[MUSIC] We are the paradoxical ape.
Bipedal, naked, large brain.
Long, the master of fire,
tools, and language.
But still trying to understand ourselves.
Aware that death is inevitable.
Yet filled with optimism.
We grow up slowly,
we hand down knowledge,
we empathize, and deceive.
We shape the future from
our shared understanding of the past.
Carta brings together experts from diverse disciplines,
to exchange insights on who we are,
and how we got here.
An exploration made possible by
the generosity of humans, like you. [MUSIC].
My name is Damian Blasi and today I'll be
telling you about human languages and their cognitions.
I would like to start by
telling you a little bit more about
the general scope of my research and my program so far.
I'll be giving you some hints
on the nature of language diversity.
Depending on how you count,
and if you have to count,
there is between 6,500 to over
8,000 languages spoken and signed into world today.
Of course, people have been amazed by
these steaming regularities in the structure of
languages that some call language universals.
There is the number of features that we get to
see in almost all languages.
Those have gotten most of the tension by linguists,
psychologists, anthropologists, and the like.
But what I care the
most about and what is the focus of my research,
is those dimensions where languages can
vary and sometimes they can vary quite a lot.
There's a huge number of ways in which this could happen.
There might be differences in the type of speech
sounds or signs that language is deployed,
there might be differences in
the types of rules that individuals
have to use when engaging in
conversation across different languages.
There are differences in the type of
information that has to be
obligatorily conveyed when we
produce a valid utterance in a given language.
There are differences in the basic concepts
that are enshrined in
the common vocabulary of a language,
and as you can see here,
a long list of
dementias are of extreme interest for science in general.
Of course, the most straightforward natural question
upon realizing of this diversity is,
where is this diversity coming from?
The most straightforward answer
is that these languages are different.
Because their histories are different
One one extremely important fact to
keep in mind is that some of the largest language groups,
which are proxy by common colors
in the map that you can see at the side of the slide,
those groups have diversified over the
last 12,000 years and most likely
within the second half of that period
of time that we refer to as the Holocene.
For instance, if you take
a language family such as Indo-European,
you have languages like English and Hindi,
Spanish, French, Sinhalese, etc.
All of those languages are believed to
derive from a common ancestor that has existed,
between 6-9,000 languages back in
the past and this is very important.
The fact that these histories are different is crucial.
Because this time period has been one of
the most eventful periods
in the history of our species period.
Within the last 12,000 years,
we went from living in small hunter-gatherer bands to
large agglomerations of individuals that
might end up paving the way for contemporary cities.
We end up occupying all of
the ecological niches that exist in the world,
we develop large political experiments
in the form of multinational empires.
We have witnessed the emergence of moralizing high gods.
We have seen the rise of
a whole new communication modality
as its written language,
and a large number of other changes that have
been crucial for defining what it means to be humans.
This is at the very core of my research program.
I work towards producing,
creating a science of language diversity that takes
into account this recent history of languages.
That is heavily interdisciplinary,
where linguistics is no more important than
anthropology or cognitive science, or history.
That is evolutionarily sound.
I'm not going to discuss that last part,
but I just wanted to get it out.
Over the last decade,
I've worked on a number of different projects,
where I engage with different types of evidence,
from grammars of long-dead languages to genetic evidence,
material culture, historiographic records,
and nography, etc.
I've done work not only on
global linguistic diversity but
also on more regional scales.
At the end of my presentation,
I'm going to provide you with a link
where you can go and check
my work and see my papers for free.
Now, onto the main message of my presentation here today.
What I would like you to keep in
mind at the end of this presentation is
that there seems to be a behemoth language bias in AI.
It shouldn't come as a surprise if I
say that language technologies and the applications that
come with them are at
the cornerstone of the development
of contemporary AI architectures.
There's going to be a number of
presentations in this impulsive.
They are going to address exactly that point.
The centrality of language and
language models to contemporary AI.
I'm not going to go over that.
I also don't think you'll be
surprised if I told you that when it
comes to scientific and technical fields,
where language plays a central role,
and this could be anything,
from the study of language pathologies to
the development of literacy interventions
and language technologies themselves,
there is a clear bias favoring
those developments in large and influential languages.
