0:00:02 > 0:00:04Human language can be complex and bewildering.
0:00:04 > 0:00:05PHONE RINGS
0:00:05 > 0:00:08Oh, dear. Sorry, I've got to take this.
0:00:10 > 0:00:12Hello? I can't talk now.
0:00:12 > 0:00:16I'm doing the Christmas lectures!
0:00:16 > 0:00:17What?
0:00:19 > 0:00:21She said DAVID didn't take his money?
0:00:23 > 0:00:26What? She said David didn't take HIS money?
0:00:27 > 0:00:31Oh, she SAID David didn't take his money.
0:00:31 > 0:00:33Why didn't you just say that, then?
0:00:33 > 0:00:37Sorry. Now, what you have there is three very different meanings from
0:00:37 > 0:00:40exactly the same sentence.
0:00:40 > 0:00:44Will anything other than another human being ever be able to cope
0:00:44 > 0:00:45with that level of complexity?
0:00:47 > 0:00:50In this lecture, I'm going to find out what makes language the ultimate
0:00:50 > 0:00:55communication tool and why humans are absolute masters of it.
0:01:19 > 0:01:22Welcome to the third Royal Institution Christmas lecture
0:01:22 > 0:01:24of 2017.
0:01:24 > 0:01:26I'm Professor Sophie Scott.
0:01:26 > 0:01:31Now humans have got an incredibly powerful ability - language.
0:01:31 > 0:01:35I can convey very precise meanings to anyone within earshot if they
0:01:35 > 0:01:40speak my language. To give you a taste, please let me introduce scientist,
0:01:40 > 0:01:43comedian and rapper, Alex Lethbridge.
0:01:54 > 0:01:56So I've listened to Doc Brown,
0:01:56 > 0:02:00Akala and Syntax, they showed me how to flow off my grammar and syntax.
0:02:00 > 0:02:02The RI told me, Alex, what's your language?
0:02:02 > 0:02:05I checked my head, do you mean English, Fante or Spanish?
0:02:05 > 0:02:07Now, my PhD's crazy, I'll do wordplay till it pays me.
0:02:07 > 0:02:09And when I get bored, a language or two.
0:02:09 > 0:02:11So while you're getting PTSD from your GCSEs
0:02:11 > 0:02:13and wondering should I RSVP to GCHQ?
0:02:13 > 0:02:15Now I'm not sure, Sophie says language is complex.
0:02:15 > 0:02:18You've got the rules like subjects, verbs and objects.
0:02:18 > 0:02:20It's more than words, you've got intonation and context.
0:02:20 > 0:02:23Final lecture, we're learning all of these concepts.
0:02:23 > 0:02:24APPLAUSE
0:02:31 > 0:02:36I don't know about you, when I'm listening to rap music, I like to count all the words.
0:02:36 > 0:02:38And I reckon in about 25 seconds there, Alex,
0:02:38 > 0:02:40you said about 110 words.
0:02:40 > 0:02:45- Yeah, exactly.- And got over about 15 ideas, does that sound about right?
0:02:45 > 0:02:47Amazing. Thank you very much, Alex.
0:02:47 > 0:02:48No worries, thanks.
0:02:54 > 0:02:58We're used to thinking of telepathy as a science-fiction concept, but
0:02:58 > 0:03:01Alex just achieved the exact same result.
0:03:01 > 0:03:04We share the content of our minds, our brains,
0:03:04 > 0:03:07whenever we want to speak, or rap, to anyone.
0:03:07 > 0:03:10Don't worry, I'm not going to rap!
0:03:10 > 0:03:14Are we unique in having these skills or will we one day have a full
0:03:14 > 0:03:16conversation with another species?
0:03:16 > 0:03:20When I was a little girl, I so wanted to be able to talk to animals.
0:03:20 > 0:03:22Will that ever happen?
0:03:22 > 0:03:26And will computers ever be able to fully get their processors around our
0:03:26 > 0:03:29language well enough to understand a joke?
0:03:29 > 0:03:33Tonight, I'm going to explore what makes language so amazing
0:03:33 > 0:03:36and so very difficult.
0:03:36 > 0:03:40But what do we mean when we talk about language?
0:03:40 > 0:03:41Languages can come in many forms.
0:03:41 > 0:03:44We can talk, we can write, we can sign.
0:03:44 > 0:03:47And I've got a very basic form of language here.
0:03:47 > 0:03:50MORSE BEEPING
0:03:56 > 0:03:58Anybody speak Morse?
0:03:59 > 0:04:01That was a cry for help!
0:04:01 > 0:04:04Now, I don't speak Morse beyond being able to do that.
0:04:04 > 0:04:08But basically you can think of language like Morse code as being
0:04:08 > 0:04:11a message which we're sending with a code.
0:04:11 > 0:04:13And to make a code,
0:04:13 > 0:04:17the first thing you need to do is to produce a signal that's got some
0:04:17 > 0:04:20kind of structure. Now, that means a signal
0:04:20 > 0:04:24that's not just a random stream of noises without any order,
0:04:24 > 0:04:29nor can it be a very simple pattern just repeated again and again.
0:04:29 > 0:04:33You need to have a capacity to send information like the short and long
0:04:33 > 0:04:34patterns of the Morse.
0:04:34 > 0:04:37Now, humans do this when we speak aloud.
0:04:37 > 0:04:40We're using the sounds of our voices that we use in our language to
0:04:40 > 0:04:42express a code.
0:04:42 > 0:04:46Can we find any signs of a similar kind of structure in other animals'
0:04:46 > 0:04:48voices? And if so,
0:04:48 > 0:04:51could we crack their code and have a proper conversation with them?
0:04:52 > 0:04:56There are some animals who are very good candidates for being able to
0:04:56 > 0:04:59produce these sorts of sounds with structured elements,
0:04:59 > 0:05:00and those are birds.
0:05:03 > 0:05:08Now, these guys, who are a couple of zebra finches,
0:05:08 > 0:05:11and a couple of canaries, they're songbirds.
0:05:13 > 0:05:15Songbirds, when they're babies,
0:05:15 > 0:05:19they learn all the songs they're going to sing when they're adults.
0:05:19 > 0:05:23The most impressive can learn over 1,000 different songs.
0:05:23 > 0:05:26Could these songs contain coded information?
0:05:26 > 0:05:29Now, I can hear a couple of cheeps coming out of here,
0:05:29 > 0:05:32but I think we have a recording of one of the canaries.
0:05:32 > 0:05:33Can we listen to that?
0:05:33 > 0:05:36CANARY CHIRPS
0:05:44 > 0:05:45It's a beautiful sound.
0:05:45 > 0:05:49But does it contain enough structure in that signal that it could be used
0:05:49 > 0:05:50to transmit a code?
0:05:52 > 0:05:55Well, I've got an example of the canary's song here.
0:05:55 > 0:06:00And what I'm showing it to you as, is what's called a spectrogram.
0:06:00 > 0:06:04Now, a spectrogram is a way of looking at the structure in a sound.
0:06:04 > 0:06:08So, what you have along this direction is time.
0:06:08 > 0:06:11So, this is the sound unfurling over time,
0:06:11 > 0:06:15how it's changing over time. This direction, we've got frequency.
0:06:15 > 0:06:17And that's roughly telling you,
0:06:17 > 0:06:20low-pitched sounds up to high-pitched sounds.
0:06:20 > 0:06:22And where the colours are warmer and brighter,
0:06:22 > 0:06:24that's where there's more energy.
0:06:24 > 0:06:27And we can see in these individual elements, these little notes,
0:06:27 > 0:06:31and we're seeing some quite structured elements to this.
0:06:31 > 0:06:33We've got a similar sequence here and repeating there.
0:06:33 > 0:06:38And then these sequences of lower and higher alternating notes.
0:06:38 > 0:06:41Now, I need to compare this with another kind of voice.
0:06:41 > 0:06:44So, I would like a human volunteer, please.
0:06:44 > 0:06:47Can I have you in the middle there, with the penguin?
0:06:47 > 0:06:48Thank you very much.
0:06:52 > 0:06:53Now, what's your name?
0:06:53 > 0:06:57- Ruth.- Ruth. I'm going to ask you to come over here and say the first two
0:06:57 > 0:07:00lines of Humpty Dumpty into my computer.
0:07:00 > 0:07:01OK, I'll tell you when to go.
0:07:01 > 0:07:03If you could just stand about there.
0:07:03 > 0:07:05Brilliant. And go now.
0:07:05 > 0:07:08Humpty Dumpty sat on the wall.
0:07:08 > 0:07:11Humpty Dumpty had a great fall.
0:07:11 > 0:07:12Brilliant. Thank you very much, Ruth.
0:07:12 > 0:07:14Exemplary, I think you'd agree.
0:07:21 > 0:07:24So, here's Ruth's version of Humpty Dumpty shown in the same way
0:07:24 > 0:07:27on a spectrogram. You can see immediately there are some differences.
0:07:27 > 0:07:30There's the canary, there's Ruth.
0:07:30 > 0:07:32You can also see some similarities.
0:07:32 > 0:07:34And I'm talking in the most general sense here,
0:07:34 > 0:07:38but Ruth is producing individual rhythm in the syllables of what she's saying,
0:07:38 > 0:07:41and you're seeing a pattern of that over the sentence.
0:07:41 > 0:07:46And you're seeing something broadly comparable in the canary.
0:07:46 > 0:07:48We're seeing structure in those sounds.
0:07:48 > 0:07:52The canary and the speech sounds have both got rhythm, pitch,
0:07:52 > 0:07:54rate information in there.
