machine jumped on that, I think.
I think the reality is probably a lot less exciting than the
headlines would lead you to believe.
Yeah. But I mean, just to hear from
you like, why do you think like this is exciting or why do you
think this is kind of important to highlight their work and why
are they deserving of being recognized here in Stockholm?
Yeah, Stockholm specifically, yeah.
So I think that the award for sure was all about their work in
machine learning and, but specifically in creating or and
developing and making progress in artificial neural networks
and. And this is important because
most of the, the machine learning and artificial
intelligence is really based on neural networks and, and this
structure for processing information and deep learning
and so on. Yeah, you could almost call it
the foundation of machine learning the the work that
they've done over the years. Yeah.
And I think it is very fascinating where we're so quick
to talk about the things that AI can do and and advancement and
so on, but it's easier to forget kind of what it builds upon.
We'll get into talking more maybe about LLMS and and the the
recent hyper on some of the innovative AI where it feels
like they are weird humans because they're basically been
trained on human knowledge and human human things, but they're
not humans. They also they're like weird
humans in a way. The the work from John and and
Joffrey, it goes even deeper in terms of looking at the brain
stuff mimicking the human brain. Yeah, yeah.
And, and it's so interesting, I sometimes I talk to computer
scientists who who are, are very familiar with artificial neural
networks and not at all familiar with the biological neural
networks. So how our brains are structured
and how our neurons talk to each other and how they fire and send
information with their chemical and electrical signals.
And so I, I've often found myself more often than you might
think, in a position where I have to explain that no,
artificial neural networks are while while they mimic the brain
and have many similarities with the structure of the brain, they
are not the same as biological neural networks.
In fact, there are some really, really important differences
between the two. So I thought maybe it could be
interesting to talk a little bit about those differences and what
actually does an artificial neural network look like versus
a biological neural network. Yeah, I I would love for you to
explain that in some ways I would trust you more than me.
How would you? In this hypothetical scenario?
They had to explain it to someone sitting across here.
How would you distinguish between the artificial and the
biological? Yeah, well, for the sake of our
behavioral scientist listeners, I'll do a little bit of the
explainer on the the machine learning part as well.
So it, I think it, it sort of helps to to visualize what this
looks like. And of course, it's much, much,
much more complicated than you could even begin to describe.
But if if you think about layers and layers and layers, think of
it in three sections, OK, you have your inputs, you have your
hidden layers, and then you have your output layers.
So if you're like reading this as you would on a screen from
left to right, you have your inputs on the left.
And then you have many, many, many layers and connections in,
in the hidden portion, which is often many more layers than you
could even imagine. And so that's where the
complexity of artificial neural networks comes from.
And then you have your output layers all the way to the right,
and then data flows from left to right as you would be reading.
Now, learning, especially in the case of of deep learning,
happens through what machine learning scientists call back
propagation. So as data are flowing in from
left to right, there's each of these nodes in the layers has a
little weight where they're they're making calculations and
math is happening. I think like the the most high
level way to think about it is like the data flows through, but
in order to learn we have to send information back through.
So going back through from the right to the left and this is
back propagation. This is the really like what the
the learning part of machine learning is.
And when that's happening, the weights in the middle are being
adjusted. So if something say say there
was a calculation at some point where it was said multiply by .3
and then you get to the end in the in the output and you
learned, Oh well there was some error there.
Our predicted value of 1 * .3 instead of being the prediction
of .3 it was actually .5 OK, well now you have a .2 error and
you send that back through the system and then in the future
iteration you have a corrected weight.
Is that too complicated? No, I think that's good.
And like this work on artificial neural networks is what they've
been kind of recognize for. And I guess again, it will be
interesting then to contrast it briefly with the biological
neural network. How are they kind of similar,
different, Yeah. So if all of that sounded very
complex, the first thing to know about human brains is human
brains are way, way, way, way more complex.
Oh my gosh. So in our brains, we have
billions of neurons. They're connected through these
through our synapses, and our synapses make trillions of
connections number. Big.
Big, big numbers more, more, more than a, a machine learning
model. And I'm actually not gonna go
into the complexity of neurons because that there is, there's
so much to unpack there, right? But I think that an important
take away is that whereas artificial neural networks can,
as a result of their structure, be very good, often better than
biological neural networks at at very specific tasks with lots
and lots and lots of data. The strength of our biological
neural networks is that they're highly dynamic.
They we, we have synaptic plasticity where we can change
the strength of our connections changes very quickly and often
with very little data. So in, in the case of AI, you
need lots and lots and lots of data and you have very little
flexibility. And with brains, you, you might
have one or two observations and you learn very, very quickly
with with very little data. And this, I'm biased as a
behavioral scientist, but makes me believe more and more just
how unbelievably incredible our brains are.
You cannot replicate a brain with math, not yet.
And we're not close at all. The fundamental structure of
artificial neural networks is such that they do not have this
ability to to be flexible. Yeah, I think very fun thing
recently has been seeing and experiencing kind of that
there's been actually a lot of people have noticed that have
increased appreciation for the complexity and the beauty of the
brain and how complexity is. I've actually had several people
that are coming more from some interest in wanting to better
understand machine learning or AI that I have started to read
more about cognitive processes and the brain.
There's so much work that just looks at like just in all of the
things that the brain can do, tries to mimic it tries to in
some ways learn from how we work.
And, and I think that's quite, Yeah, beautiful.
The brain is beautiful and really, if you think machine
learning models can be a black box, tell me how consciousness
arises from our meat machines. It's just magic.
No, but I do love that quote from from Marvin Minsky.
He's one of the original AI thinkers.
You might call him that. He says the brain is merely a
meat machine. Yeah, that is an amazing quote.
I I want to put that on a yeah AT shirt or some form of a
hoodie or a cap or something. But talk more about that quote.
Yeah. I love it.
I, I think, well, I think what's so interesting about it is to
think about our brains in terms of machines, right?
