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We have historical records that allow us to know how the ancient Greeks dressed, how they lived, how they fought ... but how did they think?
One natural idea is that the deepest aspects of human thought -- our ability to imagine, to be conscious, to dream -- have always been the same. Another possibility is that the social transformations that have shaped our culture may have also changed the structural columns of human thought.
We may all have different opinions about this. Actually, it's a long-standing philosophical debate. But is this question even amenable to science?
Here I'd like to propose that in the same way we can reconstruct how the ancient Greek cities looked just based on a few bricks, that the writings of a culture are the archaeological records, the fossils, of human thought.
And in fact, doing some form of psychological analysis of some of the most ancient books of human culture, Julian Jaynes came up in the '70s with a very wild and radical hypothesis: that only 3,000 years ago, humans were what today we would call schizophrenics. And he made this claim based on the fact that the first humans described in these books behaved consistently, in different traditions and in different places of the world, as if they were hearing and obeying voices that they perceived as coming from the Gods, or from the muses ... what today we would call hallucinations. And only then, as time went on, they began to recognize that they were the creators, the owners of these inner voices. And with this, they gained introspection: the ability to think about their own thoughts.
So Jaynes's theory is that consciousness, at least in the way we perceive it today, where we feel that we are the pilots of our own existence -- is a quite recent cultural development. And this theory is quite spectacular, but it has an obvious problem which is that it's built on just a few and very specific examples. So the question is whether the theory that introspection built up in human history only about 3,000 years ago can be examined in a quantitative and objective manner.
And the problem of how to go about this is quite obvious. It's not like Plato woke up one day and then he wrote, "Hello, I'm Plato, and as of today, I have a fully introspective consciousness."
(Laughter)
And this tells us actually what is the essence of the problem. We need to find the emergence of a concept that's never said. The word introspection does not appear a single time in the books we want to analyze.
So our way to solve this is to build the space of words. This is a huge space that contains all words in such a way that the distance between any two of them is indicative of how closely related they are. So for instance, you want the words "dog" and "cat" to be very close together, but the words "grapefruit" and "logarithm" to be very far away. And this has to be true for any two words within the space.
And there are different ways that we can construct the space of words. One is just asking the experts, a bit like we do with dictionaries. Another possibility is following the simple assumption that when two words are related, they tend to appear in the same sentences, in the same paragraphs, in the same documents, more often than would be expected just by pure chance. And this simple hypothesis, this simple method, with some computational tricks that have to do with the fact that this is a very complex and high-dimensional space, turns out to be quite effective.
And just to give you a flavor of how well this works, this is the result we get when we analyze this for some familiar words. And you can see first that words automatically organize into semantic neighborhoods. So you get the fruits, the body parts, the computer parts, the scientific terms and so on.
The algorithm also identifies that we organize concepts in a hierarchy. So for instance, you can see that the scientific terms break down into two subcategories of the astronomic and the physics terms. And then there are very fine things. For instance, the word astronomy, which seems a bit bizarre where it is, is actually exactly where it should be, between what it is, an actual science, and between what it describes, the astronomical terms.
And we could go on and on with this. Actually, if you stare at this for a while, and you just build random trajectories, you will see that it actually feels a bit like doing poetry. And this is because, in a way, walking in this space is like walking in the mind.
And the last thing is that this algorithm also identifies what are our intuitions, of which words should lead in the neighborhood of introspection. So for instance, words such as "self," "guilt," "reason," "emotion," are very close to "introspection," but other words, such as "red," "football," "candle," "banana," are just very far away.
And so once we've built the space, the question of the history of introspection, or of the history of any concept which before could seem abstract and somehow vague, becomes concrete -- becomes amenable to quantitative science.
All that we have to do is take the books, we digitize them, and we take this stream of words as a trajectory and project them into the space, and then we ask whether this trajectory spends significant time circling closely to the concept of introspection.
And with this, we could analyze the history of introspection in the ancient Greek tradition, for which we have the best available written record. So what we did is we took all the books -- we just ordered them by time -- for each book we take the words and we project them to the space, and then we ask for each word how close it is to introspection, and we just average that. And then we ask whether, as time goes on and on, these books get closer, and closer and closer to the concept of introspection.