0:07:54 > 0:07:57So it's at least possible that the songbird
0:07:57 > 0:07:59is producing something which has got
0:07:59 > 0:08:03similarities to the way we code information in our speech.
0:08:03 > 0:08:06Could it actually be a code, though?
0:08:07 > 0:08:08Well, probably not.
0:08:08 > 0:08:11It doesn't seem to be quite enough.
0:08:11 > 0:08:14So if you look at how songbirds use song,
0:08:14 > 0:08:17what you find is they don't generally change their songs
0:08:17 > 0:08:18once they've learned them.
0:08:18 > 0:08:20They will always sing the same whole song.
0:08:20 > 0:08:24And the other thing they don't do is chop songs up and rearrange them
0:08:24 > 0:08:27to make new songs. We do that all the time.
0:08:27 > 0:08:30We can use words in lots of different, very novel orders.
0:08:30 > 0:08:31The birds don't do this.
0:08:31 > 0:08:35So it's entirely possible that, complex though the songbirds are,
0:08:35 > 0:08:38they are not producing something that is conveying a complex,
0:08:38 > 0:08:40coded meaning.
0:08:40 > 0:08:41But there's another group of birds
0:08:41 > 0:08:45that can learn to say human-coded signals, use words.
0:08:45 > 0:08:47And those are parrots.
0:08:47 > 0:08:50Please meet Mike and his parrot.
0:08:53 > 0:08:56- Hello.- Hello.
0:08:59 > 0:09:02- Hi.- Hi.- Hi, Mike, who have you brought with you?
0:09:02 > 0:09:03- So this is Helly...- You all right?
0:09:03 > 0:09:05- You all right?- ..and Helly is an Amazon parrot.
0:09:05 > 0:09:08So a South American bird from the rainforests.
0:09:08 > 0:09:10And she's already said hello, hasn't she?
0:09:10 > 0:09:12She has, yes. She knows that's an introduction,
0:09:12 > 0:09:14so it's the first point of call for a conversation
0:09:14 > 0:09:16or attention gathering.
0:09:16 > 0:09:18- How many other words does she use? - You all right?
0:09:18 > 0:09:21She uses about 80 different sounds and words.
0:09:21 > 0:09:23Yeah, human words, though?
0:09:23 > 0:09:25Oh, human words, I would say she knows about five or ten.
0:09:25 > 0:09:27- Yeah.- Can you say bye?
0:09:27 > 0:09:28Bye!
0:09:30 > 0:09:32So she's doing a set of human words.
0:09:32 > 0:09:36Did you teach her those or did she just pick them up from seeing how they were used?
0:09:36 > 0:09:39Yeah, she understands that "Hello" is a greeting because it's a
0:09:39 > 0:09:41word that we would use on arrival.
0:09:41 > 0:09:44And she would then understand that the "Bye" is a departure word.
0:09:44 > 0:09:47So she places the words with timings, as well.
0:09:47 > 0:09:50- Yeah.- But throughout her life she's had lots of trainers saying,
0:09:50 > 0:09:51"Are you all right, are you all right?"
0:09:51 > 0:09:54So if she actually gets worried in something like a studio environment,
0:09:54 > 0:09:56- she will say "Are you all right?" - You all right?
0:09:56 > 0:09:58And she knows that goes with almost an emotion, as well.
0:09:58 > 0:10:03- Yes.- So she links these words with timings and emotions.
0:10:03 > 0:10:06And she can do other sounds that aren't... she can do more than words, can't she?
0:10:06 > 0:10:10Yes, she uses lots of different sounds as well as words.
0:10:10 > 0:10:11Can we have a bomb?
0:10:11 > 0:10:13WHISTLE, BOOM
0:10:13 > 0:10:15LAUGHTER
0:10:18 > 0:10:22They can also be copying other birds as well, so local birds in the wild.
0:10:22 > 0:10:25And also other birds that we house.
0:10:25 > 0:10:27Thank you very much, Mike. And thank you very much, Helly.
0:10:27 > 0:10:29Lovely meeting you. Bye-bye.
0:10:39 > 0:10:43What gives birds their amazing ability to learn to produce these sounds?
0:10:43 > 0:10:48Well, the answer may lie within their brains.
0:10:48 > 0:10:52Now, birds are more closely related to dinosaurs than they are to us.
0:10:52 > 0:10:56But we share similarities in the ways that our brains control both
0:10:56 > 0:10:59the learning and the production of sounds we make with our voices
0:10:59 > 0:11:02and the genes that build those parts of the brain.
0:11:02 > 0:11:04If we look...
0:11:05 > 0:11:06..at a bird brain,
0:11:06 > 0:11:10we can identify specific areas which are important
0:11:10 > 0:11:14in the learning of song and in the control of singing.
0:11:14 > 0:11:17And if we look at a human brain...
0:11:19 > 0:11:23..we can see a similarity, in that there are specific networks
0:11:23 > 0:11:25recruited when we are speaking.
0:11:25 > 0:11:28When we're talking, these are human brains here,
0:11:28 > 0:11:32this is the right side of the brain and the left side of the brain.
0:11:32 > 0:11:35We see some areas which are strongly associated with the control of all
0:11:35 > 0:11:39the work we have to do to make the sounds of speech.
0:11:39 > 0:11:43We also find the very specific area just on the left side of the brain
0:11:43 > 0:11:46which seems to be very important in planning speech.
0:11:46 > 0:11:50And it's also important in learning new things to do with our voices.
0:11:51 > 0:11:54How can I find out more about this system?
0:11:54 > 0:11:58We can take these snapshots of the brain in action and we can work with
0:11:58 > 0:12:01people who have had strokes and have damaged these brain areas.
0:12:01 > 0:12:05But there's another technique that we can use where we can investigate
0:12:05 > 0:12:07what would happen if we could turn off
0:12:07 > 0:12:10that part of the brain in someone, and just that part of the brain.
0:12:10 > 0:12:13What I'd like to do is introduce you to my colleague from UCL,
0:12:13 > 0:12:16Dr Ricci Hannah, and comedian Robin Ince.
0:12:19 > 0:12:23Hello Robin, hello. I've been waiting for this day.
0:12:23 > 0:12:24Hello, Ricci.
0:12:24 > 0:12:25- Hi.- Hi.
0:12:25 > 0:12:27Now, Robin.
0:12:27 > 0:12:30Robin, what we're going to do is sit you down here.
0:12:30 > 0:12:34- Right.- And then I'm going to let Ricci explain what we're going to do next. OK?
0:12:34 > 0:12:38I have to emphasise, this is a temporary state of affairs, OK?
0:12:38 > 0:12:42We're not about to do some sort of terrible live brain surgery on you!
0:12:42 > 0:12:45I was quite worried, because I was watching what else was going on and I thought,
0:12:45 > 0:12:47"They're going to prove that I'm less intelligent than a parrot!"
0:12:47 > 0:12:49So let's find out what happens.
0:12:49 > 0:12:51Ricci, can you tell us what you've got here?
0:12:51 > 0:12:55So this is a transcranial magnetic stimulator.
0:12:55 > 0:12:57And what we can use it for is to probe
0:12:57 > 0:12:59how different parts of the brain play a role
0:12:59 > 0:13:01in different aspects of behaviour.
0:13:01 > 0:13:03In this case, speech.
0:13:03 > 0:13:08OK. So can we start just by pointing out, that the way that we get this,
0:13:08 > 0:13:13we change Robin's brain activity by passing electrical current through
0:13:13 > 0:13:15- that coil, is that right?- Mm-hmm.
0:13:15 > 0:13:18And actually, according to the principles of electromagnetic forces,
0:13:18 > 0:13:21which were first described here by Michael Faraday,
0:13:21 > 0:13:25what that lets us do is induce currents inside Robin's brain
0:13:25 > 0:13:28without actually having to get inside his brain.
0:13:28 > 0:13:29It's absolutely amazing.
0:13:29 > 0:13:36- So can we start by getting Robin talking and then seeing if we can stop him talking?- OK!
0:13:37 > 0:13:39People have been trying this for years!
0:13:39 > 0:13:42- It's going to be popular!- Can I get you to shuffle back, Robin?
0:13:42 > 0:13:43- Yes.- There we go.
0:13:43 > 0:13:48- Are you OK?- OK, so I'll just position the coil.
0:13:49 > 0:13:56- What I want you to do is to say the months of the year really loudly and clearly.- OK.
0:13:56 > 0:13:58When you're ready.
0:13:58 > 0:14:03January, February, March, April, M...
0:14:03 > 0:14:04Please start talking again!
0:14:05 > 0:14:07That is weird!
0:14:07 > 0:14:09That is very...
0:14:09 > 0:14:12I think Professor Brian Cox who I do a radio show with is going to want a
0:14:12 > 0:14:15beret that I wear with that in there, so he can stop me talking!
0:14:15 > 0:14:18I don't know what it looked like to you, but it was like just...
0:14:20 > 0:14:23It was like kind of Homer with a doughnut, sort of...
0:14:24 > 0:14:26If you could bear it, can we try it again?
0:14:26 > 0:14:28Yeah! It's really... I find it amazing.
0:14:28 > 0:14:31It is extraordinary, isn't it? And then it just comes back.
0:14:31 > 0:14:33I think I prefer quiet me. Let's do it again!
0:14:34 > 0:14:38Shall I try, and see how far I can get in Jabberwocky, shall I try that?
0:14:38 > 0:14:40Yes, excellent, yes.
0:14:40 > 0:14:42- Tell me when you want me to. - When you're ready.
0:14:42 > 0:14:44'Twas brillig, and the slithy toves. Did...
0:14:48 > 0:14:49It's fantastic!
