Miguel Nicolelis: A monkey that controls a robot with its thoughts. No, really.

247,580 views ・ 2013-02-18

TED


Tafadhali bofya mara mbili manukuu ya Kiingereza hapa chini ili kucheza video.

00:00
Translator: Timothy Covell Reviewer: Morton Bast
0
0
7000
Translator: David Mvoi Reviewer: Joachim Mangilima
00:15
The kind of neuroscience that I do and my colleagues do
1
15660
2851
Hii sayansi ya ubongo ninayofanya na wenzangu
00:18
is almost like the weatherman.
2
18511
2166
ni kama mtabiri wa hali ya hewa.
00:20
We are always chasing storms.
3
20677
3516
Tunakimbizana na dhoruba kila wakati.
00:24
We want to see and measure storms -- brainstorms, that is.
4
24193
4883
Tunataka kuona na kupima dhoruba--namaanisha dhoruba za ubongo.
00:29
And we all talk about brainstorms in our daily lives,
5
29076
2768
Sisi sote huongea kuhusu dhoruba za ubongo maishani mwetu
00:31
but we rarely see or listen to one.
6
31844
3450
lakini ni nadra kuona au kuisikia mojawapo.
00:35
So I always like to start these talks
7
35294
1634
Hivyo basi mimi hupenda kuanza mazungumzo haya
00:36
by actually introducing you to one of them.
8
36928
2982
kwa kuwatambulisheni kwa mojawapo.
00:39
Actually, the first time we recorded more than one neuron --
9
39910
3427
Kusema kweli, mara yetu ya kwanza kupima zaidi ya neuron moja--
00:43
a hundred brain cells simultaneously --
10
43337
2223
seli za ubongo mia moja kwa wakati mmoja--
00:45
we could measure the electrical sparks
11
45560
2469
tungeweza pima cheche za umeme
00:48
of a hundred cells in the same animal,
12
48029
2680
za seli mia moja kutoka kwa mnyama mmoja,
00:50
this is the first image we got,
13
50709
1802
hii ndio picha ya kwanza tuliyopata,
00:52
the first 10 seconds of this recording.
14
52511
2315
sekunde kumi za kwanza za rekodi hii.
00:54
So we got a little snippet of a thought,
15
54826
3351
Sasa tukajaribu kufikiria,
00:58
and we could see it in front of us.
16
58177
2905
na tukaweza kuiona mbele yetu.
01:01
I always tell the students
17
61082
1012
mimi huwaambia wanafunzi
01:02
that we could also call neuroscientists some sort of astronomer,
18
62094
4106
kuwa tunaweza waita wanasayansi wa ubongo kama pia wataalam wa anga,
01:06
because we are dealing with a system
19
66200
1626
kwa sababu tunakabiliana na mfumo
01:07
that is only comparable in terms of number of cells
20
67826
2917
ambao unalingana kwa ncha ya nambari ya viini
01:10
to the number of galaxies that we have in the universe.
21
70743
2936
na nambari za galaksi tulizo nazo ulimwenguni.
01:13
And here we are, out of billions of neurons,
22
73679
3030
Kwa hivyo hapa ndipo tulipo, katika mabilioni ya neuroni,
01:16
just recording, 10 years ago, a hundred.
23
76709
2818
tukirekodi tu, miaka kumi iliyopita, alafu mia moja.
01:19
We are doing a thousand now.
24
79527
1583
Sasa hivi tunarekodi hadi miaka elfu moja.
01:21
And we hope to understand something fundamental about our human nature.
25
81110
5400
Na tunatumai kuelewa cha msingi kuhusu asili yetu ya kibinadamu.
01:26
Because, if you don't know yet,
26
86510
1932
Kwa sababu, kama bado hujui,
01:28
everything that we use to define what human nature is comes from these storms,
27
88442
5250
kila kitu tunachotumia kutambua asili ya binadamu kimetoka katika dhoruba hizi,
01:33
comes from these storms that roll over the hills and valleys of our brains
28
93692
4651
katika dhoruba zishukazo kutoka milima na mabonde ya akili zetu
01:38
and define our memories, our beliefs,
29
98343
3885
na ambazo zinaeleza kumbukumbu zetu, imani zetu,
01:42
our feelings, our plans for the future.
30
102228
2700
hisia zetu, mipango yetu ya siku za usoni.