And, and not only how we use the brain to model artificial
intelligence systems, right, but we then use AI sort of the, the
converse and, and technology more generally to then think
about the mind. So going both ways, we both mold
our computers after ourselves like like our neural networks,
but then we then we can't help being influenced by our
technology in return. That actually that's something
that Sherry Turkle has been studying for something like 40
years, right? So basically the the advent of
the computer age. Are you familiar with Sherry
Turkle? No, no, tell me where.
Yeah, let me, let me tell you just very briefly.
So Sherry is a sociologist at MIT and she is an incredible
person. I, I actually just Full
disclosure, I just finished her memoir, The Empathy Diaries.
So I'm now an expert on Sherry. So I, I won't shame you for not
knowing, but what was really cool about Sherry and her work
was that she captured this shift really well.
Computers were just coming about really like the personal
computer in, in around the 1970s.
Like this is like it was starting large scale, small
scale, then large scale adoption where people were moving from
thinking about humans as rational animals.
That was sort of what defined the human was you're an animal,
but with rationality to to thinking of them as emotional
machines. So in reference to the machine
now, not in reference to animals.
And really since then, I mean, my entire life for sure, we've
had this mantra that humans are unique, where these special,
unique creatures because we can express emotions and empathy.
That's what's like really special about humans.
Well, consciousness for sure. But, but, but especially when we
think about emotion and now we see our AI systems maybe not
feeling emotion, but certainly expressing emotion, expressing
empathy, making, tricking, you could say people into feeling
like they can rely on this thing or, or whatever you want to call
it. What does that mean?
But that's a big question. But I, I know we have to have to
get to our actual episode so we can save that big, big topic for
another day. But I do think that Sherry's
work exploring this interplay between humans and technology
really sort of captures the essence of what we're trying to
do with this season of the podcast.
Yeah, so you're predicting that she's going to be the next one
for a Nobel Prize? I hope so.
I hope so that would that would be due.
Yeah, no, but it's so true. And I I like that kind of frame
of of it and AI just briefly on what you're saying, I think it
is interesting that pair the original definition of Turing
test in some ways like the current AI models, they are
already succeeding. They are able to trick anyone to
believe that they're real human beings if you're chatting with
them. Like there's no way to to really
know. But at the same time, as you
say, it's also two things can be true.
That can be true in terms of Turing tests.
The traditional one at least has been kind of accomplished, but
we still probably separate a pretty big part of that to when
a large langu of mole saying, if you ask them, is that OK?
Can you feel feelings? And do you feel sad?
And like, if it would say like, yes, I feel sad and feel lonely
and so on, we would picked out with a bit of a pinch of salt in
terms of what what actually that means.
And, and we won't kind of I I guess there was some of these
news a couple years ago with some people being like, Oh my
God, I asked to shut sorry, I asked AI this and they said yes.
And I'm, I'm afraid. Well, I'm pretty I I think we
have a little more nuanced approach these days.
Yeah. And I think you and I know quite
a bit about this intersection of behavioral science and AI, but
we're learning too. And, and honestly, so much is
changing every every week, every day, that just keeping up on
what the literature and what's published at this intersection
is could be a full time job for us.
Maybe someday it will be. But for now, for now, we'll
we'll keep our side jobs. And I, I guess that brings us to
the season. Do you want to tell the audience
a little bit of what the plan is for for this season?
Yeah, we're, we're really trying to cover everything from
understanding how humans interact with intelligent
systems to using AI to to do behavioral design itself.
So how do we use it in our work? Yeah.
And I today I feel very related to I felt about 6:00 or so years
ago now when I initially started have a weekly where I felt with
behavioral design, which is really, really hard to stay on
top of what's going on. And as a petitioner to kind of,
yeah, just feel like you are in the loop of like, what are other
people doing? How are other people thinking?
I think right now it's a really triggered time for anyone as a
be able scientist to really make sense of maybe both from
identity point of view, like who am I today?
But also just feeling kind of more on solid ground when it
comes to like AI, Like what are useful things for us as
petitioners to know? What are things that are already
people are doing and what can we learn from each other and, and
really kind of do that openly and and collectively?
I think that's kind of where we're coming from, I guess.
Yeah, I think, I think we can do the work of sifting through
everything that's put out there, not everything, but many things
and share what's relevant and interesting.
That's that's really the goal. So we can be as useful as
possible. Yeah.
And I think from my end, if I'm completely honest, putting all
the cards on the table, I personally would love through
decision to have a better personal argument for used the
business case as well around payable sense and AI, 'cause I
have a lot of opinions around the importance of understanding
that AI is not a form of panacea or silver bullet or magic wand
to solve a lot of things that it's being used for.
And we've already had some, we should teach this, but like some
really good recordings of, of guests episodes where we've
already explored the importance of thoughtfully kind of marrying
behavioral science and AI at the right places and, and the really
big benefits that gives. But I'm really excited to to
better formulate that argument and that thesis of sort so that
that doesn't get lost. Yeah.
I think it's easy to find yourself on either of those
extremes where you're jumping on the bandwagon with everyone,
everyone and you're all AI everywhere.
Or you maybe you find yourself on the other end of the spectrum
where you say everyone is doing AI.
Like I just being totally dismissive and I will be honest,
I, I have been that person on that end of the spectrum at at
points in my life. And I don't think that's the
right place to be either. I think that finding the right
balance and understanding where is AI useful?
Where is it actually the best tool?
But yeah, how do we actually use this most effectively and just
understand its impact on our science and on society?
I think that is just absolutely essential.
Hey everyone, this is Maddie, your AI producer of this
episode. I just thought this was a good
opportunity to jump in real quick.
We really value listener involvement this season and we
would love to hear your thoughts, ideas, and reflections
as you listen along. So if you've got anything on
your mind, topics you want us to cover, guests you'd love to hear
from, or anything about the intersection of behavioral
science and AI, feel free to reach out.
You can e-mail us at podcast@habitweekly.com.
We read all your messages and and really want to make this
season as relevant and valuable as possible for you.