0:14:54 > 0:14:55That's...
0:15:01 > 0:15:03So what Ricci's very specifically
0:15:03 > 0:15:05focusing on here is this part of the brain,
0:15:05 > 0:15:08it's called the inferior frontal gyrus, on the left.
0:15:08 > 0:15:10And in humans it's incredibly important for
0:15:10 > 0:15:12planning and controlling speech.
0:15:12 > 0:15:15If we move to a slightly different area,
0:15:15 > 0:15:16even within the same network,
0:15:16 > 0:15:20what we find is that Robin will be able to talk absolutely fine.
0:15:20 > 0:15:21Would it be OK to try that?
0:15:21 > 0:15:22- Yeah, sure.- OK.
0:15:25 > 0:15:28- OK.- I didn't even look at the health and safety form for this!
0:15:28 > 0:15:31- I sent this to you! - I know, I didn't look, just in case!
0:15:31 > 0:15:33You're fine, you're safe.
0:15:33 > 0:15:34OK, when you're ready.
0:15:34 > 0:15:36'Twas brillig, and the slithy toves
0:15:36 > 0:15:37Did gyre and gimble in the wabe
0:15:37 > 0:15:39All mimsy were the borogoves And the mome... Yeah, that's...
0:15:39 > 0:15:41There you go, there you go.
0:15:41 > 0:15:43I think I preferred the one at the side!
0:15:43 > 0:15:46I can't thank you enough for being prepared to come out in front of
0:15:46 > 0:15:48everybody and have us try and zap your brain!
0:15:48 > 0:15:50Thank you very much, Robin. Thank you very much, Ricci.
0:15:50 > 0:15:52That was amazing, thank you.
0:15:53 > 0:15:55It was brilliant, thank you.
0:15:58 > 0:16:00Thank you.
0:16:02 > 0:16:07So you could see how precise the effect of the transcranial magnetic
0:16:07 > 0:16:12stimulation was. We were only seeing Robin stopping talking when we were
0:16:12 > 0:16:15applying the TMS over his left inferior frontal gyrus.
0:16:15 > 0:16:18When we went elsewhere in the brain, other things happened,
0:16:18 > 0:16:21but it's not stopping him from talking.
0:16:21 > 0:16:25So what we're seeing here in the humans and in the birds
0:16:25 > 0:16:27are very dedicated brain regions
0:16:27 > 0:16:30that are important in vocal control and vocal learning.
0:16:30 > 0:16:34It's a strong hint that we really are seeing some commonality in
0:16:34 > 0:16:36the brain areas that are to do with the learning
0:16:36 > 0:16:38and the producing of vocal sounds.
0:16:38 > 0:16:43But can any of these birds really understand the words they're saying?
0:16:43 > 0:16:45Well, parrots don't just say human words.
0:16:45 > 0:16:48They'll mimic pretty much anything they hear a lot of.
0:16:48 > 0:16:51Car alarms, creaking doors, alarm clocks.
0:16:51 > 0:16:53Why do they do this at all?
0:16:53 > 0:16:56Well, birds are mimicking to show off to potential mates,
0:16:56 > 0:16:57to get attention.
0:16:57 > 0:16:59To impress other birds and other humans.
0:16:59 > 0:17:01To defend their nesting sites.
0:17:01 > 0:17:04Perhaps the more impressive a sound they can make,
0:17:04 > 0:17:06the more likely they'll find a companion,
0:17:06 > 0:17:08or scare off a rival.
0:17:08 > 0:17:12So birds aren't showing a great ability to decode our language.
0:17:12 > 0:17:15But what do I mean by decoding?
0:17:15 > 0:17:22We humans are exceptionally good at working out what words are and what words mean.
0:17:22 > 0:17:26I'd like you to watch and listen to a clip that I'm about to play you,
0:17:26 > 0:17:28and there's going to be a short test afterwards.
0:17:28 > 0:17:31And this will go down on your permanent record.
0:17:32 > 0:17:34Back. Deaf. Gidge.
0:17:34 > 0:17:35Hock. Fip. Nop.
0:17:35 > 0:17:37Fib. Wreck. Sit.
0:17:37 > 0:17:38They. Fip. Fip.
0:17:38 > 0:17:40Hock. Fib. Gack.
0:17:40 > 0:17:41Gin. Hock. Hock.
0:17:41 > 0:17:43Lun. Fip. Nat.
0:17:43 > 0:17:44Fip. Ros. Hock.
0:17:44 > 0:17:45Yas. Beth.
0:17:47 > 0:17:49OK. Short test.
0:17:49 > 0:17:50What's this called?
0:17:52 > 0:17:53- CROWD:- Fip.
0:17:54 > 0:17:57It's a fip. What's this?
0:17:57 > 0:17:59- CROWD:- Hock.- Hock, excellent.
0:17:59 > 0:18:03Well done. Now, I didn't tell you to work out what was going on there.
0:18:03 > 0:18:07What you were doing was decoding the information we gave you.
0:18:07 > 0:18:11There were some novel sounds in there and they were being associated
0:18:11 > 0:18:13in quite a regular way with visual information.
0:18:13 > 0:18:16Even if you don't know that your brain is trying to do it,
0:18:16 > 0:18:20you're always trying to spot words and work out what those words mean.
0:18:20 > 0:18:24We are not the only animal who has an ability to do this.
0:18:24 > 0:18:27There's an animal you're probably all very familiar with who's actually
0:18:27 > 0:18:30really very, very good at sharing this ability with us.
0:18:30 > 0:18:31And that's dogs.
0:18:32 > 0:18:36Please give us a very nice doggy-friendly round of applause
0:18:36 > 0:18:38for Gable and his owner, Sally.
0:18:42 > 0:18:44- Hello.- Hello.- Hello.
0:18:44 > 0:18:48Hello. Hey, Gable.
0:18:49 > 0:18:51So Sally, what's different about Gable?
0:18:52 > 0:19:00Gable's got the ability to identify a large number of objects and toys by name.
0:19:00 > 0:19:04He currently knows about 150 different names for toys and objects and articles.
0:19:04 > 0:19:06Goodness.
0:19:06 > 0:19:09Now, can we see a demonstration of that?
0:19:09 > 0:19:12So we've got some of Gable's toys here and what were going to do is
0:19:12 > 0:19:14get a random selection of those out and then see.
0:19:14 > 0:19:17- OK.- I think there is somebody...
0:19:17 > 0:19:20Hi, Sean. Now, we've spent a little bit of time with Sean,
0:19:20 > 0:19:23getting Sean used to being with Gable and Gable used to being with Sean.
0:19:23 > 0:19:25Can I bring you down? Thank you.
0:19:27 > 0:19:30Now Sean, if you could come over here.
0:19:30 > 0:19:35Can you pop these gloves on and can you just randomly select 15 toys
0:19:35 > 0:19:37from these buckets and spread them out?
0:19:37 > 0:19:40When did you first realise Gable could do this?
0:19:40 > 0:19:42When he was a young puppy, actually.
0:19:42 > 0:19:44He sort of invented the game.
0:19:44 > 0:19:46I was trying to watch telly one evening and he kept pestering me.
0:19:46 > 0:19:48He wanted to do something.
0:19:48 > 0:19:51And I just remembered oh, his red toy was upstairs.
0:19:51 > 0:19:53And I just said, "Oh, go and get your red toy."
0:19:53 > 0:19:55Sort of idly just dismissing him,
0:19:55 > 0:19:57hoping he'd be gone for ages because he couldn't find it.
0:19:57 > 0:20:00And he came back with it and he put it in front of me and looked at me.
0:20:00 > 0:20:04And I just thought, "I've actually never told you that's called Red Toy."
0:20:04 > 0:20:06And so I just then thought, I wonder what happens
0:20:06 > 0:20:09if I do teach him a name, and it's gone from there, really.
0:20:10 > 0:20:12- Now...- Wow, look at all your toys. - ..Sean, don't go anywhere,
0:20:12 > 0:20:14I'm going to need you again.
0:20:14 > 0:20:19Can I ask you, Sally, to tell Gable to pick one of these toys, please?
0:20:19 > 0:20:20OK. Gable...
0:20:23 > 0:20:24triceratops.
0:20:26 > 0:20:28Get triceratops.
0:20:32 > 0:20:36- Yes! Yes, good boy!- Good boy!
0:20:36 > 0:20:37Good boy! Leave it.
0:20:39 > 0:20:41- Come round here.- Fantastic.
0:20:41 > 0:20:45So we've seen that Gable is really very good at working out what you're
0:20:45 > 0:20:48saying and trying to work out from looking at you which one you mean.
0:20:48 > 0:20:51But then Gable is your dog and he might be really,
0:20:51 > 0:20:52really familiar with your voice.
0:20:52 > 0:20:55It would be very interesting to know if we could see this happen with
0:20:55 > 0:20:56someone who's got a very different voice.
0:20:56 > 0:20:59So Sean, is it OK if Sean has a go?
0:20:59 > 0:21:03- Yes.- And what you need to do is whisper into Sean's ear
0:21:03 > 0:21:05which toy you'd like him to pick up.
0:21:05 > 0:21:07Do you remember Sean?
0:21:07 > 0:21:11- OK.- Gable, octopus.
0:21:11 > 0:21:12Get octopus.
0:21:12 > 0:21:13Go.
0:21:14 > 0:21:15Yes!
0:21:17 > 0:21:18Good boy, good boy!
0:21:18 > 0:21:20- And well done, Sean.- Leave it.
0:21:22 > 0:21:23- Can Sean do one more? - Of course, yes.
0:21:23 > 0:21:25Is that OK, Sean?