01:44
Everything that we ever do,
31
104928
2398
Kila kitu tunachofanya,
01:47
everything that every human has ever done, do or will do,
32
107326
5067
kila kitu ambacho binadamu amekifanya, anakifanya ama atakifanya,
01:52
requires the toil of populations of neurons producing these kinds of storms.
33
112393
5434
kinahitaji bidii ya idadi kubwa ya neuroni zinazozalisha dhoruba hizi.
01:57
And the sound of a brainstorm, if you've never heard one,
34
117827
2483
Na sauti ya dhoruba ya ubongo, kama hujawaisikia moja,
02:00
is somewhat like this.
35
120310
3349
huwa hivi.
02:03
You can put it louder if you can.
36
123659
3146
Unaweza ongeza sauti kama waweza.
02:06
My son calls this "making popcorn while listening to a badly-tuned A.M. station."
37
126805
6403
Mwanangu huiita "kutengeneza popcorn huku ukiskiza kituo cha redio kilichoegezwa vibaya."
02:13
This is a brain.
38
133208
1485
Huu ni ubongo.
02:14
This is what happens when you route these electrical storms to a loudspeaker
39
134693
3434
Haya ndiyo yanayotokea unapoelekeza dhoruba hizi za umeme kwenye kipaza sauti
02:18
and you listen to a hundred brain cells firing,
40
138127
2866
na usikie seli mia moja vya ubongo vikirushwa,
02:20
your brain will sound like this -- my brain, any brain.
41
140993
4622
haya ndiyo yatakayosikika katika ubongo wako--ubongo wangu, na ubongo wowote.
02:25
And what we want to do as neuroscientists in this time
42
145615
3762
Kile tunachotaka kufanya kama wanasayansi ya ubongo katika wakati huu
02:29
is to actually listen to these symphonies, these brain symphonies,
43
149377
5350
ni kuskiza kwa makini sauti hizi, sauti hizi za ubongo,
02:34
and try to extract from them the messages they carry.
44
154727
3400
na kujaribu kudondoa zile jumbe zinazobeba
02:38
In particular, about 12 years ago
45
158127
2851
Haswa, takriban miaka kumi na mbili iliyopita
02:40
we created a preparation that we named brain-machine interfaces.
46
160978
3048
tulitengeneza muundo tuliouita mashine ya akili.
02:44
And you have a scheme here that describes how it works.
47
164026
2702
Na hapa una mpango unaoeleza vile inavyotumika.
02:46
The idea is, let's have some sensors that listen to these storms, this electrical firing,
48
166728
5566
Lengo ni, tuwe na vitega hisia vinavyosikiza dhoruba hizi, vile umeme unavyozalishwa,
02:52
and see if you can, in the same time that it takes
49
172294
3082
na kuona kama inawezekana, kwa wakati huo huo wakati unaopita
02:55
for this storm to leave the brain and reach the legs or the arms of an animal --
50
175376
4969
kabla ya dhoruba hii kutoka kwa akili na kufika kwenye miguu ama mikono ya mnyama
03:00
about half a second --
51
180345
2864
kama nusu sekunde--
03:03
let's see if we can read these signals,
52
183209
2351
wacha tuone kama tunaweza kusoma ishara hizi,
03:05
extract the motor messages that are embedded in it,
53
185560
3400
kudondoa jumbe za ubongo inazobeba,
03:08
translate it into digital commands
54
188960
2272
kuitafsiri iwe amri za kikompyuta
03:11
and send it to an artificial device
55
191232
1886
na kuituma hadi kwenye kifaa kilichoundwa na binadamu
03:13
that will reproduce the voluntary motor wheel of that brain in real time.
56
193118
5893
kitakachozalisha ule mzunguko hiari wa akili wakati ule ule.
03:19
And see if we can measure how well we can translate that message
57
199011
3848
tuone kama tunaweza kupima ni vipi tunaweza tafsiri ujumbe huo vyema zaidi
03:22
when we compare to the way the body does that.
58
202859
3518
wakati tunapolinganisha na vile mwili unavyofanya kazi hio.
03:26
And if we can actually provide feedback,
59
206377
2866
Wakati tunapotoa maoni,
03:29
sensory signals that go back from this robotic, mechanical, computational actuator
60
209243
5734
viashiria hisia
03:34
that is now under the control of the brain,
61
214977
2251
sasa iliyo chini ya udhibiti wa ubongo,
03:37
back to the brain,
62
217228
1311
hadi tena kwa ubongo,
03:38
how the brain deals with that,
63
218539
2121
vile ubongo unavyokabiliana na kazi hiyo,
03:40
of receiving messages from an artificial piece of machinery.