Which I refer to as behemoth languages.
Among those, the English
language ranks very high up and it's
a language that has received the most attention and
the more scientific and technical
development of them all.
What happens is when we take these developments that
are native to English and other behemoth languages,
and we take them to other smaller,
presumably different languages,
we get to see that the quality of those techniques,
those findings, those procedures,
get impoverished, sometimes drastically.
When we move those resources
from English to other languages,
we get to see that, for instance,
we arrive to less accurate medical diagnoses,
we get lower scores in
educational and other type of intellectual testing,
we have poor access to high-quality information,
and along, etc.
Of course, language technologies are not an exception.
Last year, my colleagues and I
published a paper when we survey
the total amount of developments in
language technologies across the world's languages.
What we found is that
English and other behemoth languages,
monopolize over 80% of
all of the developments in the field.
As you can see,
most of the other behemoth languages
are Western European languages as well.
Again, I don't think you'll be surprised if
I told you that the main predictor
of the development of
language technologies in a language is not the number of
users who could benefit from
that development but instead say,
economics and totality the language has
associated with it.The more
money the users of a language have,
the more likely is that
better and more language technologies will be developed.
In this regard, we can think, well,
language technologies are the basis of AI.
We will expect that with more resources and
more equitable world we'll get to see
better AI being deployed for more and more languages.
I think there's some truth to that.
I would say that it's a second dimension that
hasn't received as much attention as this one.
In order to understand this core idea,
I need to describe briefly.
There's this two ways in which we analyze AI systems.
First, we study the performance of this systems.
Let's say, for instance, I train
a visual network to classify paintings.
So I can ask whether
the AI system is doing its job by correctly classifying,
in this case, for instance,
the Mona Lisa as such.
But then there's a second category of
analysis that have to do with the study of
the representational and developmental biases that
this systems produced while
trying to solve the task at hand.
Let me give you one particular example.
In the case of the classification of
the Mona Lisa with the system that we just introduced,
we can ask what are the features
that this system is latching into?
For instance, it might be the case
that this network is looking at
a random collection of pixels around
the painting and it's coming up to the decision.
Or that instead is
considered in this picture and it's breaking
it down into objects that look very familiar for humans,
like bodies and hands and a grin and eyes etc, etc.
That will be a representation on
analysis of the AI system.
Now in both cases,
we tend to compare the performance,
the representations and environmental biases that
these AI systems have with their human counterparts.
I'm not using human as such,
I'm using human asterisks.
With this I'm getting a bit ahead of myself,
I refer to those humans who
speak or sign a behemoth language.
Let me give you two specific examples
of what the problems of this are.
But before I'm going to refer to what I think it's
a very important and not very widely known fact
of languages and language diversity.
This is an idea that has been around for
a long time and it has been very controversial at times.
But I would say that in 2023,
there is enough evidence to make the claim
that the language one speaks or sign
have a number of important consequences for
cognition well beyond language itself.
So the language you speak or
sign might affect the nature or
the type of computations and
representations you're performing with your head.
It might impact social cognition and how
you conceptualize others and
yourself in relation to them.
It might affect the way in which you break down
sequences of causal events or memory more in general.
It might affect visual attention,
auditory attention, and many,
many other aspects of cognition
that we tend to leave have little to do with language.
This, as I was saying before,
has a number of important consequences for how we
evaluate and study AI systems
and their comparison to humans.
First example I want to give,
it's a very straightforward one and that
has to do with large language models.
Which are the, perhaps the most
famous and the most important
example of deep neural networks
that are deployed for the context of language.
What we know is that
most of these large language models are
trained on this next word prediction task
or something that is very similar.
What I mean with this is that this AI systems
are trained so that given a sequence of words,
they are able to predict
the next word or the next sequence of words.
Just by training a system to do that on
a large dataset ends up
with frankly very impressive performance
that we are all enjoying today with say, ChatGPT.
This is very surprising because for
a long time we expected that
any good performing language AI has
this large language models would have needed
some type of linguistic prior on it.
For instance, we should tell
the system some things are nouns,
that some things are verbs or that this is a phrase,
but it was much simpler to that.
