下載App 希平方
攻其不背
App 開放下載中
下載App 希平方
攻其不背
App 開放下載中
IE版本不足
你的 IE 瀏覽器太舊了 更新 IE 瀏覽器或點選連結下載 Google Chrome 瀏覽器 前往下載

免費註冊
! 這組帳號已經註冊過了
Email 帳號
密碼請填入 6 位數以上密碼
已經有帳號了?
忘記密碼
! 這組帳號已經註冊過了
您的 Email
請輸入您註冊時填寫的 Email,
我們將會寄送設定新密碼的連結給您。
寄信了!請到信箱打開密碼連結信
密碼信已寄至
沒有收到信嗎? 點這裡重寄一次
如果您尚未收到信,請前往垃圾郵件查看,謝謝!

恭喜您註冊成功!

查看會員功能

註冊未完成

《HOPE English 希平方》服務條款關於個人資料收集與使用之規定

隱私權政策
上次更新日期:2014-12-30

希平方 為一英文學習平台,我們每天固定上傳優質且豐富的影片內容,讓您不但能以有趣的方式學習英文,還能增加內涵,豐富知識。我們非常注重您的隱私,以下說明為當您使用我們平台時,我們如何收集、使用、揭露、轉移及儲存你的資料。請您花一些時間熟讀我們的隱私權做法,我們歡迎您的任何疑問或意見,提供我們將產品、服務、內容、廣告做得更好。

本政策涵蓋的內容包括:希平方 如何處理蒐集或收到的個人資料。
本隱私權保護政策只適用於: 希平方 平台,不適用於非 希平方 平台所有或控制的公司,也不適用於非 希平方 僱用或管理之人。

個人資料的收集與使用
當您註冊 希平方 平台時,我們會詢問您姓名、電子郵件、出生日期、職位、行業及個人興趣等資料。在您註冊完 希平方 帳號並登入我們的服務後,我們就能辨認您的身分,讓您使用更完整的服務,或參加相關宣傳、優惠及贈獎活動。希平方 也可能從商業夥伴或其他公司處取得您的個人資料,並將這些資料與 希平方 所擁有的您的個人資料相結合。

我們所收集的個人資料, 將用於通知您有關 希平方 最新產品公告、軟體更新,以及即將發生的事件,也可用以協助改進我們的服務。

我們也可能使用個人資料為內部用途。例如:稽核、資料分析、研究等,以改進 希平方公司 產品、服務及客戶溝通。

瀏覽資料的收集與使用
希平方 自動接收並記錄您電腦和瀏覽器上的資料,包括 IP 位址、希平方 cookie 中的資料、軟體和硬體屬性以及您瀏覽的網頁紀錄。

隱私權政策修訂
我們會不定時修正與變更《隱私權政策》,不會在未經您明確同意的情況下,縮減本《隱私權政策》賦予您的權利。隱私權政策變更時一律會在本頁發佈;如果屬於重大變更,我們會提供更明顯的通知 (包括某些服務會以電子郵件通知隱私權政策的變更)。我們還會將本《隱私權政策》的舊版加以封存,方便您回顧。

服務條款
歡迎您加入看 ”希平方”
上次更新日期:2013-09-09

歡迎您加入看 ”希平方”
感謝您使用我們的產品和服務(以下簡稱「本服務」),本服務是由 希平方 所提供。
本服務條款訂立的目的,是為了保護會員以及所有使用者(以下稱會員)的權益,並構成會員與本服務提供者之間的契約,在使用者完成註冊手續前,應詳細閱讀本服務條款之全部條文,一旦您按下「註冊」按鈕,即表示您已知悉、並完全同意本服務條款的所有約定。如您是法律上之無行為能力人或限制行為能力人(如未滿二十歲之未成年人),則您在加入會員前,請將本服務條款交由您的法定代理人(如父母、輔助人或監護人)閱讀,並得到其同意,您才可註冊及使用 希平方 所提供之會員服務。當您開始使用 希平方 所提供之會員服務時,則表示您的法定代理人(如父母、輔助人或監護人)已經閱讀、了解並同意本服務條款。 我們可能會修改本條款或適用於本服務之任何額外條款,以(例如)反映法律之變更或本服務之變動。您應定期查閱本條款內容。這些條款如有修訂,我們會在本網頁發佈通知。變更不會回溯適用,並將於公布變更起十四天或更長時間後方始生效。不過,針對本服務新功能的變更,或基於法律理由而為之變更,將立即生效。如果您不同意本服務之修訂條款,則請停止使用該本服務。