64
220660
4901
ya kupokea jumbe kutoka mashine zilizoundwa na binadamu
03:45
And that's exactly what we did 10 years ago.
65
225561
2321
Na ndivyo haswa tulivyofanya miaka kumi iliyopita.
03:47
We started with a superstar monkey called Aurora
66
227882
2961
Tulianza na nyani nyota kwa jina Aurora
03:50
that became one of the superstars of this field.
67
230843
2468
aliyekuwa nyota kwenye eneo hili.
03:53
And Aurora liked to play video games.
68
233311
2299
Na Aurora alipenda kucheza michezo ya kompyuta.
03:55
As you can see here,
69
235610
1373
Mnavyoona hapa,
03:56
she likes to use a joystick, like any one of us, any of our kids, to play this game.
70
236983
4944
anapenda kutumia kijiti, kama vile kila mmoja wetu, na watoto wetu, kucheza mchezo huu.
04:01
And as a good primate, she even tries to cheat before she gets the right answer.
71
241927
4671
Kama mnyama mwerevu, anajaribu kudanganya kabla afikie jibu sahihi.
04:06
So even before a target appears that she's supposed to cross
72
246598
4283
Kwa hivyo kabla ya hatua anayopaswa kupita
04:10
with the cursor that she's controlling with this joystick,
73
250881
2850
akitumia mshale anaodhibiti kwa kijiti,
04:13
Aurora is trying to find the target, no matter where it is.
74
253731
3951
Aurora anajaribu kufikia hatua, popote ilipo.
04:17
And if she's doing that,
75
257682
1469
Na anapofanya hivyo,
04:19
because every time she crosses that target with the little cursor,
76
259151
3314
kwa sababu kila wakati anapovuka hatua akitumia ule mshale mdogo,
04:22
she gets a drop of Brazilian orange juice.
77
262465
2950
anapata tone la juisi ya machungwa.
04:25
And I can tell you, any monkey will do anything for you
78
265415
2950
nakuambia, nyani yeyote atakufanyia chochote
04:28
if you get a little drop of Brazilian orange juice.
79
268365
3100
kama utampa tone la juisi ya machungwa.
04:31
Actually any primate will do that.
80
271465
2731
Kwa kweli mnyama yeyote anaweza kufanya hivyo.
04:34
Think about that.
81
274196
1334
Hebu tafakari hayo.
04:35
Well, while Aurora was playing this game, as you saw,
82
275530
3400
Hivyo basi, wakati Aurora alikuwa akicheza mchezo huu, vile mlivyoona,
04:38
and doing a thousand trials a day
83
278930
2435
na kufanya majaribio elfu moja kwa siku
04:41
and getting 97 percent correct and 350 milliliters of orange juice,
84
281365
3883
na kupata asilimia tisini na saba sahihi na milimita mia tatu na hamsini za juisi ya machungwa,
04:45
we are recording the brainstorms that are produced in her head
85
285248
3399
tunarekodi dhorubaza ubongo zitokazo kichwani mwake
04:48
and sending them to a robotic arm
86
288647
1647
na kuzituma kwenye mkanda wa mashine
04:50
that was learning to reproduce the movements that Aurora was making.
87
290294
3871
unaotoa zile dhoruba haswa Aurora alikuwa akitoa.
04:54
Because the idea was to actually turn on this brain-machine interface
88
294165
3783
Kwa sababu wazo lilikuwa ni kuwasha hii mashine ya ubongo
04:57
and have Aurora play the game just by thinking,
89
297948
4700
na kuona Aurora akicheza mchezo ule kwa kufikiria tu,
05:02
without interference of her body.
90
302648
2617
bila mwili wake kuingilia kati.
05:05
Her brainstorms would control an arm
91
305265
2916
Dhoruba zake za akili zitadhibiti mkono
05:08
that would move the cursor and cross the target.
92
308181
2709
utakaosogeza ule mshale na kuvuka hatua.
05:10
And to our shock, that's exactly what Aurora did.
93
310890
3191
Na kwa mshangao wetu, hivyo haswa ndivyo Aurora alifanya.
05:14
She played the game without moving her body.
94
314081
4200
Alicheza mchezo huo bila kusogeza mwili wake.
05:18
So every trajectory that you see of the cursor now,
95
318281
2237
Hivyo basi kila msogezo unaoona sasa kwenye kijiti
05:20
this is the exact first moment she got that.