Again, e-mail us at podcast@habitweekly.com.
Now let's get back to Sam and Elaine.
Wow. So yeah, I'm really excited for
this season and maybe also to share, is it useful to maybe
talk about where we're coming from, like both you and I and
all of this? Yeah, yeah, yeah.
So we did mention that we have some expertise in AI or this
intersection between behavioral science and AI.
So I think it's useful to sort of lay down our cards and say
here's why you should trust us. So I, my life's work has really
been in helping people reach the goals that they have often their
health goals, not always their health goals, but you know, how
do we, how do we help people exercise when they want to
exercise and so on. And so I've been doing that, the
digital health space for a long, long time.
And then it may be in the past five years or so, really got
interested in AI, specifically in terms of reaching these same
goals. How do we, how do, how do we
leverage new technologies to do so?
So then I joined Apple. I thought this would be a, a
good way of achieving this goal. I LED behavioral science for its
health AI team. So that was a, a really fun
experience working on high impact features, working with
fitness, plus really, really working at this intersection of
AI and behavioral science. And one thing that I should
mention is that before I entered Apple, I really had very, very
little trading and machine learning and, and the artificial
intelligence. So I, I sort of took it upon
myself to dive head first into this whole world and Oh my gosh,
like what a world it is. It's so, so wide and deep.
So I, I read books, went to conferences, took courses, like,
I even learned how to code a bit myself.
I mean, don't ask me to actually produce something, but let me
just say that I, I feel that now that I've gone through that
experience, I am, I am a much more humble person when it comes
to realizing how much I don't know.
And, and I also, I feel the a little smirk every time that I
see people on the Internet talking about AI without really
knowing the first thing about it.
And so I feel like I was that person and I can see what
they're doing, but I can also see right through what they're
doing. So I think that, yeah, our our
goal of this season is definitely going to be to expose
you and ourselves to the people who know quite a lot about
artificial intelligence and behavioral science.
So. But with that, Sam, tell me
about yourself and what's what's your cred, your street cred in
this world of? Artificial intelligence, it is
hard to compete with being the behavioral science lead at
health AI at Apple. I think it's hard to.
Yeah, it's not a competition. No, but I think it's the same,
but different in some ways. And I think that's why it's
always a little bit interesting because I had quite of a
different entry when it came to engaging with AI, where this was
when the GPT 3A model from Open AI came out.
So that was I think a year or so before.
Yeah, somewhat old school, but it was really the model that
where you start to engage with it and you're like, wow, this is
pretty cool. Like you can actually do quite a
lot of things with it. And, and so it's I think a year
or so maybe a little bit more before chat TPT came out and
very randomly I was working with some client projects where that
started to be used and and, and started to be considered and,
and I was part of really leading some of that and, and working
with that. And so I, I was tempted to
become this kind of people that do the red teaming and and so
forth, testing these models. I didn't end up going that far
in, in kind of working directly with developing the models, but
I, I feel like I quite quickly got a very intimate experience
with, with working with them. And so that's a lot of the work
that I've been doing. So maybe less on, I would say
you definitely come from having much more of AI would say rich
machine learning, understanding of, of AI.
Where a lot of my work has come in on more of the generative AI
side where it's been trying to use these models to, to make
sense in some form of the kind of multimodal aspect of either
kind of making sense of kind of images through using AI or
creating text or understanding text or creating personalized
interventions and and so on with these models and using prompt
engineering and fine tuning and dragging and some of these
things that is coming out to kind of improve the consistency
and reliability of them. So.
Well, and just to give you a little compliment here, I would
say you have mastered the art of prompt engineering, even in just
our our work doing like background research and that
sort of thing. I will throw something into
ChatGPT and and share my results with you.
And then you go and hack away at it for a couple minutes and you
come back with the like shiny emerald version of what I had.
It's just I've been awestruck by what you can do just just by
your experience tinkering around and really exploring these these
getting your hands dirty with with these technologies.
Yeah, yeah. And then I, I will definitely be
honest with that. I have done both to that degree,
but also I think recently the last year or so, I've been
really excited about the agentic side of what is happening in
terms of what we're, we're going to be exploring that a bit.
And when we mean when we say agents, what we mean is
basically that you have some form of AI set up to be a a
multi purpose tool of sort where it can do multiple things for
you depending on the task you're kind of asking it for you.
Essentially automating your workflow.
Is that what you're talking? About yeah, for example, yeah,
it's a lot of automation comes into kind of like using some
form of agentic things. And, and so yeah, I've really
become yeah, very deep into that space of like using AI for
automation purposes. And, and really, I think I find
it so interesting to understand just when it comes to
automation, how again, the human aspect were it's easier
something done to automate things because even though you
kindly could automate it, there's always this explicit
things that needs to be done, but also implicitly things we
don't think about what we're doing and, and how how that
plays into things. So, so yeah, that's a long, long
with a way of saying that I, yeah, I, I've had my fair share
of exposure to, to AI, but as you, I feel also very humble.
I feel so much so much that I feel still that I, I want to
know more about and understand better.
And, and I think that's, that's also where I've really enjoyed
our private discussions. And I feel like I've learned so
much from from you. We're kind of privately talking
about some of these things. So, so yeah, I'm really excited
to have this season up and running again and having this
recorded conversations too, and. Yeah, I, I feel like I have to
mention how our, our current professional lives intersect
with this as well. Because in, in the in the
meantime, since I guess since we've since we've last released
an episode, we have created a company together with some
friends nuanced behavior And at that company we are working on
advising on AI driven tech. So this is exactly what we do,
sort of injecting behavioral design and behavioral into
products that use AI. They don't have to use AI, but
many of them do. Yeah.
And I think that's what's interesting.
We, we ended up calling this nuanced behavior and it's easy
to kind of find yourself using the word nuanced quite often.
But it is where we're coming from where we have a lot of
experience when it comes to applied rural science.