And this is exactly what happens in the ancient Greek tradition. So you can see that for the oldest books in the Homeric tradition, there is a small increase with books getting closer to introspection. But about four centuries before Christ, this starts ramping up very rapidly to an almost five-fold increase of books getting closer, and closer and closer to the concept of introspection. And one of the nice things about this is that now we can ask whether this is also true in a different, independent tradition.
So we just ran this same analysis on the Judeo-Christian tradition, and we got virtually the same pattern. Again, you see a small increase for the oldest books in the Old Testament, and then it increases much more rapidly in the new books of the New Testament. And then we get the peak of introspection in "The Confessions of Saint Augustine," about four centuries after Christ. And this was very important, because Saint Augustine had been recognized by scholars, philologists, historians, as one of the founders of introspection. Actually, some believe him to be the father of modern psychology.
So our algorithm, which has the virtue of being quantitative, of being objective, and of course of being extremely fast -- it just runs in a fraction of a second -- can capture some of the most important conclusions of this long tradition of investigation. And this is in a way one of the beauties of science, which is that now this idea can be translated and generalized to a whole lot of different domains.
So in the same way that we asked about the past of human consciousness, maybe the most challenging question we can pose to ourselves is whether this can tell us something about the future of our own consciousness. To put it more precisely, whether the words we say today can tell us something of where our minds will be in a few days, in a few months or a few years from now.
And in the same way many of us are now wearing sensors that detect our heart rate, our respiration, our genes, on the hopes that this may help us prevent diseases, we can ask whether monitoring and analyzing the words we speak, we tweet, we email, we write, can tell us ahead of time whether something may go wrong with our minds. And with Guillermo Cecchi, who has been my brother in this adventure, we took on this task. And we did so by analyzing the recorded speech of 34 young people who were at a high risk of developing schizophrenia.
And so what we did is, we measured speech at day one, and then we asked whether the properties of the speech could predict, within a window of almost three years, the future development of psychosis. But despite our hopes, we got failure after failure. There was just not enough information in semantics to predict the future organization of the mind. It was good enough to distinguish between a group of schizophrenics and a control group, a bit like we had done for the ancient texts, but not to predict the future onset of psychosis.
But then we realized that maybe the most important thing was not so much what they were saying, but how they were saying it. More specifically, it was not in which semantic neighborhoods the words were, but how far and fast they jumped from one semantic neighborhood to the other one. And so we came up with this measure, which we termed semantic coherence, which essentially measures the persistence of speech within one semantic topic, within one semantic category.
And it turned out to be that for this group of 34 people, the algorithm based on semantic coherence could predict, with 100 percent accuracy, who developed psychosis and who will not. And this was something that could not be achieved -- not even close -- with all the other existing clinical measures.
And I remember vividly, while I was working on this, I was sitting at my computer and I saw a bunch of tweets by Polo -- Polo had been my first student back in Buenos Aires, and at the time he was living in New York. And there was something in this tweets -- I could not tell exactly what because nothing was said explicitly -- but I got this strong hunch, this strong intuition, that something was going wrong. So I picked up the phone, and I called Polo, and in fact he was not feeling well. And this simple fact, that reading in between the lines, I could sense, through words, his feelings, was a simple, but very effective way to help.
What I tell you today is that we're getting close to understanding how we can convert this intuition that we all have, that we all share, into an algorithm. And in doing so, we may be seeing in the future a very different form of mental health, based on objective, quantitative and automated analysis of the words we write, of the words we say.
Gracias.
(Applause)
我們有歷史紀錄可循,可以讓我們知道 古希臘人如何穿著、 如何生活、 如何打仗... 但他們如何思考呢?
有一個很自然的方法就是, 去探索人類最深層的想法—— 我們的想像力、 自覺力、 夢想力、 是否是一樣的。 另一種可能是, 去探索造就我們文化的社會變革, 這些變革也許就是 改變人類想法的主要因素。
對這一點,大家或許有不同的看法。 實際上,這是一個存在已久的哲學辯論。 究竟這個問題是否可以 經由科學來處理?