96
320518
3212
hivyo ndivyo haswa alivyofanya mara ya kwanza.
05:23
That's the exact first moment
97
323730
1784
Hiyo ndiyo ilikuwa mara ya kwanza
05:25
a brain intention was liberated from the physical domains of a body of a primate
98
325514
6767
nia kwenye ubongo ilitolewa kutoka kwenye mwili wa nyani
05:32
and could act outside, in that outside world,
99
332281
3700
na iliweza kufanya kazi nje ya mwili, hapa ulimwengu wa nje,
05:35
just by controlling an artificial device.
100
335981
2966
kwa kudhibiti mashine.
05:38
And Aurora kept playing the game, kept finding the little target
101
338947
4917
Na Aurora aliendelea kucheza mchezo huo, aliendelea kufikia ile hatua
05:43
and getting the orange juice that she wanted to get, that she craved for.
102
343864
3917
na kupata juisi ya machungwa aliyotaka, aliyotamani.
05:47
Well, she did that because she, at that time, had acquired a new arm.
103
347781
6701
Kwa kweli, alifanya hivyo kwa sababu, wakati ule, yeye alipata mkono mpya.
05:54
The robotic arm that you see moving here 30 days later,
104
354482
2963
Mkono ule wa roboti unaoona ukitembea hapa siku thelathini baadaye,
05:57
after the first video that I showed to you,
105
357445
2686
baada ya ile video ya kwanza niliyowaonyesheni,
06:00
is under the control of Aurora's brain
106
360131
2650
uko chini ya udhibiti wa ubongo ya Aurora
06:02
and is moving the cursor to get to the target.
107
362781
3168
na unasongeza mshale ule ili kufikia ile hatua.
06:05
And Aurora now knows that she can play the game with this robotic arm,
108
365949
3899
Na Aurora sana anajua kuwa anaweza kucheza mchezo huu akitumia mkono mashine,
06:09
but she has not lost the ability to use her biological arms to do what she pleases.
109
369848
5716
lakini hajapoteza uwezo wa kutumia mkono wake asili kwa chochote angependa kufanya.
06:15
She can scratch her back, she can scratch one of us, she can play another game.
110
375564
4067
Anaweza kujikuna mgongo, anaweza kukuna mmoja wetu, anaweza kucheza mchezo wowote mwingine.
06:19
By all purposes and means,
111
379631
1600
Kwa nia zote na madhumuni,
06:21
Aurora's brain has incorporated that artificial device
112
381231
4116
akili ya Aurora imehusisha kile kifaa bandia
06:25
as an extension of her body.
113
385347
2750
kama muendelezo wa mwili wake.
06:28
The model of the self that Aurora had in her mind
114
388097
3533
Huu mfano wa ubinafsi ambao Aurora alikuwa nao kwa akili yake
06:31
has been expanded to get one more arm.
115
391630
4084
umepanuliwa kupata mkono mmoja zaidi.
06:35
Well, we did that 10 years ago.
116
395714
2350
Tulifanya hivyo miaka kumi iliyopita.
06:38
Just fast forward 10 years.
117
398064
2833
Sasa songea mbele miaka kumi.
06:40
Just last year we realized that you don't even need to have a robotic device.
118
400897
4983
Mwaka uliopita tu tuligundua kuwa huhutaji kuwa na kifaa cha mashine.
06:45
You can just build a computational body, an avatar, a monkey avatar.
119
405880
5484
Unaweza tu kutengeneza kifaa cha kompyuta.
06:51
And you can actually use it for our monkeys to either interact with them,
120
411364
4250
Na unaweza kuitumia kwa nyani zetu kuleta uhusiano kati yao
06:55
or you can train them to assume in a virtual world
121
415614
4439
ama unaweza wafunza kudhania ulimwengu gushi
07:00
the first-person perspective of that avatar
122
420053
3044
maono ya mtu kuhusiana na mashine ile
07:03
and use her brain activity to control the movements of the avatar's arms or legs.
123
423097
5651
na kutumia msisimko wa akili yake kudhibiti matembezi ya mikono na miguu ya mashine.
07:08
And what we did basically was to train the animals
124
428748
2766
Na cha msingi tulichofanya kilikuwa kufunza hawa wanyama
07:11
to learn how to control these avatars
125
431514
3050
njia ya kudhiiti mashine hizi
07:14
and explore objects that appear in the virtual world.
126
434564
3899
na kuchunguza vidude vinavyojitokeza katika ulimwengu gushi.
