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15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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TOEIC・英語 大学生・専門学校生・社会人

下線部(1)の文構造が分かりません。特に2行目の文構造が分かりません。強調のdoであることは分かりますが、その後のthat以降が関係詞?かすらも分からないので、誰か教えて下さい!

次の英文は1991年に出版された本からのもので、 研究分野としての「人工知 能」 (Artificial Intelligence) について述べています。 下線部(1)~(3)を日本語に訳 しなさい。 What is Artificial Intelligence (AI)? Just about the only characterization of Al that would meet with universal acceptance is that it involves trying to make machines do tasks which are normally seen as requiring intelligence. There are countless refinements of this characterization: what sort of machines we want to consider; how we decide what tasks require intelligence and so on. One of the most important questions concerns the reasons why we want to make machines do such tasks. AI has always been split between people who want to make machines do tasks that require intelligence because they want more useful machines, and people who want to do it because they see it as a way of exploring how humans do such tasks. We will call the two approaches the engineering approach and the cognitive-science respectively. (2) (1) approach The techniques required for the two approaches are not always very different. For many of the tasks that engineering AI wants solutions to, the only systems we know about that can perform them are humans), so that, at least initially, the obvious way to design solutions is to try to mimic what we know about humans. For many of the tasks that cognitive-science Al wants solutions to, the evidence on how humans do them is too hard to interpret to enable us to construct computational models, so the only approach is to try to design solutions from scratch" and then see how well they fit what we know about humans. The main visible difference between the two approaches is in (3) their criteria for success; an engineer would be delighted to have create something that outperformed a person; a cognitive scientist would regard it as a failure. -1- M7 (492-61

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TOEIC・英語 大学生・専門学校生・社会人

至急お願いします!

次の1~5の日本文の意味を表すように、 語を補いなさい。 )内の語(句)を使って、下線部分に適切な英 1. 彼女は給油するためにガソリンスタンドに立ち寄った。 (car, fill up) 《英語》She stopped 2. 私は日本に私の服を送るための箱を探している。 (clothes, box, Japan, send) 《英語》I am looking for 3.彼はファイナンシャルプランナーになるために勉強している。 (become, financial, study) 《英語》 He is 4. 彼女は子供に料理を教えることのできる場所を借ります。 (cooking, kids, place, teach) 《英語》 She rents 5. キャシーは彼に仕事を引き継いでもらいたがっていた。 (business, take over) 《英語》 Cathy wanted at a gas station. planner. Ⅱ 次の英文を読んで、 下記のペアワークやグループワークに取り組みましょう。 ◎ CD 70 DL 70 A good work-life balance enables us to divide our energy between our home and work priorities. It also enables us to reduce stress and anxiety both at work and at home. In an effort to strike an optimum work-life balance, I struggle to find anything like a balance between work and doing something for myself at all. I want to travel to places in Asia to diversify my life. I hope to stay physically and mentally fit. I hope that my life will not always be as busy as it is right now. Notes 1. enable O to do 「○が…することを可能にする」 2. divide ○ ○ を分ける」 3. priority 「優先事項」 4. reduce 「○ を減らす」 5. optimum 「最適な」 6. struggle to do 「・・・ しようと努力する」 7. at all 「とにかく」 8. diversify ○ ○ に厚みを持たせる」 9. stay C 「Cのままでいる」 Pair/Group Work ペアまたはグループになって質問をしたり、答えたりしましょう。

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