The Word Royal Institution Christmas Lectures


The Word

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Human language can be complex and bewildering.

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PHONE RINGS

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Oh, dear. Sorry, I've got to take this.

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Hello? I can't talk now.

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I'm doing the Christmas lectures!

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What?

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She said DAVID didn't take his money?

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What? She said David didn't take HIS money?

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Oh, she SAID David didn't take his money.

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Why didn't you just say that, then?

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Sorry. Now, what you have there is three very different meanings from

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exactly the same sentence.

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Will anything other than another human being ever be able to cope

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with that level of complexity?

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In this lecture, I'm going to find out what makes language the ultimate

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communication tool and why humans are absolute masters of it.

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Welcome to the third Royal Institution Christmas lecture

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of 2017.

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I'm Professor Sophie Scott.

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Now humans have got an incredibly powerful ability - language.

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I can convey very precise meanings to anyone within earshot if they

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speak my language. To give you a taste, please let me introduce scientist,

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comedian and rapper, Alex Lethbridge.

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So I've listened to Doc Brown,

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Akala and Syntax, they showed me how to flow off my grammar and syntax.

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The RI told me, Alex, what's your language?

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I checked my head, do you mean English, Fante or Spanish?

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Now, my PhD's crazy, I'll do wordplay till it pays me.

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And when I get bored, a language or two.

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So while you're getting PTSD from your GCSEs

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and wondering should I RSVP to GCHQ?

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Now I'm not sure, Sophie says language is complex.

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You've got the rules like subjects, verbs and objects.

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It's more than words, you've got intonation and context.

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Final lecture, we're learning all of these concepts.

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APPLAUSE

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I don't know about you, when I'm listening to rap music, I like to count all the words.

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And I reckon in about 25 seconds there, Alex,

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you said about 110 words.

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-Yeah, exactly.

-And got over about 15 ideas, does that sound about right?

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Amazing. Thank you very much, Alex.

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No worries, thanks.

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We're used to thinking of telepathy as a science-fiction concept, but

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Alex just achieved the exact same result.

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We share the content of our minds, our brains,

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whenever we want to speak, or rap, to anyone.

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Don't worry, I'm not going to rap!

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Are we unique in having these skills or will we one day have a full

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conversation with another species?

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When I was a little girl, I so wanted to be able to talk to animals.

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Will that ever happen?

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And will computers ever be able to fully get their processors around our

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language well enough to understand a joke?

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Tonight, I'm going to explore what makes language so amazing

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and so very difficult.

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But what do we mean when we talk about language?

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Languages can come in many forms.

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We can talk, we can write, we can sign.

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And I've got a very basic form of language here.

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MORSE BEEPING

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Anybody speak Morse?

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That was a cry for help!

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Now, I don't speak Morse beyond being able to do that.

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But basically you can think of language like Morse code as being

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a message which we're sending with a code.

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And to make a code,

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the first thing you need to do is to produce a signal that's got some

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kind of structure. Now, that means a signal

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that's not just a random stream of noises without any order,

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nor can it be a very simple pattern just repeated again and again.

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You need to have a capacity to send information like the short and long

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patterns of the Morse.

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Now, humans do this when we speak aloud.

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We're using the sounds of our voices that we use in our language to

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express a code.

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Can we find any signs of a similar kind of structure in other animals'

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voices? And if so,

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could we crack their code and have a proper conversation with them?

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There are some animals who are very good candidates for being able to

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produce these sorts of sounds with structured elements,

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and those are birds.

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Now, these guys, who are a couple of zebra finches,

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and a couple of canaries, they're songbirds.

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Songbirds, when they're babies,

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they learn all the songs they're going to sing when they're adults.

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The most impressive can learn over 1,000 different songs.

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Could these songs contain coded information?

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Now, I can hear a couple of cheeps coming out of here,

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but I think we have a recording of one of the canaries.

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Can we listen to that?

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CANARY CHIRPS

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It's a beautiful sound.

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But does it contain enough structure in that signal that it could be used

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to transmit a code?

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Well, I've got an example of the canary's song here.

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And what I'm showing it to you as, is what's called a spectrogram.

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Now, a spectrogram is a way of looking at the structure in a sound.

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So, what you have along this direction is time.

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So, this is the sound unfurling over time,

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how it's changing over time. This direction, we've got frequency.

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And that's roughly telling you,

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low-pitched sounds up to high-pitched sounds.

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And where the colours are warmer and brighter,

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that's where there's more energy.

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And we can see in these individual elements, these little notes,

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and we're seeing some quite structured elements to this.

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We've got a similar sequence here and repeating there.

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And then these sequences of lower and higher alternating notes.

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Now, I need to compare this with another kind of voice.

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So, I would like a human volunteer, please.

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Can I have you in the middle there, with the penguin?

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Thank you very much.

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Now, what's your name?

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-Ruth.

-Ruth. I'm going to ask you to come over here and say the first two

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lines of Humpty Dumpty into my computer.

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OK, I'll tell you when to go.

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If you could just stand about there.

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Brilliant. And go now.

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Humpty Dumpty sat on the wall.

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Humpty Dumpty had a great fall.

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Brilliant. Thank you very much, Ruth.

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Exemplary, I think you'd agree.

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So, here's Ruth's version of Humpty Dumpty shown in the same way

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on a spectrogram. You can see immediately there are some differences.

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There's the canary, there's Ruth.

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You can also see some similarities.

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And I'm talking in the most general sense here,

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but Ruth is producing individual rhythm in the syllables of what she's saying,

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and you're seeing a pattern of that over the sentence.

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And you're seeing something broadly comparable in the canary.

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We're seeing structure in those sounds.

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The canary and the speech sounds have both got rhythm, pitch,

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rate information in there.

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So it's at least possible that the songbird

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is producing something which has got

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similarities to the way we code information in our speech.

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Could it actually be a code, though?

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Well, probably not.

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It doesn't seem to be quite enough.

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So if you look at how songbirds use song,

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what you find is they don't generally change their songs

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once they've learned them.

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They will always sing the same whole song.

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And the other thing they don't do is chop songs up and rearrange them

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to make new songs. We do that all the time.

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We can use words in lots of different, very novel orders.

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The birds don't do this.

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So it's entirely possible that, complex though the songbirds are,

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they are not producing something that is conveying a complex,

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coded meaning.

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But there's another group of birds

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that can learn to say human-coded signals, use words.

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And those are parrots.

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Please meet Mike and his parrot.

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-Hello.

-Hello.

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-Hi.

-Hi.

-Hi, Mike, who have you brought with you?

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-So this is Helly...

-You all right?

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-You all right?

-..and Helly is an Amazon parrot.

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So a South American bird from the rainforests.

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And she's already said hello, hasn't she?

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She has, yes. She knows that's an introduction,

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so it's the first point of call for a conversation

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or attention gathering.

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-How many other words does she use?

-You all right?

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She uses about 80 different sounds and words.

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Yeah, human words, though?

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Oh, human words, I would say she knows about five or ten.

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-Yeah.

-Can you say bye?

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Bye!

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So she's doing a set of human words.

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Did you teach her those or did she just pick them up from seeing how they were used?

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Yeah, she understands that "Hello" is a greeting because it's a

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word that we would use on arrival.

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And she would then understand that the "Bye" is a departure word.

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So she places the words with timings, as well.

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-Yeah.

-But throughout her life she's had lots of trainers saying,

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"Are you all right, are you all right?"

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So if she actually gets worried in something like a studio environment,

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-she will say "Are you all right?"

-You all right?

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And she knows that goes with almost an emotion, as well.

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-Yes.

-So she links these words with timings and emotions.

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And she can do other sounds that aren't... she can do more than words, can't she?

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Yes, she uses lots of different sounds as well as words.

