TED2019
Janelle Shane: The danger of AI is weirder than you think
詹妮爾‧沙恩: 人工智慧的危險性比你想的還要怪異
Filmed:
Readability: 4.2
376,501 views
人工智慧研究者詹妮爾‧沙恩說，人工智慧的危險性並不在於它會反抗我們，而是它會完全照我們所指示的去做。沙恩分享了人工智慧演算法在解決人類問題（如創造新的冰淇淋口味，或在道路上辨識汽車）時，所發生既詭異、有時還讓人擔憂的事。沙恩也說明了為什麼人工智慧遠比不上真實的大腦。
Janelle Shane - AI researcher
While moonlighting as a research scientist, Janelle Shane found fame documenting the often hilarious antics of AI algorithms. Full bio
While moonlighting as a research scientist, Janelle Shane found fame documenting the often hilarious antics of AI algorithms. Full bio
Double-click the English transcript below to play the video.
00:13
So, artificial intelligence
0
1765
3000
人工智慧
00:16
is known for disrupting
all kinds of industries.
all kinds of industries.
1
4789
3529
顛覆各產業為人所知。
00:20
What about ice cream?
2
8961
2043
那冰淇淋呢？
00:23
What kind of mind-blowing
new flavors could we generate
new flavors could we generate
3
11879
3639
有了先進的人工智慧，
我們能變出什麼驚人的新口味？
00:27
with the power of an advanced
artificial intelligence?
artificial intelligence?
4
15542
2976
00:31
So I teamed up with a group of coders
from Kealing Middle School
from Kealing Middle School
5
19011
4161
我和基林中學的
一組程式設計師合作，
一組程式設計師合作，
00:35
to find out the answer to this question.
6
23196
2241
想找出這個問題的答案。
00:37
They collected over 1,600
existing ice cream flavors,
existing ice cream flavors,
7
25461
5081
他們收集了既有的
一千六百種冰淇淋口味，
一千六百種冰淇淋口味，
00:42
and together, we fed them to an algorithm
to see what it would generate.
to see what it would generate.
8
30566
5522
將這些資料輸入到演算法中，
看看會產出什麼。
看看會產出什麼。
00:48
And here are some of the flavors
that the AI came up with.
that the AI came up with.
9
36112
3753
以下是人工智慧想出來的一些口味。
00:52
[Pumpkin Trash Break]
10
40444
1471
〔南瓜垃圾〕
00:53
(Laughter)
11
41939
1402
（笑聲）
00:55
[Peanut Butter Slime]
12
43365
2469
〔花生醬黏液〕
00:58
[Strawberry Cream Disease]
13
46822
1343
〔草莓奶油疾病〕
01:00
(Laughter)
14
48189
2126
（笑聲）
01:02
These flavors are not delicious,
as we might have hoped they would be.
as we might have hoped they would be.
15
50339
4597
這些口味並不如我們預期的可口。
01:06
So the question is: What happened?
16
54960
1864
所以問題是：到底怎麼了？
哪裡出問題了？
哪裡出問題了？
01:08
What went wrong?
17
56848
1394
01:10
Is the AI trying to kill us?
18
58266
1959
人工智慧想要害死我們嗎？
01:13
Or is it trying to do what we asked,
and there was a problem?
and there was a problem?
19
61027
4310
或它只是照我們的指示去做，
卻出了問題？
01:18
In movies, when something
goes wrong with AI,
goes wrong with AI,
20
66567
2464
在電影中，人工智慧如果出問題，
01:21
it's usually because the AI has decided
21
69055
2712
通常都是因為人工智慧決定
01:23
that it doesn't want to obey
the humans anymore,
the humans anymore,
22
71791
2272
不要繼續服從人類了，
01:26
and it's got its own goals,
thank you very much.
thank you very much.
23
74087
2623
它有自己的目標，非常謝謝。
01:29
In real life, though,
the AI that we actually have
the AI that we actually have
24
77266
3216
不過，在我們現實生活中的人工智慧
01:32
is not nearly smart enough for that.