我的建議是 如同僅藉由一些磚頭, 我們得以重建希臘古都的外貌, 也可用同樣的方式, 藉由一些文化作品、建築歷史、 化石,來了解人類的想法。
而實際上, 因為做了一些 人類古老文化書籍的心理分析, 裘利安.傑尼斯在70年代, 發表了一個相當大膽激進的假說: 他說,3000年前的人類, 是我們現在俗稱的 「精神分裂症患者」。 他會如此主張的原因是, 在世界各地不同的傳統及地方, 這些書籍裡面所描述的人類行為 似乎不約而同地都會服從 他們認為是從神祗那邊傳來的聲音。 而如今,我們會稱之為 「幻聽」或「幻覺」。 隨著時間的洗禮, 他們開始認知到 那些聲音是他們自己創造的, 他們就是那些內在聲音的主人。 有了這樣的認知,他們學會了自省: 一種反思自己想法的能力。
所以傑尼斯對「意識」的理論就是, 至少現今我們覺察到「意識」、 感受到我們自己就是 人生導師的體悟 是相當近代的文化發展。 這理論相當特別, 但它有一個很明顯的問題就是, 它是建立在極少又特定的案例上。 所以問題是, 3000年來人類才建立起 自省能力的這個理論, 是否可以經得起「量化」 且「客觀」的考驗。
至於要如何做的問題, 也是相當簡單明瞭。 但我的意思並非,比如, 柏拉圖有一天突然醒來說, 「哈囉!我是柏拉圖, 我今天,擁有完整的自省意識了」 那樣的簡單而已。
(笑聲)
而這告訴我們,我們要找出 問題的本質為何。 我們必須找到從來沒有被 談論過的概念。 「自省」這個字, 在這些書本中從未出現過一次。
所以為了解決這個問題, 我們要建立一個文字的空間。 在這個大空間裡, 包含了相當多的字, 用這種方式,可以量測出 兩個字彼此之間的 關聯性程度。 舉個例子, 你會想,「狗」、「貓」 應該是比較有關聯性的, 但「葡萄柚」和「對數」 就沒甚麼關聯了。 而在這個空間裡的任何兩個字, 都必須是可以被量測出來的。
而我們有很多方式 可以建立起這些字的空間架構, 方法一,是只要請教專家就行了, 有點類似查字典。 另一個可行的方法是, 當兩個字出現關聯性時, 去追蹤它們的預設狀況, 它們可能會出現在同一句、 同一段落、 或同一文件中, 多於「偶然」地出現。 在這個簡單的前提下, 這個單純且帶有運算技巧 的方法必須好用, 而這個複雜且高維度的空間, 事後證明,相當有效。
向各位介紹一下,它多有效, 我們分析了一些經常用到的字, 首先你可以看到, 這些詞彙會自動地歸納成 語義相近的相鄰群組, 所以你可看到,水果跟身體部位, 電腦與科學字彙等等。
演算法也可以把我們要 整理的概念分門別類出來。 舉個例子, 你可以看到,科學的字彙 被拆解成兩個子類, 分別是太空與物理的詞彙。 然後你會發現一件好玩的事, 舉個例子,「天文學」這個詞彙, 它應該擺的位置 與它現在的位置 好像不太搭嘎, 它現在介於真實科學與 天文學之間,偏向科學的位置, 而它自己卻是一個天文學的字彙。
我們可以持續尋找 其它類似的情況。 實際上,如果你盯著這些字一陣子, 然後隨機搭配連結一下這些字, 你會覺得好像自己在吟詩。 那是因為,在某種程度上, 在這些空間字彙裡漫遊, 就像是在腦海中吟詩一樣。
最後, 演算法也能辨識出人類的直覺字彙, 並歸納到內省的相鄰字彙中。 舉個例子, 像是自我、內疚、理由、情緒 與內省相關的字彙非常接近, 但其它的字, 像是,紅色、足球、蠟燭、香蕉 就差很遠了。
所以一旦我們建立起 這樣的詞彙空間, 有關於內省的歷史, 有關與任何概念的歷史, 以前被認為是抽象或是有點模糊的字彙, 都可以變成紮紮實實 可以被量化的科學。
而我們要做的就是, 拿起這些書, 把它們數位化, 然後把這些字,像子彈一樣 射到這些字彙空間裡面, 然後我們問電腦, 這些字彙所行經的軌跡 花了多少的時間 才達到內省概念的字彙中。