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Can we have a bomb?

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WHISTLE, BOOM

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LAUGHTER

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They can also be copying other birds as well, so local birds in the wild.

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And also other birds that we house.

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Thank you very much, Mike. And thank you very much, Helly.

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Lovely meeting you. Bye-bye.

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What gives birds their amazing ability to learn to produce these sounds?

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Well, the answer may lie within their brains.

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Now, birds are more closely related to dinosaurs than they are to us.

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But we share similarities in the ways that our brains control both

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the learning and the production of sounds we make with our voices

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and the genes that build those parts of the brain.

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If we look...

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..at a bird brain,

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we can identify specific areas which are important

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in the learning of song and in the control of singing.

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And if we look at a human brain...

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..we can see a similarity, in that there are specific networks

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recruited when we are speaking.

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When we're talking, these are human brains here,

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this is the right side of the brain and the left side of the brain.

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We see some areas which are strongly associated with the control of all

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the work we have to do to make the sounds of speech.

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We also find the very specific area just on the left side of the brain

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which seems to be very important in planning speech.

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And it's also important in learning new things to do with our voices.

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How can I find out more about this system?

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We can take these snapshots of the brain in action and we can work with

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people who have had strokes and have damaged these brain areas.

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But there's another technique that we can use where we can investigate

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what would happen if we could turn off

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that part of the brain in someone, and just that part of the brain.

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What I'd like to do is introduce you to my colleague from UCL,

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Dr Ricci Hannah, and comedian Robin Ince.

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Hello Robin, hello. I've been waiting for this day.

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Hello, Ricci.

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-Hi.

-Hi.

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Now, Robin.

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Robin, what we're going to do is sit you down here.

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-Right.

-And then I'm going to let Ricci explain what we're going to do next. OK?

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I have to emphasise, this is a temporary state of affairs, OK?

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We're not about to do some sort of terrible live brain surgery on you!

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I was quite worried, because I was watching what else was going on and I thought,

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"They're going to prove that I'm less intelligent than a parrot!"

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So let's find out what happens.

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Ricci, can you tell us what you've got here?

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So this is a transcranial magnetic stimulator.

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And what we can use it for is to probe

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how different parts of the brain play a role

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in different aspects of behaviour.

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In this case, speech.

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OK. So can we start just by pointing out, that the way that we get this,

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we change Robin's brain activity by passing electrical current through

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-that coil, is that right?

-Mm-hmm.

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And actually, according to the principles of electromagnetic forces,

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which were first described here by Michael Faraday,

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what that lets us do is induce currents inside Robin's brain

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without actually having to get inside his brain.

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It's absolutely amazing.

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-So can we start by getting Robin talking and then seeing if we can stop him talking?

-OK!

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People have been trying this for years!

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-It's going to be popular!

-Can I get you to shuffle back, Robin?

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-Yes.

-There we go.

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-Are you OK?

-OK, so I'll just position the coil.

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-What I want you to do is to say the months of the year really loudly and clearly.

-OK.

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When you're ready.

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January, February, March, April, M...

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Please start talking again!

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That is weird!

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That is very...

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I think Professor Brian Cox who I do a radio show with is going to want a

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beret that I wear with that in there, so he can stop me talking!

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I don't know what it looked like to you, but it was like just...

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It was like kind of Homer with a doughnut, sort of...

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If you could bear it, can we try it again?

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Yeah! It's really... I find it amazing.

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It is extraordinary, isn't it? And then it just comes back.

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I think I prefer quiet me. Let's do it again!

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Shall I try, and see how far I can get in Jabberwocky, shall I try that?

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Yes, excellent, yes.

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-Tell me when you want me to.

-When you're ready.

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'Twas brillig, and the slithy toves. Did...

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It's fantastic!

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That's...

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So what Ricci's very specifically

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focusing on here is this part of the brain,

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it's called the inferior frontal gyrus, on the left.

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And in humans it's incredibly important for

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planning and controlling speech.

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If we move to a slightly different area,

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even within the same network,

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what we find is that Robin will be able to talk absolutely fine.

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Would it be OK to try that?

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-Yeah, sure.

-OK.

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-OK.

-I didn't even look at the health and safety form for this!

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-I sent this to you!

-I know, I didn't look, just in case!

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You're fine, you're safe.

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OK, when you're ready.

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'Twas brillig, and the slithy toves

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Did gyre and gimble in the wabe

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All mimsy were the borogoves And the mome... Yeah, that's...

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There you go, there you go.

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I think I preferred the one at the side!

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I can't thank you enough for being prepared to come out in front of

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everybody and have us try and zap your brain!

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Thank you very much, Robin. Thank you very much, Ricci.

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That was amazing, thank you.

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It was brilliant, thank you.

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Thank you.

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So you could see how precise the effect of the transcranial magnetic

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stimulation was. We were only seeing Robin stopping talking when we were

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applying the TMS over his left inferior frontal gyrus.

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When we went elsewhere in the brain, other things happened,

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but it's not stopping him from talking.

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So what we're seeing here in the humans and in the birds

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are very dedicated brain regions

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that are important in vocal control and vocal learning.

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It's a strong hint that we really are seeing some commonality in

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the brain areas that are to do with the learning

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and the producing of vocal sounds.

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But can any of these birds really understand the words they're saying?

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Well, parrots don't just say human words.

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They'll mimic pretty much anything they hear a lot of.

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Car alarms, creaking doors, alarm clocks.

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Why do they do this at all?

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Well, birds are mimicking to show off to potential mates,

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to get attention.

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To impress other birds and other humans.

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To defend their nesting sites.

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Perhaps the more impressive a sound they can make,

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the more likely they'll find a companion,

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or scare off a rival.

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So birds aren't showing a great ability to decode our language.

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But what do I mean by decoding?

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We humans are exceptionally good at working out what words are and what words mean.

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I'd like you to watch and listen to a clip that I'm about to play you,

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and there's going to be a short test afterwards.

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And this will go down on your permanent record.

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Back. Deaf. Gidge.

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Hock. Fip. Nop.

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Fib. Wreck. Sit.

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They. Fip. Fip.

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Hock. Fib. Gack.

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Gin. Hock. Hock.

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Lun. Fip. Nat.

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Fip. Ros. Hock.

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Yas. Beth.

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OK. Short test.

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What's this called?

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-CROWD:

-Fip.

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It's a fip. What's this?

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-CROWD:

-Hock.

-Hock, excellent.

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Well done. Now, I didn't tell you to work out what was going on there.

0:17:590:18:03

What you were doing was decoding the information we gave you.

0:18:030:18:07

There were some novel sounds in there and they were being associated

0:18:070:18:11

in quite a regular way with visual information.

0:18:110:18:13

Even if you don't know that your brain is trying to do it,

0:18:130:18:16

you're always trying to spot words and work out what those words mean.

0:18:160:18:20

We are not the only animal who has an ability to do this.

0:18:200:18:24

There's an animal you're probably all very familiar with who's actually

0:18:240:18:27

really very, very good at sharing this ability with us.

0:18:270:18:30

And that's dogs.

0:18:300:18:31

Please give us a very nice doggy-friendly round of applause

0:18:320:18:36

for Gable and his owner, Sally.

0:18:360:18:38

-Hello.

-Hello.

-Hello.

0:18:420:18:44

Hello. Hey, Gable.

0:18:440:18:48

So Sally, what's different about Gable?

0:18:490:18:51

Gable's got the ability to identify a large number of objects and toys by name.

0:18:520:19:00

He currently knows about 150 different names for toys and objects and articles.

0:19:000:19:04

Goodness.

0:19:040:19:06

Now, can we see a demonstration of that?

0:19:060:19:09

So we've got some of Gable's toys here and what were going to do is

0:19:090:19:12

get a random selection of those out and then see.

