♪ (Music fades in) ♪
(Chirping)
(Vocalizations, different languages)
(Talking overlaps in background)
(Laughing)
(Computerized beeping)
(Beep)
(Man) We come into this world with the innate ability to learn to interact
with other sentient beings.
(Child vocalizing)
(Man) Suppose you had to interact with other people by writing little messages.
(Child vocalizes)
(Man) It'd be a real pain.
(Man) And that's how we interact with computers.
It's much easier just to talk to them... just so much easier...
(Child vocalizes)
(Man) If the computers could understand what we're saying.
For that, you need really good speech recognition.
(Narrator) The first speech recognition system was developed by Bell Laboratories
in 1952. It could only recognize numbers spoken by one person.
In the 1970s, Carnegie-Mellon came out with the Harpy System.
This was able to recognize over 1,000 words and different pronunciations
(Narrator) of the same word. - (Man) Tomato - (Woman) Tomato
(Narrator) Speech recognition continued in the 80s with the introduction of the
Hidden Markov Model, which used a more mathematical approach
to analyzing sound waves that led to many breakthroughs we have today.
You're taking in very raw audio wave forms
like you get through a microphone
on your phone
or whatever...
(Woman) We chop it into small pieces and it tries to identify which phoneme
was spoken in that piece of speech.
- Phoneme is a primitive unit for expressing words.
(voicing phonemes shown above)
And then it stitches those together into likely words like Palo Alto.
- Speech recognition today is good at transcribing what you've said...
(Man, to phone) What's the weather like in Topeka?
(Man) You can talk about travels, your contacts, like, "Where can I get pizza?"
(Phone) Here are the listings for Pizza.
(Man) "How tall is the Eiffel Tower?" (Phone) The Eiffel Tower is ...
(Woman) We've made tremendous improvements very quickly.
(Man, to phone) Who is the 21st President of the United States?
(Phone beeps) (Phone) Chester A. Arthur was the 21st...
(Man, to phone) Okay, Google, where is he from?
(Man) Years ago, you had to be an engineer to interact with computers.
Today, everybody can interact.
- One thing still in its infancy is understanding.
- We need a far more sophisticated language understanding model
that understands what the sentence means.
We're still a very long way from that.
(Beeping)
♪ (Soft background music) ♪
(Woman) Our ability to use language is one of the things that helps us have culture.
It's one of the things that helps us pass on traditions across generations.
Figuring out how the system of language works, even though it seems easy,
turns out to be very hard, but is one that every baby understands by 2 years old.
(Girl) There's two of them. (Woman) There's two Ls, yeah (spells word)
- Language is extremely complex and sophisticated...
- From the semantics
- (Man in chair) Ironies... - (Woman) Strong accents...
- (Man) Facial expressions...
- Human emotions, because that's part of how we communicate.
- Humor...
(Aside) Do I have to be careful not to offend the dinosaur?
- Language has so many different layers and that's why it's
such a difficult problem.
(Man) The present human brain and the learning algorithms in it
are far, far better at things like language understanding
and they're still a lot better at pun recognition.
- Whether or not we replicate exactly what the brain does, to understand
language and speech, is still a question.
(Beeping)
(Man) For many years, we believed that neural networks should work better than
the dumb existing technology that's basically just "table look-up."
Then, in 2009, two of my students (with some help from me) got it
working better. The first time it was just a little better.
But it was obvious that this could be improved to work much better.
(Man) The brain has this system of neurons all computing in parallel.
All knowledge in the brain is in the strength of connection between neurons.
What I mean by "neural net" is something that is simulated on a conventional
computer, but is designed to work in roughly the same ways as the brain.
Until quite recently, people got features by hand engineering them.
They looked at sine waves and did fourier analysis and tried to figure out
what features they should feed to the pattern recognition system.
The thing about neural networks is that they learn their own features.
In particular, they can learn features and features of features, etc,
and that's lead to huge improvement in speech recognition.
- But you can also use them for language understanding tasks.
How you do this is to represent words in very high-dimensional spaces.
- (Man) We can now deal with analogies where a word is represented as a list
of numbers.
For example, if I take 100 numbers that represent "Paris," and I subtract from it
"France" and add "Italy," if I look at the numbers I have, the closest
thing is a list of numbers that represents "Rome."
By first converting words into numbers, using a neural net, you can actually
do this analogical reasoning.
I predict that, in the next five years, it will be clear that these neural networks
with new learning algorithms will give us much better language understanding.
(Woman) When we started out, we thought things like chess or mathematics or logic
would be things that were really hard.
They're not that hard. We ended up with a machine that played as well as
a Grand Master at chess.
What we thought would be easy for a computer system, like language,
has turned out incredibly hard.
(Man) I can't even imagine the moment of success quite yet because there are so many
pieces of this puzzle that are unsolved, both from a science point of view
as well as a technical implementation point of view.
There are a lot of unknowns.
(Woman) Those are the great revolutions. Not just what we fiddle with what
we already know, but when we discover something completely new and unexpected.
(Man) Once you are in the area of
human-level performance, that will be pretty remarkable.
(Beep)