第三人網站的連結 本服務或協力廠商可能會提供連結至其他網站或網路資源的連結。您可能會因此連結至其他業者經營的網站,但不表示希平方與該等業者有任何關係。其他業者經營的網站均由各該業者自行負責,不屬希平方控制及負責範圍之內。

兒童及青少年之保護 兒童及青少年上網已經成為無可避免之趨勢,使用網際網路獲取知識更可以培養子女的成熟度與競爭能力。然而網路上的確存有不適宜兒童及青少年接受的訊息,例如色情與暴力的訊息,兒童及青少年有可能因此受到心靈與肉體上的傷害。因此,為確保兒童及青少年使用網路的安全,並避免隱私權受到侵犯,家長(或監護人)應先檢閱各該網站是否有保護個人資料的「隱私權政策」,再決定是否同意提出相關的個人資料;並應持續叮嚀兒童及青少年不可洩漏自己或家人的任何資料(包括姓名、地址、電話、電子郵件信箱、照片、信用卡號等)給任何人。

為了維護 希平方 網站安全,我們需要您的協助:

您承諾絕不為任何非法目的或以任何非法方式使用本服務,並承諾遵守中華民國相關法規及一切使用網際網路之國際慣例。您若係中華民國以外之使用者,並同意遵守所屬國家或地域之法令。您同意並保證不得利用本服務從事侵害他人權益或違法之行為,包括但不限於:
A. 侵害他人名譽、隱私權、營業秘密、商標權、著作權、專利權、其他智慧財產權及其他權利;
B. 違反依法律或契約所應負之保密義務;
C. 冒用他人名義使用本服務;
D. 上載、張貼、傳輸或散佈任何含有電腦病毒或任何對電腦軟、硬體產生中斷、破壞或限制功能之程式碼之資料;
E. 干擾或中斷本服務或伺服器或連結本服務之網路,或不遵守連結至本服務之相關需求、程序、政策或規則等,包括但不限於:使用任何設備、軟體或刻意規避看 希平方 - 看 YouTube 學英文 之排除自動搜尋之標頭 (robot exclusion headers);

服務中斷或暫停
本公司將以合理之方式及技術,維護會員服務之正常運作,但有時仍會有無法預期的因素導致服務中斷或故障等現象,可能將造成您使用上的不便、資料喪失、錯誤、遭人篡改或其他經濟上損失等情形。建議您於使用本服務時宜自行採取防護措施。 希平方 對於您因使用(或無法使用)本服務而造成的損害,除故意或重大過失外,不負任何賠償責任。

版權宣告
上次更新日期:2013-09-16

希平方 內所有資料之著作權、所有權與智慧財產權,包括翻譯內容、程式與軟體均為 希平方 所有,須經希平方同意合法才得以使用。
希平方歡迎你分享網站連結、單字、片語、佳句,使用時須標明出處,並遵守下列原則:

  • 禁止用於獲取個人或團體利益,或從事未經 希平方 事前授權的商業行為
  • 禁止用於政黨或政治宣傳,或暗示有支持某位候選人
  • 禁止用於非希平方認可的產品或政策建議
  • 禁止公佈或傳送任何誹謗、侮辱、具威脅性、攻擊性、不雅、猥褻、不實、色情、暴力、違反公共秩序或善良風俗或其他不法之文字、圖片或任何形式的檔案
  • 禁止侵害或毀損希平方或他人名譽、隱私權、營業秘密、商標權、著作權、專利權、其他智慧財產權及其他權利、違反法律或契約所應付支保密義務
  • 嚴禁謊稱希平方辦公室、職員、代理人或發言人的言論背書,或作為募款的用途

網站連結
歡迎您分享 希平方 網站連結,與您的朋友一起學習英文。

抱歉傳送失敗!