0:19:120:19:14

-OK.

-I think there is somebody...

0:19:140:19:17

Hi, Sean. Now, we've spent a little bit of time with Sean,

0:19:170:19:20

getting Sean used to being with Gable and Gable used to being with Sean.

0:19:200:19:23

Can I bring you down? Thank you.

0:19:230:19:25

Now Sean, if you could come over here.

0:19:270:19:30

Can you pop these gloves on and can you just randomly select 15 toys

0:19:300:19:35

from these buckets and spread them out?

0:19:350:19:37

When did you first realise Gable could do this?

0:19:370:19:40

When he was a young puppy, actually.

0:19:400:19:42

He sort of invented the game.

0:19:420:19:44

I was trying to watch telly one evening and he kept pestering me.

0:19:440:19:46

He wanted to do something.

0:19:460:19:48

And I just remembered oh, his red toy was upstairs.

0:19:480:19:51

And I just said, "Oh, go and get your red toy."

0:19:510:19:53

Sort of idly just dismissing him,

0:19:530:19:55

hoping he'd be gone for ages because he couldn't find it.

0:19:550:19:57

And he came back with it and he put it in front of me and looked at me.

0:19:570:20:00

And I just thought, "I've actually never told you that's called Red Toy."

0:20:000:20:04

And so I just then thought, I wonder what happens

0:20:040:20:06

if I do teach him a name, and it's gone from there, really.

0:20:060:20:09

-Now...

-Wow, look at all your toys.

-..Sean, don't go anywhere,

0:20:100:20:12

I'm going to need you again.

0:20:120:20:14

Can I ask you, Sally, to tell Gable to pick one of these toys, please?

0:20:140:20:19

OK. Gable...

0:20:190:20:20

triceratops.

0:20:230:20:24

Get triceratops.

0:20:260:20:28

-Yes! Yes, good boy!

-Good boy!

0:20:320:20:36

Good boy! Leave it.

0:20:360:20:37

-Come round here.

-Fantastic.

0:20:390:20:41

So we've seen that Gable is really very good at working out what you're

0:20:410:20:45

saying and trying to work out from looking at you which one you mean.

0:20:450:20:48

But then Gable is your dog and he might be really,

0:20:480:20:51

really familiar with your voice.

0:20:510:20:52

It would be very interesting to know if we could see this happen with

0:20:520:20:55

someone who's got a very different voice.

0:20:550:20:56

So Sean, is it OK if Sean has a go?

0:20:560:20:59

-Yes.

-And what you need to do is whisper into Sean's ear

0:20:590:21:03

which toy you'd like him to pick up.

0:21:030:21:05

Do you remember Sean?

0:21:050:21:07

-OK.

-Gable, octopus.

0:21:070:21:11

Get octopus.

0:21:110:21:12

Go.

0:21:120:21:13

Yes!

0:21:140:21:15

Good boy, good boy!

0:21:170:21:18

-And well done, Sean.

-Leave it.

0:21:180:21:20

-Can Sean do one more?

-Of course, yes.

0:21:220:21:23

Is that OK, Sean?

0:21:230:21:25

Gable, get hammer.

0:21:250:21:27

Get hammer.

0:21:270:21:28

Yes! Amazing.

0:21:310:21:34

Good boy!

0:21:340:21:36

So this really does indicate that Gable must have some understanding of what

0:21:370:21:41

words mean above and beyond just associating your voice with things.

0:21:410:21:44

That's fantastic. Thank you so much, Sean, thank you so much, Sally,

0:21:440:21:48

and particularly thank you so much, Gable.

0:21:480:21:50

Come on, then!

0:21:500:21:51

That's amazing.

0:21:550:21:57

So, what's happening in Gable's brain so that he can do this?

0:21:570:22:03

Well, there's a group of scientists in Hungary who have been doing some

0:22:030:22:06

quite extraordinary experiments.

0:22:060:22:09

They've been training dogs to lie very still

0:22:090:22:11

and then they've been putting them into brain scanners.

0:22:110:22:14

The brain scanners don't work if you move so the dog has to stay very still.

0:22:160:22:21

And then what they are doing is taking pictures of the activity inside the

0:22:220:22:25

dog's brains while they're listening to different sounds and words.

0:22:250:22:30

When I scan people, they're not normally that happy!

0:22:300:22:35

So this is called functional magnetic resonance imaging and it lets us

0:22:350:22:38

take photographs of the brain in action.

0:22:380:22:41

And this is showing the results.

0:22:420:22:44

So a dog's brain, as you can see,

0:22:440:22:46

it's different in shape to a human brain, but it has some of the same

0:22:460:22:49

structures in there. And quite strikingly,

0:22:490:22:54

one of the things they are finding in the results is when dogs hear words they understand,

0:22:540:22:58

we see greater activation in the left side of the brain,

0:22:580:23:01

particularly in brain areas to do with processing sound.

0:23:010:23:04

And that is very similar to what you find in our brains.

0:23:040:23:10

So it looks like it's at least possible that more than any other animal

0:23:100:23:14

we've looked at, dogs might be sharing some of our ability

0:23:140:23:18

to decode spoken words and they may even be doing it in similar ways to us.

0:23:180:23:23

But of course, amazing as they are, dog's brains do have their limits.

0:23:240:23:30

The largest number of words that any dog has been found to understand,

0:23:300:23:34

and this was an exceptional dog, is about 1,000 words.

0:23:340:23:37

Which sounds fantastic.

0:23:370:23:39

Gable knows about 150 words,

0:23:390:23:41

but every one of you could understand 1,000 words when you were three years old.

0:23:410:23:46

And of course human language is more than just single words.

0:23:460:23:49

We don't walk around going, "Steps, camera, man".

0:23:490:23:54

We are putting words together into sequences.

0:23:540:23:57

And when we put words into sequences,

0:23:570:24:00

we actually add in an extra level of coded meaning.

0:24:000:24:04

So perhaps, if we want to look for some more humanlike ability to put

0:24:060:24:10

symbols into sequences like we do with sentences,

0:24:100:24:13

we should be looking closer in the evolutionary tree.

0:24:130:24:17

Perhaps we should be looking to our closest cousins, other apes.

0:24:170:24:21

Now, because I'm not David Attenborough,

0:24:210:24:24

I don't get to play with the chimpanzee.

0:24:240:24:25

But we have the next best thing.

0:24:270:24:29

Please release the apes.

0:24:290:24:32

THEY IMITATE MONKEYS

0:24:320:24:36

This didn't happen to David Attenborough!

0:24:500:24:52

That's just brilliant.

0:24:540:24:55

These are human apes.

0:24:570:24:58

Please welcome Neil and Ace.

0:25:010:25:03

So you guys have been working a lot with our ape cousins?

0:25:030:25:06

We have, yeah. We were actually very fortunate to work with Andy Serkis

0:25:060:25:09

on Planet Of The Apes Last Frontier, which is an interactive movie.

0:25:090:25:12

Hold on a second, sorry.

0:25:120:25:14

OK, just reassurance.

0:25:140:25:17

We were very lucky, we got to study apes for quite a while before we

0:25:170:25:20

started creating our own characters from ape work.

0:25:200:25:23

We watched their movements, their behaviour patterns, hierarchies -

0:25:230:25:27

here's some of the work we did - and also the speech they use,

0:25:270:25:30

which is five different parts of language.

0:25:300:25:33

Grunts, barks, hoots, whimpers and screams, which they do use as chimps.

0:25:330:25:37

And this is my ape actually, Bryn.

0:25:370:25:39

-That's you?

-Yes, that's me.

0:25:390:25:41

-That's amazing.

-Yes.