07:18
And these objects are visually identical,
127
438463
2301
Na vidude hivi vimefanana
07:20
but when the avatar crosses the surface of these objects,
128
440764
3883
lakini wakati mashine inapovuka mbele ya vidude hivi,
07:24
they send an electrical message that is proportional to the microtactile texture of the object
129
444647
6400
zinatuma ujumbe wa umeme uliyosawia na uso wa kile kidude
07:31
that goes back directly to the monkey's brain,
130
451047
4016
ambao unaenda moja kwa moja hadi kwenye ubongo wa nyani,
07:35
informing the brain what it is the avatar is touching.
131
455063
5052
ukieleza ubongo kile hasa machine ile inagusa.
07:40
And in just four weeks, the brain learns to process this new sensation
132
460115
4765
Na kwa muda wa wiki nne tu, akili inajifunza kuhisi hii hisia mpya
07:44
and acquires a new sensory pathway -- like a new sense.
133
464880
6434
na inapata njia mpya ya hisia--kama hisia mpya.
07:51
And you truly liberate the brain now
134
471314
2416
Sasa unaiacha akili iwe huru
07:53
because you are allowing the brain to send motor commands to move this avatar.
135
473730
4384
kwa sababu unaikubali akili kutuma jumbe ili kuthibiti mashine hii.
07:58
And the feedback that comes from the avatar is being processed directly by the brain
136
478114
5000
Na maoni yanayotoka kwenye mashine yanachanganuliwa kwenye ubongo moja kwa moja
08:03
without the interference of the skin.
137
483114
2433
bila ya ngozi kuingilia kati.
08:05
So what you see here is this is the design of the task.
138
485547
2534
Kwa hivyo kile mnachoona hapa ni ule ubunifu wa kazi ile.
08:08
You're going to see an animal basically touching these three targets.
139
488081
4250
Mtaweza kuona mnyama akigusa sehemu au hatua hizi tatu.
08:12
And he has to select one because only one carries the reward,
140
492331
4349
Lazima achague moja kwa vile ni moja pekee inayoelekea palipo na zawadi,
08:16
the orange juice that they want to get.
141
496680
1867
ile juisi ya machungwa ambayo wanayoitaka.
08:18
And he has to select it by touch using a virtual arm, an arm that doesn't exist.
142
498547
5633
Na inambidi aichague kwa mguso akitumia mkono gushi, mkono amao haupo.
08:24
And that's exactly what they do.
143
504180
2000
Na hivyo ndivyo hasa wanavyofanya.
08:26
This is a complete liberation of the brain
144
506180
3435
Huu ni uhuru kamili wa akili
08:29
from the physical constraints of the body and the motor in a perceptual task.
145
509615
4282
kutokana na vikwazo vya kimwili na kazi ya akili ya kuona.
08:33
The animal is controlling the avatar to touch the targets.
146
513897
4167
Yule mnyama anathibiti mashine ile kugusa malengo hayo.
08:38
And he's sensing the texture by receiving an electrical message directly in the brain.
147
518064
5651
Na anahisi vile ilivyo kwa kupokea ujumbe umeme moja kwa moja kwenye ubongo.
08:43
And the brain is deciding what is the texture associated with the reward.
148
523715
3883
Na huo ubongo unaamua ni hisia ipi inayoashiria ile zawadi.
08:47
The legends that you see in the movie don't appear for the monkey.
149
527598
3832
Wale wakongwe uwaonao kwenye filamu hawawakilishi nyani huyu.
08:51
And by the way, they don't read English anyway,
150
531430
2484
Na kusema kweli, hata hawawezi kusoma Kiingereza,
08:53
so they are here just for you to know that the correct target is shifting position.
151
533914
5216
kwa hivyo wako hapa kuwaonyesheni ya kwamba lengo sahihi linabadilika badilika.
08:59
And yet, they can find them by tactile discrimination,
152
539130
3934
Hata hivyo, wanawezazipata kwa kubagua,
09:03
and they can press it and select it.
153
543064
3217
na wanawezazibonyeza na kuzichagua.
09:06
So when we look at the brains of these animals,
154
546281
2682
Hivyo basi tunapoangalia bongo za wanyama hawa,
09:08
on the top panel you see the alignment of 125 cells
155
548963
3667
katika sehemu ya juu mtaona mpangilio wa viini mia na ishirini na tano
09:12
showing what happens with the brain activity, the electrical storms,
156
552630
4201
vikionyesha kile kinachotokea kwenye ubongo, zile dhoruba umeme,
09:16
of this sample of neurons in the brain
157
556831
2067
za sampuli hii ya neuron kwenye ubongo
09:18
when the animal is using a joystick.
