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TED2003

Jeff Hawkins: How brain science will change computing

ジェフ・ホーキンスが語る「脳科学がコンピューティングを変える」

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脳は素早い情報処理装置ではなく、経験を記憶、再生することで次の出来事を予測する利口な記憶装置なのです。Treo考案者のジェフ・ホーキンスが新たな脳の見方について力説します。

- Computer designer, brain researcher
Jeff Hawkins pioneered the development of PDAs such as the Palm and Treo. Now he's trying to understand how the human brain really works, and adapt its method -- which he describes as a deep system for storing memory -- to create new kinds of computers and tools. Full bio

I do two things: I design mobile computers and I study brains.
私は携帯型コンピュータの設計と、脳の研究をしています
00:25
And today's talk is about brains and,
今日のトークの主題は脳で、
00:29
yay, somewhere I have a brain fan out there.
ヤッター、脳ファンがいるね
00:31
(Laughter)
(笑)
00:33
I'm going to, if I can have my first slide up here,
一枚目のスライドを出してもらえますか
00:35
and you'll see the title of my talk and my two affiliations.
これがこのトークの題名と私が所属してる二つの組織です
00:37
So what I'm going to talk about is why we don't have a good brain theory,
今日は、なぜいい脳理論が存在しないのかについてお話しましょう
00:41
why it is important that we should develop one and what we can do about it.
なぜ理論を築く必要があり、そのためにどうすべきか
00:45
And I'll try to do all that in 20 minutes. I have two affiliations.
全部20分で話してみせましょう。私は二つの組織に所属してます
00:48
Most of you know me from my Palm and Handspring days,
PalmやHandspring時代の私しか知らない人が多いですが
00:51
but I also run a nonprofit scientific research institute
実はメンローパークにある非営利研究所の
00:54
called the Redwood Neuroscience Institute in Menlo Park,
レッドウッド神経科学研究所も運営してます
00:57
and we study theoretical neuroscience,
そこで私達は理論神経科学や
00:59
and we study how the neocortex works.
大脳新皮質の仕組みについて研究してます
01:01
I'm going to talk all about that.
今日はそういったこともお話しします
01:03
I have one slide on my other life, the computer life, and that's the slide here.
これはコンピュータの方の仕事についてのスライドです
01:05
These are some of the products I've worked on over the last 20 years,
これらは、この20年間に私がつくった製品の一部で
01:08
starting back from the very original laptop to some of the first tablet computers
最も初期のノート パソコンやタブレット コンピュータなどから
01:11
and so on, and ending up most recently with the Treo,
最近出たばかりのTreoまであります
01:15
and we're continuing to do this.
そしてまだまだつくり続けてます
01:17
And I've done this because I really believe that mobile computing
私は本当にモバイル コンピュータが将来
01:19
is the future of personal computing, and I'm trying to make the world
今のパソコンに取って代わると信じているから
01:21
a little bit better by working on these things.
少しでも世のためにと、こういうのを考案してきました
01:24
But this was, I have to admit, all an accident.
でも正直言って全て単なる偶然でした
01:27
I really didn't want to do any of these products
本当はこんな製品には全く興味がなくて
01:29
and very early in my career I decided
早いうちから
01:31
I was not going to be in the computer industry.
コンピュータ業界の仕事はしないと決めてました
01:33
And before I tell you about that, I just have to tell you
でもその話をする前に、この間ネットで見つけた
01:36
this one little picture of graffiti there I picked off the web the other day.
「グラフィティ」についてどうしてもお話したいのです
01:38
I was looking for a picture of graffiti, little text input language,
テキスト入力言語のグラフィティについて検索してると
01:40
and I found the website dedicated to teachers who want to make these,
あるウェブサイトを見つけたのです
01:43
you know, the script writing things across the top of their blackboard,
黒板に掲げて文字を記入するやつを作る先生用サイトで
01:46
and they had added graffiti to it, and I'm sorry about that.
グラフィティつまり落書きも書き込まれていました。あー、気の毒に
01:49
(Laughter)
(笑)
01:52
So what happened was, when I was young and got out of engineering school
さて、私は1979年にコーネル大学の工学科を卒業した後
01:54
at Cornell in '79, I decided -- I went to work for Intel and
インテルで働くことにしました
01:59
I was in the computer industry -- and three months into that,
コンピュータ業界に踏み込んで3ヶ月後
02:03
I fell in love with something else, and I said, "I made the wrong career choice here,"
「職業選択を誤ってしまった」と気付き
02:06
and I fell in love with brains.
全く違うもの、つまり脳にはまってしまいました
02:10
This is not a real brain. This is a picture of one, a line drawing.
これは現物の脳ではなく、脳のスケッチです
02:13
But I don't remember exactly how it happened,
きっかけははっきり覚えてないけど、
02:16
but I have one recollection, which was pretty strong in my mind.
かなり強烈に記憶に残っている出来事があります
02:19
In September 1979, Scientific American came out
1979年のサイエンティフィック アメリカン9月号は
02:22
with a single topic issue about the brain. And it was quite good.
一冊全てが脳についてだったのです
02:25
It was one of the best issues ever. And they talked about the neuron
一番いいテーマで、ニューロン、発達、病気、視覚など
02:28
and development and disease and vision and all the things
脳について知りたいことは
02:31
you might want to know about brains. It was really quite impressive.
全て説明してあって実に印象的でした
02:33
And one might have the impression that we really knew a lot about brains.
脳の研究はかなり進んでる気がするかもしれないけど、
02:36
But the last article in that issue was written by Francis Crick of DNA fame.
DNAで有名なフランシス クリックは、記事の最後で ---
02:39
Today is, I think, the 50th anniversary of the discovery of DNA.
今日は確かDNA発見50周年ですね、
02:43
And he wrote a story basically saying,
--- 彼はこんなことを書いてました
02:46
well, this is all well and good, but you know what,
この雑誌に載ってることは、全てそれはそれでいいけど
02:48
we don't know diddley squat about brains
実は脳の仕組みについて
02:51
and no one has a clue how these things work,
なーんにもわかっちゃいないんだから
02:53
so don't believe what anyone tells you.