0:25:410:25:43

So what this is letting you do is have ape actors which are based on humans,

0:25:430:25:46

so you can get them to do things which you could never actually ask

0:25:460:25:49

-another ape to do?

-Absolutely,

0:25:490:25:51

and we get to create a bit of drama in the family.

0:25:510:25:53

It's actually really about family, this interactive film,

0:25:530:25:56

so it is centred on this particular family of apes that have splintered

0:25:560:25:58

away from Caesar's group.

0:25:580:26:00

So you've had to work really closely with the apes to actually learn

0:26:000:26:02

about their body language and movements.

0:26:020:26:04

Did you pick up on anything that felt like communication that you were looking at?

0:26:040:26:08

Yeah, we studied a lot of their gesticulation and body language,

0:26:080:26:11

especially between dominant and subservient male apes.

0:26:110:26:15

Because what you get is a lot of different behaviour patterns that

0:26:150:26:18

have been formed by a simple sign, for instance, the touch of hands.

0:26:180:26:21

If somebody is in the dominant position,

0:26:210:26:22

it's quite important to be able to...

0:26:220:26:24

So if you were offering,

0:26:240:26:25

if you're trying to get my attention and forgiveness, for instance. Go on.

0:26:250:26:29

He gives me respect and I give it back to him.

0:26:290:26:33

-They are communicating with gestures.

-They are, yes.

0:26:330:26:35

Amazing. Thank you so much, thank you.

0:26:350:26:37

So Neil and Ace aren't just imitating the apes very well,

0:26:480:26:51

they are clearly picking up on some aspects of communication,

0:26:510:26:54

but is there really some kind of

0:26:540:26:57

ape conversation going on?

0:26:570:26:59

And would it look at all like the way we use language?

0:26:590:27:02

To find out, please welcome chimpanzee researcher from the University of St Andrews,

0:27:030:27:08

Dr Cat Hobaiter.

0:27:080:27:10

Hello. Lovely to meet you.

0:27:120:27:14

So you've been asking some really interesting questions about ape language

0:27:160:27:20

and can you tell us more about your work?

0:27:200:27:22

Yes, absolutely,

0:27:220:27:23

so what people have done in the past was try to teach apes our language,

0:27:230:27:29

so human language or sign language,

0:27:290:27:31

or they've looked at their vocalisations.

0:27:310:27:33

But what we've been looking at is their gestures.

0:27:330:27:36

-OK.

-And it turns out, they've got a lot of different gestures.

0:27:360:27:39

So their own natural communication contains 60 or 70

0:27:390:27:44

different hand and body movements that they're using every day to ask come here, go away,

0:27:440:27:50

I want that, all the little meanings.

0:27:500:27:52

And how do you work out what those gestures mean?

0:27:520:27:55

What we do is we look for a particular gesture

0:27:550:27:58

and then we are looking for what happens next.

0:27:580:28:01

And if I were to do this and you responded back to me,

0:28:010:28:06

one or two cases it could be anything or you could misunderstand me or I

0:28:060:28:10

could keep going. But what I'm looking for is what stops me from

0:28:100:28:15

signalling. So if I'm asking you for something,

0:28:150:28:17

then the thing you do that makes me happy as a signaller

0:28:170:28:21

-is the thing that I wanted.

-Yes, so you have to look at the whole context.

0:28:210:28:25

Yes, so we need to look at the signaller, at the recipient, the gesture,

0:28:250:28:29

then we need to look at not just one or two but hundreds of cases so we

0:28:290:28:33

can see the patterns emerging in the behaviour.

0:28:330:28:35

And you've been using the general public to help you with your research as

0:28:350:28:39

-well, haven't you?

-Yes,

0:28:390:28:40

so we were able over a few years to look at all the other apes but there

0:28:400:28:44

was one of them missing, which was us.

0:28:440:28:46

And we know we have language but we don't know if we still have access

0:28:460:28:51

to some of the communication that the apes also use.

0:28:510:28:54

The sort of gestural stuff they are using, yes.

0:28:540:28:56

Exactly. So whether or not if we showed a member of the public a

0:28:560:29:00

particular gesture, could they guess what it meant.

0:29:000:29:02

And what we did was put up lots of videos online and ask everyone to

0:29:020:29:06

come along and sort of play the game and have a go.

0:29:060:29:08

Can we have a go at that now?

0:29:080:29:10

-Yes, please.

-Fantastic, so I think we've got a clip to start with.

0:29:100:29:12

We are going to watch this closely because we are going to ask you what you think is going on.

0:29:120:29:16

So the gesture there was that kind of arm.

0:29:200:29:22

Little arm raise from Charlotte.

0:29:220:29:24

OK, and is that a baby?

0:29:240:29:26

Yes, she's just a little one.

0:29:260:29:28

Do we think that's A - I want that, B - move closer, or C - go away?

0:29:280:29:33

ALL: B.

0:29:360:29:37

Yes, you guys are all well versed in chimpanzee already.

0:29:370:29:40

That was move closer.

0:29:400:29:42

OK, can we try another one?

0:29:420:29:43

OK, we are just highlighting this because it happens very quickly.

0:29:450:29:48

Yes, this is a really subtle one so it's just that little foot movement

0:29:480:29:52

as she looks back and gives a little foot wiggle to her son.

0:29:520:29:54

What happens next?

0:29:570:29:58

Is that play with me, climb on me or come here?

0:30:000:30:04

ALL: B.

0:30:040:30:05

B, yeah. It's rather sweet, that one. Very, very swift.

0:30:050:30:08

-Yes.

-Really subtle.

0:30:080:30:09

So this is really fascinating.

0:30:100:30:12

It does look like you are seeing a sign language,

0:30:120:30:15

you're seeing really gestural use of communication.

0:30:150:30:18

They're not just waving their arms around passionately,

0:30:180:30:20

there is a very precise meaning being conveyed here.

0:30:200:30:23

But of course the real power of human language is that we can combine our

0:30:230:30:28

words and symbols into sequences and that gives us a more complex level of meaning.

0:30:280:30:33

Can the chimps ever do this?

0:30:330:30:35

Do you ever see any kind of examples of sequences?

0:30:350:30:39

Definitely. So what we see sometimes are these one-off gestures but they

0:30:390:30:43

will also put gestures together.

0:30:430:30:45

Sometimes a couple at once,

0:30:450:30:47

that was a big scratch and shaking the object and he's actually waiting

0:30:470:30:51

there. And whatever that gesture was,

0:30:510:30:53

it doesn't seem to have done the trick because Rohara is ignoring him.

0:30:530:30:57

-He's going to do it again.

-He's going to do it again.

-And the arm went up.

0:30:570:31:00

So we've got a scratch, a tree wiggle and the arm going up.

0:31:000:31:02

Yes.

0:31:020:31:04

And it worked. Oh, all right then, I'll get down.

0:31:040:31:07

She's a bit of an old lady.

0:31:070:31:09

So the tree waving is the sort of "Come here, I'm in charge".

0:31:090:31:14

-The scratching?

-Scratching usually means groom, let's groom,

0:31:140:31:18

let's get together and groom.

0:31:180:31:20

Our big question for us now is when they are putting these gestures into

0:31:200:31:24

sequences, if it was human words the order of the sequence would make a

0:31:240:31:28

difference to the meaning, and we are trying to work out now if that's the same for the chimps.

0:31:280:31:32

Amazing.

0:31:320:31:33

Thank you so much, Cat, thank you.

0:31:330:31:35

Now, why is this so exciting?

0:31:420:31:45

Well, if other apes can also combine symbols into a specific order to

0:31:450:31:50

produce something with complex meaning,

0:31:500:31:53

it unlocks a great deal more of the power of the language.

0:31:530:31:57

Combining symbols into sequences allows us to describe the world in

0:31:570:32:01

much more complex ways.