And it's been really interesting to to meet a lot of clients that
maybe have a really strong capability when it comes to some
of these aspects, potentially even in AI in some cases, but
also helping them get the most value from the behavioral
science side. Firstly, when understanding the
human, like how do we really support humans in leading better
lives? And whether it's sustainability
or in health or in in more financial related context, how
can we can we both with the technological understanding and
capability, but still not forgetting that in the end, we
have to understand the human side of it and we have to make
the most of that. I think that's been really,
really fun to to complement with an income really from that side
as well. Yeah, absolutely.
So given that this is our intro episode to the season, we
thought that it might be useful to share some overarching themes
about AI and behavioral science, share some terminology that's
going to come up throughout the season and really kind of get
into what are the myths out there?
We can, we can sort of debunk those those myths and set the
record straight. And I thought that the, the
version of this that we could do is AI1O1 for behavioral
scientists. And, and I want to do this in
the spirit of a, a course that that existed back when I was an
undergrad at Reed College. It was called physics for
philosophers. And you can just tell by the by
the name of the course, this is not your hardcore physics.
So this is our version of AI for behavioral scientists, just to
give kind of a general what's what.
And I think there are some really basic basics to know
about AI for, for the very beginner and, and in terms of
just how we think about AI, of course, we've already spoken a
little bit already about about neural networks.
But if you think of I, I think at their very core AI systems
are and in and in particular machine learning systems.
I'm not even going to get into teasing those two apart, but if
you if you think of them as super powered prediction
machines, right, you are taking data and from that data finding
patterns in the data, often massive amounts of data, and
then categorizing that data, classifying it and making a
prediction. Yeah.
I think that's a really helpful way to simplify it and really
think in my asset super powered prediction machine also reminds
me of this conversation we had at some point with Lisa Feldman
Barrett about emotion and so on where she talked about us and
how we engage and understand our emotion, this kind of prediction
machines. And then it ties into this whole
thing that that episode started with and and that of course we
are also prediction machine. But I when we're talking about
artificial system, that's, yeah, narrow, narrow pitch machines.
Narrow pitch machines? Not so.
Good at common sense or information transfer or or
really generalizing at a conceptual level that that's a
human thing. For now, SIM, what do you think
are the term the absolutely essential terms that our
listeners have to know in order to understand any conversation
about AI? Where should we start?
Yeah. So let's start sharing maybe
three key terms that I think is helpful to have heard of and
have some understanding around. So I think let's start with
large language models or LLMS. So when people talk about
degenerative AI or Gen. AI, in some ways, they're often
referring to large language models, since these models are
what typically drive any type of generative AI content
generation. And so when it comes to LLMS,
the more popular ones or the, I would say the ones that maybe
even some of our listeners have been using or testing, obviously
you have open AIS GPT 4. And important to understand is
that these models, you should have two components.
They have one maybe shot version, which is like shot GPT
and so on. But they also have AAPI
component, meaning that they can be used directly within others
softwares or in other products. And so when it comes to opening
the eye, a lot of people think about shot GPT, but they
underestimate the fact that their underlying GPT 4 O, for
example, API is really what they're often times interacting
with in many, many places outside of directly shut GPT.
And so then we also have Meta who have their Llama model and
that's quite popular for more kind of open source use cases
and Entropics Cloud or their various cloud models.
And they are probably like the biggest direct competitor right
now to open AI in terms of exactly how they're trying to
structure their their models. And so they're all similar in
that they all generate content and they've been trained on
insane amount of data. Like they've been trained on so
much data from everywhere from Wikipedia to Reddit.
And it's, yeah, it's basically a large degree of whatever we have
available to us from whatever has been recorded within human
knowledge online is being used and has been used to train many
of these models. And so when we talk about them
being kind of weird humans in some ways, that's why.
It's because they are trained on things that we have written on
Wikipedia or Reddit or in books and and so on.
And so overall they are designed to again create text, but
they've also become multi modal in recent years, meaning that
they can often times handle images, audio and even video in
some cases. And so that's why they're also
becoming kind of increasingly powerful because, you know, you
can use the same model to basically look at what you see
in front of you. Like you can take a photo of
what you see and you can ask a model what it, what do I see?
And then it can give you a response in text or an audio
form. And so that can be extremely
powerful in many contexts. And we see now with the
versatility that these models are actually able to perform
about 50% of tasks done by knowledge workers.
It's kind of estimated. Obviously, this is when the LLMS
are set up correctly and the tasks are like explicitly
defined in a very, very clear and simple way.
So, so yeah, they're capable of everything from summarizing to
generating code to even creating personalized interventions and
and so on. And what kind of limits their
power is often times based on the input they're given.
And that brings us to prompt engineering, which is the
process of guiding these models to generate useful and
consistent results. So for example, if you simply
ask the model, like, hey, write me an e-mail, you will probably
get something very generic in return.
But if you provide the in the prompt some clear context, like
write a formal e-mail to a new client introducing our services
and so on. And then when it comes to prompt
engineering, often times it goes much, much, much deeper.
It's often times looks at the idea of providing multiple
examples of the output that you want.
So multi kind of shot prompt or non non zero shot prompts.
So it can also include some form of chain of thought prompting
basically where you're kind of trying to get the model to a
review and reason and and think about what is trying to provide
before actually gives it as an output.
And so hopefully when done properly, you'll get something
much more relevant this way. And I feel like a lot of people,
I guess, get disappointed because they don't realize that
even though these models are very powerful, they still need
clear instructions. And we often talk about the idea
of kind of having 1000 AI interns because that's what
often feels like is that they can do a lot of things.
And they are like, you know, with scale, you can do so much
at same time. You have to keep things simple
and be very clear about the context, and vague prompts
honestly lead to vague results or generic results and so on.
And that's oftentimes the first step to work consistency.
And there's always more advanced kind of steps to take.
We won't get into this too much today, but you can use things
like RAG retrieval, augmented generation, or RLAHF, meaning
reinforcement learning with human feedbacks and so on.