まだ何も信じてはいけない、と
02:55
This is a quote from that article. He said, "What is conspicuously lacking,"
彼の書いたその記事から引用します。
02:57
he's a very proper British gentleman so, "What is conspicuously lacking
紳士的な言葉使いです。「明らかに欠けてるのは」
03:00
is a broad framework of ideas in which to interpret these different approaches."
「様々な考え方を解釈するための大まかな枠組みです」
03:04
I thought the word framework was great.
枠組みとは実に的確な表現だと思いました
03:07
He didn't say we didn't even have a theory. He says,
彼は理論がないとは言っていません
03:09
we don't even know how to begin to think about it --
何から考え始めたらいいのか分からない つまり
03:11
we don't even have a framework.
理論的枠組みがない
03:13
We are in the pre-paradigm days, if you want to use Thomas Kuhn.
いわばトーマス クーンのいうパラダイム前の時代です
03:15
And so I fell in love with this, and said look,
私はこの考え方にはまってしまい、
03:18
we have all this knowledge about brains. How hard can it be?
脳についてこんなに分かっているのだから、無理な訳ない
03:21
And this is something we can work on my lifetime. I felt I could make a difference,
生涯にその枠組みを見つけ、社会貢献しようと思いました
03:24
and so I tried to get out of the computer business, into the brain business.
そのためコンピュータ業界をやめて脳に関わることにしました
03:27
First, I went to MIT, the AI lab was there,
まずMITの人工知能研究所に行き
03:31
and I said, well, I want to build intelligent machines, too,
私も知能機械をつくりたい、
03:33
but the way I want to do it is to study how brains work first.
まずは脳の仕組みについて研究したい、と言ったんです
03:35
And they said, oh, you don't need to do that.
でも、そんなことしなくていい、と言われました
03:38
We're just going to program computers; that's all we need to do.
ただコンピュータのプログラミングだけをすればいい、と
03:41
And I said, no, you really ought to study brains. They said, oh, you know,
脳を研究すべき、と私が言っても、間違いだ、と言うから
03:43
you're wrong. And I said, no, you're wrong, and I didn't get in.
間違いはあなただと返したら、不合格でした
03:46
(Laughter)
(笑)
03:48
But I was a little disappointed -- pretty young -- but I went back again
かなりがっかりしたけど、数年後今度は
03:50
a few years later and this time was in California, and I went to Berkeley.
カリフォルニア大のバークリー校で再挑戦することにして
03:52
And I said, I'll go in from the biological side.
今度は生物学的方面から迫ってみることにしたのです
03:55
So I got in -- in the Ph.D. program in biophysics, and I was, all right,
生物物理学の大学院に合格したから
03:59
I'm studying brains now, and I said, well, I want to study theory.
さて、脳理論を研究したい、と言いいました
04:02
And they said, oh no, you can't study theory about brains.
でも、脳理論なんて研究しちゃいかん、
04:05
That's not something you do. You can't get funded for that.
そんなことのための研究費はない、
04:07
And as a graduate student, you can't do that. So I said, oh my gosh.
大学院生がそんなことしたらだめだ、と言われてしまいました
04:09
I was very depressed. I said, but I can make a difference in this field.
非常にがっくりしたけど、
04:13
So what I did is I went back in the computer industry
この業界を変えるぞと思いつつ、コンピュータ業界に戻り
04:15
and said, well, I'll have to work here for a while, do something.
しばらくここで働くしかないな、と決心し、
04:18
That's when I designed all those computer products.
後ろの製品は全部そのころデザインしたのです
04:20
(Laughter)
(笑)
04:23
And I said, I want to do this for four years, make some money,
4年間こんなことをしてちょっとお金を稼いだり、
04:24
like I was having a family, and I would mature a bit,
家庭を築いたりしてちょっと成長しよう、と思いました
04:27
and maybe the business of neuroscience would mature a bit.
その間に脳科学業界もちょっとは成長するかな、と
04:31
Well, it took longer than four years. It's been about 16 years.
ただ4年なんて考えはあまくて、かれこれ16年
04:34
But I'm doing it now, and I'm going to tell you about it.
でも今はしていますからお話しします
04:37
So why should we have a good brain theory?
さて、なぜいい脳理論が必要なのだろうか?
04:39
Well, there's lots of reasons people do science.
人が科学に取り組む動機はいろいろあります
04:42
One is -- the most basic one is -- people like to know things.
まず最も基本的な理由は、人は学ぶことが好きで
04:45
We're curious, and we just go out and get knowledge, you know?
興味があればもっと知りたい
04:48
Why do we study ants? Well, it's interesting.
なぜアリについて研究するのか。それはおもしろいから
04:50
Maybe we'll learn something really useful about it, but it's interesting and fascinating.
役立つ知識だという以前に、興味深く、魅力的です
04:52
But sometimes, a science has some other attributes
でも、他に、非常に興味をそそられる
04:55
which makes it really, really interesting.
特質をもつ科学もあります
04:57
Sometimes a science will tell something about ourselves,
ときに科学の研究は人類について何かを
04:59
it'll tell us who we are.
明らかにします
05:02
Rarely, you know: evolution did this and Copernicus did this,
まれに、例えば進化やコペルニクスの発見は人類について
05:03
where we have a new understanding of who we are.
新知識を与えてくれました
05:06
And after all, we are our brains. My brain is talking to your brain.
人間の根本は脳なのです。今、私の脳があなたの脳に話しかけてます
05:08
Our bodies are hanging along for the ride, but my brain is talking to your brain.
体もくっついて来てるけど、会話をしてるのは私達の脳
05:12
And if we want to understand who we are and how we feel and perceive,
気持ちや知覚の仕組みについて本当に理解するためには
05:15
we really understand what brains are.