不明原因問題造成傳送失敗,請儘速與我們聯繫!
希平方 x ICRT

「Tricia Wang:大數據所忽略的人的視角」- The Human Insights Missing from Big Data


框選或點兩下字幕可以直接查字典喔!

In ancient Greece, when anyone from slaves to soldiers, poets and politicians, needed to make a big decision on life's most important questions, like, "Should I get married?" or "Should we embark on this voyage?" or "Should our army advance into this territory?" they all consulted the oracle.

So this is how it worked: you would bring her a question and you would get on your knees, and then she would go into this trance. It would take a couple of days, and then eventually she would come out of it, giving you her predictions as your answer.

From the oracle bones of ancient China to ancient Greece to Mayan calendars, people have craved for prophecy in order to find out what's going to happen next. And that's because we all want to make the right decision. We don't want to miss something. The future is scary, so it's much nicer knowing that we can make a decision with some assurance of the outcome.

Well, we have a new oracle, and it's name is big data, or we call it "Watson" or "deep learning" or "neural net." And these are the kinds of questions we ask of our oracle now, like, "What's the most efficient way to ship these phones from China to Sweden?" Or, "What are the odds of my child being born with a genetic disorder?" Or, "What are the sales volume we can predict for this product?"

I have a dog. Her name is Elle, and she hates the rain. And I have tried everything to unchain her. But because I have failed at this, I also have to consult an oracle, called Dark Sky, every time before we go on a walk, for very accurate weather predictions in the next 10 minutes. She's so sweet. So because of all of this, our oracle is a $122 billion industry.

Now, despite the size of this industry, the returns are surprisingly low. Investing in big data is easy, but using it is hard. Over 73 percent of big data projects aren't even profitable, and I have executives coming up to me saying, "We're experiencing the same thing. We invested in some big data system, and our employees aren't making better decisions. And they're certainly not coming up with more breakthrough ideas."

So this is all really interesting to me, because I'm a technology ethnographer. I study and I advise companies on the patterns of how people use technology, and one of my interest areas is data. So why is having more data not helping us make better decisions, especially for companies who have all these resources to invest in these big data systems? Why isn't it getting any easier for them?

So, I've witnessed the struggle firsthand. In 2009, I started a research position with Nokia. And at the time, Nokia was one of the largest cell phone companies in the world, dominating emerging markets like China, Mexico and India—all places where I had done a lot of research on how low-income people use technology. And I spent a lot of extra time in China getting to know the informal economy. So I did things like working as a street vendor selling dumplings to construction workers. Or I did fieldwork, spending nights and days in internet cafes, hanging out with Chinese youth so I could understand how they were using games and mobile phones and using it between moving from the rural areas to the cities.

And through all of this qualitative evidence that I was gathering, I was starting to see so clearly that a big change was about to happen among low-income Chinese people. Even though they were surrounded by advertisements for luxury products like fancy toilets—who wouldn't want one?—and apartments and cars, through my conversations with them, I found out that the ads the actually enticed them the most were the ones for iPhones, promising them this entry into this high-tech life. And even when I was living with them in urban slums like this one, I saw people investing over half of their monthly income into buying a phone, and increasingly, they were "shanzhai," which are affordable knock-offs of iPhones and other brands. They're very usable. Does the job.

And after years of living with migrants and working with them and just really doing everything that they were doing, I started piecing all these data points together—from the things that seem random, like me selling dumplings, to the things that were more obvious, like tracking how much they were spending on their cell phone bills. And I was able to create this much more holistic picture of what was happening. And that's when I started to realize that even the poorest in China would want a smartphone, and that they would do almost anything to get their hands on one.