There's a lot of things that could come into play to fine
tune or to improve or to get better kind of content that it
retrieves for its output. But yeah, we'll, we'll, we'll
want to get into those. For now, we'll probably go cover
them more in this season, but yeah, finally the last term so I
covered. Large language models the role
of prompt engineering and then AI agents.
So AI agents are autonomous systems assigned to perform
tasks without needing constant supervision.
So one example is having some form of customer service chat
bot that handles some form of routine queries.
Another one is setting up an AI agent to automatically draft
emails for you. And so the difference here with
an agent is that you can set it up so that it knows how to kind
of perform certain types of writing styles.
So it knows when to adjust the tone and style based on the
recipient. So that when you draft an e-mail
to a colleague, that is different to the e-mail that's
drafted for a client or your partner and so on.
So that's increasingly what my work has been around, you know,
working with these kind of AI agents combined with, you know,
prompt in earring and so on. And they can really create some
powerful, often times automated systems or when when you talk
about automating workflows, they become extremely powerful.
And yeah, the these are the three key concepts that I guess
I wanted to then start with. So we've covered large language
models, a little bit of intro of what they are and some of the
different models out there, the importance of prompt engineering
and how it's just not the same as just writing something into
the model. It's about kind of ensuring
consistency and then how you can scale some of this work when
you're bringing AI agents into to play.
So together they enable us to kind of optimize and scale and,
and automate the tasks in a way that I think, you know, just a
few years ago was completely unthinkable and, and something
we never imagined would be possible today.
So yeah, but there's obviously much more to talk about here.
And so maybe Eileen, you can share something more maybe on
recommender systems or I briefly mention reinforcement learning.
And yeah, what would you want to share with with people?
Yeah, I would love to. I think there are really three
major categories. Recommender systems is 1,
Reinforcement learning is another.
And then I feel like it's easy to forget because we we don't
see as much of this in the at the intersection of AI and
behavioral science. But there's also just our sort
of classical machine learning where you have a bunch of data
and here you say you train your model on a million cat pictures
and then you have some new data and you want to learn is, is
this photo of a cat or a not cat?
You can with fairly fairly good accuracy, understand with many
of these classic machine learning models, whether a photo
is a cat or not cat. And I'm, I'm borrowing from
Cassie Kozikov's famous cat, not cat example because I really
admire her work. But but that's, that's sort of
like the most basic version of a of a classic ML model.
The the other categories that you mentioned, recommender
systems, we talk a lot about recommender systems in our
episode with Carrie Morwich. And so, so you'll have a, a
recommender systems one O 1 in there.
So you'll wait for that launch. And then of course, we have
reinforcement learning, which we really dive into with both Amy
Bucher and Susan Murphy, and we really look at the potential for
using reinforcement learning within behavioral science as a
as a method for doing behavioral science.
So yeah, that covers some useful terminology and we'll attach
this in the show notes as well. We can refer to if, if in doubt,
hopefully that's going to be a little helpful.
But then we also wanted to cover a bit of myths because I think
we both feel like there's quite a lot of those going around when
it comes to talking about AI and especially like this
intersection of behavioral science and AI.
And I think 1 myths or slash like oversimplification that I
feel like I hear a lot is just really equating intelligence in
the same way. Like you have a large language
model that is taking an IQ test and then it's very easy to say,
oh, it's as intelligent as a human being.
So that means that it's as human level intelligence.
And that is true. But I think a lot of people also
don't extend that to say that it's human like.
And I think again, it's really, really, really, really important
to not let us may be seduced or confused or made us to think
that they are one of the same because they are again, really,
really different. I think as I'm seeing the data
is coming out, how impressive some of the models are now at
performing certain tasks, at providing certain types of
expertise. It certainly is leveling or
exceeding human level intelligence in some areas in.
Some. Areas in some areas, in some
areas and it's still a very, very, very big difference to
what is AGI like what, what does it look like, but it we're still
so. Far say what AGI means for our.
Yeah. So artificial general
intelligence, you actually referenced the study of narrow
intelligence because you got things, important things to
really hit home that we're still in a very narrow set of
intelligence where, OK, these large language models can
operate and do a lot of different tasks and a lot of
different things, but they're still very narrow.
That's why we often talk about having 1000 AI interns and this
idea of generalized intelligence where they can highly and
reliably perform in a lot of general contexts and really
understand what we know as human beings have like human like
intelligence. It's still far away.
And you're right, it is extremely seductive when you see
that oh ChatGPT passed the bar exam or ChatGPT outperforms
physicians in XYZ. But you know, then the next
second fails. A very basic cognitive
reflection task. Or are you familiar with the
Nick Bostrom's paper clip? Example, yeah, coming from
effective ultrasound, that's like one of the classic.
Yeah. So this thought experiment is,
say you give an AIA task and it is maximize the production of
paper clips, then maybe the AI with access to all the resources
that it needs can maximize paper clips.
And if that's your goal, maybe that's a good thing.
But of course, there are always unintended consequences.
And when, while maximizing paper clips, you, you might find that
there that there are actually other goals that you want to
have in mind as well, such as not killing humans while you're
at it. And and so then you might tell
the AI, OK, but in, in order to maximize paper clips, please
also don't kill humans. OK?
So like it's, it's given that direct order, it'll do that.
But then you think about all of the other indirect ways that you
might kill humans by destroying the environment by by by
excavating all of the resources that you need in order to
maximize paper clips. And you can sort of go on and on
and on with all of these edge cases.
And you can tell an AI system to directly do all of these things.
But the the contrast is for the human, you don't you, you it's,
it's so, it's common sense, right?
It's so obvious that you don't want to deforest the entire
planet in order to maximize paper clips that that that's not
even something you would have to consider saying.
However, if your only goal as an AI system is to maximize paper
clips, then you might you know, you don't have that
consideration. You don't have that human
element of of common sense to guide your decision making.
You only have the inputs that you're given.
Yeah, and I I mentioned this and I think that's speaks to a lot
of people's experience where I think it's a little bit sad in
some ways as well. But like when they for the first
time they open shat GBT and ask it something and they just get
like a really shitty response because these assets something
very generic and simple and we get some kind of generic and
simple back. That's me if you're describing
me. OK, OK.