脳の徹底的な理解が必要です
05:18
Another thing is sometimes science
またときに科学はものすごい社会的貢献、
05:20
leads to really big societal benefits and technologies,
テクノロジーやビジネスの進化につながる
05:22
or businesses, or whatever, that come out of it. And this is one, too,
脳科学もその一例です
05:24
because when we understand how brains work, we're going to be able
というのは、脳の仕組みが分かれば
05:26
to build intelligent machines, and I think that's actually a good thing on the whole,
知能機械をつくることができるでしょう
05:29
and it's going to have tremendous benefits to society,
それは、基盤技術のように最終的には
05:32
just like a fundamental technology.
社会にすごい利益をもたらすと私は思ってます
05:34
So why don't we have a good theory of brains?
なぜいい脳理論がないのだろう?
05:36
And people have been working on it for 100 years.
もう100年前から研究されている課題にも関わらず
05:38
Well, let's first take a look at what normal science looks like.
まず一般に科学はどんなものでしょう
05:41
This is normal science.
普通の科学とはこんなものです
05:43
Normal science is a nice balance between theory and experimentalists.
普通の科学の場合、理論と実験がバランス良く存在してます
05:45
And so the theorist guys say, well, I think this is what's going on,
理論家が、こんなふうでしょう、と言ったら
05:49
and the experimentalist says, no, you're wrong.
実験主義者が、いや、違う、と却下する
05:51
And it goes back and forth, you know?
このように議論が行き来し続けるのです
05:53
This works in physics. This works in geology. But if this is normal science,
物理や地質学ならこれでいいけど
05:55
what does neuroscience look like? This is what neuroscience looks like.
これは普通の科学の話で、脳科学はこんな感じです
05:57
We have this mountain of data, which is anatomy, physiology and behavior.
解剖学、生理学、行動から成る山のようなデータがあります
06:00
You can't imagine how much detail we know about brains.
脳についてもう信じられないほど詳しく分かってます
06:05
There were 28,000 people who went to the neuroscience conference this year,
今年の脳科学会議の出席者数は2万8000人にのぼり
06:08
and every one of them is doing research in brains.
全員が脳について研究してるのです
06:12
A lot of data. But there's no theory. There's a little, wimpy box on top there.
データは膨大でも理論がない。一番上に貧弱な箱がありますね
06:14
And theory has not played a role in any sort of grand way in the neurosciences.
理論が脳科学に大きな影響を与えたことはまだないのです
06:18
And it's a real shame. Now why has this come about?
非常に残念です。なぜでしょう?
06:23
If you ask neuroscientists, why is this the state of affair,
脳科学者にこの質問をなげがけると
06:26
they'll first of all admit it. But if you ask them, they'll say,
とりあえず現状を認めます。でも
06:28
well, there's various reasons we don't have a good brain theory.
いい脳理論がない理由は様々だと言い訳します
06:31
Some people say, well, we don't still have enough data,
データ不足なんだ、まだ理解できないことが多いから
06:34
we need to get more information, there's all these things we don't know.
もっと情報が必要なのだ、と言ったりします
06:36
Well, I just told you there's so much data coming out your ears.
でもさっき言ったようにデータはあり余ってるのです
06:39
We have so much information, we don't even know how to begin to organize it.
情報がありすぎて、どう整理し始めるか考えつかないのに
06:42
What good is more going to do?
増やしてどうなる?
06:45
Maybe we'll be lucky and discover some magic thing, but I don't think so.
運良く魔法のデータでも見つかるとは思えません
06:47
This is actually a symptom of the fact that we just don't have a theory.
実はこの現状は理論がないということのしるしなのです
06:50
We don't need more data -- we need a good theory about it.
もうデータはいらない。必要なのはいい理論
06:53
Another one is sometimes people say, well, brains are so complex,
脳は複雑すぎるんだ、という人もいます
06:56
it'll take another 50 years.
あと50年はかかるだろう、と
06:59
I even think Chris said something like this yesterday.
クリスでさえ、昨日言ってたね
07:01
I'm not sure what you said, Chris, but something like,
君のせりふは正確に覚えてないけど
07:03
well, it's one of the most complicated things in the universe. That's not true.
脳はこの世で最も複雑だ、とか。でもそうじゃない
07:05
You're more complicated than your brain. You've got a brain.
人は脳より複雑さ。脳を持ってるんだから
07:08
And it's also, although the brain looks very complicated,
それに脳は複雑に見えるけど
07:10
things look complicated until you understand them.
何でも、理解するまでは複雑に見えるものなのです
07:12
That's always been the case. And so all we can say, well,
昔からそうでした。それに、私が興味を持ってる
07:15
my neocortex, which is the part of the brain I'm interested in, has 30 billion cells.
脳の大脳新皮質という部分にある細胞の数は300億だけど
07:18
But, you know what? It's very, very regular.
それはとっても規則的なのです
07:22
In fact, it looks like it's the same thing repeated over and over and over again.
同じものが何度も何度も繰り返されてるかのようで
07:24
It's not as complex as it looks. That's not the issue.
思うほど複雑じゃない。問題は別にあります
07:27
Some people say, brains can't understand brains.
脳は脳を理解できないという人もいます
07:30
Very Zen-like. Whoo. (Laughter)
うわー、非常に禅っぽいですね
07:32
You know,
(笑)
07:35
it sounds good, but why? I mean, what's the point?
聞こえはいいけど、何の役に立つの?
07:36
It's just a bunch of cells. You understand your liver.
脳も、たかが細胞の固まり。肝臓なら分かるでしょ
07:39
It's got a lot of cells in it too, right?
肝臓にも多数の細胞がありますよね
07:42
So, you know, I don't think there's anything to that.
だからそんな考えも無意味だと思います
07:44
And finally, some people say, well, you know,
そして最後に、自分が
07:46
I don't feel like a bunch of cells, you know. I'm conscious.
たかが細胞の固まりだなんて気がしない、という人もいます。意識があり、
07:48
I've got this experience, I'm in the world, you know.
今物事を経験しながら、この世に存在してるんだから
07:52
I can't be just a bunch of cells. Well, you know,
ただの細胞の固まりだなんてあり得ない、と
07:54
people used to believe there was a life force to be living,
昔、生命は目に見えない力に支えられているという考えがあったけど
07:56
and we now know that's really not true at all.