Just training AI systems on
this next word prediction task lead
to incredible results.
What we can do then is to
compare the performance of these humans asterisk with
the performance and the behavior of
this large language models and it turns
out they seem to be very aligned,
even more so than when comparing in other tasks.
Let me put it in simpler terms.
What seems that what humans are
doing and what these machines are doing is in
many respects and their respects that we
can measure and observe very
similar and this has lead to a number of
very intriguing and very exciting question.
For instance, maybe there's
something that this large language models.
Maybe there's something about this large language models
when they are being trained on data that it's
similar to a type of learning that takes place in
the head of actual humans all the way around.
It might be the case that
this similarity is telling you something deeper
about the connection between
large language models and humans.
Maybe humans when acquiring and developing language are
solving something that is similar to
the next word prediction task as these machines do.
That's should be super exciting because it means
that we can study the structure of
language by looking at this networks instead of
looking at the networks of actual humans,
at least in some regards.
The problem or the limitation is
that it seems that prediction and this
eagerness for prediction that we get to see in humans
doesn't seem to generalize to
all languages we have access to.
Again, there's no time to go into
the linguistic details but there are
some languages like Turkish,
where it seems that it's much less
important to make predictions about the next word
rather than coming up with a model of
the entire sentence as it unfolds over time.
This is, again, very important
because it could be telling us that there
might be different ways in which humans can arrive to
the system and one of those good prediction,
but that's not the exclusive one.
The second example I wanted to discuss with you today,
have to do with the
so-called convolutional neural networks,
which are the most famous architecture
for visual AI that we have access to.
Again, there's no time
for me to describe how it actually works,
but this is the standard architecture
for tasks such as the one I
introduced before which is say
the classification of paintings or natural images.
Now, we could consider the following thought experiment.
Let's say that I train
my CNN and the CNN observes this object,
that it's labeled as a dax So there's a picture,
this object appears and it's labeled as being a dax.
Then I can ask my system after training,
which one of these objects is more
likely of being classified as a dax?
And if you can see there's two objects that have
different types of similarities
with the original dax I introduced.
The one at the very right of the sequence
shares the same texture
as the original dax, but a different shape.
Instead, the second to
last shares the same shape
but it has a different texture.
Something that is very, very
interesting is that when we train
CNNs on large datasets of natural occurring images,
we get to see clearly emerging shape bias.
But instead, when we look into humans asterisk behavior,
we get see a very clear shape bias arising step.
Again, we encounter the same situation
where this will be
the complimentary situation as the one introduced before.
Because in this case, there is no match between
humans asterisk and AI systems
instead there is a mismatch.
This led people to think, well,
maybe we can instill
the shape bias into CNNs and we see what happens.
What happens is that
this CNN seems to be performing better when they
are informed by shape bias than by
their native texture bias.
This is very interesting because
you put something and you find in humans,
in AI system and suddenly it's performing better.
This leads to all kinds of natural questions
about why is that shape bias exists to start with?
Some people have suggested, for instance,
the shape bias might be something like
a marginally optimal solution when you
have to communicate about objects in the real-world.
But again, here is
language diversity coming up with counterexamples.
For instance, speakers of Tsimane,
which is a language found in Bolivia,
have found to exhibit a very weakened version of
the shape bias or to go for
other features that have nothing to do with shape at all.
This two examples illustrate
the main takeaway message that I
wanted to share and discuss with you today,
which is comparing humans and machines into
conscious for AI is extremely
important and a very productive endeavor.
We can come up with better AI for a number of tasks,
some of which are important now and some
of which might become very important in the future.
But I think by overfitting our comparisons to
whatever speakers of behemoth languages
and very in particular English,
we are losing track of all
of these luxurious diversity that
happens outside of the places
that we have study more extensively so far.
So I'm quite sure that it's a number of
amazing discoveries about ways in which
humans can conceive, represent,
and compute different types
of information that we haven't uncovered
yet and those lie in
languages other than the behemoth languages.
With this, I want to thank you and thank
all the organizers for this amazing event and
here's my contact details and you have access to
my emails and I look
forward to keeping this discussion going on.
[MUSIC]