158
558898
2116
wakati mnyama huyo anatumia kijiti.
09:21
And that's a picture that every neurophysiologist knows.
159
561014
2600
Na hiyo ndio picha kila mwanafiziolojia ayoinajua.
09:23
The basic alignment shows that these cells are coding for all possible directions.
160
563614
5183
Mpangilio wa kimsingi unaonyesha kuwa viini hivi vinafuata kila mwelekeo.
09:28
The bottom picture is what happens when the body stops moving
161
568797
5683
Picha iliyo chini inaonyesha kinachotokea wakati mwili uachapo kusongea
09:34
and the animal starts controlling either a robotic device or a computational avatar.
162
574480
6134
na yule mnyama aanzapo kuthibiti kidude cha roboti ama mashine ya kikompyuta.
09:40
As fast as we can reset our computers,
163
580614
3066
Kwa kasi ile ile tunayoweza kubadilisha kompyuta zetu,
09:43
the brain activity shifts to start representing this new tool,
164
583680
5818
kazi inayoendelea akilini hubadilika ili kuwakilisha kifaa hiki kipya,
09:49
as if this too was a part of that primate's body.
165
589498
5250
kana kwamba ilikuwa sehemu ya mwili wa mnyama huyo.
09:54
The brain is assimilating that too, as fast as we can measure.
166
594748
4715
Akili inaichanganua pia, kwa kasi ile ile tunayopima nayo.
09:59
So that suggests to us that our sense of self
167
599463
3618
Hiyo inatuashiria kuwa hisia za kibinafsi
10:03
does not end at the last layer of the epithelium of our bodies,
168
603081
4150
haziishi kwenye safu ya mwisho ya ngozi ya miili yetu,
10:07
but it ends at the last layer of electrons of the tools that we're commanding with our brains.
169
607231
5718
bali inaisha kwenye safu ya mwisho ya electroni za vifaa tunavyotumia akili zetu kuvithibiti.
10:12
Our violins, our cars, our bicycles, our soccer balls, our clothing --
170
612949
4764
Vayolini zetu, magari yetu, baiskeli zetu, mipira yetu ya kandanda, nguo zetu
10:17
they all become assimilated by this voracious, amazing, dynamic system called the brain.
171
617713
6851
zote zinabadilishwa na hiki chombo thabiti, huu mfumo badilifu unaoitwa ubongo.
10:24
How far can we take it?
172
624564
1699
Ni umbali upi tunaoweza kuipeleka?
10:26
Well, in an experiment that we ran a few years ago, we took this to the limit.
173
626263
4218
Katika jaribio tulilofanya miaka chache iliyopita, tuliipeleka hadi kikomo.
10:30
We had an animal running on a treadmill
174
630481
2482
Tulikuwa na mnyama aliyekimbia kwenye baiskeli zoezi
10:32
at Duke University on the East Coast of the United States,
175
632963
2267
katika chuo kikuu cha Duke katika mashariki ya pwani ya Marekani,
10:35
producing the brainstorms necessary to move.
176
635230
2700
ikizalisha dhoruba bongo zinazohitajika kusonga.
10:37
And we had a robotic device, a humanoid robot,
177
637930
4091
Na tukawa na chombo cha roboti, roboti ya kibinadamu,
10:42
in Kyoto, Japan at ATR Laboratories
178
642021
2394
huko Kyoto, Ujapani katika maabara ya ATR
10:44
that was dreaming its entire life to be controlled by a brain,
179
644415
6094
iliyopanga maisha yake yote kuthibitiwa na ubongo,
10:50
a human brain, or a primate brain.
180
650509
3273
akili ya binadamu, ama ya mnyama.
10:53
What happens here is that the brain activity that generated the movements in the monkey
181
653782
4598
Kile kinachotokea hapa ni kwamba shighuli katika ubongo uliozalisha msongeo katika nyani huyo
10:58
was transmitted to Japan and made this robot walk
182
658380
3467
ulisambazwa hadi Ujapani na ukafanya roboti kutembea
11:01
while footage of this walking was sent back to Duke,
183
661847
4067
na ukanda ya matembezi haya ukapelekwa hadi Duke,
11:05
so that the monkey could see the legs of this robot walking in front of her.