25
80506
1863
並沒有聰明到能做出那樣的事。
01:34
It has the approximate computing power
26
82781
2982
它大概只有蚯蚓程度的計算能力，
01:37
of an earthworm,
27
85787
1276
或頂多到一隻蜜蜂的程度，
01:39
or maybe at most a single honeybee,
28
87087
3403
01:42
and actually, probably maybe less.
29
90514
2215
其實，可能還更低。
01:44
Like, we're constantly learning
new things about brains
new things about brains
30
92753
2594
對於大腦我們不斷有新的發現，
01:47
that make it clear how much our AIs
don't measure up to real brains.
don't measure up to real brains.
31
95371
4360
讓我們更清楚知道，
人工智慧遠遠比不上真實大腦。
人工智慧遠遠比不上真實大腦。
01:51
So today's AI can do a task
like identify a pedestrian in a picture,
like identify a pedestrian in a picture,
32
99755
5663
所以，現今的人工智慧可以做到
在圖片中辨識出行人之類的事，
01:57
but it doesn't have a concept
of what the pedestrian is
of what the pedestrian is
33
105442
2983
但它不知道什麼是行人，
02:00
beyond that it's a collection
of lines and textures and things.
of lines and textures and things.
34
108449
4824
只知道行人是許多
線條、結構、東西的組合。
線條、結構、東西的組合。
02:05
It doesn't know what a human actually is.
35
113792
2521
它不知道人類是什麼。
02:08
So will today's AI
do what we ask it to do?
do what we ask it to do?
36
116822
3282
所以，現今的人工智慧
會照我們要求的做嗎？
會照我們要求的做嗎？
02:12
It will if it can,
37
120128
1594
如果能的話，它會，
02:13
but it might not do what we actually want.
38
121746
2726
但它可能不會照我們
真正想要它做的去做。
真正想要它做的去做。
02:16
So let's say that you
were trying to get an AI
were trying to get an AI
39
124496
2415
比如，你想要人工智慧
02:18
to take this collection of robot parts
40
126935
2619
把這一堆機器人的零件
02:21
and assemble them into some kind of robot
to get from Point A to Point B.
to get from Point A to Point B.
41
129578
4197
組裝成某種機器人，
從 A 點走到 B 點。
從 A 點走到 B 點。
02:25
Now, if you were going to try
and solve this problem
and solve this problem
42
133799
2481
如果你是用傳統的電腦程式方法來寫，
02:28
by writing a traditional-style
computer program,
computer program,
43
136304
2351
02:30
you would give the program
step-by-step instructions
step-by-step instructions
44
138679
3417
你就得要給程式一步一步的指令，
02:34
on how to take these parts,
45
142120
1329
告訴它要拿哪些零件，
如何組裝成有腳的機器人，
如何組裝成有腳的機器人，
02:35
how to assemble them
into a robot with legs
into a robot with legs
46
143473
2407
02:37
and then how to use those legs
to walk to Point B.
to walk to Point B.
47
145904
2942
接著告訴它如何用腳來走到 B 點。
02:41
But when you're using AI
to solve the problem,
to solve the problem,
48
149441
2340
但，若用人工智慧來解決
這個問題，做法就不同了。
這個問題，做法就不同了。
02:43
it goes differently.
49
151805
1174
02:45
You don't tell it
how to solve the problem,
how to solve the problem,
50
153003
2382
你不用告訴它如何解決問題，
只要給它一個目標，
只要給它一個目標，
02:47
you just give it the goal,
51
155409
1479
02:48
and it has to figure out for itself
via trial and error
via trial and error
52
156912
3262
它自己要用嘗試錯誤法
來想辦法達成目標。
來想辦法達成目標。
02:52
how to reach that goal.
