跟读练习: How to manage your time more effectively (according to machines) - Brian Christian - 通过YouTube学习英语口语
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The Earth is a great day.
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The Earth is a great day.
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The Earth is a great day.
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The Earth is a great day.
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The Earth is a great day.
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The Earth is a great day.
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The Earth is a great day.
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The Earth is a great day.
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In the summer of 1997,
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NASA's Pathfinder spacecraft landed on the surface of Mars and began transmitting incredible,
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iconic images back to Earth.
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But several days in, something went terribly wrong the transmissions stopped.
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Pathfinder was, in effect, procrastinating,
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keeping itself fully occupied, but failing to do its most important work.
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What was going on?
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There was a bug, it turned out, in its scheduler.
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Every operating system has something called the scheduler
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that tells the CPU how long to work on each task before switching and what to switch to.
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Done right, computers move so fluidly between their various responsibilities,
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they give the illusion of doing everything simultaneously.
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But we all know what happens when things go wrong.
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This should give us, if nothing else, some measure of consolation.
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Even computers get overwhelmed sometimes.
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Maybe learning about the computer science of scheduling can give us some ideas about our own human struggles with time.
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One of the first insights is that all the time you spend prioritizing your work is time you aren't spending doing it.
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For instance, let's say when you check your inbox,
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you scan all the messages,
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choosing which is the most important.
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Once you've dealt with that one, you repeat.
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Seems sensible, but there's a problem here.
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This is what's known as a quadratic time algorithm.
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With an inbox that's twice as full,
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these passes will take twice as long,
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and you'll need to do twice as many of them.
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This means four times the work.
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The programmers of the operating system Linux encountered a similar problem in 2003.
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Linux would rank every single one of its tasks in order of importance
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and sometimes spent more time ranking tasks than doing them.
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The programmer's counterintuitive solution was to replace this full ranking with a limited number of priority buckets.
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The system was less precise about what to do next,
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but more than made up for it by spending more time making progress.
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So with your emails, insisting on always doing the very most important thing first could lead to a meltdown.
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Waking up to an inbox three times fuller than normal could take nine times longer to clear.
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You'd be better off replying in chronological order or even at random.
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Surprisingly, sometimes giving up on doing things in the perfect order may be the key to getting them done.
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Another insight that emerges from computer scheduling has to do with one of the most prevalent features of modern life, interruptions.
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When a computer goes from one task to another,
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it has to do what's called a context switch,
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bookmarking its place in one task,
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moving old data out of its memory, and new data in.
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Each of these actions comes at a cost.
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The insight here is that there's a fundamental trade-off between productivity and responsiveness.
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Getting serious work done means minimizing context switches.
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But being responsive means reacting any time something comes up.
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These two principles are fundamentally in tension.
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Recognizing this tension allows us to decide where we want to strike that balance.
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The obvious solution is to minimize interruptions.
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The less obvious one is to group them.
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If no notification or email requires a response more urgently than once an hour,
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say, then that's exactly how often you should check them.
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No more.
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In computer science, this idea goes by the name of interrupt coalescing.
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Rather than dealing with things as they come up,
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oh, the mouse was moved,
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a key was pressed, more of that file downloaded,
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the system groups these interruptions together based on how long they can afford to wait.
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In 2013, interrupt coalescing triggered a massive improvement in laptop battery life.
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This is because deferring interruptions lets a system check everything at once,
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then quickly re-enter a low-power state.
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As with computers, so it is with us.
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Perhaps adopting a similar approach might allow us users to reclaim our own attention
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and give us back one of the things that feels so rare in modern life— rest.
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Are you interested in improving your own time and task management skills?
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If so, we highly recommend you check out Algorithms to Live By,
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the computer science of human decisions.
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You can download an audio version of the book for free at audible.com slash ted.
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And every free trial created encourages Audible to continue supporting TedEd's nonprofit mission.
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上下文与背景
在1997年夏季，NASA的探测器“探路者”成功着陆火星，并开始向地球传输令人惊叹的影像。然而，几天后，传输停止，探测器似乎在无所事事地拖延自己的主要工作。这一现象引发了我们对时间管理的思考，甚至连计算机也会因任务调度不当而陷入困境。通过对计算机科学中调度机制的理解，我们能够找到一些时间管理的启示，帮助我们更有效地处理生活和工作中的事务。
日常交流的五个常用短语
- What’s the most important task?（最重要的任务是什么？）
- How do I minimize interruptions?（我如何减少干扰？）
- Can you group the tasks?（你能将任务归类吗？）
- Let’s prioritize effectively.（让我们有效地优先处理。）
- I’ll check my emails in an hour.（我将在一小时后检查邮件。）
逐步跟读指导
为了克服视频中的语言难点并提高您的英语口语能力，以下是针对该视频的逐步跟读指南：
- 选择片段：从视频中选择一小段，最好是清晰且表达重要观念的句子。
- 慢速播放：将视频播放速度设置为较慢，以便更好地捕捉发音和语调。
- 逐句模仿：在每句之后暂停，重复说出该句，以增强记忆与发音的准确性。这项练习称为“shadowspeak”，有助于提升您的英语口语练习能力。
- 录音对比：将自己的跟读录音与视频中的原音进行对比，纠正发音和语调上的差异，进一步提高口语能力。
- 定期复习：建议每周至少进行两次复习，保持对这些短语的熟悉度，提升融会贯通的能力。
观看视频时，利用这个跟读指导，不仅能够增强听力理解，还有助于提升雅思口语练习的效果。通过“看YouTube学英语”，您将能更好地掌握口语技巧，并在日常交流中应用这些短语。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。