0:21:25 > 0:21:27Gable, get hammer.
0:21:27 > 0:21:28Get hammer.
0:21:31 > 0:21:34Yes! Amazing.
0:21:34 > 0:21:36Good boy!
0:21:37 > 0:21:41So this really does indicate that Gable must have some understanding of what
0:21:41 > 0:21:44words mean above and beyond just associating your voice with things.
0:21:44 > 0:21:48That's fantastic. Thank you so much, Sean, thank you so much, Sally,
0:21:48 > 0:21:50and particularly thank you so much, Gable.
0:21:50 > 0:21:51Come on, then!
0:21:55 > 0:21:57That's amazing.
0:21:57 > 0:22:03So, what's happening in Gable's brain so that he can do this?
0:22:03 > 0:22:06Well, there's a group of scientists in Hungary who have been doing some
0:22:06 > 0:22:09quite extraordinary experiments.
0:22:09 > 0:22:11They've been training dogs to lie very still
0:22:11 > 0:22:14and then they've been putting them into brain scanners.
0:22:16 > 0:22:21The brain scanners don't work if you move so the dog has to stay very still.
0:22:22 > 0:22:25And then what they are doing is taking pictures of the activity inside the
0:22:25 > 0:22:30dog's brains while they're listening to different sounds and words.
0:22:30 > 0:22:35When I scan people, they're not normally that happy!
0:22:35 > 0:22:38So this is called functional magnetic resonance imaging and it lets us
0:22:38 > 0:22:41take photographs of the brain in action.
0:22:42 > 0:22:44And this is showing the results.
0:22:44 > 0:22:46So a dog's brain, as you can see,
0:22:46 > 0:22:49it's different in shape to a human brain, but it has some of the same
0:22:49 > 0:22:54structures in there. And quite strikingly,
0:22:54 > 0:22:58one of the things they are finding in the results is when dogs hear words they understand,
0:22:58 > 0:23:01we see greater activation in the left side of the brain,
0:23:01 > 0:23:04particularly in brain areas to do with processing sound.
0:23:04 > 0:23:10And that is very similar to what you find in our brains.
0:23:10 > 0:23:14So it looks like it's at least possible that more than any other animal
0:23:14 > 0:23:18we've looked at, dogs might be sharing some of our ability
0:23:18 > 0:23:23to decode spoken words and they may even be doing it in similar ways to us.
0:23:24 > 0:23:30But of course, amazing as they are, dog's brains do have their limits.
0:23:30 > 0:23:34The largest number of words that any dog has been found to understand,
0:23:34 > 0:23:37and this was an exceptional dog, is about 1,000 words.
0:23:37 > 0:23:39Which sounds fantastic.
0:23:39 > 0:23:41Gable knows about 150 words,
0:23:41 > 0:23:46but every one of you could understand 1,000 words when you were three years old.
0:23:46 > 0:23:49And of course human language is more than just single words.
0:23:49 > 0:23:54We don't walk around going, "Steps, camera, man".
0:23:54 > 0:23:57We are putting words together into sequences.
0:23:57 > 0:24:00And when we put words into sequences,
0:24:00 > 0:24:04we actually add in an extra level of coded meaning.
0:24:06 > 0:24:10So perhaps, if we want to look for some more humanlike ability to put
0:24:10 > 0:24:13symbols into sequences like we do with sentences,
0:24:13 > 0:24:17we should be looking closer in the evolutionary tree.
0:24:17 > 0:24:21Perhaps we should be looking to our closest cousins, other apes.
0:24:21 > 0:24:24Now, because I'm not David Attenborough,
0:24:24 > 0:24:25I don't get to play with the chimpanzee.
0:24:27 > 0:24:29But we have the next best thing.
0:24:29 > 0:24:32Please release the apes.
0:24:32 > 0:24:36THEY IMITATE MONKEYS
0:24:50 > 0:24:52This didn't happen to David Attenborough!
0:24:54 > 0:24:55That's just brilliant.
0:24:57 > 0:24:58These are human apes.
0:25:01 > 0:25:03Please welcome Neil and Ace.
0:25:03 > 0:25:06So you guys have been working a lot with our ape cousins?
0:25:06 > 0:25:09We have, yeah. We were actually very fortunate to work with Andy Serkis
0:25:09 > 0:25:12on Planet Of The Apes Last Frontier, which is an interactive movie.
0:25:12 > 0:25:14Hold on a second, sorry.
0:25:14 > 0:25:17OK, just reassurance.
0:25:17 > 0:25:20We were very lucky, we got to study apes for quite a while before we
0:25:20 > 0:25:23started creating our own characters from ape work.
0:25:23 > 0:25:27We watched their movements, their behaviour patterns, hierarchies -
0:25:27 > 0:25:30here's some of the work we did - and also the speech they use,
0:25:30 > 0:25:33which is five different parts of language.
0:25:33 > 0:25:37Grunts, barks, hoots, whimpers and screams, which they do use as chimps.
0:25:37 > 0:25:39And this is my ape actually, Bryn.
0:25:39 > 0:25:41- That's you?- Yes, that's me.
0:25:41 > 0:25:43- That's amazing.- Yes.
0:25:43 > 0:25:46So what this is letting you do is have ape actors which are based on humans,
0:25:46 > 0:25:49so you can get them to do things which you could never actually ask
0:25:49 > 0:25:51- another ape to do?- Absolutely,
0:25:51 > 0:25:53and we get to create a bit of drama in the family.
0:25:53 > 0:25:56It's actually really about family, this interactive film,
0:25:56 > 0:25:58so it is centred on this particular family of apes that have splintered
0:25:58 > 0:26:00away from Caesar's group.
0:26:00 > 0:26:02So you've had to work really closely with the apes to actually learn
0:26:02 > 0:26:04about their body language and movements.
0:26:04 > 0:26:08Did you pick up on anything that felt like communication that you were looking at?
0:26:08 > 0:26:11Yeah, we studied a lot of their gesticulation and body language,
0:26:11 > 0:26:15especially between dominant and subservient male apes.
0:26:15 > 0:26:18Because what you get is a lot of different behaviour patterns that
0:26:18 > 0:26:21have been formed by a simple sign, for instance, the touch of hands.
0:26:21 > 0:26:22If somebody is in the dominant position,
0:26:22 > 0:26:24it's quite important to be able to...
0:26:24 > 0:26:25So if you were offering,
0:26:25 > 0:26:29if you're trying to get my attention and forgiveness, for instance. Go on.
0:26:29 > 0:26:33He gives me respect and I give it back to him.
0:26:33 > 0:26:35- They are communicating with gestures.- They are, yes.
0:26:35 > 0:26:37Amazing. Thank you so much, thank you.
0:26:48 > 0:26:51So Neil and Ace aren't just imitating the apes very well,
0:26:51 > 0:26:54they are clearly picking up on some aspects of communication,
0:26:54 > 0:26:57but is there really some kind of
0:26:57 > 0:26:59ape conversation going on?
0:26:59 > 0:27:02And would it look at all like the way we use language?
0:27:03 > 0:27:08To find out, please welcome chimpanzee researcher from the University of St Andrews,
0:27:08 > 0:27:10Dr Cat Hobaiter.
0:27:12 > 0:27:14Hello. Lovely to meet you.
0:27:16 > 0:27:20So you've been asking some really interesting questions about ape language
0:27:20 > 0:27:22and can you tell us more about your work?
0:27:22 > 0:27:23Yes, absolutely,
0:27:23 > 0:27:29so what people have done in the past was try to teach apes our language,
0:27:29 > 0:27:31so human language or sign language,
0:27:31 > 0:27:33or they've looked at their vocalisations.
0:27:33 > 0:27:36But what we've been looking at is their gestures.
0:27:36 > 0:27:39- OK.- And it turns out, they've got a lot of different gestures.
0:27:39 > 0:27:44So their own natural communication contains 60 or 70
0:27:44 > 0:27:50different hand and body movements that they're using every day to ask come here, go away,
0:27:50 > 0:27:52I want that, all the little meanings.
0:27:52 > 0:27:55And how do you work out what those gestures mean?
0:27:55 > 0:27:58What we do is we look for a particular gesture
0:27:58 > 0:28:01and then we are looking for what happens next.
0:28:01 > 0:28:06And if I were to do this and you responded back to me,
0:28:06 > 0:28:10one or two cases it could be anything or you could misunderstand me or I
0:28:10 > 0:28:15could keep going. But what I'm looking for is what stops me from
0:28:15 > 0:28:17signalling. So if I'm asking you for something,
0:28:17 > 0:28:21then the thing you do that makes me happy as a signaller
0:28:21 > 0:28:25- is the thing that I wanted.- Yes, so you have to look at the whole context.
0:28:25 > 0:28:29Yes, so we need to look at the signaller, at the recipient, the gesture,
0:28:29 > 0:28:33then we need to look at not just one or two but hundreds of cases so we
0:28:33 > 0:28:35can see the patterns emerging in the behaviour.
0:28:35 > 0:28:39And you've been using the general public to help you with your research as
0:28:39 > 0:28:40- well, haven't you?- Yes,
0:28:40 > 0:28:44so we were able over a few years to look at all the other apes but there
0:28:44 > 0:28:46was one of them missing, which was us.
0:28:46 > 0:28:51And we know we have language but we don't know if we still have access
0:28:51 > 0:28:54to some of the communication that the apes also use.
0:28:54 > 0:28:56The sort of gestural stuff they are using, yes.
0:28:56 > 0:29:00Exactly. So whether or not if we showed a member of the public a
0:29:00 > 0:29:02particular gesture, could they guess what it meant.