今、それは全くの嘘だったと分かってます
07:59
And there's really no evidence that says -- well, other than people
人の行動を細胞が実現するという発想への疑念以外
08:01
just have disbelief that cells can do what they do.
何の根拠もありません
08:04
And so, if some people have fallen into the pit of metaphysical dualism,
つまり何らかの形而上学的二元論にはまってる人もいて、
08:06
some really smart people, too, but we can reject all that.
とても頭のいい人もいるけど、全て却下しましょう
08:09
(Laughter)
(笑)
08:12
No, I'm going to tell you there's something else,
私が考える、他の、とても基本的なことについて
08:14
and it's really fundamental, and this is what it is:
話しましょう。つまり
08:17
there's another reason why we don't have a good brain theory,
脳についていい定理がないのは、
08:19
and it's because we have an intuitive, strongly-held,
直感的には正しいために堅く信じている
08:21
but incorrect assumption that has prevented us from seeing the answer.
誤解があって、それが答えを導く妨げになっているのです
08:24
There's something we believe that just, it's obvious, but it's wrong.
私達が信じていることで、明らかな間違いがあります
08:29
Now, there's a history of this in science and before I tell you what it is,
それがいったい何なのか話す前に、
08:32
I'm going to tell you a bit about the history of it in science.
科学史上でのその存在についてお話しましょう
08:36
You look at some other scientific revolutions,
その他の科学的革命を見てみると、
08:38
and this case, I'm talking about the solar system, that's Copernicus,
例えばコペルニクスの太陽系とか
08:40
Darwin's evolution, and tectonic plates, that's Wegener.
ダーウィンの進化やウェグナーの構造プレートなど
08:42
They all have a lot in common with brain science.
これらは全て脳科学と多くの共通点を持ってます
08:45
First of all, they had a lot of unexplained data. A lot of it.
第一に、説明できないデータが非常にたくさんありました
08:48
But it got more manageable once they had a theory.
でも理論が構築されてからずっと扱い易くなったのです
08:51
The best minds were stumped -- really, really smart people.
それまではものすごーく頭のいい人達さえ途方にくれていたのに
08:54
We're not smarter now than they were then.
私達はそのころの人達より利口なわけではないのです
08:57
It just turns out it's really hard to think of things,
ただ、何かについて本当に理解するのは難しいけれど
08:59
but once you've thought of them, it's kind of easy to understand it.
一度考えてみてしまえば、結構分かり易いものなのです
09:01
My daughters understood these three theories
私の娘達は幼稚園生になったころには
09:03
in their basic framework by the time they were in kindergarten.
これら三つの理論を基本的に理解していました
09:05
And now it's not that hard, you know, here's the apple, here's the orange,
例えば、リンゴとミカンを使って地球の公転を理解するのは
09:08
you know, the Earth goes around, that kind of stuff.
別に大して難しくないのです
09:11
Finally, another thing is the answer was there all along,
そして最後に、答えはずっと前から分かってたけど
09:14
but we kind of ignored it because of this obvious thing, and that's the thing.
ある明らかなことのためにみんな無視していたのです
09:16
It was an intuitive, strong-held belief that was wrong.
直感的には正しいために、堅く信じていたある誤解
09:19
In the case of the solar system, the idea that the Earth is spinning
太陽系の場合、地球が自転していて
09:22
and the surface of the Earth is going like a thousand miles an hour,
地表が時速1000マイルくらいで動き、
09:25
and the Earth is going through the solar system about a million miles an hour.
地球は太陽系を時速100万マイルくらいで動いています
09:28
This is lunacy. We all know the Earth isn't moving.
狂気的です。地球は動いてないと思ってますから
09:31
Do you feel like you're moving a thousand miles an hour?
時速100万マイルで動いてる気がしますか?
09:33
Of course not. You know, and someone who said,
もうちろんしませんよ
09:35
well, it was spinning around in space and it's so huge,
地球が宇宙で勢いよく回転してるとか言ったら、
09:37
they would lock you up, and that's what they did back then.
狂人扱いされます、昔はそうだったのです
09:39
(Laughter)
(笑)
09:41
So it was intuitive and obvious. Now what about evolution?
直感的には正しく、明らかでした。さて進化の場合は?
09:42
Evolution's the same thing. We taught our kids, well, the Bible says,
進化も同様。聖書によると神が全ての生き物を創造して
09:45
you know, God created all these species, cats are cats, dogs are dogs,
猫は猫、犬は犬、人は人、植物は植物、と
09:48
people are people, plants are plants, they don't change.
一生変わらないんだと子供達に教えました
09:50
Noah put them on the Ark in that order, blah, blah, blah. And, you know,
その順番でノアが箱船に乗せたから、かくかくしかじか
09:53
the fact is, if you believe in evolution, we all have a common ancestor,
でも実際には、もし進化を信じるとしたら、私達みんなに共通祖先がいて
09:57
and we all have a common ancestry with the plant in the lobby.
私達とロビーの植木にも共通祖先がいるはずなのです
10:01
This is what evolution tells us. And, it's true. It's kind of unbelievable.
これが進化です。信じ難いけどこれは事実なのです
10:04
And the same thing about tectonic plates, you know?
構造プレートについても同様
10:07
All the mountains and the continents are kind of floating around
山と大陸が地上に浮かんでる
10:10
on top of the Earth, you know? It's like, it doesn't make any sense.
意味不明ですよね
10:12
So what is the intuitive, but incorrect assumption,
それではその直感的には正しいようだけど
10:16
that's kept us from understanding brains?
脳の理解を妨げる誤解とは?
10:20
Now I'm going to tell it to you, and it's going to seem obvious that that is correct,
これから答えを説明しましょう
10:22
and that's the point, right? Then I'm going to have to make an argument
そしたら私はなぜ反対意見が
10:24
why you're incorrect about the other assumption.
間違っているのか議論しなければなりません
10:26
The intuitive but obvious thing is that somehow intelligence
その直感的には正しいけど明らかな誤解とは
10:28
is defined by behavior,
知性は行動によって定義されるということです
10:31
that we are intelligent because of the way that we do things
つまり、私達の知能の高さは知性的な行動によって
10:33
and the way we behave intelligently, and I'm going to tell you that's wrong.