You have to keep in mind, iPhones had just come out, it was 2009, so this was, like, eight years ago, and Androids had just started looking like iPhones. And a lot of very smart and realistic people said, "Those smartphones—that's just a fad. Who wants to carry around these heavy things where batteries drain quickly and they break every time you drop them?" But I had a lot of data, and I was very confident about my insights, so I was very excited to share them with Nokia.

But Nokia was not convinced, because it wasn't big data. They said, "We have millions of data points, and we don't see any indicators of anyone wanting to buy a smartphone, and your data set of 100, as diverse as it is, is too weak for us to even take seriously." And I said, "Nokia, you're right. Of course you wouldn't see this, because you're sending out surveys assuming that people don't know what a smartphone is, so of course you're not going to get any data back about people wanting to buy a smartphone in two years. Your surveys, your methods have been designed to optimize an existing business model, and I'm looking at these emergent human dynamics that haven't happened yet. We're looking outside of market dynamics so that we can get ahead of it." Well, you know what happened to Nokia? Their business fell off a cliff. This—this is the cost of missing something. It was unfathomable.

But Nokia's not alone. I see organizations throwing out data all the time because it didn't come from a quant model or it doesn't fit in one. But it's not big data's fault. It's the way we use big data; it's our responsibility. Big data's reputation for success comes from quantifying very specific environments, like electricity power grids or delivery logistics or genetic code, when we're quantifying in systems that are more or less contained.

But not all systems are as neatly contained. When you're quantifying and systems are more dynamic, especially systems that involve human beings, forces are complex and unpredictable, and these are things that we don't know how to model so well. Once you predict something about human behavior, new factors emerge, because conditions are constantly changing. That's why it's a never-ending cycle. You think you know something, and then something unknown enters the picture. And that's why just relying on big data alone increases the chance that we'll miss something, while giving us this illusion that we already know everything.

And what makes it really hard to see this paradox and even wrap our brains around it is that we have this thing that I call the quantification bias, which is the unconscious belief of valuing the measurable over the immeasurable. And we often experience this at our work. Maybe we work alongside colleagues who are like this, or even our whole entire company may be like this, where people become so fixated on that number that they can't see anything outside of it, even when you present them evidence right in front of their face. And this is a very appealing message, because there's nothing wrong with quantifying; it's actually very satisfying. I get a great sense of comfort from looking at an Excel spreadsheet, even very simple ones.

It's just kind of like, "Yes! The formula worked. It's all okay. Everything is under control."

But the problem is that quantifying is addictive. And when we forget that and when we don't have something to kind of keep that in check, it's very easy to just throw out data because it can't be expressed as a numerical value. It's very easy just to slip into silver-bullet thinking, as if some simple solution existed. Because this is a great moment of danger for any organization, because oftentimes, the future we need to predict—it isn't in that haystack, but it's that tornado that's bearing down on us outside of the barn. There is no greater risk than being blind to the unknown. It can cause you to make the wrong decisions. It can cause you to miss something big.

But we don't have to go down this path. It turns out that the oracle of ancient Greece holds the secret key that shows us the path forward. Now, recent geological research has shown that the Temple of Apollo, where the most famous oracle sat, was actually built over two earthquake faults. And these faults would release these petrochemical fumes from underneath the Earth's crust, and the oracle literally sat right above these faults, inhaling enormous amounts of ethylene gas, these fissures. It's true. It's all true, and that's what made her babble and hallucinate and go into this trance-like state. She was high as a kite!

So how did anyone—how did anyone get any useful advice out of her in this state? Well, you see those people surrounding the oracle? You see those people holding her up, because she's, like, a little woozy? And you see that guy on your left-hand side holding the orange notebook? Well, those were the temple guides, and they worked hand in hand with the oracle. When inquisitors would come and get on their knees, that's when the temple guides would get to work, because after they asked her questions, they would observe their emotional state, and then they would ask them follow-up questions, like, "Why do you want to know this prophecy? Who are you? What are you going to do with this information?" And then the temple guides would take this more ethnographic, this more qualitative information, and interpret the oracle's babblings. So the oracle didn't stand alone, and neither should our big data systems.