0:29:02 > 0:29:06And what we did was put up lots of videos online and ask everyone to
0:29:06 > 0:29:08come along and sort of play the game and have a go.
0:29:08 > 0:29:10Can we have a go at that now?
0:29:10 > 0:29:12- Yes, please.- Fantastic, so I think we've got a clip to start with.
0:29:12 > 0:29:16We are going to watch this closely because we are going to ask you what you think is going on.
0:29:20 > 0:29:22So the gesture there was that kind of arm.
0:29:22 > 0:29:24Little arm raise from Charlotte.
0:29:24 > 0:29:26OK, and is that a baby?
0:29:26 > 0:29:28Yes, she's just a little one.
0:29:28 > 0:29:33Do we think that's A - I want that, B - move closer, or C - go away?
0:29:36 > 0:29:37ALL: B.
0:29:37 > 0:29:40Yes, you guys are all well versed in chimpanzee already.
0:29:40 > 0:29:42That was move closer.
0:29:42 > 0:29:43OK, can we try another one?
0:29:45 > 0:29:48OK, we are just highlighting this because it happens very quickly.
0:29:48 > 0:29:52Yes, this is a really subtle one so it's just that little foot movement
0:29:52 > 0:29:54as she looks back and gives a little foot wiggle to her son.
0:29:57 > 0:29:58What happens next?
0:30:00 > 0:30:04Is that play with me, climb on me or come here?
0:30:04 > 0:30:05ALL: B.
0:30:05 > 0:30:08B, yeah. It's rather sweet, that one. Very, very swift.
0:30:08 > 0:30:09- Yes.- Really subtle.
0:30:10 > 0:30:12So this is really fascinating.
0:30:12 > 0:30:15It does look like you are seeing a sign language,
0:30:15 > 0:30:18you're seeing really gestural use of communication.
0:30:18 > 0:30:20They're not just waving their arms around passionately,
0:30:20 > 0:30:23there is a very precise meaning being conveyed here.
0:30:23 > 0:30:28But of course the real power of human language is that we can combine our
0:30:28 > 0:30:33words and symbols into sequences and that gives us a more complex level of meaning.
0:30:33 > 0:30:35Can the chimps ever do this?
0:30:35 > 0:30:39Do you ever see any kind of examples of sequences?
0:30:39 > 0:30:43Definitely. So what we see sometimes are these one-off gestures but they
0:30:43 > 0:30:45will also put gestures together.
0:30:45 > 0:30:47Sometimes a couple at once,
0:30:47 > 0:30:51that was a big scratch and shaking the object and he's actually waiting
0:30:51 > 0:30:53there. And whatever that gesture was,
0:30:53 > 0:30:57it doesn't seem to have done the trick because Rohara is ignoring him.
0:30:57 > 0:31:00- He's going to do it again. - He's going to do it again. - And the arm went up.
0:31:00 > 0:31:02So we've got a scratch, a tree wiggle and the arm going up.
0:31:02 > 0:31:04Yes.
0:31:04 > 0:31:07And it worked. Oh, all right then, I'll get down.
0:31:07 > 0:31:09She's a bit of an old lady.
0:31:09 > 0:31:14So the tree waving is the sort of "Come here, I'm in charge".
0:31:14 > 0:31:18- The scratching?- Scratching usually means groom, let's groom,
0:31:18 > 0:31:20let's get together and groom.
0:31:20 > 0:31:24Our big question for us now is when they are putting these gestures into
0:31:24 > 0:31:28sequences, if it was human words the order of the sequence would make a
0:31:28 > 0:31:32difference to the meaning, and we are trying to work out now if that's the same for the chimps.
0:31:32 > 0:31:33Amazing.
0:31:33 > 0:31:35Thank you so much, Cat, thank you.
0:31:42 > 0:31:45Now, why is this so exciting?
0:31:45 > 0:31:50Well, if other apes can also combine symbols into a specific order to
0:31:50 > 0:31:53produce something with complex meaning,
0:31:53 > 0:31:57it unlocks a great deal more of the power of the language.
0:31:57 > 0:32:01Combining symbols into sequences allows us to describe the world in
0:32:01 > 0:32:03much more complex ways.
0:32:03 > 0:32:07It gives us the power to share much more complex ideas.
0:32:07 > 0:32:11It's a really useful skill but it's not in any way a simple skill.
0:32:12 > 0:32:16And to look at exactly what kind of thing the chimps are up against in
0:32:16 > 0:32:21terms of the difficulty of understanding sequences of symbols and decoding them
0:32:21 > 0:32:24accurately, I need two volunteers.
0:32:25 > 0:32:28Can I have you there in the Los Angeles...
0:32:28 > 0:32:32Can I have you in the Santa - sorry, Rudolph.
0:32:32 > 0:32:34Fantastic, thank you very much, thank you...
0:32:35 > 0:32:36You come round here.
0:32:38 > 0:32:40You come round this side. Thank you very much.
0:32:40 > 0:32:42Now, what's your name?
0:32:42 > 0:32:44- Gracie.- Gracie, lovely to meet you, Gracie.
0:32:44 > 0:32:46- And what's your name?- Ryan.
0:32:46 > 0:32:47- Ryan?- Yes.- Fantastic.
0:32:47 > 0:32:48Now, Ryan and Gracie,
0:32:48 > 0:32:53what we need to do is put some general covers on you.
0:32:53 > 0:32:55OK, we are just going to, sort of, from top to toe, cover you up.
0:32:55 > 0:32:59Thank you. So what we are going to do is I'm going to ask you one at a
0:32:59 > 0:33:03time to read some instructions and to mix some things together.
0:33:03 > 0:33:04And, Gracie, I'm going to ask you to go first.
0:33:04 > 0:33:08So, Ryan, so that you don't see what's going on,
0:33:08 > 0:33:11I need you to pop on a blindfold and some ear defenders, OK?
0:33:11 > 0:33:15Just so that you don't give away the game.
0:33:15 > 0:33:18Right, so in a second I'm going to step forward with you
0:33:18 > 0:33:20and we are going to turn over your instructions.
0:33:20 > 0:33:23An important thing to remember, when we get to the end
0:33:23 > 0:33:25of the instructions, is you step back with me.
0:33:25 > 0:33:26Are you all right?
0:33:26 > 0:33:28OK, let's start.
0:33:38 > 0:33:41I think you might need to... You've got it, you've got it.
0:33:41 > 0:33:43Good code-cracking.
0:33:44 > 0:33:45And step back.
0:33:47 > 0:33:49To see an alarming change of colour.
0:33:49 > 0:33:50Fantastic, Gracie.
0:33:50 > 0:33:52OK, now don't go anywhere.
0:33:52 > 0:33:53We are now going to turn to Ryan.
0:33:53 > 0:33:56OK, so what we are going to do, we are going to turn over your instructions
0:33:56 > 0:33:58in just a second and I want
0:33:58 > 0:34:01you to follow them with these chemicals here. OK?
0:34:01 > 0:34:05Yes. First mix A into B.
0:34:05 > 0:34:07A into B.
0:34:07 > 0:34:09- So I just put...- A into B.
0:34:09 > 0:34:11So I just put them like this?
0:34:11 > 0:34:12Yes, very good.
0:34:19 > 0:34:21- OK.- Add C.
0:34:22 > 0:34:23And then...
0:34:25 > 0:34:27you step back.
0:34:27 > 0:34:29LAUGHTER
0:34:32 > 0:34:35Are you OK, there?
0:34:35 > 0:34:36Amazing.
0:34:37 > 0:34:39Well done.
0:34:40 > 0:34:42Thank you so much.
0:34:42 > 0:34:44Now, we had two different outcomes there.
0:34:44 > 0:34:48We had either a dramatic change of colour or an actual foam explosion,
0:34:48 > 0:34:51depending on the order in which the chemicals were mixed.
0:34:51 > 0:34:55And the only reason why Gracie and Ryan mixed them in different orders
0:34:55 > 0:34:57was just how we'd punctuated the instructions.
0:34:57 > 0:34:59So they both have the same instructions,
0:34:59 > 0:35:02and they were just understanding them differently based on where we
0:35:02 > 0:35:05put the full stops. Thank you very much, Gracie, thank you very much, Ryan.
0:35:09 > 0:35:11So you can see that the exact grammar,
0:35:11 > 0:35:13in this case the punctuation of the sentences,
0:35:13 > 0:35:17has completely changed the meaning of those sentences
0:35:17 > 0:35:21and that's why this tiny glimpse of the ability to combine gestures
0:35:21 > 0:35:23in the apes is so exciting.
0:35:23 > 0:35:28So if we think about the animals that we have talked about this evening,
0:35:28 > 0:35:31we've seen birds who are able to produce very complex sounds
0:35:31 > 0:35:36and they can mimic many other sounds, but they don't seem to understand the meaning of
0:35:36 > 0:35:38those words. We've seen dogs,
0:35:38 > 0:35:42now dogs don't talk but dogs are incredibly good at working out
0:35:42 > 0:35:45what human words mean and what they refer to.
0:35:45 > 0:35:47And we've seen the chimpanzees,
0:35:47 > 0:35:52they are both using gestures to have quite precise meanings and they are
0:35:52 > 0:35:58also using sequences of those gestures to have more sentence-like
0:35:58 > 0:36:02structures. But they are not the same as the kind of codes humans use.
0:36:02 > 0:36:06Why are there these distinctions between humans and other animals?
0:36:06 > 0:36:11Well, I think a big difference is likely to do with our brains.
0:36:11 > 0:36:14I just want you to look at these different ones here.