定められる。私はこの考えが誤解だと言いたいのです
10:35
What it is is intelligence is defined by prediction.
実は知性は予測能力によって定義されます
10:38
And I'm going to work you through this in a few slides here,
これから見せるスライドとある例を通して
10:40
give you an example of what this means. Here's a system.
どういうことか詳しく説明しましょう。ここにシステムがあります
10:43
Engineers like to look at systems like this. Scientists like to look at systems like this.
工学者、科学者はシステムをこのように考えています
10:47
They say, well, we have a thing in a box, and we have its inputs and its outputs.
箱の中に何かがあって、入力と出力があるのです
10:50
The AI people said, well, the thing in the box is a programmable computer
人工知能の研究者いわく箱の中身はプログラム可能なコンピュータで、
10:53
because that's equivalent to a brain, and we'll feed it some inputs
脳に相当し、入力を提供すれば
10:56
and we'll get it to do something, have some behavior.
何らかの行動を示す
10:58
And Alan Turing defined the Turing test, which is essentially saying,
アラン チューリングはチューリング テストを定義することで
11:00
we'll know if something's intelligent if it behaves identical to a human.
人間同様の行動が確認できれば知性もつ物体だ、と説きました
11:03
A behavioral metric of what intelligence is,
こんな行動を基にした知能の測定方法を
11:06
and this has stuck in our minds for a long period of time.
私達は長いこと信頼してきました
11:09
Reality though, I call it real intelligence.
でも、実際には「真の知能」は
11:12
Real intelligence is built on something else.
別のものを基に構成されているのです
11:14
We experience the world through a sequence of patterns, and we store them,
人はこの世をパターンの時間的並びとして経験、記憶し、
11:16
and we recall them. And when we recall them, we match them up
後に思い出します。そして思い出すと同時に
11:20
against reality, and we're making predictions all the time.
現実と照らし合わせ、常に次の出来事を予測してます
11:23
It's an eternal metric. There's an eternal metric about us sort of saying,
つまり私達には永久の測定基準が存在し、常に
11:27
do we understand the world? Am I making predictions? And so on.
この世を理解し予測してるか、などと自問しているのです
11:30
You're all being intelligent right now, but you're not doing anything.
今何もしてないけど、あなた達は皆知性的ですよね
11:33
Maybe you're scratching yourself, or picking your nose,
体をかいて、鼻をほじってるかもしれないけど、
11:35
I don't know, but you're not doing anything right now,
今別に特別なことはしてません
11:37
but you're being intelligent; you're understanding what I'm saying.
でも知性的なのです。私の話を理解してます
11:39
Because you're intelligent and you speak English,
知能をもち英語を話すため、文末の言葉も
11:42
you know what word is at the end of this -- (Silence)
分かり(...)
11:44
sentence.
ます。
11:45
The word came into you, and you're making these predictions all the time.
次の言葉が自然と浮かぶように、常に予測をしてるのです
11:47
And then, what I'm saying is,
この永久的予測が
11:50
is that the eternal prediction is the output in the neocortex.
大脳新皮質の出力なのです
11:52
And that somehow, prediction leads to intelligent behavior.
そして予測は、何らかの方法で知性的行動につながります
11:54
And here's how that happens. Let's start with a non-intelligent brain.
どんな方法か、まず知能のない脳を見てみましょう
11:57
Well I'll argue a non-intelligent brain, we got hold of an old brain,
知能のない脳、昔の脳を手に入れたとします
12:00
and we're going to say it's like a non-mammal, like a reptile,
哺乳類でなくて、例えば爬虫類のもの
12:04
so I'll say, an alligator; we have an alligator.
そう、ワニの脳としましょう
12:07
And the alligator has some very sophisticated senses.
ワニは様々な洗練された感覚をもっています
12:09
It's got good eyes and ears and touch senses and so on,
するどい目と耳、触感、
12:12
a mouth and a nose. It has very complex behavior.
口と鼻。そして非常に複雑な行動を示します
12:15
It can run and hide. It has fears and emotions. It can eat you, you know.
逃げたり隠れたり、恐れたり興奮したりします。あなたを食べることだってできる
12:19
It can attack. It can do all kinds of stuff.
あなたを襲うかもしれない。いろんなことができます
12:23
But we don't consider the alligator very intelligent, not like in a human sort of way.
でも、ワニに、人間のような高い知能があると考えたりはしません
12:27
But it has all this complex behavior already.
既にこんなに複雑な行動を示すのに
12:32
Now, in evolution, what happened?
さて、進化の過程で何が起きたんだろう?
12:34
First thing that happened in evolution with mammals,
最初に哺乳類に起きた進化は
12:36
we started to develop a thing called the neocortex.
大脳新皮質の発達でした
12:39
And I'm going to represent the neocortex here,
大脳新皮質を、この昔の脳から
12:41
by this box that's sticking on top of the old brain.
突き出てる箱だとしましょう
12:43
Neocortex means new layer. It is a new layer on top of your brain.
大脳新皮質とは、新しい層つまり脳の表面の新しい脳です
12:45
If you don't know it, it's the wrinkly thing on the top of your head that,
知らない人のために説明しますが、頭のてっぺんの
12:48
it's got wrinkly because it got shoved in there and doesn't fit.
大きすぎて無理に入れたらしわくちゃになった、あれです
12:51
(Laughter)
(笑)
12:54
No, really, that's what it is. It's about the size of a table napkin.
本気ですよ。テーブル ナプキンくらいの大きさで、
12:55
And it doesn't fit, so it gets all wrinkly. Now look at how I've drawn this here.
大きさが合わずくしゃっとなったんです。さあこれを見て
12:57
The old brain is still there. You still have that alligator brain.
昔の、脳はまだ残ってます。ワニの脳はまだあるのです
13:00
You do. It's your emotional brain.
そう、これは感情的な脳
13:04
It's all those things, and all those gut reactions you have.
本能的な反応などの根源なのです
13:06
And on top of it, we have this memory system called the neocortex.
その上に大脳新皮質という記憶装置があります
13:09
And the memory system is sitting over the sensory part of the brain.