But in that case it is easy to feel like, OK, this is shit, but
it again is because these models and and so they're currently
quite far away from real understanding the context that
we're in. And we we're doing our best to
kind of kind of communicate and, and make the context come across
in an efficient way, but it's still still very narrow in in
nature. So yeah.
OK. So what, on your end, do you
feel similarly about as a myth? All right, so we have human
level is does not equal human. Like that's our myth #1 and you
just mentioned Gen. AI, so I wanna throw in a myth
that I think exists about Gen. AI, which is that it boosts
creativity. And this is this is a really fun
one for me because some research recently came out that explored
exactly this looking at OK, it's have have some users ask them to
create a story and either give them access to ChatGPT or not.
And then just objectively assess the, the, the products that they
come up with, how creative is the story that they write?
And it's really interesting that giving access to ChatGPT does
increase creativity on average. So across the board, if you, if
you rate the creativity of these stories, it seems like, yeah,
OK, that that's a that's a plus. But then if you actually drill
down into the the stories, if you look at people who were more
creative at at the start, the sort of your high skill workers,
they really didn't benefit from ChatGPT at all.
This lift was really can be contributed to helping the those
who were not very creative or or or not very skilled in writing
in the 1st place. So you actually find that that
the ChatGPT is much more helpful for for less skilled workers.
This is this is kind of a another myth in itself.
But another side effect of this actually is that even though
the, the, the stories were more creative overall for the, for
the individual users, if you look at the content in a
collective sense, the diversity of stories is actually reduced.
So everyone is kind of getting the same ideas from Chachi BT
and they're all converging on similar stories.
And that to me is not a good thing that that that is really
just making the the content in our world much more homogeneous
and much less interesting. Yeah, and I can speak a little
bit just based on just having a partner who's a motion designer
and and artist coming from an art background.
It's really like I, I see the frustration when she's trying to
use some of these tools because like she either has the option
to do it herself in a full control and rely on her own
unique creativity and so on. Or it's just kind of like almost
tie an hour behind her back and be kind of reliant on a shitty
version of that through some of the quote UN quote creative
tools that can't really do things as well as she can.
And where she doesn't have full control.
And she's kind of have to be like, no, I didn't mean in this
way or like, I mean in this way. And like, no, like this is what
I mean. Like instead of actually just
being able to, to do it and just having the control.
And so while someone who has never created some form of a
digital artifact, we can now do that.
And that's really cool. It is also, as you said, like
it's really important to understand like what, what are
we really risking if we throw away the the people who do it
really well? And yeah, I think we risk a lot
of lot of that collective collective gain or collective
good. OK, so myth #2 Jenny, I doesn't
boost creativity overall. Yeah, I would say that let me
share one pro tip, a personal Aleen pro tip and I think this
so this is related to using ChatGPT for for creative tasks.
Some advice that I hear out there is, oh, use it to
brainstorm and get ideas and like, like basically use it for
your first draft of something. And I think that is a terrible,
terrible, terrible idea. I think you absolutely have to
use for all people use your brain as the first draft.
If you need help structuring it, clarifying your ideas, seeing
what you've missed, fine. But to, to use ChatGPT as your
first draft, then, then what do you have?
You now have a new status quo, which is this fully fleshed out
report. And one thing we know as
behavioral scientists about humans is we we are attached to
the things that we own. So it's like this is the the
endowment effect in in play right here.
We don't want to lose by editing, getting rid of the
suggestions that ChatGPT made. We don't want to lose that
content. Yeah, you're, it's a great,
great, great, great point. I think you as you're like
alluding to like you basically sell yourself short by just
molding yourself by the most generic responses given to you
by Shajibati. It's really both important not
to let these models and so on think for us too much like it's
really useful to get feedback and, and and expand our thinking
and test our thinking about not to do anything.
And, and sadly, because it is actually what I've noticed
myself, because I really agree with your advice, but at some
time also noticing that it is tempting to start also being
like, hey, give me 4 ideas, give me 4 things.
Give me, give me. But instead of jumping that
straight away, taking a little bit of a moment to reflect of
again, what do I think, what are my thoughts, what I believe,
what are my ideas? And then starting from there.
And then you can always like build on that.
I've never seen that not being the best way for like, I've
never seen the way where I've started what he is like asking
JPT to just do something for me and not in the end I feel I've
wasted 30 minutes where it just giving me some generic stuff
where I have to. In the end you start over and,
you know, give it what I actually need.
All right, so that was myth #2 Gen.
AI boost creativity. No, it doesn't.
Myth busted. OK, next one.
So I think there's this this tendency to talk about AI as
being this huge risk in terms of user privacy.
And I think that this misses some really important nuance.
And that is just that not all AI is equally unsafe.
It's not all equally non private.
And, and, and really you can create models depending you
being a company, a product manager, whoever's in control of
the AI product, you can, you can create it differently depending
on your privacy goals. And some companies have really
made a real effort towards towards using and developing
privacy preserving AI. And this is a has become a whole
subfield in itself. And you can look at what does
that mean for maintaining privacy in your training data,
in your input data and your output data.
And one pretty popular way of doing this is called Federated
learning. And this is basically just a
version of on device machine learning.
So the data never leaves the user's device.
It means there are some implications of this, some
downsides. You might say the the AI models
have to be smaller. You can't fit ChatGPT on your
phone. ChatGPT is extremely energy
inefficient, so you can't do that.
But your, your data never leaves your phone.
Like that's a beautiful, important thing.
And, and I feel like that is not talked about maybe as as much as
it should be. So your, your personal data is
totally secure. It is.
It belongs to you and it isn't given to anyone else.
Yeah, I think that also looking at comparing the large language
models that are open source that you can kind of self host and
and store locally compared to the ones that you can't like in
the case of opening eyes or and tropics.
The the difference is becoming I think less in many ways.