184
665914
5233
ili nyani huyo aone miguu ya yule roboti ikitembea mbele yake.
11:11
So she could be rewarded, not by what her body was doing
185
671147
4067
Ilmradi azawadiwe, sio kwa kile mwili wake ulikuwa ukifanya
11:15
but for every correct step of the robot on the other side of the planet
186
675214
4961
bali kwa kila hatua sahihi iliyochukuliwa na roboti aliyekuwa sehemu ya pili ya ulimwengu
11:20
controlled by her brain activity.
187
680175
2609
ikithibitiwa na shughuli ya akili yake.
11:22
Funny thing, that round trip around the globe took 20 milliseconds less
188
682784
7118
Cha kuchekesha ni kwamba, safari hiyo ilichukua milisekunde ishirini chini
11:29
than it takes for that brainstorm to leave its head, the head of the monkey,
189
689902
4150
ya wakati dhoruba bongo inayochukua kutoka kichwani mwake, kichwa cha nyani,
11:34
and reach its own muscle.
190
694052
3870
hadi kwenye msuli wake.
11:37
The monkey was moving a robot that was six times bigger, across the planet.
191
697922
6030
Nyani alikuwa akisongeza roboti iliyokuwa na ukubwa mara sita wake yeye, kutoka sehemu moja ya ulimwengu hadi nyengine.
11:43
This is one of the experiments in which that robot was able to walk autonomously.
192
703952
6400
Hii ni moja wapo ya majaribio ambapo roboti iliweza kutembea bila usaidizi.
11:50
This is CB1 fulfilling its dream in Japan
193
710352
5267
Hii ni CB1 ikitimiza ndoto yake Ujapani
11:55
under the control of the brain activity of a primate.
194
715619
3700
chini ya uthibiti wa shughuli ya ubongo wa mnyama.
11:59
So where are we taking all this?
195
719319
1989
Kwa hivyo ni wapi tunapopeleka haya yote?
12:01
What are we going to do with all this research,
196
721308
2343
Ni wapi tunapoenda na huu utafiti,
12:03
besides studying the properties of this dynamic universe that we have between our ears?
197
723651
5668
kando na kusomea tabia za ulimwengu huu badilifu tulionao katikati ya masikio yetu?
12:09
Well the idea is to take all this knowledge and technology
198
729319
4833
Nia yetu ni kutumia usomi huu na teknolojia hii
12:14
and try to restore one of the most severe neurological problems that we have in the world.
199
734152
5484
na duniani.kujaribu kurekebisha mojawapo ya shida kubwa zaidi katika ufahamu wa ubongo tulizonazo hapa ulimwenguni.
12:19
Millions of people have lost the ability to translate these brainstorms
200
739636
4583
Mamilioni ya watu wamepoteza uwezo wakutafsiri hizi dhoruba za ubongo
12:24
into action, into movement.
201
744219
2116
ziwe hatua, au harakati.
12:26
Although their brains continue to produce those storms and code for movements,
202
746335
5234
Ingawaje akili zao zinazidi kuzaa dhoruba hizo na matembezi,
12:31
they cannot cross a barrier that was created by a lesion on the spinal cord.
203
751569
5167
haziwezi kilichoundwa kwa lesheni kwenye uti wa mgongo.kupita kizuizi
12:36
So our idea is to create a bypass,
204
756736
2450
Kwa hivyo lengo letu ni kutengeneza njia,
12:39
is to use these brain-machine interfaces to read these signals,
205
759186
4032
tukitumia hizi mashine za ubongo kutafsiri viashiria hivi,
12:43
larger-scale brainstorms that contain the desire to move again,
206
763218
4050
dhoruba kubwa akilini zilizo na hamu ya kutembea tena,
12:47
bypass the lesion using computational microengineering
207
767268
3969
kupita ile lesheni tukitumia uhandisi wa kikompyuta
12:51
and send it to a new body, a whole body called an exoskeleton,
208
771237
7114
na kuituma hadi kwenye mwili mpya, mwili mpya kabisa unaoitwa eksoskeletoni,
12:58
a whole robotic suit that will become the new body of these patients.
209
778351
5567
suti mpya ya kiroboti inatakayokuwa mwili mpya wa wagonjwa hawa.
13:03
And you can see an image produced by this consortium.
210
783918
4126
Na unaweza kuona picha inayojitokeza kutokana na muungano huu.
