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IV. 科技應該放大科技的優點和人性的優點。

One of the hallmarks of poorly designed systems is that they force the human user to act like a machine in order to successfully complete a task . Machines shouldn’t act like humans—at least, not in the current design environment that lacks the framework to effectively integrate “humanness” with devices—and humans shouldn’t act like machines . Amplify the best of each, without expecting them to do each other’s jobs . “Affective Computing” (https://en .wikipedia. org/wiki/Affective_ computin ) involves studying and developing devices that can “recognize, interpret, process, and simulate human affects .” The term was first introduced in 1995 by Rosalind Picard, professor of media arts and sciences at MIT. We’ll say more about affective technology soon .
設計不良系統的一個特點是,它們迫使人類用戶像機器一樣行事才能成功完成任務。機器不應該像人類一樣行事,至少在目前缺乏有效整合「人性」與設備的設計環境中不應該如此,而人類也不應該像機器一樣行事。增強每個人的優勢,而不指望它們做對方的工作。 「情感計算」(https://en.wikipedia.org/wiki/Affective_computing) 涉及研究和開發能夠「識別、解釋、處理和模擬人類情感」的設備。這個術語最早由麻省理工學院媒體藝術與科學教授羅莎琳德·皮卡德於 1995 年首次提出。我們很快會更多地談論情感科技。

Think of the automatic faucet that turns on the water for you, but requires you to hold your hands in a very narrowly defined location during the entire process of washing—something few humans naturally do

The best technology, on the other hand, amplifies the best parts of both machines and people. It never crosses their roles, or forgets who is who . All tech is designed by people at some point The responsibility is on us to make it not just more efficient, but more accepting of the humanness of its users . A person’s primary task should not be computing; it should be being human.

Being human means seeking food, fun, and social connection. It means improving the local environment, participating in the community, meeting or finding friends and family, participating in rituals or festivals . It means finding, creating, and performing meaningful work; learning constantly; and developing skills . Humans are problem solvers, but we also feel pain, love and friendship, jealousy, fear, happiness and joy. We feel a sense of accomplishment when we achieve our goals . We study religion and history, and ache for belonging .

We are also the ones who create the next steps in a field—something technology, by itself, cannot do . A machine can run a piece of code and even evolve that code if a human programs it to do so, but the human specialty of jumping a layer of abstraction and coming up with an insight that changes the fundamental way we do things is something that is unpredictable

We have a history and a set of skills that we’ve developed in response to our culture and our environment. Humans understand context. Computers cannot understand context unless humans train them to . Originally the problem of teaching a machine to identify an object was thought to be a trivial task, but decades later it remains one of the most difficult problems in machine learning Humans are still the best at object recognition, and machines can take those human insights and index them in order to make them available to other humans

No matter how much human knowledge is put into a computer, it will never have the same needs as a living organism . It won’t seek friendship or experience hunger, need to pee or clean itself It doesn’t care about its environment as long as it’s able to function Computers don’t form families, or hang out in groups

During the ’90s, my father worked on voice concatenation systems (combining individual prerecorded words to create meaning) for a large telecom company in the Midwest . His task was to build a digital directory assistance system that allowed people to call a number and have an automated voice respond to it . First he worked with voice talent to record hundreds of thousands of words and phrases . Then he worked with linguists to stitch the words together so that text could be read back in a smooth and human-like way
在 90 年代,我父親在中西部的一家大型電信公司工作,從事語音串接系統(將個別預先錄製的詞語組合起來以創造意義)的工作。他的任務是建立一個數位電話查詢系統,讓人們可以撥打一個號碼,然後由自動語音回應。一開始,他與語音專業人員合作錄製了數十萬個詞語和片語。然後,他與語言學家合作將這些詞語拼接在一起,以便文本可以以流暢且類似人類的方式被朗讀。

I used to sit at the dinner table and discuss artificial intelligence for hours with my dad He didn’t like the idea of artificial intelligence, and insisted on reading me bedtime stories from a pair of books called The Evolution of Consciousness by Robert E. Ornstein and Naturally Intelligent Systems by Maureen McHugh .

When my dad and I discussed voice recognition and automated systems, he would always point out how difficult these systems were to build: “Computers don’t have human forms. They don’t grow up,” he told me . “They don’t understand what it’s like to walk outside into the sun, or feel grass beneath their toes . They’re a brain without a body. Because of that, they don’t understand things the way that humans do . The best thing computers can do is to connect humans to one another.”

What this ultimately got me to realize was that no computer can understand humans as well as we can understand one another. Therefore, the best interfaces don’t connect us to technology; they connect us to other people. Google is indispensable not because it provides all the answers, but because it connects us to what others have discovered or written— and they have the answers . The Google interface itself is almost invisible . We don’t look at it; we look at the search results . Google Search doesn’t try to act human—it helps humans to find one another
這讓我最終意識到的是,沒有任何電腦能像我們彼此之間理解一樣理解人類。因此,最好的介面不是將我們連接到科技,而是將我們連接到其他人。Google 是不可或缺的,不是因為它提供所有答案,而是因為它將我們連接到其他人所發現或撰寫的內容,而他們有答案。Google 介面本身幾乎是看不見的。我們不看它;我們看搜索結果。Google 搜尋並不試圖表現得像人類,它幫助人類找到彼此。

Google Search is an example of a system that amplifies humanness and makes the best use of a machine . You can think of it as a switchboard connecting humans to humans, through a series of bots that index the majority of human digital knowledge . Without bots indexing data, we could never find anything Google doesn’t determine the best result for us, but it does give us a series of results we can choose from, prioritized by their importance to other humans . From a given list of results, we can then understand which ones best pertain to our problem The bots themselves only index human knowledge and help with the search results; they do not choose the result for us
Google 搜尋是一個能夠增強人性並充分利用機器的系統的例子。你可以把它想像成一個將人與人連接的交換機,透過一系列的機器人來索引大部分的人類數位知識。如果沒有機器人索引數據,我們將永遠找不到任何東西。Google 不會為我們確定最佳結果,但它會給我們一系列我們可以選擇的結果,按照它們對其他人的重要性排序。從給定的結果清單中,我們可以了解哪些最適合我們的問題。這些機器人只是索引人類知識並幫助搜尋結果;它們不會為我們選擇結果。

Mouse inventor Douglas Engelbart defined “augmenting human intellect” as the use of technology to increase capability people to be able to better approach complex problems and situations, to gain knowledge to suit specific needs, and to finally derive solutions to problems d The lesson for designers and engineers is to focus on optimizing your technology so that it amplifies the tasks that humans are better at that machines; tasks like curation, working with context, understanding, being flexible, and improvisation . A computer can’t truly [1]

understand or curate, and once it’s been programmed, it’s relatively inflexible . The better a system supports humans to do these things, the better the result!

These may seem like obvious differences, but they’re worth acknowledging explicitly as we decide how to design the interactions between these two very different intelligences

  • [1] Engelbart, Douglas . “Augmenting Human Intellect: A Conceptual Framework . ”SRI Summary Report AFOSR-3223, 1962. (http://www.dougengelbart.org/pubs/augment-3906, htm'i)
    Engelbart, Douglas.「擴增人類智慧:概念框架」。SRI 摘要報告 AFOSR-3223,1962 年。(http://www.dougengelbart.org/pubs/augment-3906.htm)
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