And yeah, it's also both for the bigger models, if you wanted to
use them with your company data and so on, they can certainly be
self hosted and privately stored and nothing leaves the company.
But as you said, like also what what is exciting is this kind of
like increase kind of promise of these maybe tiny models that can
exist even on the device. When I think that's kind of
where, yeah, you, you obviously you can't maybe do exactly all
of the things in the same way. But as you say, like it's you
can do a lot of things, yeah. A lot of things, in fact.
That leads me to our next myth, which is that more complex
models are better because they often are not.
There are quite a few downsides to having more complex models,
including their lack of transparency.
So when we talk about the black box, the bigger and more complex
your model is, the less explainable it is.
So you don't have what we call interpretable or explainable AI
where you can say here's what the model is doing.
So if, if you have a really big complex model, you can no longer
do that. And that's that there's a very
clear downside to that. If you don't understand it, you,
you, you not only cannot tell your users what's going on, but
you yourself don't know what's going on behind the scenes.
And so that there's some obvious, some very real and
obvious dangers there, but also the, the plastic downside of
complex models is that you have a risk of overfitting.
So this, this is what happens when you say you, you train
some, a model on some piece of data, but you train it so
closely to that data that, that it, it can only predict that
data. When you then send it out into
the real world, it does a, a pretty poor job.
And this is overfitting to the training data.
And then of course, more, bigger, more complex models are
slower, they take more energy. So if you don't have to do that,
if you can use a some a linear regression model, then do that
by all means. Yeah, yeah, 100%.
I think that's so true. And yeah, we covered quite a bit
of myths there. Any any final myth or a little
thing that you wanted to mention before we get to our final
showdown of this episode? I think I would only say one
final thing, and this is going to have to be an episode on its
own because it's such a huge, huge topic.
And this is one that Sendhil Mulanathan has, has really
tackled. And this is the idea of
algorithmic bias and human bias and, and sort of comparing the
two. So the myth is that algorithmic
bias is worse than human bias because in in the public
conversation, we talk all about how terrible biases in our
algorithms and how AI is so biased and so on.
And Sendal makes the very good point that humans are very, very
hard to change. Algorithms may be hard too, but
surely not as hard as changing humans.
And so that that that has been something that I think about a
lot. And obviously work has to go
into making algorithms non biased or biased in the in the
correct way, right? Maybe correcting for bias out in
the real world, but in in terms of the lift compared to changing
humans, as behavioral scientists we know just how difficult it is
to change people. So if we can just change some
math. Not to understate the
difficulty, but surely relatively much easier.
Yeah. And yeah, Sandhu, if you're
listening, let us know and and we'll book you in for episode.
Awesome. So finally, we have a new quick
fire round for this season. It's called to AI or not to AI.
Basically, we're really interested to hear from my guest
in terms of their thoughts about really using AI or not in the
day-to-day or their work. And so we're kind of providing
with some tasks and asking them, is this something that you think
AI should do that it's well suited to or not?
And so for this kind of first episode, we thought it'd be fun
to use. Yeah.
Ask each other a few of those to warm, warm ourselves up.
And also just, yeah, we've kept this secret so we don't know
what the other person can ask. But I have a few ready for you.
Eileen, I'm. Ready.
OK so my first one for you to AI or not to AI let your Tesla
drive your son to daycare on its own using autonomous mode.
Absolutely not. Never, never.
Or like another. Really hard to imagine a time
when I would be, Yeah. I mean, he certainly won't be in
daycare, but he probably won't even be in school by the time
that that autonomous vehicles are ready for that.
Even if you knew that you were probably on average a worse
driver than most autonomous vehicles, yeah.
Would you still? Would you still wanna do it
yourself? So I probably am a worse driver.
That's the reality, especially in the Tesla's too big for me.
But yeah, I don't trust it. I've had too many incidents
where it does a crazy thing and you have to you have to take
over. And it doesn't feel like the
technology is moving fast enough in order for me to trust it.
I want so badly for that day to come.
It just doesn't feel close at all.
So maybe my answer is eventually, but not before.
My son is an old man himself. OK Sam, for a podcast on
behavioral design, generate guest BIOS and episode summaries
and then combine that into a script for hosts to read
hypothetically. Hypothetically, some of it, yes,
some of it, no. Maybe.
I do think there's amazing things you can do today in terms
of if you have a very strong and clear understanding what the
output you want to be like, let's say like a bio or a
transcript or various things that you want to include in your
podcast. Definitely that can be done.
And maybe it's being done for for this podcast.
But in terms of like providing the extension of a longer script
or like actually having some form of artificial holds that
would do some form of thing, No, not not that, but but yeah,
introducing the guests, yeah, that kind of stuff, yeah.
Interesting. OK, OK, my turn.
OK, Eileen, to AI or not to AI? Create a virtual version of
yourself that speaks Swiss German really well to provide
care and companionship for your older relative.
Aw, that's would be gosh, I have such mixed feelings about that
because. They can chat with someone that
can maybe. Well, yeah, it's it's so
interesting. In the general case, I feel
pretty strong anti anti feelings to not AI.
However, in this specific case, Mike Grossi is 90 something
years old and it's not like I'm in Switzerland with her.
So it's not replacing me. I'm not there.
And so, gosh, wow, you, you really made made me see the flip
side that I hadn't seen before. OK, I'm going to say I'm willing
to try it, but now I can't let any of my relatives listen to
this episode. I'd like to oversee it for a few
years at least. OK.
To AI or not to AIA marriage
counselor? Actually, it's funny because
that is my next to you. I have to not to AI have AI
relationship coach analyze your marriage and suggest
improvements. Yeah, I would say to AI
actually, I would be quite open to to getting feedback and yeah,
ideas from from an artificial marriage coach I think.
I think so too. I think so too because I think
it's easy for you as a couple to discredit the AI suggestions if
you don't like them. Easier than a person, and maybe
it'll bring you together to have a common enemy.