0:36:14 > 0:36:18So here we've got a bird brain, that's actually a chicken brain.
0:36:18 > 0:36:19That's a dog's brain.
0:36:20 > 0:36:26This is a replica chimpanzee brain, and this is a human brain.
0:36:27 > 0:36:29Now, they look different.
0:36:29 > 0:36:33One obvious difference is size, and the human brain is enormous,
0:36:33 > 0:36:37much larger than any of the other brains on the table.
0:36:37 > 0:36:39But big brains aren't everything.
0:36:39 > 0:36:41To make an analogy to computers,
0:36:41 > 0:36:45it's not perhaps just about the raw processing power of the brain,
0:36:45 > 0:36:48maybe we need to think about the operating system as well.
0:36:48 > 0:36:52And we seem to have a very efficient operating system
0:36:52 > 0:36:55for dealing with symbol-based codes.
0:36:56 > 0:37:00I always wanted the superpower of being able to talk to animals,
0:37:00 > 0:37:05but maybe our mismatching brains mean that I never will.
0:37:05 > 0:37:10Maybe there's something else in our world with which we communicate far more frequently,
0:37:10 > 0:37:13and maybe I need to stop thinking so much about how I could have
0:37:13 > 0:37:17conversations with other animals and maybe think a bit more about talking
0:37:17 > 0:37:18to machines.
0:37:20 > 0:37:23This year's big Christmas present gadgets are machines that we can talk to,
0:37:23 > 0:37:25digital assistants.
0:37:25 > 0:37:28A quarter of us now either command our phones via our voice
0:37:28 > 0:37:31or use devices like these around the home.
0:37:33 > 0:37:37Hey, computer, please tell me what the weather will be like in London tomorrow.
0:37:37 > 0:37:41There will be showers there tomorrow with a high of nine and a low of three.
0:37:41 > 0:37:43OK.
0:37:43 > 0:37:47Now that's nowhere near being anything like a human brain, but when I was doing my PhD
0:37:47 > 0:37:50on speech processing 25 years ago,
0:37:50 > 0:37:53I would have never believed that one day we would be walking around with
0:37:53 > 0:37:56phones in our pockets that we could talk to.
0:37:56 > 0:37:59It's incredibly hard to overestimate
0:37:59 > 0:38:01how quickly this field has developed.
0:38:01 > 0:38:03What's happening inside that box?
0:38:03 > 0:38:06How are computers managing to interact with us?
0:38:06 > 0:38:09And is it ever having a proper conversation with us?
0:38:09 > 0:38:12Are we ever going to have a meaningful dialogue with a computer?
0:38:12 > 0:38:15Will it ever understand the jokes we tell?
0:38:15 > 0:38:19Now this is an incredibly complex area and we could easily fill three
0:38:19 > 0:38:23lectures just on this topic, so I'm going to give you a tiny insight
0:38:23 > 0:38:25into aspects of how this works.
0:38:25 > 0:38:27But first, let's break down the question.
0:38:27 > 0:38:31What do computers need to do to be able to understand us?
0:38:31 > 0:38:35And the first thing is it needs to take the sounds that we make and
0:38:35 > 0:38:37decode that into words.
0:38:37 > 0:38:41This is called speech recognition and it's what is known in science as
0:38:41 > 0:38:43a ridiculously difficult question.
0:38:43 > 0:38:47And that's because speech is hard and speech is complex.
0:38:47 > 0:38:50I'm going to play you a sentence spoken in Estonian
0:38:50 > 0:38:54and what I want you to do is count the words you can hear.
0:38:54 > 0:38:57OK? So just listen out for words and see how many there are.
0:38:57 > 0:39:00VOICE SPEAKS IN ESTONIAN
0:39:07 > 0:39:09OK, how many words did you think were there?
0:39:10 > 0:39:13Sorry? 13, that's a good guess.
0:39:13 > 0:39:15Pink top, glasses.
0:39:15 > 0:39:19- 32?- 32, OK, a big jump there, about twice as many.
0:39:19 > 0:39:21OK. Let's see the actual sentences.
0:39:21 > 0:39:26So we've got 22 words, so actually it's a meaningless question to ask you,
0:39:26 > 0:39:30because if you don't speak Estonian why would the words stand out to you?
0:39:30 > 0:39:34But this is what the computer is confronted with.
0:39:34 > 0:39:39It's hearing this continuous flow of sound and it's got to find some way
0:39:39 > 0:39:42of getting a toehold on what those words could be.
0:39:42 > 0:39:43Now listen to this sentence.
0:39:43 > 0:39:46What I'm going to do is record myself speaking.
0:39:49 > 0:39:50Where are the words?
0:39:53 > 0:39:58So this is the spectrogram of me saying "Where are the words" that I've just recorded.
0:39:58 > 0:40:00But where ARE the words?
0:40:00 > 0:40:02There's no individual words there at all.
0:40:02 > 0:40:05What you're getting is a sort of smear of energy and what looks like
0:40:05 > 0:40:11one thing happening at the end there is actually the "ss" sound at the end of words.
0:40:11 > 0:40:15When you hear gaps between words when someone is talking to you,
0:40:15 > 0:40:17that's because you understand those words.
0:40:17 > 0:40:20If you don't understand the words, you don't hear the gaps,
0:40:20 > 0:40:22as we saw with the Estonian.
0:40:22 > 0:40:25So how do computers deal with this?
0:40:25 > 0:40:29How do computers find the starts and ends of words when there actually
0:40:29 > 0:40:32are no physical gaps there necessarily at all?
0:40:32 > 0:40:37Well the first thing a computer does is it breaks up the incoming stream
0:40:37 > 0:40:39of sound into smaller chunks of sound
0:40:39 > 0:40:43and we've got a demonstration of this here.
0:40:43 > 0:40:47So this is an incoming sentence and I don't know what this is,
0:40:47 > 0:40:50I've just got the spectrogram to work off and I'm a computer for the
0:40:50 > 0:40:52purposes of this point.
0:40:52 > 0:40:55And what I'm going to do is split this up
0:40:55 > 0:40:58into different slices of information.
0:40:58 > 0:41:04And what I'm going to do then is take those different slices
0:41:04 > 0:41:07and go to look them up in the library that I've got,
0:41:07 > 0:41:13which will help me try and get the best estimate of what speech sound I'm probably trying to look at.
0:41:13 > 0:41:15And my library has to be very... Has to sort of
0:41:15 > 0:41:20have an idealised version of speech sounds because we all talk
0:41:20 > 0:41:24differently. So instead of trying to find an exact match for the speech sound,
0:41:24 > 0:41:27the computer is looking for almost like a best guess.
0:41:27 > 0:41:30So if we do this with the first speech sound in my sequence...
0:41:33 > 0:41:36I've got a little slice of sound here.
0:41:38 > 0:41:42Now if I look at that in isolation, what I can see
0:41:43 > 0:41:47is that there is a broad sort of smear of energy there.
0:41:47 > 0:41:49There's no bright hotspots.
0:41:49 > 0:41:54Remember when you get a more intense bit of energy in a speech sound or
0:41:54 > 0:41:57any sound in the spectrogram, you see it as a brighter colour?
0:41:57 > 0:42:01There aren't really any bright colours there, but there's a big stretch of red
0:42:01 > 0:42:03and it's going almost the whole length.
0:42:03 > 0:42:05If I go over here,
0:42:05 > 0:42:11I think I might be looking at a "T" so my best guess for that first sound is
0:42:11 > 0:42:15that it is "T". Now who would like to help me guess the next sound?
0:42:16 > 0:42:18Can I have you in the blue top, please?
0:42:18 > 0:42:19Thank you very much.
0:42:21 > 0:42:23- Now what's your name?- Joe.
0:42:23 > 0:42:25- Sorry?- Joe.- Right, Joe,
0:42:25 > 0:42:28you're going to help me be a computer processor and work out what the next sound is.
0:42:28 > 0:42:30We've got one here.
0:42:31 > 0:42:33Now, Joe,
0:42:33 > 0:42:37you can see this looks quite different to that sound we were just looking for.
0:42:37 > 0:42:41Now can we find anything in our library that looks like that?
0:42:41 > 0:42:44I think you're right,
0:42:44 > 0:42:46I think we are dealing with "EE."
0:42:46 > 0:42:47Can you put that there for me?
0:42:47 > 0:42:50Now not to labour the point, if we carry on like this,
0:42:50 > 0:42:54you're not getting like really whip quick recognition of the speech,
0:42:54 > 0:42:57are you? We are taking our time so what I'm going to do is throw more
0:42:57 > 0:43:00processors at the problem, just as a computer would.
0:43:00 > 0:43:02I'm going to take a couple more volunteers please,
0:43:02 > 0:43:05so can I have you with the glasses, please,
0:43:05 > 0:43:09and can I have you with the polo neck sweater.
0:43:09 > 0:43:11Thank you very much. Can I have you?
0:43:11 > 0:43:13Thank you very much. Got to have a unicorn.
0:43:17 > 0:43:21OK, so, Unicorn, what's your name?
0:43:21 > 0:43:22- Sasha.- Sasha, lovely to meet you, Sasha.
0:43:22 > 0:43:24- Hi.- Evie.
0:43:24 > 0:43:26Evie. And your name is?
0:43:26 > 0:43:27- Kit.- Kit, excellent.
0:43:27 > 0:43:29Now there's four speech sounds left.
0:43:29 > 0:43:33I want you to each grab one and see if you can match it up with any of
0:43:33 > 0:43:35the speech sounds here, OK?
0:43:35 > 0:43:36There you go.
0:43:36 > 0:43:38And what have you got here?