53
160198
1484
02:54
And it turns out that the way AI tends
to solve this particular problem
to solve this particular problem
54
162254
4102
結果發現，人工智慧
解決這個問題的方法
解決這個問題的方法
02:58
is by doing this:
55
166380
1484
傾向於用這種方式：
02:59
it assembles itself into a tower
and then falls over
and then falls over
56
167888
3367
它會把它自己組裝成
一座塔，然後倒向 B，
一座塔，然後倒向 B，
03:03
and lands at Point B.
57
171279
1827
就會到達 B 點了。
03:05
And technically, this solves the problem.
58
173130
2829
技術上來說，問題的確解決了。
它的確抵達了 B 點。
它的確抵達了 B 點。
03:07
Technically, it got to Point B.
59
175983
1639
03:09
The danger of AI is not that
it's going to rebel against us,
it's going to rebel against us,
60
177646
4265
人工智慧的危險性
並不在於它會反抗我們，
並不在於它會反抗我們，
03:13
it's that it's going to do
exactly what we ask it to do.
exactly what we ask it to do.
61
181935
4274
而是它會「完全」照我們的要求去做。
03:18
So then the trick
of working with AI becomes:
of working with AI becomes:
62
186876
2498
所以使用人工智慧的秘訣就變成是：
03:21
How do we set up the problem
so that it actually does what we want?
so that it actually does what we want?
63
189398
3828
我們要如何把問題設定好，
讓它真能照我們所想的去做？
讓它真能照我們所想的去做？
03:26
So this little robot here
is being controlled by an AI.
is being controlled by an AI.
64
194726
3306
這個小機器人是由人工智慧控制。
03:30
The AI came up with a design
for the robot legs
for the robot legs
65
198056
2814
人工智慧構思出機器人的腳，
03:32
and then figured out how to use them
to get past all these obstacles.
to get past all these obstacles.
66
200894
4078
接著它再想出要如何
用腳來越過這些障礙。
用腳來越過這些障礙。
03:36
But when David Ha set up this experiment,
67
204996
2741
但，當大衛‧哈在設計這個實驗時，
03:39
he had to set it up
with very, very strict limits
with very, very strict limits
68
207761
2856
他得要訂下非常非常嚴格的限制，
03:42
on how big the AI
was allowed to make the legs,
was allowed to make the legs,
69
210641
3292
限制人工智慧能把腳做到多大，
03:45
because otherwise ...
70
213957
1550
因為，若不限制……
03:55
(Laughter)
71
223058
3931
（笑聲）
04:00
And technically, it got
to the end of that obstacle course.
to the end of that obstacle course.
72
228563
3745
技術上，它也的確到了
障礙道的另一端。
障礙道的另一端。
04:04
So you see how hard it is to get AI
to do something as simple as just walk.
to do something as simple as just walk.
73
232332
4942
所以大家可以了解，讓人工智慧
做出走路這麼簡單的事有多難了。
做出走路這麼簡單的事有多難了。
04:09
So seeing the AI do this,
you may say, OK, no fair,
you may say, OK, no fair,
74
237298
3820
看到人工智慧這麼做，
你可能會說，好，這不公平，
你可能會說，好，這不公平，
04:13
you can't just be
a tall tower and fall over,
a tall tower and fall over,
75
241142
2580
你不能變成高塔然後倒下來就到位，
04:15
you have to actually, like,
use legs to walk.
use legs to walk.
76
243746
3435
你必須要真的用腳來走路。
04:19
And it turns out,
that doesn't always work, either.
that doesn't always work, either.
77
247205
2759
結果發現，那也行不通。
04:21
This AI's job was to move fast.
78
249988
2759
這個人工智慧的工作
是要達成快速移動。
是要達成快速移動。
04:25
They didn't tell it that it had
to run facing forward
to run facing forward
79
253115
3593
他們沒有告訴人工智慧說
跑步時一定要面對前方，
跑步時一定要面對前方，
04:28
or that it couldn't use its arms.
80
256732
2258
也沒說它不可以用手臂。
04:31
So this is what you get
when you train AI to move fast,
when you train AI to move fast,
81
259487
4618
所以如果你訓練人工智慧要做到
快速移動，就會得到這種結果，
快速移動，就會得到這種結果，
04:36
you get things like somersaulting
and silly walks.
and silly walks.