0:32:010:32:03

It gives us the power to share much more complex ideas.

0:32:030:32:07

It's a really useful skill but it's not in any way a simple skill.

0:32:070:32:11

And to look at exactly what kind of thing the chimps are up against in

0:32:120:32:16

terms of the difficulty of understanding sequences of symbols and decoding them

0:32:160:32:21

accurately, I need two volunteers.

0:32:210:32:24

Can I have you there in the Los Angeles...

0:32:250:32:28

Can I have you in the Santa - sorry, Rudolph.

0:32:280:32:32

Fantastic, thank you very much, thank you...

0:32:320:32:34

You come round here.

0:32:350:32:36

You come round this side. Thank you very much.

0:32:380:32:40

Now, what's your name?

0:32:400:32:42

-Gracie.

-Gracie, lovely to meet you, Gracie.

0:32:420:32:44

-And what's your name?

-Ryan.

0:32:440:32:46

-Ryan?

-Yes.

-Fantastic.

0:32:460:32:47

Now, Ryan and Gracie,

0:32:470:32:48

what we need to do is put some general covers on you.

0:32:480:32:53

OK, we are just going to, sort of, from top to toe, cover you up.

0:32:530:32:55

Thank you. So what we are going to do is I'm going to ask you one at a

0:32:550:32:59

time to read some instructions and to mix some things together.

0:32:590:33:03

And, Gracie, I'm going to ask you to go first.

0:33:030:33:04

So, Ryan, so that you don't see what's going on,

0:33:040:33:08

I need you to pop on a blindfold and some ear defenders, OK?

0:33:080:33:11

Just so that you don't give away the game.

0:33:110:33:15

Right, so in a second I'm going to step forward with you

0:33:150:33:18

and we are going to turn over your instructions.

0:33:180:33:20

An important thing to remember, when we get to the end

0:33:200:33:23

of the instructions, is you step back with me.

0:33:230:33:25

Are you all right?

0:33:250:33:26

OK, let's start.

0:33:260:33:28

I think you might need to... You've got it, you've got it.

0:33:380:33:41

Good code-cracking.

0:33:410:33:43

And step back.

0:33:440:33:45

To see an alarming change of colour.

0:33:470:33:49

Fantastic, Gracie.

0:33:490:33:50

OK, now don't go anywhere.

0:33:500:33:52

We are now going to turn to Ryan.

0:33:520:33:53

OK, so what we are going to do, we are going to turn over your instructions

0:33:530:33:56

in just a second and I want

0:33:560:33:58

you to follow them with these chemicals here. OK?

0:33:580:34:01

Yes. First mix A into B.

0:34:010:34:05

A into B.

0:34:050:34:07

-So I just put...

-A into B.

0:34:070:34:09

So I just put them like this?

0:34:090:34:11

Yes, very good.

0:34:110:34:12

-OK.

-Add C.

0:34:190:34:21

And then...

0:34:220:34:23

you step back.

0:34:250:34:27

LAUGHTER

0:34:270:34:29

Are you OK, there?

0:34:320:34:35

Amazing.

0:34:350:34:36

Well done.

0:34:370:34:39

Thank you so much.

0:34:400:34:42

Now, we had two different outcomes there.

0:34:420:34:44

We had either a dramatic change of colour or an actual foam explosion,

0:34:440:34:48

depending on the order in which the chemicals were mixed.

0:34:480:34:51

And the only reason why Gracie and Ryan mixed them in different orders

0:34:510:34:55

was just how we'd punctuated the instructions.

0:34:550:34:57

So they both have the same instructions,

0:34:570:34:59

and they were just understanding them differently based on where we

0:34:590:35:02

put the full stops. Thank you very much, Gracie, thank you very much, Ryan.

0:35:020:35:05

So you can see that the exact grammar,

0:35:090:35:11

in this case the punctuation of the sentences,

0:35:110:35:13

has completely changed the meaning of those sentences

0:35:130:35:17

and that's why this tiny glimpse of the ability to combine gestures

0:35:170:35:21

in the apes is so exciting.

0:35:210:35:23

So if we think about the animals that we have talked about this evening,

0:35:230:35:28

we've seen birds who are able to produce very complex sounds

0:35:280:35:31

and they can mimic many other sounds, but they don't seem to understand the meaning of

0:35:310:35:36

those words. We've seen dogs,

0:35:360:35:38

now dogs don't talk but dogs are incredibly good at working out

0:35:380:35:42

what human words mean and what they refer to.

0:35:420:35:45

And we've seen the chimpanzees,

0:35:450:35:47

they are both using gestures to have quite precise meanings and they are

0:35:470:35:52

also using sequences of those gestures to have more sentence-like

0:35:520:35:58

structures. But they are not the same as the kind of codes humans use.

0:35:580:36:02

Why are there these distinctions between humans and other animals?

0:36:020:36:06

Well, I think a big difference is likely to do with our brains.

0:36:060:36:11

I just want you to look at these different ones here.

0:36:110:36:14

So here we've got a bird brain, that's actually a chicken brain.

0:36:140:36:18

That's a dog's brain.

0:36:180:36:19

This is a replica chimpanzee brain, and this is a human brain.

0:36:200:36:26

Now, they look different.

0:36:270:36:29

One obvious difference is size, and the human brain is enormous,

0:36:290:36:33

much larger than any of the other brains on the table.

0:36:330:36:37

But big brains aren't everything.

0:36:370:36:39

To make an analogy to computers,

0:36:390:36:41

it's not perhaps just about the raw processing power of the brain,

0:36:410:36:45

maybe we need to think about the operating system as well.

0:36:450:36:48

And we seem to have a very efficient operating system

0:36:480:36:52

for dealing with symbol-based codes.

0:36:520:36:55

I always wanted the superpower of being able to talk to animals,

0:36:560:37:00

but maybe our mismatching brains mean that I never will.

0:37:000:37:05

Maybe there's something else in our world with which we communicate far more frequently,

0:37:050:37:10

and maybe I need to stop thinking so much about how I could have

0:37:100:37:13

conversations with other animals and maybe think a bit more about talking

0:37:130:37:17

to machines.

0:37:170:37:18

This year's big Christmas present gadgets are machines that we can talk to,

0:37:200:37:23

digital assistants.

0:37:230:37:25

A quarter of us now either command our phones via our voice

0:37:250:37:28

or use devices like these around the home.

0:37:280:37:31

Hey, computer, please tell me what the weather will be like in London tomorrow.

0:37:330:37:37

There will be showers there tomorrow with a high of nine and a low of three.

0:37:370:37:41

OK.

0:37:410:37:43

Now that's nowhere near being anything like a human brain, but when I was doing my PhD

0:37:430:37:47

on speech processing 25 years ago,

0:37:470:37:50

I would have never believed that one day we would be walking around with

0:37:500:37:53

phones in our pockets that we could talk to.

0:37:530:37:56

It's incredibly hard to overestimate

0:37:560:37:59

how quickly this field has developed.

0:37:590:38:01

What's happening inside that box?

0:38:010:38:03

How are computers managing to interact with us?

0:38:030:38:06

And is it ever having a proper conversation with us?

0:38:060:38:09

Are we ever going to have a meaningful dialogue with a computer?

0:38:090:38:12

Will it ever understand the jokes we tell?

0:38:120:38:15

Now this is an incredibly complex area and we could easily fill three

0:38:150:38:19

lectures just on this topic, so I'm going to give you a tiny insight

0:38:190:38:23

into aspects of how this works.

0:38:230:38:25

But first, let's break down the question.

0:38:250:38:27

What do computers need to do to be able to understand us?

0:38:270:38:31

And the first thing is it needs to take the sounds that we make and

0:38:310:38:35

decode that into words.