Yeah. And it's it's probably also
where it's one of the cases where at least initially short
term, it would be easier to probably be able to talk about
anything and be more open. As long as you feel like your
privacy is protected, then yeah, I wouldn't mind trying it in
terms of, and I would say this is a good example where I do
think long term, even though it could perform as well in terms
of use, like task for task perform as well.
There is I think some inherent value in actually having a human
being that you feel accountable to and that you trust and so on.
That's kind of there to support your relationship that will make
you work a little bit harder, be a little more engaged and so on.
Whereas you might not be as motivated to show up in time or
like do whatever thing you need to do and you are starting less
seriously than if you actually have to shop for a person and
pay for it and so on. So I think there is some part of
the traditional setup that I do think it's hard to replace in
that sense. So this one is a little bit
longer, but again, to AI or not to AI to use some form of AI
that tracks your day through, let's say your smartwatch or
smartphone or maybe smart glasses.
So it tracks your day. And then at the end of the day,
each night, it gives you feedback on whether you lived up
to your best self, like whether you like, you can set the
standard in some ways, like this is who I want to be.
And then every evening getting a little bit of like a mean a
report of like, hey, we'll talk. Eileen, here's how you've been
performing today. I think if I would be able to
set the seriousness of my commitment to that goal and also
not get that report on a daily basis, I think maybe less
frequent. I could handle like a weekly
check in or even a monthly check in and then at those check
insurance, set new goals for the next the next time period.
Yeah, I'd be up for that. I mean, if I'm controlling
everything, sure. I don't, yeah.
And and it's, I think I just say, I think it, I would
definitely be more up to it also like on a monthly basis, but
like on a daily basis, like I think eventually, because it's
very uncomfortable to be putting that pressure on yourself.
And I think that's what we're doing as society today.
And that's why I put it like it's more extreme to that.
But yeah, I'm sorry. What's your What's your final?
My final one it it's related to my your your Swiss question, but
this is in the afterlife. So to AI or not to AI your great
grandfather. So for example, in a virtual
reality world, you have a fully formed great grandfather who
walks like him, talks like him, you can talk to him, you can
play catch with him, you can visit him anytime you want.
Not really. No.
Yeah, yeah, it does Like this so much.
Speaks to all of my sci-fi interests in terms of like
Westworld and and there's an episode of Black Mirror that's
kind of mirrors this concept and.
But no, not for, not for me. I do think it's nice to be able
to let go and to, yeah, to. Maybe not nice, but important.
Important, Yeah. It's important to to be able to
let go. And it's important to in some
way, I feel like it also diminish what actually was there
before because you can like holding on to some form of
fragment of who they were or who they are, but it's actually not.
And it's not actually them. Yeah.
Yeah, this is this. This reminds me of since I'm on
a Sherry Turkle kick. She tells a story about meeting
a virtual Steve Jobs and she's done.
She's done some more recent work on artificial intimacy and
looking at people, for example, who have created a version of
someone that they knew who is who is passed on and they want
to continue interacting with them.
So she met a virtual Steve Jobs as part of this research and she
actually knew Steve Jobs when he was alive.
And her experience with the virtual Steve Jobs was, oh, like
maybe they uploaded his mannerisms and everything that
he's ever said. And they have this, the wealth
of everything on Steve Jobs. And he's a public figure.
So there's a whole lot of information about Steve Jobs out
there. But her experience was like, he
would never say that this is the, like, there are some things
that maybe feel like a likeness, but he would hate the virtual
Steve Jobs. Like, he would not approve.
Yeah. Fidelity, it's hard to, yeah,
establish. And yeah, I do think it's
interesting that we're both kind of shows a lot of these
questions that centered around significant relationships in our
life, like our connection to our kids or elder relatives or
partners, and I guess ourselves as well.
And I do think there's, I'm excited for this season to talk
about how AI can be in terms of either a tool or convenience
when it comes to recommending certain things regarding, I
guess classic stuff like recommended mention components
or recommending TV shows and so on.
I think that is interesting. But I do also think it's, it's
super interesting and profound to think about how AI can
influence who we are as, as people.
And kind of it does confront this notion of who are we as
humans? What are we?
What is it to be us? What is it not to be?
What is it be artificial? And, and how do we grapple with
that? And I think in some ways I think
that's, that's really real, especially as we starting to use
AI to be redefined as we have replica replicating partners, we
have a replicating, you know, customer service agents, these
things that is trying to imitate and as you say like creative
jobs or whatever. So it does gets to some very
deep and human questions and I I think if we can become a little
bit better understanding of how we can relate to those and how
we can kind of, yeah, feel comfortable there, I think
that's a big win as well in this season if we can approach on
this thing. So yeah, just a final reflection
on my end, but I think that's super interesting.
Yeah, I mean, all of these questions are just so perfectly
suited for behavioral science and just to toot our own horn
like that is why we have to do this.
I remember early on we were, we were sort of teeter tottering
about whether we really wanted to do AI because of the hype.
We felt the backlash and we decided that it would be
irresponsible of us not to do this.
So that's that's why we're here. Yeah.
And we're excited to share this with you and, and we in terms of
listeners and we have a e-mail address which is podcast at
habaweekly.com. And we're very, very keen to get
your thoughts, questions it asks people you want to hear from,
questions you want to get answered.
Send us all of that and anything or on that e-mail.
So podcast at habaweekly.com. And yeah, to wrap up this one,
get excited. We have the first interview
coming out next week. We have some fantastic,
honestly, I can say because we're recording them already,
some fantastic episodes that are already lined up for you.
So yeah, hopefully this was a little bit of a primer and yeah,
time to get excited for some some really interesting
conversations on paper science on AI and and beyond.
So so yeah, I'm excited, Eileen. I'm excited.
Yeah, and that's a wrap. You've been listening to the
Behavioral Design Podcast, brought to you by Habit Weekly
and Nuanced Behavior. Sam and Elaine 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. The.
The brain is beautiful and really, if you think machine
learning models can be a black box, tell me how consciousness
arises from our meat machines.