この記憶装置は脳の感覚を司る部分の上に乗っかってます
13:12
And so as the sensory input comes in and feeds from the old brain,
感覚の入力を受けた昔の脳からの出力は
13:16
it also goes up into the neocortex. And the neocortex is just memorizing.
大脳新皮質にも上がって来ます。そして大脳新皮質はただ記憶してるのです
13:19
It's sitting there saying, ah, I'm going to memorize all the things that are going on:
そこで大脳新皮質は、ああ、どこに行き、誰と会い、何を聞いたかとか
13:23
where I've been, people I've seen, things I've heard, and so on.
全部記憶しよう、と考えます
13:27
And in the future, when it sees something similar to that again,
そして将来もし似た環境、全く同じような環境で、
13:29
so in a similar environment, or the exact same environment,
似たものを見かけたら、再生します
13:33
it'll play it back. It'll start playing it back.
過去の経験を再生しだすのです
13:36
Oh, I've been here before. And when you've been here before,
ああ、来たことある。次はこんなことが起きるぞ
13:38
this happened next. It allows you to predict the future.
未来を予測することを可能にします
13:40
It allows you to, literally it feeds back the signals into your brain;
本当に予測できるのです。過去の信号が脳に戻ってくることで
13:43
they'll let you see what's going to happen next,
次に起こることが分かり
13:47
will let you hear the word "sentence" before I said it.
声に出す前に、文末の言葉が聞こえたのです
13:49
And it's this feeding back into the old brain
これは古い方の脳へ出力が返っているということで
13:52
that'll allow you to make very more intelligent decisions.
これによって、より知性的な判断が可能になります
13:55
This is the most important slide of my talk, so I'll dwell on it a little bit.
このスライドは一番重要だから入念に説明します
13:58
And so, all the time you say, oh, I can predict the things.
常に、ああ、予測できるな、と考えてるのです
14:01
And if you're a rat and you go through a maze, and then you learn the maze,
ネズミの場合、迷路を通過すれば道を覚え、
14:05
the next time you're in a maze, you have the same behavior,
次にまた迷路に出くわしたら同じ行動をとります
14:08
but all of a sudden, you're smarter
でも途端に、より利口になるのです
14:10
because you say, oh, I recognize this maze, I know which way to go,
つまり、この迷路覚えてるぞ、行先は分かるぞ
14:12
I've been here before, I can envision the future. And that's what it's doing.
前に来たから予想できるぞと。実際その通りです
14:15
In humans -- by the way, this is true for all mammals;
これは全ての哺乳類に関して同様に言えるけど
14:18
it's true for other mammals -- and in humans, it got a lot worse.
人間の場合はずっとひどくなったのです
14:21
In humans, we actually developed the front part of the neocortex
人間の場合、大脳新皮質の前部が発達して
14:23
called the anterior part of the neocortex. And nature did a little trick.
自然の女神はちょっとしたいたずらをしました
14:26
It copied the posterior part, the back part, which is sensory,
脳の後部、感覚の部分をコピーして
14:30
and put it in the front part.
前部にくっつけたのです
14:32
And humans uniquely have the same mechanism on the front,
人間だけは後部と同じ仕組みを前部に持ち、
14:34
but we use it for motor control.
運動操作のために使っています
14:36
So we are now able to make very sophisticated motor planning, things like that.
だから人は今、複雑な動作を計画し実行できます
14:38
I don't have time to get into all this, but if you want to understand how a brain works,
詳しく解説する時間はないけど、脳の仕組みを理解したいなら
14:41
you have to understand how the first part of the mammalian neocortex works,
哺乳類の大脳新皮質がどのようにパターンを記憶し、予測するかを
14:44
how it is we store patterns and make predictions.
理解しなければならないのです
14:47
So let me give you a few examples of predictions.
それでは「予測」の例をいくつか挙げてみましょう
14:49
I already said the word "sentence." In music,
さっき話した文中の言葉もその一例です
14:52
if you've heard a song before, if you heard Jill sing those songs before,
音楽の場合、例えばジルの曲を聞いたことがあったら
14:54
when she sings them, the next note pops into your head already --
彼女が歌ってるうちから次の音が浮かんできます
14:57
you anticipate it as you're going. If it was an album of music,
曲を聞きながら次の音を予想します
15:00
the end of one album, the next song pops into your head.
アルバムでは曲の終わりに次の曲が頭に浮かんできます
15:02
And these things happen all the time. You're making these predictions.
常にこうやって予測してるのです
15:05
I have this thing called the altered door thought experiment.
「改造されたドアの記憶」という実験があります
15:07
And the altered door thought experiment says, you have a door at home,
この実験によるとこんなことが言えます
15:10
and when you're here, I'm changing it, I've got a guy
あなたがここにいる間に、あなたの家のドアを改造します
15:13
back at your house right now, moving the door around,
今ある男があなたの家の
15:16
and they're going to take your doorknob and move it over two inches.
ドアノブを5センチ移動させてる
15:18
And when you go home tonight, you're going to put your hand out there,
今夜あなたが帰宅したら
15:20
and you're going to reach for the doorknob and you're going to notice
ドアノブへ手を伸ばしては
15:22
it's in the wrong spot, and you'll go, whoa, something happened.
位置が違うと気付き、何か違うぞ、と思うでしょう
15:24
It may take a second to figure out what it was, but something happened.
すぐにはなぜか分からないけど、何か違う
15:27
Now I could change your doorknob in other ways.
他にも、ドアノブのサイズを変えたり、
15:29
I can make it larger or smaller, I can change its brass to silver,
素材を銀に変えたりしてもいいのです
15:31
I could make it a lever. I can change your door, put colors on;
ドアノブをレバーにしたり、ドアの色を変えたり
15:33
I can put windows in. I can change a thousand things about your door,
窓を付けたり、どんな違いだとしても
15:35
and in the two seconds you take to open your door,
ドアを開けるために必要な数秒間で
15:38
you're going to notice that something has changed.