13:08
This is a nonprofit consortium called the Walk Again Project
211
788044
4059
Huu ni muungano uitwao Walk Again Project
13:12
that is putting together scientists from Europe,
212
792103
2783
unaoleta pamoja wanasayansi kutoka Uropa,
13:14
from here in the United States, and in Brazil
213
794886
1865
kutoka hapa Marekani, na Brazili
13:16
together to work to actually get this new body built --
214
796751
4517
kufanya kazi ili kutengeneza mwili huu mpya—
13:21
a body that we believe, through the same plastic mechanisms
215
801268
3334
mwili tunaoamini, katika mfumo plastiki ule ule
13:24
that allow Aurora and other monkeys to use these tools through a brain-machine interface
216
804602
5802
uliowezesha Aurora na nyani wengine kutumia hivi vifaa kupitia kwa mashine ya ubongo
13:30
and that allows us to incorporate the tools that we produce and use in our daily life.
217
810404
5630
na ambayo ilituwezesha kuingiza vifaa tunavyoweza kutengeneza na kutumia katika maisha yetu, siku baada ya siku.
13:36
This same mechanism, we hope, will allow these patients,
218
816034
3684
Mfumo huu huu, tunatumai, utawezesha wagonjwa hawa,
13:39
not only to imagine again the movements that they want to make
219
819718
3768
Mfumo huu huu, tunatumai, utawezesha wagonjwa hawa,
13:43
and translate them into movements of this new body,
220
823486
3207
bali pia kutafsiri mafikira hayo kuwa matembezi ya mwili huu mpya,
13:46
but for this body to be assimilated as the new body that the brain controls.
221
826693
6758
lakini kwa mwili huu kubadilika kama ule mwili mpya unaothibitiwa na ubongo.
13:53
So I was told about 10 years ago
222
833451
3851
Niliambiwa miaka kumi iliyopita
13:57
that this would never happen, that this was close to impossible.
223
837302
5066
kuwa haya yote hayatatokea, ati hii ilikuwa haiwezekani.
14:02
And I can only tell you that as a scientist,
224
842368
2451
Na naweza kuwaamia kama mwanasayansi,
14:04
I grew up in southern Brazil in the mid-'60s
225
844819
2986
nilikua huko Brazili ya kusini katika miaka ya sitini
14:07
watching a few crazy guys telling [us] that they would go to the Moon.
226
847805
5048
nikiangalia wale wenye maono wakituambia kuwa wataenda Mwezini.
14:12
And I was five years old,
227
852853
1461
Na nilikuwa na umri wa miaka mitano,
14:14
and I never understood why NASA didn't hire Captain Kirk and Spock to do the job;
228
854314
4240
na sikuwahi kuelewa sababu gani NASA haikuwaajiri manahodha Kirk na Spock kufanya kazi hiyo;
14:18
after all, they were very proficient --
229
858554
2432
kwani, si walikuwa na ustadi wa hali ya juu—
14:20
but just seeing that as a kid
230
860986
3450
lakini kuona tu kama mtoto
14:24
made me believe, as my grandmother used to tell me,
231
864436
2985
ilinifanya kuamini, kama nyanyangu alivyokuwa akiniambia,
14:27
that "impossible is just the possible
232
867421
1845
kwamba "kisichowezekana ni kile tu kinachowezekana
14:29
that someone has not put in enough effort to make it come true."
233
869266
3904
lakini mtu hajatia bidii ya kutosha kukitimiza."
14:33
So they told me that it's impossible to make someone walk.
234
873170
3799
Kwa hivyo waliniambia kuwa haiwezekani kufanya mtu atembee.
14:36
I think I'm going to follow my grandmother's advice.
235
876969
3251
Nafikiri nitafuata wasia wa nyanyangu.
14:40
Thank you.
236
880220
1450
Asanteni.
14:41
(Applause)
237
881670
8029
(Makofi)
Kuhusu tovuti hii

Tovuti hii itakuletea video za YouTube ambazo ni muhimu kwa kujifunza Kiingereza. Utaona masomo ya Kiingereza yanayofundishwa na walimu wa kiwango cha juu kutoka duniani kote. Bofya mara mbili kwenye manukuu ya Kiingereza yanayoonyeshwa kwenye kila ukurasa wa video ili kucheza video kutoka hapo. Manukuu yanasonga katika kusawazishwa na uchezaji wa video. Ikiwa una maoni au maombi yoyote, tafadhali wasiliana nasi kwa kutumia fomu hii ya mawasiliano.

https://forms.gle/WvT1wiN1qDtmnspy7