0:38:350:38:37

This is called speech recognition and it's what is known in science as

0:38:370:38:41

a ridiculously difficult question.

0:38:410:38:43

And that's because speech is hard and speech is complex.

0:38:430:38:47

I'm going to play you a sentence spoken in Estonian

0:38:470:38:50

and what I want you to do is count the words you can hear.

0:38:500:38:54

OK? So just listen out for words and see how many there are.

0:38:540:38:57

VOICE SPEAKS IN ESTONIAN

0:38:570:39:00

OK, how many words did you think were there?

0:39:070:39:09

Sorry? 13, that's a good guess.

0:39:100:39:13

Pink top, glasses.

0:39:130:39:15

-32?

-32, OK, a big jump there, about twice as many.

0:39:150:39:19

OK. Let's see the actual sentences.

0:39:190:39:21

So we've got 22 words, so actually it's a meaningless question to ask you,

0:39:210:39:26

because if you don't speak Estonian why would the words stand out to you?

0:39:260:39:30

But this is what the computer is confronted with.

0:39:300:39:34

It's hearing this continuous flow of sound and it's got to find some way

0:39:340:39:39

of getting a toehold on what those words could be.

0:39:390:39:42

Now listen to this sentence.

0:39:420:39:43

What I'm going to do is record myself speaking.

0:39:430:39:46

Where are the words?

0:39:490:39:50

So this is the spectrogram of me saying "Where are the words" that I've just recorded.

0:39:530:39:58

But where ARE the words?

0:39:580:40:00

There's no individual words there at all.

0:40:000:40:02

What you're getting is a sort of smear of energy and what looks like

0:40:020:40:05

one thing happening at the end there is actually the "ss" sound at the end of words.

0:40:050:40:11

When you hear gaps between words when someone is talking to you,

0:40:110:40:15

that's because you understand those words.

0:40:150:40:17

If you don't understand the words, you don't hear the gaps,

0:40:170:40:20

as we saw with the Estonian.

0:40:200:40:22

So how do computers deal with this?

0:40:220:40:25

How do computers find the starts and ends of words when there actually

0:40:250:40:29

are no physical gaps there necessarily at all?

0:40:290:40:32

Well the first thing a computer does is it breaks up the incoming stream

0:40:320:40:37

of sound into smaller chunks of sound

0:40:370:40:39

and we've got a demonstration of this here.

0:40:390:40:43

So this is an incoming sentence and I don't know what this is,

0:40:430:40:47

I've just got the spectrogram to work off and I'm a computer for the

0:40:470:40:50

purposes of this point.

0:40:500:40:52

And what I'm going to do is split this up

0:40:520:40:55

into different slices of information.

0:40:550:40:58

And what I'm going to do then is take those different slices

0:40:580:41:04

and go to look them up in the library that I've got,

0:41:040:41:07

which will help me try and get the best estimate of what speech sound I'm probably trying to look at.

0:41:070:41:13

And my library has to be very... Has to sort of

0:41:130:41:15

have an idealised version of speech sounds because we all talk

0:41:150:41:20

differently. So instead of trying to find an exact match for the speech sound,

0:41:200:41:24

the computer is looking for almost like a best guess.

0:41:240:41:27

So if we do this with the first speech sound in my sequence...

0:41:270:41:30

I've got a little slice of sound here.

0:41:330:41:36

Now if I look at that in isolation, what I can see

0:41:380:41:42

is that there is a broad sort of smear of energy there.

0:41:430:41:47

There's no bright hotspots.

0:41:470:41:49

Remember when you get a more intense bit of energy in a speech sound or

0:41:490:41:54

any sound in the spectrogram, you see it as a brighter colour?

0:41:540:41:57

There aren't really any bright colours there, but there's a big stretch of red

0:41:570:42:01

and it's going almost the whole length.

0:42:010:42:03

If I go over here,

0:42:030:42:05

I think I might be looking at a "T" so my best guess for that first sound is

0:42:050:42:11

that it is "T". Now who would like to help me guess the next sound?

0:42:110:42:15

Can I have you in the blue top, please?

0:42:160:42:18

Thank you very much.

0:42:180:42:19

-Now what's your name?

-Joe.

0:42:210:42:23

-Sorry?

-Joe.

-Right, Joe,

0:42:230:42:25

you're going to help me be a computer processor and work out what the next sound is.

0:42:250:42:28

We've got one here.

0:42:280:42:30

Now, Joe,

0:42:310:42:33

you can see this looks quite different to that sound we were just looking for.

0:42:330:42:37

Now can we find anything in our library that looks like that?

0:42:370:42:41

I think you're right,

0:42:410:42:44

I think we are dealing with "EE."

0:42:440:42:46

Can you put that there for me?

0:42:460:42:47

Now not to labour the point, if we carry on like this,

0:42:470:42:50

you're not getting like really whip quick recognition of the speech,

0:42:500:42:54

are you? We are taking our time so what I'm going to do is throw more

0:42:540:42:57

processors at the problem, just as a computer would.

0:42:570:43:00

I'm going to take a couple more volunteers please,

0:43:000:43:02

so can I have you with the glasses, please,

0:43:020:43:05

and can I have you with the polo neck sweater.

0:43:050:43:09

Thank you very much. Can I have you?

0:43:090:43:11

Thank you very much. Got to have a unicorn.

0:43:110:43:13

OK, so, Unicorn, what's your name?

0:43:170:43:21

-Sasha.

-Sasha, lovely to meet you, Sasha.

0:43:210:43:22

-Hi.

-Evie.

0:43:220:43:24

Evie. And your name is?

0:43:240:43:26

-Kit.

-Kit, excellent.

0:43:260:43:27

Now there's four speech sounds left.

0:43:270:43:29

I want you to each grab one and see if you can match it up with any of

0:43:290:43:33

the speech sounds here, OK?

0:43:330:43:35

There you go.

0:43:350:43:36

And what have you got here?

0:43:360:43:38

Look at this one. Can you see any that have got sloping shapes?

0:43:380:43:40

-Is it that one?

-I think you might be right there.

0:43:400:43:43

So if you can remember, you came from number four.

0:43:440:43:46

I think I have an S sound.

0:43:470:43:48

I think you're absolutely right.

0:43:480:43:50

You grab an S sound and pop it up.

0:43:500:43:52

Now you have got the really difficult one here

0:43:530:43:55

because when the speaker said it, they really underarticulated it,

0:43:550:43:59

so what you have is this sound here

0:43:590:44:04

and that's going to have to be our best guess, OK?

0:44:040:44:07

OK, so if you can pop that in, we can see what we've got.

0:44:070:44:09

Thank you very much.

0:44:090:44:11

It could be team mates, it could be tea mate.

0:44:110:44:16

We don't know still what the words are but we've now got a good guess

0:44:160:44:20

what our speech sounds are and that takes us to our next stage.

0:44:200:44:23

What I want to do first is say thank you very much

0:44:230:44:25

to Joe, Sasha, Evie, Kit.

0:44:250:44:27

Thank you very much, thank you.

0:44:270:44:29

So the addition of processing power

0:44:340:44:36

has really helped us be able to speed up this process

0:44:360:44:38

and that's why you can talk to your phone without it

0:44:380:44:40

taking that amount of time.

0:44:400:44:43

And what we are doing here is pulling out what the speech sounds are

0:44:430:44:47

but we still don't know what those words are,

0:44:470:44:49

we don't know where the edges are.

0:44:490:44:51

What we need to do is go to another level and what computers do

0:44:510:44:54

is go to what's called a language level

0:44:540:44:57

to start to break this stream of sounds up into words and into sentences.

0:44:570:45:01

I need two new volunteers to decode a stream of speech for me, just like

0:45:010:45:06

you were a computer. Can I have...

0:45:060:45:09

you in the middle with the blue T-shirt?