0:43:38 > 0:43:40Look at this one. Can you see any that have got sloping shapes?
0:43:40 > 0:43:43- Is it that one? - I think you might be right there.
0:43:44 > 0:43:46So if you can remember, you came from number four.
0:43:47 > 0:43:48I think I have an S sound.
0:43:48 > 0:43:50I think you're absolutely right.
0:43:50 > 0:43:52You grab an S sound and pop it up.
0:43:53 > 0:43:55Now you have got the really difficult one here
0:43:55 > 0:43:59because when the speaker said it, they really underarticulated it,
0:43:59 > 0:44:04so what you have is this sound here
0:44:04 > 0:44:07and that's going to have to be our best guess, OK?
0:44:07 > 0:44:09OK, so if you can pop that in, we can see what we've got.
0:44:09 > 0:44:11Thank you very much.
0:44:11 > 0:44:16It could be team mates, it could be tea mate.
0:44:16 > 0:44:20We don't know still what the words are but we've now got a good guess
0:44:20 > 0:44:23what our speech sounds are and that takes us to our next stage.
0:44:23 > 0:44:25What I want to do first is say thank you very much
0:44:25 > 0:44:27to Joe, Sasha, Evie, Kit.
0:44:27 > 0:44:29Thank you very much, thank you.
0:44:34 > 0:44:36So the addition of processing power
0:44:36 > 0:44:38has really helped us be able to speed up this process
0:44:38 > 0:44:40and that's why you can talk to your phone without it
0:44:40 > 0:44:43taking that amount of time.
0:44:43 > 0:44:47And what we are doing here is pulling out what the speech sounds are
0:44:47 > 0:44:49but we still don't know what those words are,
0:44:49 > 0:44:51we don't know where the edges are.
0:44:51 > 0:44:54What we need to do is go to another level and what computers do
0:44:54 > 0:44:57is go to what's called a language level
0:44:57 > 0:45:01to start to break this stream of sounds up into words and into sentences.
0:45:01 > 0:45:06I need two new volunteers to decode a stream of speech for me, just like
0:45:06 > 0:45:09you were a computer. Can I have...
0:45:09 > 0:45:12you in the middle with the blue T-shirt?
0:45:12 > 0:45:14Yes, there you go, fantastic.
0:45:14 > 0:45:16Can I have you? Thank you very much, thank you.
0:45:22 > 0:45:24- Now, what's your name?- Max.
0:45:24 > 0:45:26Lovely to meet you, Max. And your name is?
0:45:26 > 0:45:28- Cammy.- Cammy, fantastic.
0:45:28 > 0:45:30What I'm going to give you is the same task a computer has got when
0:45:30 > 0:45:34it's worked out the speech sounds but it doesn't know what the words are.
0:45:34 > 0:45:37What you're going to do is see a list of speech sounds and I want you
0:45:37 > 0:45:39to try and work out what words could be in there.
0:45:39 > 0:45:43Sometimes the computer doesn't know what a sound is at all.
0:45:43 > 0:45:45You'll just see a question mark, maybe there was a cough,
0:45:45 > 0:45:49maybe there was a noise, so sometimes you're going to have to guess, OK?
0:45:49 > 0:45:52So I can just ask you to step over here, Max, thank you very much.
0:45:52 > 0:45:56OK and we will see it appear on the screen, OK?
0:45:56 > 0:45:58So reading down in that direction,
0:45:59 > 0:46:01Try reading that aloud.
0:46:01 > 0:46:04THEY READ WORDS ON SCREEN SLOWLY
0:46:17 > 0:46:20Now, think about the building that we are in.
0:46:20 > 0:46:23- The Royal Institution. - The Royal Institution, absolutely,
0:46:23 > 0:46:24so context can help you.
0:46:24 > 0:46:27You go back at that, we are at the Royal Institution.
0:46:27 > 0:46:29One last time.
0:46:29 > 0:46:32TOGETHER: Drums are really
0:46:32 > 0:46:37loud, so we have to
0:46:37 > 0:46:42use ear
0:46:44 > 0:46:47defenders at the Royal Institution.
0:46:47 > 0:46:49Well done.
0:46:51 > 0:46:53Thank you. Thank you, Cammy, thank you, Max.
0:46:53 > 0:46:55Thank you.
0:46:55 > 0:47:00So that's an extreme example, but what you are seeing there was essentially
0:47:00 > 0:47:02the same problem a computer is trying to solve.
0:47:02 > 0:47:03It's trying to find the edges,
0:47:03 > 0:47:05it's trying to work out where the words could be,
0:47:05 > 0:47:08and it's helped in this because it knows what the words are.
0:47:08 > 0:47:12It has a database of thousands and thousands of words and it also knows
0:47:12 > 0:47:15something about how sentences go together.
0:47:15 > 0:47:19It knows how probable it is that a word will follow another word.
0:47:19 > 0:47:21And if a word becomes more likely,
0:47:21 > 0:47:24the probability of it occurring actually changes in the computer
0:47:24 > 0:47:26so it's more easily activated.
0:47:26 > 0:47:28And, in fact, our brains do something similar.
0:47:28 > 0:47:32If you are listening to speech and you hear something which is highly
0:47:32 > 0:47:34predictable like "the ship sailed across the bay"
0:47:34 > 0:47:37then you will understand that sentence more easily.
0:47:37 > 0:47:41So we are seeing actually, in terms of a lot of the code of language,
0:47:41 > 0:47:46humans and computers are not as differently matched as we used to be.
0:47:46 > 0:47:50Computers are catching up with a lot of our linguistic abilities.
0:47:50 > 0:47:53Computers have become huge and very powerful,
0:47:53 > 0:47:57they can be searching through very large databases very quickly,
0:47:57 > 0:48:00and the internet means a little box like that can be connected to those
0:48:00 > 0:48:02large online databases.
0:48:02 > 0:48:04It doesn't need to have all the information there.
0:48:04 > 0:48:10But, of course, this is not the full story of how human language works.
0:48:10 > 0:48:13There is an entire level of communication
0:48:13 > 0:48:17that we use all the time that we have barely mentioned.
0:48:17 > 0:48:20Rather than just what we say when we're talking,
0:48:20 > 0:48:24we are always sending out information in how we say words.
0:48:26 > 0:48:32Everything I've talked about so far has been, if we think about brains,
0:48:32 > 0:48:35associated with properties of the left side of the brain.
0:48:37 > 0:48:42The left side of the brain in humans, for most people, is associated with
0:48:42 > 0:48:45how we decode speech, how we control our own voices.
0:48:46 > 0:48:50And the right side of the brain is a lot less interested in these
0:48:50 > 0:48:54linguistic properties of communication like words and sentences,
0:48:54 > 0:48:57and a lot more interested in all the other things that are going on when
0:48:57 > 0:48:59we are talking to people:
0:48:59 > 0:49:00who we are talking to.
0:49:00 > 0:49:02Are they being emotional?
0:49:02 > 0:49:03Are they telling a brilliant joke like,
0:49:03 > 0:49:06what is round and sounds like a trumpet?
0:49:06 > 0:49:08A crumpet.
0:49:08 > 0:49:09GROANS
0:49:09 > 0:49:14So to have a meaningful conversation with a machine or understand my
0:49:14 > 0:49:18brilliant jokes, we need to do something much more difficult.
0:49:18 > 0:49:24We need to add in to our computer the missing right half of its brain.
0:49:24 > 0:49:28Because actually when we are talking, when you're listening to somebody,
0:49:28 > 0:49:32there are very important aspects of our communication which you need to
0:49:32 > 0:49:34pick up on to really understand what somebody is saying.
0:49:34 > 0:49:36Not just when it's written down.
0:49:36 > 0:49:39The information when someone is speaking is very often being expressed as
0:49:39 > 0:49:42much by how they are talking as what they are saying.
0:49:42 > 0:49:45And this often refers to something called intonation.
0:49:45 > 0:49:49Intonation is how we vary the pitch, the speed,
0:49:49 > 0:49:52the melody of our voice when we are speaking.
0:49:52 > 0:49:56An example would be that we don't talk to each other like that.
0:49:56 > 0:49:58We would consider it to be quite strange.
0:49:58 > 0:50:02We are always using intonation to clarify, enhance,
0:50:02 > 0:50:04put in emphasis and emotion.
0:50:04 > 0:50:06In our brains,
0:50:06 > 0:50:09intonation is processed very differently
0:50:09 > 0:50:11from the words that you're listening to.
0:50:11 > 0:50:14On the whole, intonation is strongly found to be something
0:50:14 > 0:50:18that the right hemisphere deals with and is interested in.
0:50:18 > 0:50:22And often, it can be as important if not more important to the real
0:50:22 > 0:50:24meaning of what somebody is saying.
0:50:25 > 0:50:29We've got some recordings here of someone speaking in an emotional style
0:50:29 > 0:50:33and we have stripped out all the auditory information that tells you about
0:50:33 > 0:50:35the words they are saying and it's just leaving you with the intonation.
0:50:35 > 0:50:37See if you can guess the emotion.
0:50:37 > 0:50:40BUZZING
0:50:40 > 0:50:42Any guesses, does that sound happy?
0:50:42 > 0:50:45Angry? It sounds annoyed, doesn't it?
0:50:45 > 0:50:46Yeah, that was angry.
0:50:46 > 0:50:47That was very angry.
0:50:47 > 0:50:48Another one.
0:50:48 > 0:50:51BUZZING
0:50:53 > 0:50:57Yeah, lots of downward inflections.
0:50:57 > 0:50:59Sounds softer.
0:50:59 > 0:51:03That was a sad voice so you are definitely using intonation to get aspects
0:51:03 > 0:51:07of emotion out, but that's by no means the whole story.