何か違うということに気付くでしょう
15:40
Now, the engineering approach to this, the AI approach to this,
さて、工学的、人工知能的に検証する場合
15:43
is to build a door database. It has all the door attributes.
ドアの特徴を全部含むデータベースを作成します
15:45
And as you go up to the door, you know, let's check them off one at time.
ドアに近づいては一つずつ特徴を確認していく、
15:48
Door, door, door, you know, color, you know what I'm saying.
ドアの...色とか...
15:51
We don't do that. Your brain doesn't do that.
でも私達の脳はそんなことはしません
15:53
What your brain is doing is making constant predictions all the time
実際には、脳は常時、
15:55
about what is going to happen in your environment.
次に何が起こるのか予測してるのです
15:57
As I put my hand on this table, I expect to feel it stop.
テーブルに手をあてれば、手が止まると分かります
15:59
When I walk, every step, if I missed it by an eighth of an inch,
歩いているとき、もし少しでもずれたら
16:02
I'll know something has changed.
何か違う、と気付きます
16:05
You're constantly making predictions about your environment.
常に周りの環境について予測してるのです
16:07
I'll talk about vision here briefly. This is a picture of a woman.
視覚について簡単に説明しましょう。女性の絵です
16:09
And when you look at people, your eyes are caught
他人を眺めるとき、目は2、3秒で
16:12
over at two to three times a second.
移転してます
16:14
You're not aware of this, but your eyes are always moving.
無意識のうちに、目は常に動いているのです
16:15
And so when you look at someone's face,
だから他人の顔を眺めるときは
16:17
you'd typically go from eye to eye to eye to nose to mouth.
通常目、目、鼻、口、と焦点を移転させます
16:19
Now, when your eye moves from eye to eye,
さて、もし2つの目の間に
16:21
if there was something else there like, a nose,
別のものがあった場合
16:23
you'd see a nose where an eye is supposed to be,
例えば目があるはずのとこに鼻があったとしたら
16:25
and you'd go, oh shit, you know --
あれ、え~、と思うでしょう
16:27
(Laughter)
(笑)
16:30
There's something wrong about this person.
何か変だぞ、と
16:31
And that's because you're making a prediction.
それは、予測をしてるからなのです
16:33
It's not like you just look over there and say, what am I seeing now?
そっちに目を向けてから、今何を見てるのかな、
16:35
A nose, that's okay. No, you have an expectation of what you're going to see.
鼻だ、よし、ではなく、何を見るか予測しています
16:37
(Laughter)
(笑)
16:40
Every single moment. And finally, let's think about how we test intelligence.
そう、常に。さあ、最後に知能の測定方法について考えてみましょう
16:41
We test it by prediction. What is the next word in this, you know?
予測能力を指標とします。次にくる言葉を聞いたり
16:45
This is to this as this is to this. What is the next number in this sentence?
これらと同じ関係がこれらなら、次の数字は?とか
16:48
Here's three visions of an object.
物体の絵が三つある。四つ目は?とか
16:51
What's the fourth one? That's how we test it. It's all about prediction.
これが予測能力の測定法です。これが予測です
16:53
So what is the recipe for brain theory?
それではいい脳理論を築く秘訣は?
16:57
First of all, we have to have the right framework.
まず第一に、ふさわしい枠組みが必要です
17:00
And the framework is a memory framework,
それは記憶の枠組みです
17:03
not a computation or behavior framework. It's a memory framework.
計算とか行動ではなく、記憶
17:05
How do you store and recall these sequences or patterns? It's spatio-temporal patterns.
順序やパターンをどう記憶し思い出すか?それは時空間的なパターンを使うのです
17:07
Then, if in that framework, you take a bunch of theoreticians.
そしてこの枠組みの次は理論学者
17:11
Now biologists generally are not good theoreticians.
一般的に生物学者は理論に強くない
17:14
It's not always true, but in general, there's not a good history of theory in biology.
必ずそうとは限らないけど、生物学にはいい理論がないのです
17:16
So I found the best people to work with are physicists,
だから経験からいうと物理学者、工学者、数学者など
17:20
engineers and mathematicians, who tend to think algorithmically.
アルゴリズム的な考えをする傾向のある人達が一番です
17:23
Then they have to learn the anatomy, and they've got to learn the physiology.
次に彼らに解剖学、そして生理学を学ばせます
17:26
You have to make these theories very realistic in anatomical terms.
この理論に用いられる生体用語はとても現実的でなければいけない
17:29
Anyone who gets up and tells you their theory about how the brain works
脳理論と言いながら、脳の内部がどうやってつながっていて
17:33
and doesn't tell you exactly how it's working in the brain
動作しているのかを厳密に説明しないものは
17:37
and how the wiring works in the brain, it is not a theory.
理論とはいえません
17:39
And that's what we're doing at the Redwood Neuroscience Institute.
レッド ウッド神経科学研究所では、まさにそういうことに注目してます
17:41
I would love to have more time to tell you we're making fantastic progress in this thing,
研究は、ものすごい勢いで進歩してます
17:44
and I expect to be back up on this stage,
残りの時間では無理でけど、また近いうちにこの舞台で、
17:48
maybe this will be some other time in the not too distant future and tell you about it.
このことについてお話したいですね
17:50
I'm really, really excited. This is not going to take 50 years at all.
本当に楽しみです。全然50年もかからないでしょう
17:52
So what will brain theory look like?
さて、どんな脳理論になるのでしょうか?
17:55
First of all, it's going to be a theory about memory.
第一に、記憶中心の理論でしょう
17:57
Not like computer memory. It's not at all like computer memory.
でもコンピュータのメモリみたいのものではありませんよ
17:59
It's very, very different. And it's a memory of these very
メモリとは全然違って、
18:02
high-dimensional patterns, like the things that come from your eyes.
目から入力されるような、高次元のパターンを記憶します
18:04
It's also memory of sequences.
さらに順序も記憶します
18:07
You cannot learn or recall anything outside of a sequence.
順序なしで、記憶や再生はできません
18:09
A song must be heard in sequence over time,
曲を記憶するときは、それを順に耳にし
18:11
and you must play it back in sequence over time.