Now to be clear, I'm not saying that big data systems are huffing ethylene gas, or that they're even giving invalid predictions. The total opposite. But what I am saying is that in the same way that the oracle needed her temple guides, our big data systems need them, too. They need people like ethnographers and user researchers who can gather what I call thick data. This is precious data from humans, like stories, emotions and interactions that cannot be quantified. It's the kind of data that I collected for Nokia that comes in in the form of a very small sample size, but delivers incredible depth of meaning.

And what makes it so thick and meaty is the experience of understanding the human narrative. And that's what helps to see what's missing in our models. Thick data grounds our business questions in human questions, and that's why integrating big and thick data forms a more complete picture. Big data is able to offer insights at scale and leverage the best of machine intelligence, whereas thick data can help us rescue the context loss that comes from making big data usable, and leverage the best of human intelligence. And when you actually integrate the two, that's when things get really fun, because then you're no longer just working with data you've already collected. You get to also work with data that hasn't been collected. You get to ask questions about why: Why is this happening?

Now, when Netflix did this, they unlocked a whole new way to transform their business. Netflix is known for their really great recommendation algorithm, and they had this $1 million prize for anyone who could improve it. And there were winners. But Netflix discovered the improvements were only incremental. So to really find out what was going on, they hired an ethnographer, Grant McCracken, to gather thick data insights. And what he discovered was something that they hadn't seen initially in the quantitative data. He discovered that people loved to binge-watch. In fact, people didn't even feel guilty about it. They enjoyed it.

So Netflix was like, "Oh. This is a new insight." So they went to their data science team, and they were able to scale this big data insight in with their quantitative data. And once they verified it and validated it, Netflix decided to do something very simple but impactful. They said, instead of offering the same show from different genres or more of the different shows from similar users, we'll just offer more of the same show. We'll just make it easier for you to binge-watch. And they didn't stop there. They did all these things to redesign their entire viewer experience, to really encourage binge-watching. It's why people and friends disappear for whole weekends at a time, catching up on shows like "Master of None." By integrating big data and thick data, they not only improved their business, but they transformed how we consume media. And now their stocks are projected to double in the next few years.

But this isn't just about watching more videos or selling more smartphones. For some, integrating thick data insights into the algorithm could mean life or death, especially for the marginalized. All around the country, police departments are using big data for predictive policing, to set bond amounts and sentencing recommendations in ways that reinforce existing biases. NSA's Skynet machine learning algorithm has possibly aided in the deaths of thousands of civilians in Pakistan from misreading cellular device metadata. As all of our lives become more automated, from automobiles to health insurance or to employment, it is likely that all of us will be impacted by the quantification bias.

Now, the good news is that we've come a long way from huffing ethylene gas to make predictions. We have better tools, so let's just use them better. Let's integrate the big data with the thick data. Let's bring our temple guides with the oracles, and whether this work happens in companies or nonprofits or government or even in the software, all of it matters, because that means we're collectively committed to making better data, better algorithms, better outputs and better decisions. This is how we'll avoid missing that something.

播放本句

登入使用學習功能

使用Email登入

HOPE English 播放器使用小提示

  • 功能簡介

    單句重覆、上一句、下一句:顧名思義,以句子為單位重覆播放,單句重覆鍵顯示橘色時為重覆播放狀態;顯示灰色時為正常播放狀態。按上一句鍵、下一句鍵時就會自動重覆播放該句。
    收錄佳句:點擊可增減想收藏的句子。

    中、英文字幕開關:中、英文字幕按鍵為綠色為開啟,灰色為關閉。鼓勵大家搞懂每一句的內容以後,關上字幕聽聽看,會發現自己好像在聽中文說故事一樣,會很有成就感喔!
    收錄單字:用滑鼠框選英文單字可以收藏不會的單字。
  • 分享
    如果您覺得本篇短片很有趣或很喜歡,在短片結束時有分享連結,可以分享給朋友一同欣賞,一起看YouTube學英文!

    或是您有收錄很優秀的句子時,也可以分享佳句給大家,一同看佳句學英文!