0:51:07 > 0:51:10And to help me demonstrate why intonation is so very important to
0:51:10 > 0:51:13communication, please welcome news presenter and journalist
0:51:13 > 0:51:16Krishnan Guru-Murthy.
0:51:22 > 0:51:26Krishnan, obviously the words you say are absolutely critical to your job,
0:51:26 > 0:51:30but how you are saying them must be something that you're always thinking about.
0:51:30 > 0:51:34We use intonation to do all sorts of things on the news, all the
0:51:34 > 0:51:35time, and it's very, very complex.
0:51:35 > 0:51:39At the beginning of the news, we're saying, "This is urgent,
0:51:39 > 0:51:40"it's exciting, you've got to watch,
0:51:40 > 0:51:43"something really important happened today and I've got to tell you about
0:51:43 > 0:51:46- "it."- Yeah.- And then once we get into the news,
0:51:46 > 0:51:50we are trying to use the right intonation for the type of story
0:51:50 > 0:51:54we are telling people, so we've got to try and hit the right note, literally.
0:51:54 > 0:51:57So if it's something very surprising,
0:51:57 > 0:52:00"The new president of the United States is Donald Trump!"
0:52:00 > 0:52:02- That's quite surprising. - That is quite surprising.
0:52:02 > 0:52:07If it's very serious or frightening news, something terrible has happened,
0:52:07 > 0:52:10"The new president of the United States is Donald Trump."
0:52:10 > 0:52:13It's a different thing.
0:52:13 > 0:52:17You wouldn't normally do that, but if you were sort of talking about
0:52:17 > 0:52:19an attack or a war or something like that,
0:52:19 > 0:52:22this is a very serious thing and you're trying to get people in the
0:52:22 > 0:52:25right frame of mind. But you're also trying not to frighten them
0:52:25 > 0:52:27and you've got to use intonation for that.
0:52:27 > 0:52:30Now we've got a very short experiment to do with Krishnan,
0:52:30 > 0:52:33who's going to read out some football results.
0:52:33 > 0:52:37Just by the intonation on his voice, I want you to...
0:52:37 > 0:52:40He's going to stop before he gets to the final score and we're going to
0:52:40 > 0:52:45work out if the last team had a higher score or a lower score than
0:52:45 > 0:52:48the first team. OK, shall we try one?
0:52:48 > 0:52:52Manchester City 2, West Bromwich Albion...
0:52:52 > 0:52:53Higher or lower?
0:52:54 > 0:52:56- Nil.- Nil, there you go, lower.
0:52:58 > 0:53:01West Ham United one, Tottenham Hotspur...
0:53:02 > 0:53:04- Higher, yes.- Five.
0:53:05 > 0:53:07Chelsea one, Liverpool...
0:53:07 > 0:53:09One - exactly, it's a draw.
0:53:11 > 0:53:12This is absolutely amazing.
0:53:12 > 0:53:15What Krishnan is doing, is over the whole course of the sentence,
0:53:15 > 0:53:18he's doing a kind of dance with his intonation
0:53:18 > 0:53:21that's completely keeping pace with the meaning of what he's saying.
0:53:21 > 0:53:24You've got weaving the intonation in and out of the words and you're
0:53:24 > 0:53:27picking up on that, you know what that means.
0:53:27 > 0:53:31Can you do an example of it incorrectly so we can hear what that sounds like?
0:53:33 > 0:53:35Stoke City four, Huddersfield...
0:53:36 > 0:53:37five.
0:53:39 > 0:53:43And when you say that, it sounds like it's the words that are wrong, whereas it's the
0:53:43 > 0:53:45intonation that's wrong, it's really striking.
0:53:45 > 0:53:47Thank you very, very much, Krishnan.
0:53:47 > 0:53:48- Thank you.- Thank you, Sophie.
0:53:53 > 0:53:56So we are using intonation all the time in regular conversation,
0:53:56 > 0:54:02and sometimes we think that that's all we do to pick up on emotion in the voice.
0:54:02 > 0:54:05Actually emotion in the voice is incredibly complex.
0:54:05 > 0:54:08There's a great deal going on because emotions change how your bodies work
0:54:08 > 0:54:11and how they feel. They can change many different aspects of your voice.
0:54:11 > 0:54:13Up to now,
0:54:13 > 0:54:16computers have really struggled with this kind of information.
0:54:16 > 0:54:20But that's changing.
0:54:20 > 0:54:23And people are starting to make more progress.
0:54:23 > 0:54:25For our last demonstration,
0:54:25 > 0:54:28I'm going to look at something really amazing -
0:54:28 > 0:54:31a computer that can read emotions from human voices.
0:54:31 > 0:54:34Now the acoustics in here are quite reverberant
0:54:34 > 0:54:38and this computer has been built to work in the home,
0:54:38 > 0:54:41and so what I'm going to do is just step outside where there's a lovely
0:54:41 > 0:54:43carpet and it's a little bit more like being in someone's living room.
0:54:43 > 0:54:45Let's go and meet Olly.
0:54:47 > 0:54:50- Hi. Hi, Raymond.- Hello.
0:54:50 > 0:54:54Now, tell me about Olly.
0:54:54 > 0:54:57So Olly can actually understand what you're saying, as well as
0:54:57 > 0:54:59emotions of your voice, the tone of your voice.
0:54:59 > 0:55:02Excellent, what kind of thing do you do with Olly?
0:55:02 > 0:55:06Olly actually enhances the communication between human and technology.
0:55:06 > 0:55:10So he could be a digital assistant who's really understanding how you
0:55:10 > 0:55:12feel, not just what you are saying?
0:55:12 > 0:55:14- Exactly.- Yes.- Can we find out more about how he works?
0:55:14 > 0:55:16- Sure.- Is it easiest to demonstrate that?
0:55:16 > 0:55:19Yes, of course. So what you need to do is just stand a sensible distance
0:55:19 > 0:55:22from Olly, speak normal as usual,
0:55:22 > 0:55:25- and then maybe you can add some emotion to it when you speak.- OK.
0:55:25 > 0:55:27So, are you ready?
0:55:27 > 0:55:28One, two, three.
0:55:29 > 0:55:32SADLY: Hey, Ollie, what's the weather like in London today?
0:55:35 > 0:55:38Oh, there we go, yes.
0:55:40 > 0:55:44So is Ollie getting the emotion out of your voice by the sound of your
0:55:44 > 0:55:47voice or the words you're saying, or is it both?
0:55:47 > 0:55:48Actually it's both.
0:55:48 > 0:55:52It recognises some acoustic components of what you said,
0:55:52 > 0:55:56and it's also using the syntax and semantics of the words you used.
0:55:56 > 0:55:58It's very clever. Thank you very much,
0:55:58 > 0:56:00- thank you.- Thank you very much.
0:56:03 > 0:56:08Olly is doing an amazing job of decoding emotion as well as words
0:56:08 > 0:56:13from the human voice, and you can see from this just how hard emotion
0:56:13 > 0:56:17is to read and how extremely nuanced it can be.
0:56:17 > 0:56:22We find it very easy, because we've grown up around people using language,
0:56:22 > 0:56:25emotion, social meaning in their voices all the time,
0:56:25 > 0:56:29so it seems easy to us but it's absolutely vital to understanding language.
0:56:34 > 0:56:36All around the world,
0:56:36 > 0:56:42animals are communicating in very simple and very complex ways and we
0:56:42 > 0:56:44have so much more to learn.
0:56:44 > 0:56:48I've touched on just the surface of animal communication today.
0:56:49 > 0:56:55Within this, humans do seem to be extraordinarily complex in their linguistic abilities.
0:56:55 > 0:56:59But we're only really using language with each other.
0:56:59 > 0:57:02Could there ever be another life form that could crack our code -
0:57:02 > 0:57:05perhaps one we haven't even encountered yet?
0:57:06 > 0:57:10Watching Carl Sagan describe his work on the Voyager space probes
0:57:10 > 0:57:1340 years ago in the 1977 Christmas lectures,
0:57:13 > 0:57:16literally set me on course to standing here today -
0:57:16 > 0:57:18that's why I became a scientist.
0:57:19 > 0:57:22The Voyagers contain golden records,
0:57:22 > 0:57:24and the golden records contain the sounds of Earth,
0:57:24 > 0:57:28including greetings in 55 different human languages.
0:57:28 > 0:57:30GREETINGS IN DIFFERENT LANGUAGES
0:57:35 > 0:57:39Now, the sounds of the Earth's languages
0:57:39 > 0:57:41have long since left our solar system.
0:57:41 > 0:57:45The Voyager probes are now 13 billion miles away from Earth.
0:57:45 > 0:57:48They are the most distant objects ever created by humans.
0:57:49 > 0:57:53Now, if those spacecraft ever encounter an alien life form,
0:57:53 > 0:57:57maybe they can use these sounds to start to decode our language.
0:57:57 > 0:57:59And if they have brains that work like ours,
0:57:59 > 0:58:03maybe one day we could have conversations with them.
0:58:03 > 0:58:07This year's lectures have explored our fundamental urge to communicate.
0:58:07 > 0:58:10We've looked at where it comes from and why we're so very good at it,
0:58:10 > 0:58:14and we just can't help but reach out to others.
0:58:14 > 0:58:18The big question is whether there is another form of life out there
0:58:18 > 0:58:19that could crack our code,
0:58:19 > 0:58:23and I hope you've realised that it will need to have an incredible brain -
0:58:23 > 0:58:27at least as incredible as your brains, to do so.
0:58:27 > 0:58:29Thank you.