82
264129
3534
你會得到筋斗翻和很蠢的走路姿勢。
04:39
It's really common.
83
267687
1400
這很常見。
04:41
So is twitching along the floor in a heap.
84
269667
3179
「成堆地沿著地板抽動」亦然。
04:44
(Laughter)
85
272870
1150
（笑聲）
我認為更詭異的
04:47
So in my opinion, you know what
should have been a whole lot weirder
should have been a whole lot weirder
86
275241
3254
是《魔鬼終結者》的機器人。
04:50
is the "Terminator" robots.
87
278519
1396
04:52
Hacking "The Matrix" is another thing
that AI will do if you give it a chance.
that AI will do if you give it a chance.
88
280256
3755
如果你給人工智慧機會，
它也會駭入《駭客任務》的母體。
它也會駭入《駭客任務》的母體。
04:56
So if you train an AI in a simulation,
89
284035
2517
如果在模擬狀況中訓練人工智慧，
04:58
it will learn how to do things like
hack into the simulation's math errors
hack into the simulation's math errors
90
286576
4113
它會學習如何做出的事包括
駭入模擬的數學錯誤中
駭入模擬的數學錯誤中
05:02
and harvest them for energy.
91
290713
2207
並獲取它們作為能量。
05:04
Or it will figure out how to move faster
by glitching repeatedly into the floor.
by glitching repeatedly into the floor.
92
292944
5475
或者，它會重覆在地板
鑽上鑽下使自己移動得更快。
鑽上鑽下使自己移動得更快。
05:10
When you're working with AI,
93
298443
1585
和人工智慧合作比較像是
和某種大自然的詭異力量合作，
和某種大自然的詭異力量合作，
05:12
it's less like working with another human
94
300052
2389
05:14
and a lot more like working
with some kind of weird force of nature.
with some kind of weird force of nature.
95
302465
3629
而不太像是和人類合作。
05:18
And it's really easy to accidentally
give AI the wrong problem to solve,
give AI the wrong problem to solve,
96
306562
4623
一不小心就會叫人工智慧
去解決不正確的問題，
去解決不正確的問題，
05:23
and often we don't realize that
until something has actually gone wrong.
until something has actually gone wrong.
97
311209
4538
且通常出錯後我們才會發現。
05:28
So here's an experiment I did,
98
316242
2080
我做了一個實驗，
05:30
where I wanted the AI
to copy paint colors,
to copy paint colors,
99
318346
3182
我希望人工智慧能複製顏料顏色，
05:33
to invent new paint colors,
100
321552
1746
發明新的顏料顏色，
05:35
given the list like the ones
here on the left.
here on the left.
101
323322
2987
給它左側的這個清單。
05:38
And here's what the AI
actually came up with.
actually came up with.
102
326798
3004
這些是人工智慧創造出來的顏色。
05:41
[Sindis Poop, Turdly, Suffer, Gray Pubic]
103
329826
3143
〔辛迪司便便、混蛋、
苦難、灰色陰部〕
苦難、灰色陰部〕
05:44
(Laughter)
104
332993
4230
（笑聲）
05:51
So technically,
105
339177
1886
技術上來說，
05:53
it did what I asked it to.
106
341087
1864
它照我的意思做了。
05:54
I thought I was asking it for,
like, nice paint color names,
like, nice paint color names,
107
342975
3308
我以為我要求人工智慧
給我好聽的色彩名稱，
給我好聽的色彩名稱，
05:58
but what I was actually asking it to do
108
346307
2307
但我實際上是在要求它
06:00
was just imitate the kinds
of letter combinations
of letter combinations
109
348638
3086
模仿它在原始顏色中
所見到的那些字母組合。
所見到的那些字母組合。
06:03
that it had seen in the original.