0:45:090:45:12

Yes, there you go, fantastic.

0:45:120:45:14

Can I have you? Thank you very much, thank you.

0:45:140:45:16

-Now, what's your name?

-Max.

0:45:220:45:24

Lovely to meet you, Max. And your name is?

0:45:240:45:26

-Cammy.

-Cammy, fantastic.

0:45:260:45:28

What I'm going to give you is the same task a computer has got when

0:45:280:45:30

it's worked out the speech sounds but it doesn't know what the words are.

0:45:300:45:34

What you're going to do is see a list of speech sounds and I want you

0:45:340:45:37

to try and work out what words could be in there.

0:45:370:45:39

Sometimes the computer doesn't know what a sound is at all.

0:45:390:45:43

You'll just see a question mark, maybe there was a cough,

0:45:430:45:45

maybe there was a noise, so sometimes you're going to have to guess, OK?

0:45:450:45:49

So I can just ask you to step over here, Max, thank you very much.

0:45:490:45:52

OK and we will see it appear on the screen, OK?

0:45:520:45:56

So reading down in that direction,

0:45:560:45:58

Try reading that aloud.

0:45:590:46:01

THEY READ WORDS ON SCREEN SLOWLY

0:46:010:46:04

Now, think about the building that we are in.

0:46:170:46:20

-The Royal Institution.

-The Royal Institution, absolutely,

0:46:200:46:23

so context can help you.

0:46:230:46:24

You go back at that, we are at the Royal Institution.

0:46:240:46:27

One last time.

0:46:270:46:29

TOGETHER: Drums are really

0:46:290:46:32

loud, so we have to

0:46:320:46:37

use ear

0:46:370:46:42

defenders at the Royal Institution.

0:46:440:46:47

Well done.

0:46:470:46:49

Thank you. Thank you, Cammy, thank you, Max.

0:46:510:46:53

Thank you.

0:46:530:46:55

So that's an extreme example, but what you are seeing there was essentially

0:46:550:47:00

the same problem a computer is trying to solve.

0:47:000:47:02

It's trying to find the edges,

0:47:020:47:03

it's trying to work out where the words could be,

0:47:030:47:05

and it's helped in this because it knows what the words are.

0:47:050:47:08

It has a database of thousands and thousands of words and it also knows

0:47:080:47:12

something about how sentences go together.

0:47:120:47:15

It knows how probable it is that a word will follow another word.

0:47:150:47:19

And if a word becomes more likely,

0:47:190:47:21

the probability of it occurring actually changes in the computer

0:47:210:47:24

so it's more easily activated.

0:47:240:47:26

And, in fact, our brains do something similar.

0:47:260:47:28

If you are listening to speech and you hear something which is highly

0:47:280:47:32

predictable like "the ship sailed across the bay"

0:47:320:47:34

then you will understand that sentence more easily.

0:47:340:47:37

So we are seeing actually, in terms of a lot of the code of language,

0:47:370:47:41

humans and computers are not as differently matched as we used to be.

0:47:410:47:46

Computers are catching up with a lot of our linguistic abilities.

0:47:460:47:50

Computers have become huge and very powerful,

0:47:500:47:53

they can be searching through very large databases very quickly,

0:47:530:47:57

and the internet means a little box like that can be connected to those

0:47:570:48:00

large online databases.

0:48:000:48:02

It doesn't need to have all the information there.

0:48:020:48:04

But, of course, this is not the full story of how human language works.

0:48:040:48:10

There is an entire level of communication

0:48:100:48:13

that we use all the time that we have barely mentioned.

0:48:130:48:17

Rather than just what we say when we're talking,

0:48:170:48:20

we are always sending out information in how we say words.

0:48:200:48:24

Everything I've talked about so far has been, if we think about brains,

0:48:260:48:32

associated with properties of the left side of the brain.

0:48:320:48:35

The left side of the brain in humans, for most people, is associated with

0:48:370:48:42

how we decode speech, how we control our own voices.

0:48:420:48:45

And the right side of the brain is a lot less interested in these

0:48:460:48:50

linguistic properties of communication like words and sentences,

0:48:500:48:54

and a lot more interested in all the other things that are going on when

0:48:540:48:57

we are talking to people:

0:48:570:48:59

who we are talking to.

0:48:590:49:00

Are they being emotional?

0:49:000:49:02

Are they telling a brilliant joke like,

0:49:020:49:03

what is round and sounds like a trumpet?

0:49:030:49:06

A crumpet.

0:49:060:49:08

GROANS

0:49:080:49:09

So to have a meaningful conversation with a machine or understand my

0:49:090:49:14

brilliant jokes, we need to do something much more difficult.

0:49:140:49:18

We need to add in to our computer the missing right half of its brain.

0:49:180:49:24

Because actually when we are talking, when you're listening to somebody,

0:49:240:49:28

there are very important aspects of our communication which you need to

0:49:280:49:32

pick up on to really understand what somebody is saying.

0:49:320:49:34

Not just when it's written down.

0:49:340:49:36

The information when someone is speaking is very often being expressed as

0:49:360:49:39

much by how they are talking as what they are saying.

0:49:390:49:42

And this often refers to something called intonation.

0:49:420:49:45

Intonation is how we vary the pitch, the speed,

0:49:450:49:49

the melody of our voice when we are speaking.

0:49:490:49:52

An example would be that we don't talk to each other like that.

0:49:520:49:56

We would consider it to be quite strange.

0:49:560:49:58

We are always using intonation to clarify, enhance,

0:49:580:50:02

put in emphasis and emotion.

0:50:020:50:04

In our brains,

0:50:040:50:06

intonation is processed very differently

0:50:060:50:09

from the words that you're listening to.

0:50:090:50:11

On the whole, intonation is strongly found to be something

0:50:110:50:14

that the right hemisphere deals with and is interested in.

0:50:140:50:18

And often, it can be as important if not more important to the real

0:50:180:50:22

meaning of what somebody is saying.

0:50:220:50:24

We've got some recordings here of someone speaking in an emotional style

0:50:250:50:29

and we have stripped out all the auditory information that tells you about

0:50:290:50:33

the words they are saying and it's just leaving you with the intonation.

0:50:330:50:35

See if you can guess the emotion.

0:50:350:50:37

BUZZING

0:50:370:50:40

Any guesses, does that sound happy?

0:50:400:50:42

Angry? It sounds annoyed, doesn't it?

0:50:420:50:45

Yeah, that was angry.

0:50:450:50:46

That was very angry.

0:50:460:50:47

Another one.

0:50:470:50:48

BUZZING

0:50:480:50:51

Yeah, lots of downward inflections.

0:50:530:50:57

Sounds softer.

0:50:570:50:59

That was a sad voice so you are definitely using intonation to get aspects

0:50:590:51:03

of emotion out, but that's by no means the whole story.

0:51:030:51:07

And to help me demonstrate why intonation is so very important to

0:51:070:51:10

communication, please welcome news presenter and journalist

0:51:100:51:13

Krishnan Guru-Murthy.

0:51:130:51:16

Krishnan, obviously the words you say are absolutely critical to your job,

0:51:220:51:26

but how you are saying them must be something that you're always thinking about.

0:51:260:51:30

We use intonation to do all sorts of things on the news, all the

0:51:300:51:34

time, and it's very, very complex.

0:51:340:51:35

At the beginning of the news, we're saying, "This is urgent,

0:51:350:51:39

"it's exciting, you've got to watch,

0:51:390:51:40

"something really important happened today and I've got to tell you about

0:51:400:51:43

-"it."

-Yeah.

-And then once we get into the news,

0:51:430:51:46

we are trying to use the right intonation for the type of story

0:51:460:51:50

we are telling people, so we've got to try and hit the right note, literally.