有了這些數據, 我們就可以分析古希臘傳統中, 有關於內省的歷史, 因為有著最完整的文字記錄。 所以,我們先把這些書, 按照時間排列, 然後把這些字 投射到字彙空間裡面, 然後我們問電腦,這些字 與內省有多少的相關性, 再把它們平均起來。 然後,我們不斷地問電腦問題, 這些書就會 越來越接近內省的概念。
而這正是古希臘所發生的事。 各位可以看到在 荷馬時代最古老的書籍, 與內省的相關性只有一點點。 但約在西元前400年左右, 快速成長了五倍, 這些書與內省的概念 越來越接近。 最棒的是, 我們可以問電腦, 在不同的、獨立的傳統文化中, 是否也有一樣的現象。
所以,我們用同樣的方法, 分析了傳統猶太基督教的書籍, 也得到了類似的趨勢。 在最古老的舊約聖經中, 你可以看到它緩慢地增加, 之後在新約聖經中, 它快速地增長, 大約西元400年,聖奧古斯丁的《懺悔錄》 內省達到了最高峰。 這個方法相當重要, 因為聖奧古斯丁已經被多位學者、 心理學家、歷史學家公認為 是內省的創始人之一。 有些人認為他是現代心理學之父。
所以,我們演算法的優點 不僅可以量化、 而且客觀、 當然速度也相當快—— 幾秒就可以跑完—— 並捕捉到若使用傳統方法 必須費長時間調查 才能抓到的一些重點。 這也是科學美好的地方, 它可以可以解讀、歸納這想法, 然後廣泛應用在許多不同的領域上。
或許,最具挑戰性的問題是, 我們用電腦來分析過去的 自我意識發展的方法, 是不是亦可以告訴我們 自我意識的未來趨向呢? 更精確地說, 我們現在說的話, 是否可以告訴我們 接下來的幾天、幾個月或幾年後, 我們的心智會達到什樣的情況。
同樣的方式,我們現在很多人 都使用穿戴式偵測器, 可以偵測我們的心跳、 呼吸、 基因, 讓我們可以預防疾病的發生, 我們是否已可以藉由 偵測分析我們所說的話、 推的文、郵寄的信、寫的文字, 來提前告訴我們,我們的心智 可能要發生問題了? 我跟我的兄弟,吉列爾莫.切基, 扛起了這項任務。 我們紀錄分析了 34 位年輕人的談話。 他們過去曾經是罹患 精神分裂症的高風險族群。
我們測量了他們第一天的談話, 然後問電腦,從他們的話中, 是否可以預測出, 未來三年內, 他們會不會精神錯亂。 但我們大失所望, 一次又一次的失敗。 因為沒有足夠的語義資訊 來預測未來的心智發展。 它在分辨精神病患及控制組上 已經有足夠的能力, 因為這有點像我們之前 做古文字的分析, 但沒辦法預測未來 精神錯亂的發病。
後來我們了解到, 也許最重要的關鍵 不是他們說了甚麼, 而是他們怎麼說。 更精確地說, 不是他們說的「話」落在哪個 語義相近的群組裡, 而是他們說話的「方式」 是否會在這幾個語義相近的群組裡 快速地跳來跳去。 所以我們想出了一個 叫做「語義連貫性」的量測方法, 本質上就是測量談話的持續性 是否會落在同一個 語義主題或類別上。
結果顯示,剛剛的 34 位年輕人, 透過這個語義連貫性演算法, 預測誰會精神錯亂的正確率 達到百分之百。 目前臨床上所有測量方式 都無法達到、或接近這個數字。
在我做這項研究的時候, 清楚地記得一件事, 當時我坐在電腦前面, 看到之前我回到布宜諾斯艾利斯的第一個學生 ——保羅,傳了一堆信息給我, 當時他住在紐約。 我發現訊息不太對勁—— 雖然我講不出個所以然來, 因為他寫得不清不楚—— 但我有一個強烈的直覺, 一定是出事了。 所以,我打電話給保羅, 沒錯,他當時感覺不太舒服。 用這樣一個單純的辨認方式, 從他的字裡行間, 我可以隱約感受到他的感覺, 並在第一時間有效地幫助他。
今天我要告訴各位的是, 我們已經越來越能理解 如何把我們共有的直覺, 轉換成演算法。 經由這樣做, 未來我們也許可以看到一種 全然不同的心理健康模式, 而且是基於一種客觀、量化的方式 來自動分析出我們所寫的字、 我們所說的話。
謝謝。
(掌聲)
来自: kevingiao > 《Ted》
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