110
351748
1905
06:05
And I didn't tell it anything
about what words mean,
about what words mean,
111
353677
3098
我沒有告訴它字的意思，
06:08
or that there are maybe some words
112
356799
2560
也沒告訴它可能有一些字
06:11
that it should avoid using
in these paint colors.
in these paint colors.
113
359383
2889
不太適合用在顏料顏色上。
06:15
So its entire world
is the data that I gave it.
is the data that I gave it.
114
363141
3494
它所有的訊息就僅是
我給它的資料而已。
我給它的資料而已。
06:18
Like with the ice cream flavors,
it doesn't know about anything else.
it doesn't know about anything else.
115
366659
4028
和冰淇淋口味的例子一樣，
其他的它什麼都不知道。
其他的它什麼都不知道。
06:24
So it is through the data
116
372491
1638
通常會因為資料內容的關係，
06:26
that we often accidentally tell AI
to do the wrong thing.
to do the wrong thing.
117
374153
4044
無意間讓人工智慧去執行錯誤的運作。
06:30
This is a fish called a tench.
118
378694
3032
這是一種叫丁鱖的魚。
06:33
And there was a group of researchers
119
381750
1815
有一群研究人員
06:35
who trained an AI to identify
this tench in pictures.
this tench in pictures.
120
383589
3874
訓練人工智慧在照片中辨識出丁鱖。
06:39
But then when they asked it
121
387487
1296
但當他們問人工智慧，它是用
圖上的哪個部分來辨識出丁鱖，
圖上的哪個部分來辨識出丁鱖，
06:40
what part of the picture it was actually
using to identify the fish,
using to identify the fish,
122
388807
3426
06:44
here's what it highlighted.
123
392257
1358
結果它標記出這些部分。
06:47
Yes, those are human fingers.
124
395203
2189
是的，這些是人類的手指。
06:49
Why would it be looking for human fingers
125
397416
2059
如果它的目標是要辨識出魚類，
為什麼要去找人類的手指？
為什麼要去找人類的手指？
06:51
if it's trying to identify a fish?
126
399499
1921
06:54
Well, it turns out that the tench
is a trophy fish,
is a trophy fish,
127
402126
3164
結果發現，丁鱖是釣客的戰利品，
06:57
and so in a lot of pictures
that the AI had seen of this fish
that the AI had seen of this fish
128
405314
3811
所以，人工智慧在訓練期間
所看到的大量丁鱖照片，
所看到的大量丁鱖照片，
07:01
during training,
129
409149
1151
看起來像這樣。（笑聲）
07:02
the fish looked like this.
130
410324
1490
07:03
(Laughter)
131
411838
1635
07:05
And it didn't know that the fingers
aren't part of the fish.
aren't part of the fish.
132
413497
3330
人工智慧不知道手指並非魚的一部分。
07:10
So you see why it is so hard
to design an AI
to design an AI
133
418808
4120
這就是為什麼難以設計出
能看懂眼前事物為何的人工智慧。
07:14
that actually can understand
what it's looking at.
what it's looking at.
134
422952
3319
07:18
And this is why designing
the image recognition
the image recognition
135
426295
2862
這也就是為什麼在自動駕駛汽車上
設計影像辨識系統如此困難。
07:21
in self-driving cars is so hard,
136
429181
2067
07:23
and why so many self-driving car failures
137
431272
2205
很多自動駕駛汽車會失敗
07:25
are because the AI got confused.
138
433501
2885
是因為困惑的人工智慧。
07:28
I want to talk about an example from 2016.
139
436410
4008
我想要談談 2016 年的一個例子。
07:32
There was a fatal accident when somebody
was using Tesla's autopilot AI,
was using Tesla's autopilot AI,
140
440442
4455
某人在使用特斯拉的自動駕駛
人工智慧時發生了致命的意外，
人工智慧時發生了致命的意外，
07:36
but instead of using it on the highway
like it was designed for,
like it was designed for,
141
444921
3414
它原本是設計行駛在高速公路上的，
但他們卻讓它行駛在城市街道上。
07:40
they used it on city streets.
