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《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:希平方學英文 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: 希平方學英文 平台,不適用於非 希平方學英文 平台所有或控制的公司,也不適用於非 希平方學英文 僱用或管理之人。

個人資料的收集與使用
當您註冊 希平方學英文 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 希平方學英文 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。希平方學英文 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 希平方學英文 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 希平方學英文 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
希平方學英文 自動接收並記錄您電腦和瀏覽器上的資料,包括 IP 位址、希平方學英文 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
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上次更新日期:2013-09-09

歡迎您加入看 ”希平方學英文”
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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,但不表示希平方學英文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,不屬希平方學英文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。

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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:
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上次更新日期:2013-09-16

希平方學英文 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 希平方學英文 所有,須經希平方學英文同意合法才得以使用。
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「Matt Beane:我們如何與智慧型儀器共事」- How Do We Learn to Work with Intelligent Machines?

觀看次數:1828  • 

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It's 6:30 in the morning, and Kristen is wheeling her prostate patient into the OR. She's a resident, a surgeon in training. It's her job to learn. Today, she's really hoping to do some of the nerve-sparing, extremely delicate dissection that can preserve erectile function. That'll be up to the attending surgeon, though, but he's not there yet. She and the team put the patient under, and she leads the initial eight-inch incision in the lower abdomen. Once she's got that clamped back, she tells the nurse to call the attending. He arrives, gowns up, And from there on in, their four hands are mostly in that patient—with him guiding but Kristin leading the way. When the prostates out (and, yes, he let Kristen do a little nerve sparing), he rips off his scrubs. He starts to do paperwork. Kristen closes the patient by 8:15, with a junior resident looking over her shoulder. And she lets him do the final line of sutures. Kristen feels great. Patient's going to be fine, and no doubt she's a better surgeon than she was at 6:30.

Now this is extreme work. But Kristin's learning to do her job the way that most of us do: watching an expert for a bit, getting involved in easy, safe parts of the work and progressing to riskier and harder tasks as they guide and decide she's ready. My whole life I've been fascinated by this kind of learning. It feels elemental, part of what makes us human. It has different names: apprenticeship, coaching, mentorship, on the job training. In surgery, it's called "see one, do one, teach one." But the process is the same, and it's been the main path to skill around the globe for thousands of years. Right now, we're handling AI in a way that blocks that path. We're sacrificing learning in our quest for productivity.

I found this first in surgery while I was at MIT, but now I've got evidence it's happening all over, in very different industries and with very different kinds of AI. If we do nothing, millions of us are going to hit a brick wall as we try to learn to deal with AI. Let's go back to surgery to see how.

Fast forward six months. It's 6:30am again, and Kristen is wheeling another prostate patient in, but this time to the robotic OR. The attending leads attaching a four-armed, thousand-pound robot to the patient. They both rip off their scrubs, head to control consoles 10 or 15 feet away, and Kristen just watches. The robot allows the attending to do the whole procedure himself, so he basically does. He knows she needs practice. He wants to give her control. But he also knows she'd be slower and make more mistakes, and his patient comes first. So Kristin has no hope of getting anywhere near those nerves during this rotation. She'll be lucky if she operates more than 15 minutes during a four-hour procedure. And she knows that when she slips up, he'll tap a touch screen, and she'll be watching again, feeling like a kid in the corner with a dunce cap.

Like all the studies of robots and work I've done in the last eight years, I started this one with a big, open question: How do we learn to work with intelligent machines? To find out, I spent two and a half years observing dozens of residents and surgeons doing traditional and robotic surgery, interviewing them and in general hanging out with the residents as they tried to learn. I covered 18 of the top US teaching hospitals, and the story was the same. Most residents were in Kristen's shoes. They got to "see one" plenty, but the "do one" was barely available. So they couldn't struggle, and they weren't learning.

This was important news for surgeons, but I needed to know how widespread it was: Where else was using AI blocking learning on the job? To find out, I've connected with a small but growing group of young researchers who've done boots-on-the-ground studies of work involving AI in very diverse settings like start-ups, policing, investment banking and online education. Like me, they spent at least a year and many hundreds of hours observing, interviewing and often working side-by-side with the people they studied. We shared data, and I looked for patterns. No matter the industry, the work, the AI, the story was the same. Organizations were trying harder and harder to get results from AI, and they were peeling learners away from expert work as they did it. Start-up managers were outsourcing their customer contact. Cops had to learn to deal with crime forecasts without experts support. Junior bankers were getting cut out of complex analysis, and professors had to build online courses without help. And the effect of all of this was the same as in surgery. Learning on the job was getting much harder.

This can't last. McKinsey estimates that between half a billion and a billion of us are going to have to adapt to AI in our daily work by 2030. And we're assuming that on-the-job learning will be there for us as we try. Accenture's latest workers survey showed that most workers learned key skills on the job, not in formal training. So while we talk a lot about its potential future impact, the aspect of AI that may matter most right now is that we're handling it in a way that blocks learning on the job just when we need it most.

Now across all our sites, a small minority found a way to learn. They did it by breaking and bending rules. Approved methods weren't working, so they bent and broke rules to get hands-on practice with experts. In my setting, residents got involved in robotic surgery in medical school at the expense of their generalist education. And they spent hundreds of extra hours with simulators and recordings of surgery, when you were supposed to learn in the OR. And maybe most importantly, they found ways to struggle in live procedures with limited expert supervision. I call all this "shadow learning," because it bends the rules and learner's do it out of the limelight. And everyone turns a blind eye because it gets results. Remember, these are the star pupils of the bunch.

Now, obviously, this is not OK, and it's not sustainable. No one should have to risk getting fired to learn the skills they need to do their job. But we do need to learn from these people. They took serious risks to learn. They understood they needed to protect struggle and challenge in their work so that they could push themselves to tackle hard problems right near the edge of their capacity. They also made sure there was an expert nearby to offer pointers and to backstop against catastrophe. Let's build this combination of struggle and expert support into each AI implementation.

Here's one clear example I could get of this on the ground. Before robots, if you were a bomb disposal technician, you dealt with an IED by walking up to it. A junior officer was hundreds of feet away, so could only watch and help if you decided it was safe and invited them downrange. Now you sit side-by-side in a bomb-proof truck. You both watched the video feed. They control a distant robot, and you guide the work out loud. Trainees learn better than they did before robots. We can scale this to surgery, start-ups, policing, investment banking, online education and beyond. The good news is we've got new tools to do it. The internet and the cloud mean we don't always need one expert for every trainee, for them to be physically near each other or even to be in the same organization. And we can build AI to help: to coach learners as they struggle, to coach experts as they coach and to connect those two groups in smart ways.

There are people at work on systems like this, but they've been mostly focused on formal training. And the deeper crisis is in on-the-job learning. We must do better. Today's problems demand we do better to create work that takes full advantage of AI's amazing capabilities while enhancing our skills as we do it. That's the kind of future I dreamed of as a kid. And the time to create it is now.

Thank you.

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