- Thank you.
So, we're now going to
start with our first lightning talks of the day.
And this session is
a session that's focused on the theme of
intelligence inspired by humans.
And first up we have Michael Frank,
who is a professor in our psychology department,
and he's the David and Lucile Packard Foundation professor
of human biology, Michael.
- Thanks very much.
I'd like to tell you a little bit
about my work on children's early language learning
and why I think this can be an inspiration for social AI.
Language learning is one of the key puzzles
of cognitive science, linguistics, and psychology.
How do speechless, wordless infants
so quickly become toddlers who can understand language
and use language to make their way through the world?
This process happens incredibly quickly.
Here's my daughter, Madeline, at 18 months.
At that point, the longest thing she had said was "happy-b",
which I think meant happy birthday.
At 19 months she combined words for the first time
and said "blue ball".
At 20 months, 23 months she observed
that "spike doggy no food eat dirt".
She was talking about my mother-in-law's dog, Spike,
and it's true, he doesn't eat food, he eats dirt.
(audience laughs)
At 26 months she was a teenager already,
she said "dada move own body, my need lilbit more space".
(audience laughs)
So you can see her here at 26 months,
she's about two pounds heavier,
her hair's a little bit longer
and she's got more dirt on her face.
But these physical transformations
pale in comparison to the expressive transformation
that she's gone through.
She's able to use language in a productive way,
saying things that I never said to her, to get her way.
This input to uptake relationship
has been studied in the cognitive sciences for many years.
And in this history
models from artificial intelligence and machine learning
have been a tremendous inspiration,
helping us understand the relationship
between language input
and language output in generalization.
This architecture here is a transformer network.
It's been responsible for some recent
state of the art results in language modeling,
producing incredibly interesting
generalizations from language input.
But I'd like to highlight one difference
between this transformer and children.
This figure shows differences in language input
to different kinds of models.
Each dot shows one million words
of training input to a model.
The red dots show what's received
by an average two-year-old, 10 million words.
In contrast, recent state of the art language models
receive more than 10 billion words of input.
In other words, it takes 1000 times less data
for a two-year-old to learn to speak
than for a language model.
Why is this and how can we use less data
to train our own models?
What I want to argue for you today
is that the social context of language learning
allows children to do more with less data.
How does this happen?
First, children let others help them
solve puzzles, receiving supervision,
in the computer science sense, from the adults around them.
Adults point out when something is ambiguous,
help clarify the input for children
in ways that allow them to learn.
In my lab we study the social ecology
of these parent-child conversations, using sensors
like cameras on the children's and parent's heads
to get a good sense of what social information
is available to the kids.
But beyond just using that information,
adults and children, when they hear language,
they think about what others are thinking
and they use that to make sense of the language they hear.
For the past 10 years my colleague,
Noah Goodman, and I have been studying
this kind of pragmatic understanding of language in context.
We show people displays like this one
and we say "my friend has glasses".
Many people share the intuition
that my friend is the face with glasses but not a hat,
going beyond the literal meaning of the word
to infer the intention in context.
We've constructed models
of this process of recursive reasoning
that describe the way that we can refine
the initial meaning of a linguistic utterance
to figure out what it actually means
in a particular context.
Critically, our experiments have shown
that children can actually use this kind of information
to learn the meaning of a novel word.
So when we show three and four-year-olds
these two dinosaurs,
and point to this one and say
"this is a dinosaur with a gazzer",
children correctly infer that the gazzer is the bandana,
the novel, more informative, more interesting,
more pragmatically relevant feature of the dinosaur.
The last thing that children know how to do
much better than artificial agents
is to ask for help when they need it.
When we show a child a object they've never seen before
and we say "give me the dax",
they correctly infer what it is
and give it to the experimenter.
But when we show them two objects
and we say the same kind of thing, "give me the fep",
they look around, huh, and they look to the experimenter
seeking social gaze for help.
In all of these ways and more
social reasoning skills allow children
to do more with less data.
And I believe by studying children
and understanding this process
we can create AI that is more efficient with data
and uses its social context more appropriately
to learn to communicate.
Thank you.
(audience applause)