142
448359
2205
07:43
And what happened was,
143
451239
1175
事情的經過是：有台卡車
經過這台車的前面，
經過這台車的前面，
07:44
a truck drove out in front of the car
and the car failed to brake.
and the car failed to brake.
144
452438
3396
這台車沒有煞車。
07:48
Now, the AI definitely was trained
to recognize trucks in pictures.
to recognize trucks in pictures.
145
456507
4762
人工智慧一定有被訓練過
如何辨識出照片中的卡車。
如何辨識出照片中的卡車。
07:53
But what it looks like happened is
146
461293
2145
但發生的狀況似乎是
07:55
the AI was trained to recognize
trucks on highway driving,
trucks on highway driving,
147
463462
2931
人工智慧被訓練辨識出
在高速公路上行駛的卡車，
在高速公路上行駛的卡車，
07:58
where you would expect
to see trucks from behind.
to see trucks from behind.
148
466417
2899
在高速公路上你會看到的
應該是卡車的車尾。
應該是卡車的車尾。
08:01
Trucks on the side is not supposed
to happen on a highway,
to happen on a highway,
149
469340
3420
高速公路上不應該會看到卡車的側面，
08:04
and so when the AI saw this truck,
150
472784
3455
所以，當人工智慧看到這台卡車時，
08:08
it looks like the AI recognized it
as most likely to be a road sign
as most likely to be a road sign
151
476263
4827
人工智慧似乎把它當作是路上的號誌，
08:13
and therefore, safe to drive underneath.
152
481114
2273
因此判斷可以安全地從下方行駛過去。
08:16
Here's an AI misstep
from a different field.
from a different field.
153
484114
2580
再來是另一個領域中的人工智慧過失。
08:18
Amazon recently had to give up
on a résumé-sorting algorithm
on a résumé-sorting algorithm
154
486718
3460
亞馬遜最近必須要放棄他們
努力研發的履歷排序演算法，
努力研發的履歷排序演算法，
08:22
that they were working on
155
490202
1220
08:23
when they discovered that the algorithm
had learned to discriminate against women.
had learned to discriminate against women.
156
491446
3908
因為他們發現演算法學會歧視女性。
他們用過去僱用員工的記錄資料
08:27
What happened is they had trained it
on example résumés
on example résumés
157
495378
2716
當作訓練人工智慧的範例。
08:30
of people who they had hired in the past.
158
498118
2242
從範例中，人工智慧學到不要選擇
08:32
And from these examples, the AI learned
to avoid the résumés of people
to avoid the résumés of people
159
500384
4023
上過女子大學的人，
08:36
who had gone to women's colleges
160
504431
2026
08:38
or who had the word "women"
somewhere in their resume,
somewhere in their resume,
161
506481
2806
也不要選擇在履歷中某處
寫到「女」字的人，
寫到「女」字的人，
08:41
as in, "women's soccer team"
or "Society of Women Engineers."
or "Society of Women Engineers."
162
509311
4576
比如「女子足球隊」
或「女工程師協會」。
或「女工程師協會」。
08:45
The AI didn't know that it wasn't supposed
to copy this particular thing
to copy this particular thing
163
513911
3974
人工智慧看到人類這麼做，
它並不知道它不該複製這種模式。
08:49
that it had seen the humans do.
164
517909
1978
08:51
And technically, it did
what they asked it to do.
what they asked it to do.
165
519911
3177
技術上來說，它也照著
他們給它的指示做了。
他們給它的指示做了。
08:55
They just accidentally asked it
to do the wrong thing.
to do the wrong thing.
166
523112
2797
他們只是不小心
叫人工智慧做了錯的事。
叫人工智慧做了錯的事。
08:58
And this happens all the time with AI.
167
526653
2895
人工智慧常常會發生這種狀況。
09:02
AI can be really destructive
and not know it.
and not know it.