順に再生しなければなりません
18:14
And these sequences are auto-associatively recalled, so if I see something,
こういう順序を伴うパターンは自動的に連想されるから
18:17
I hear something, it reminds me of it, and then it plays back automatically.
何か見聞きすれば、機械的に関連するものを思い出したり
18:20
It's an automatic playback. And prediction of future inputs is the desired output.
自動的記憶再生するのです。そして次の入力に対する予測こそ望ましい出力なのです
18:23
And as I said, the theory must be biologically accurate,
また、さっき言ったようにその理論は生物学の面で正確で
18:27
it must be testable, and you must be able to build it.
試したり、築き上げられたりできなくちゃいけません
18:30
If you don't build it, you don't understand it. So, one more slide here.
理論を築き上げなければ、理解することはできないのです。さて、スライドをもう一枚
18:32
What is this going to result in? Are we going to really build intelligent machines?
これはどんなことをもたらすのでしょうか? 知能機械をつくるとか?
18:36
Absolutely. And it's going to be different than people think.
もちろん。でも人が想像してるのとは違います
18:40
No doubt that it's going to happen, in my mind.
創作に成功することは間違いないと思うけど
18:44
First of all, it's going to be built up, we're going to build the stuff out of silicon.
どう違うかというと、第一に、材料はシリコン
18:47
The same techniques we use for building silicon computer memories,
シリコンでコンピュータのメモリをつくるのと同じ技術を
18:51
we can use for here.
使用すればいい
18:54
But they're very different types of memories.
ただし、全く違う種類のメモリになります
18:55
And we're going to attach these memories to sensors,
メモリをセンサーにつなげれば
18:57
and the sensors will experience real-live, real-world data,
センサーはリアルタイムに外の世界のデータを感知し、
18:59
and these things are going to learn about their environment.
周囲の環境について学びます
19:02
Now it's very unlikely the first things you're going to see are like robots.
最初からロボットみたいなものが創作される可能性は低いでしょう
19:04
Not that robots aren't useful and people can build robots.
ロボットも役に立たなくもなくて、つくる技術はあるけど
19:07
But the robotics part is the hardest part. That's the old brain. That's really hard.
ロボットっぼいところは昔の脳の部分だから、とても複雑で
19:10
The new brain is actually kind of easier than the old brain.
新しい脳は昔の脳よりずっと単純です
19:14
So the first thing we're going to do are the things that don't require a lot of robotics.
だから、あまりロボットっぽさのないところから始めます
19:16
So you're not going to see C-3PO.
C-3POみたいなのはずっと先です
19:19
You're going to more see things like, you know, intelligent cars
初めは知能的な自動車などが開発されるでしょう
19:21
that really understand what traffic is and what driving is
例えば車の往来や運転を理解していて、
19:23
and have learned that certain types of cars with the blinkers on for half a minute
方向指示器を30秒点滅させている車は角を曲がらない
19:26
probably aren't going to turn, things like that.
などと予測できる車とか
19:29
(Laughter)
(笑)
19:31
We can also do intelligent security systems.
あとは知能的な警備システムとか
19:32
Anywhere where we're basically using our brain, but not doing a lot of mechanics.
このように脳を使ってるけど機械的なことをしていない領域で
19:34
Those are the things that are going to happen first.
まず適用されだすでしょう
19:38
But ultimately, the world's the limit here.
でも最終的には、適応領域の限界はありせん
19:40
I don't know how this is going to turn out.
どんな結果につながるかはまだ分からないのです
19:42
I know a lot of people who invented the microprocessor
マイクロ プロセッサーを発明した人達も
19:44
and if you talk to them, they knew what they were doing was really significant,
何か重大なものをつくっているとは分かっていたけど
19:46
but they didn't really know what was going to happen.
発明によって何が起こるかは知らなかったのです
19:51
They couldn't anticipate cell phones and the Internet and all this kind of stuff.
電卓や信号機制御装置ぐらいはできると考えていましたが
19:54
They just knew like, hey, they were going to build calculators
携帯電話やインターネットは予想外でした
19:59
and traffic light controllers. But it's going to be big.
とにかくすごいものができるぞ、と
20:01
In the same way, this is like brain science and these memories
同様に、脳科学と今お話したようなメモリは
20:03
are going to be a very fundamental technology, and it's going to lead
基盤技術となり、今後100年の間に
20:06
to very unbelievable changes in the next 100 years.
信じられないような変化をもたらすでしょう
20:09
And I'm most excited about how we're going to use them in science.
一番楽しみなのは、科学の領域でどのように使用するか、ということです
20:12
So I think that's all my time, I'm over it, and I'm going to end my talk
さてもう時間切れだから、ここでこのトークは
20:16
right there.
おしまいにしましょう
20:19
Translated by Natsu Fukui
Reviewed by Satoshi Tatsuhara

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About the speaker:

Jeff Hawkins - Computer designer, brain researcher
Jeff Hawkins pioneered the development of PDAs such as the Palm and Treo. Now he's trying to understand how the human brain really works, and adapt its method -- which he describes as a deep system for storing memory -- to create new kinds of computers and tools.

Why you should listen

Jeff Hawkins' Palm PDA became such a widely used productivity tool during the 1990s that some fanatical users claimed it replaced their brains. But Hawkins' deepest interest was in the brain itself. So after the success of the Palm and Treo, which he brought to market at Handspring, Hawkins delved into brain research at the Redwood Center for Theoretical Neuroscience in Berkeley, Calif., and a new company called Numenta.

Hawkins' dual goal is to achieve an understanding of how the human brain actually works -- and then develop software to mimic its functionality, delivering true artificial intelligence. In his book On Intelligence (2004) he lays out his compelling, controversial theory: Contrary to popular AI wisdom, the human neocortex doesn't work like a processor; rather, it relies on a memory system that stores and plays back experiences to help us predict, intelligently, what will happen next. He thinks that "hierarchical temporal memory" computer platforms, which mimic this functionality (and which Numenta might pioneer), could enable groundbreaking new applications that could powerfully extend human intelligence.

More profile about the speaker
Jeff Hawkins | Speaker | TED.com