0:51:500:51:54

So if it's something very surprising,

0:51:540:51:57

"The new president of the United States is Donald Trump!"

0:51:570:52:00

-That's quite surprising.

-That is quite surprising.

0:52:000:52:02

If it's very serious or frightening news, something terrible has happened,

0:52:020:52:07

"The new president of the United States is Donald Trump."

0:52:070:52:10

It's a different thing.

0:52:100:52:13

You wouldn't normally do that, but if you were sort of talking about

0:52:130:52:17

an attack or a war or something like that,

0:52:170:52:19

this is a very serious thing and you're trying to get people in the

0:52:190:52:22

right frame of mind. But you're also trying not to frighten them

0:52:220:52:25

and you've got to use intonation for that.

0:52:250:52:27

Now we've got a very short experiment to do with Krishnan,

0:52:270:52:30

who's going to read out some football results.

0:52:300:52:33

Just by the intonation on his voice, I want you to...

0:52:330:52:37

He's going to stop before he gets to the final score and we're going to

0:52:370:52:40

work out if the last team had a higher score or a lower score than

0:52:400:52:45

the first team. OK, shall we try one?

0:52:450:52:48

Manchester City 2, West Bromwich Albion...

0:52:480:52:52

Higher or lower?

0:52:520:52:53

-Nil.

-Nil, there you go, lower.

0:52:540:52:56

West Ham United one, Tottenham Hotspur...

0:52:580:53:01

-Higher, yes.

-Five.

0:53:020:53:04

Chelsea one, Liverpool...

0:53:050:53:07

One - exactly, it's a draw.

0:53:070:53:09

This is absolutely amazing.

0:53:110:53:12

What Krishnan is doing, is over the whole course of the sentence,

0:53:120:53:15

he's doing a kind of dance with his intonation

0:53:150:53:18

that's completely keeping pace with the meaning of what he's saying.

0:53:180:53:21

You've got weaving the intonation in and out of the words and you're

0:53:210:53:24

picking up on that, you know what that means.

0:53:240:53:27

Can you do an example of it incorrectly so we can hear what that sounds like?

0:53:270:53:31

Stoke City four, Huddersfield...

0:53:330:53:35

five.

0:53:360:53:37

And when you say that, it sounds like it's the words that are wrong, whereas it's the

0:53:390:53:43

intonation that's wrong, it's really striking.

0:53:430:53:45

Thank you very, very much, Krishnan.

0:53:450:53:47

-Thank you.

-Thank you, Sophie.

0:53:470:53:48

So we are using intonation all the time in regular conversation,

0:53:530:53:56

and sometimes we think that that's all we do to pick up on emotion in the voice.

0:53:560:54:02

Actually emotion in the voice is incredibly complex.

0:54:020:54:05

There's a great deal going on because emotions change how your bodies work

0:54:050:54:08

and how they feel. They can change many different aspects of your voice.

0:54:080:54:11

Up to now,

0:54:110:54:13

computers have really struggled with this kind of information.

0:54:130:54:16

But that's changing.

0:54:160:54:20

And people are starting to make more progress.

0:54:200:54:23

For our last demonstration,

0:54:230:54:25

I'm going to look at something really amazing -

0:54:250:54:28

a computer that can read emotions from human voices.

0:54:280:54:31

Now the acoustics in here are quite reverberant

0:54:310:54:34

and this computer has been built to work in the home,

0:54:340:54:38

and so what I'm going to do is just step outside where there's a lovely

0:54:380:54:41

carpet and it's a little bit more like being in someone's living room.

0:54:410:54:43

Let's go and meet Olly.

0:54:430:54:45

-Hi. Hi, Raymond.

-Hello.

0:54:470:54:50

Now, tell me about Olly.

0:54:500:54:54

So Olly can actually understand what you're saying, as well as

0:54:540:54:57

emotions of your voice, the tone of your voice.

0:54:570:54:59

Excellent, what kind of thing do you do with Olly?

0:54:590:55:02

Olly actually enhances the communication between human and technology.

0:55:020:55:06

So he could be a digital assistant who's really understanding how you

0:55:060:55:10

feel, not just what you are saying?

0:55:100:55:12

-Exactly.

-Yes.

-Can we find out more about how he works?

0:55:120:55:14

-Sure.

-Is it easiest to demonstrate that?

0:55:140:55:16

Yes, of course. So what you need to do is just stand a sensible distance

0:55:160:55:19

from Olly, speak normal as usual,

0:55:190:55:22

-and then maybe you can add some emotion to it when you speak.

-OK.

0:55:220:55:25

So, are you ready?

0:55:250:55:27

One, two, three.

0:55:270:55:28

SADLY: Hey, Ollie, what's the weather like in London today?

0:55:290:55:32

Oh, there we go, yes.

0:55:350:55:38

So is Ollie getting the emotion out of your voice by the sound of your

0:55:400:55:44

voice or the words you're saying, or is it both?

0:55:440:55:47

Actually it's both.

0:55:470:55:48

It recognises some acoustic components of what you said,

0:55:480:55:52

and it's also using the syntax and semantics of the words you used.

0:55:520:55:56

It's very clever. Thank you very much,

0:55:560:55:58

-thank you.

-Thank you very much.

0:55:580:56:00

Olly is doing an amazing job of decoding emotion as well as words

0:56:030:56:08

from the human voice, and you can see from this just how hard emotion

0:56:080:56:13

is to read and how extremely nuanced it can be.

0:56:130:56:17

We find it very easy, because we've grown up around people using language,

0:56:170:56:22

emotion, social meaning in their voices all the time,

0:56:220:56:25

so it seems easy to us but it's absolutely vital to understanding language.

0:56:250:56:29

All around the world,

0:56:340:56:36

animals are communicating in very simple and very complex ways and we

0:56:360:56:42

have so much more to learn.

0:56:420:56:44

I've touched on just the surface of animal communication today.

0:56:440:56:48

Within this, humans do seem to be extraordinarily complex in their linguistic abilities.

0:56:490:56:55

But we're only really using language with each other.

0:56:550:56:59

Could there ever be another life form that could crack our code -

0:56:590:57:02

perhaps one we haven't even encountered yet?

0:57:020:57:05

Watching Carl Sagan describe his work on the Voyager space probes

0:57:060:57:10

40 years ago in the 1977 Christmas lectures,

0:57:100:57:13

literally set me on course to standing here today -

0:57:130:57:16

that's why I became a scientist.

0:57:160:57:18

The Voyagers contain golden records,

0:57:190:57:22

and the golden records contain the sounds of Earth,

0:57:220:57:24

including greetings in 55 different human languages.

0:57:240:57:28

GREETINGS IN DIFFERENT LANGUAGES

0:57:280:57:30

Now, the sounds of the Earth's languages

0:57:350:57:39

have long since left our solar system.

0:57:390:57:41

The Voyager probes are now 13 billion miles away from Earth.

0:57:410:57:45

They are the most distant objects ever created by humans.

0:57:450:57:48

Now, if those spacecraft ever encounter an alien life form,

0:57:490:57:53

maybe they can use these sounds to start to decode our language.

0:57:530:57:57

And if they have brains that work like ours,

0:57:570:57:59

maybe one day we could have conversations with them.

0:57:590:58:03

This year's lectures have explored our fundamental urge to communicate.

0:58:030:58:07

We've looked at where it comes from and why we're so very good at it,

0:58:070:58:10

and we just can't help but reach out to others.

0:58:100:58:14

The big question is whether there is another form of life out there

0:58:140:58:18

that could crack our code,

0:58:180:58:19

and I hope you've realised that it will need to have an incredible brain -

0:58:190:58:23

at least as incredible as your brains, to do so.

0:58:230:58:27

Thank you.

0:58:270:58:29

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