168
530120
3591
人工智慧可能造成破壞卻不自覺。
09:05
So the AIs that recommend
new content in Facebook, in YouTube,
new content in Facebook, in YouTube,
169
533735
5078
所以，在臉書、Youtube 上
負責做推薦的人工智慧，
負責做推薦的人工智慧，
09:10
they're optimized to increase
the number of clicks and views.
the number of clicks and views.
170
538837
3539
它們進行了優化以增加點閱率次數。
09:14
And unfortunately, one way
that they have found of doing this
that they have found of doing this
171
542400
3436
不幸的是，它們發現，
達到目標的方法之一
達到目標的方法之一
09:17
is to recommend the content
of conspiracy theories or bigotry.
of conspiracy theories or bigotry.
172
545860
4503
就是推薦關於陰謀論或偏執的內容。
09:22
The AIs themselves don't have any concept
of what this content actually is,
of what this content actually is,
173
550902
5302
人工智慧本身
對於推薦的內容一無所知，
09:28
and they don't have any concept
of what the consequences might be
of what the consequences might be
174
556228
3395
也對推薦這些內容會造成的後果
09:31
of recommending this content.
175
559647
2109
一無所悉。
09:34
So, when we're working with AI,
176
562296
2011
當我們使用人工智慧時，
09:36
it's up to us to avoid problems.
177
564331
4182
必須要由我們來避免問題。
09:40
And avoiding things going wrong,
178
568537
2323
我們若要避免出錯，
09:42
that may come down to
the age-old problem of communication,
the age-old problem of communication,
179
570884
4526
可能就得歸結到溝通的老問題上，
09:47
where we as humans have to learn
how to communicate with AI.
how to communicate with AI.
180
575434
3745
我們人類得要學習
如何和人工智慧溝通。
如何和人工智慧溝通。
09:51
We have to learn what AI
is capable of doing and what it's not,
is capable of doing and what it's not,
181
579203
4039
我們得要了解人工智慧
能夠做什麼、不能做什麼，
能夠做什麼、不能做什麼，
且要知道，人工智慧
只有小蟲等級的大腦，
只有小蟲等級的大腦，
09:55
and to understand that,
with its tiny little worm brain,
with its tiny little worm brain,
182
583266
3086
09:58
AI doesn't really understand
what we're trying to ask it to do.
what we're trying to ask it to do.
183
586376
4013
它不知道我們真正想要它做什麼。
10:03
So in other words, we have
to be prepared to work with AI
to be prepared to work with AI
184
591148
3321
換言之，我們得有心理準備，
我們所使用的人工智慧
我們所使用的人工智慧
10:06
that's not the super-competent,
all-knowing AI of science fiction.
all-knowing AI of science fiction.
185
594493
5258
並非科幻小說裡那種
超能、無所不知的人工智慧。
超能、無所不知的人工智慧。
10:11
We have to prepared to work with an AI
186
599775
2862
我們必須準備好與現今還是
小蟲大腦等級的人工智慧共事。
小蟲大腦等級的人工智慧共事。
10:14
that's the one that we actually have
in the present day.
in the present day.
187
602661
2938
10:17
And present-day AI is plenty weird enough.
188
605623
4205
而現今的人工智慧是非常怪異的。
10:21
Thank you.
189
609852
1190
謝謝。
10:23
(Applause)
190
611066
5225
（掌聲）
ABOUT THE SPEAKER
Janelle Shane - AI researcherWhile moonlighting as a research scientist, Janelle Shane found fame documenting the often hilarious antics of AI algorithms.
Why you should listen
Janelle Shane's humor blog, AIweirdness.com, looks at, as she tells it, "the strange side of artificial intelligence." Her upcoming book, You Look Like a Thing and I Love You: How AI Works and Why It's Making the World a Weirder Place, uses cartoons and humorous pop-culture experiments to look inside the minds of the algorithms that run our world, making artificial intelligence and machine learning both accessible and entertaining.
According to Shane, she has only made a neural network-written recipe once -- and discovered that horseradish brownies are about as terrible as you might imagine.
Janelle Shane | Speaker | TED.com