跟读练习: Learning Software Engineering During the Era of AI | Raymond Fu | TEDxCSTU - 通过YouTube学习英语口语
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Transcriber: Brenda Meza Reviewer: Emilia Soso At the turn of the century, when I started to learn software engineering, one of my professors told us that in the future, every job will be a programming job.
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Transcriber: Brenda Meza Reviewer: Emilia Soso At the turn of the century, when I started to learn software engineering, one of my professors told us that in the future, every job will be a programming job.
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That was in 2001.
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And he said that we’re holding a golden ticket to job security.
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Just last month, the CEO of GitHub said that the future of programming is natural language.
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It looks like the prediction of my professor at the turn of the century is going to become true, but probably not in the way that he had imagined.
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Artificial intelligence is capable of writing code for you through a natural language prompt.
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GitHub Copilot can complete code for you and fix bugs for you.
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And ChatGPT can create an entire project for you within seconds.
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And all these tools are available to anyone.
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So I find myself wondering, have we lost our golden tickets to job security?
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And as a CSTU professor and a father to a daughter who studied Computer Science, there's a bigger question for me.
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If AI is going to do programming, is it still worth it for us to learn software engineering anymore?
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Today, I would like to explore this question with all of you guys.
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Let’s talk about what AI can do and more importantly, how our students of software engineering can prepare for the future roles of a real software engineer.
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So let’s dive in.
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First, let’s talk about what AI is good at.
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In terms of programming, AI is really good at generating thousands of lines of code.
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It translates between programming languages.
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It can create user interfaces and fix bugs for you.
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And it excels at repetitive tasks, and, you know, pattern recognition.
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You know, once I asked ChatGPT to create a project for me, a dating app like Tinder in Python.
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And within seconds it actually created a complete application with user profiles, the swiping logic, and even a sample database.
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The only thing it didn't do for me is find me a date.
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(Laughs) But AI has a lot of limitations. We have to accept that.
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It still doesn’t understand the why behind all the tasks we ask them to do.
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It needs your human input for real-world context and scenarios.
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It may not work well, prioritizing long-term business goals and assessing trade-offs.
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And last but not least, it's not reliable.
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It hallucinates and sometimes gives the wrong answer.
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The statistics say that 55% of the developers today are actually starting to use Copilot, but only 30% of them are accepting the outcome without any changes.
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So if you are a developer and you are not in the first 55%, that means you’re not using AI, and you’re in trouble.
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But if you are in the 30%, that means you trust AI too much.
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You may be in bigger trouble.
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So all the leading AIs today are built on top of large language models, and it’s trained on the text of human knowledge.
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It’s impressive.
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If you give a clear prompt, it’ll give you very good results.
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But all the strategic thinking are still us. It’s the human.
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You can think of AI as a brilliant junior developer that you hire to your team, and they can do a lot of jobs very quickly and efficiently.
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But it's up to us human to define the vision, to validate the results and ensure what we're building is good for the society.
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So there’s another thing that I want to talk about that AI is struggling with.
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It's struggling to communicate and collaborate with human beings.
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Well, maybe you will say this is more of a human problem, right?
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We humans sometimes deal with the same problem too.
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But this is something we will have to work out.
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Let AI do what AI is good at, and we humans can take care of the boring jobs such as handling office politics.
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So talk about the capabilities and limitations of AI.
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Now we can take a look at the software engineering roles.
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So software engineering roles is not just about writing code.
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It actually is about how we need to understand what the user needs.
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We need to collaborate across roles and also make tough decisions with empathy and responsibility.
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This is what a software engineer should be doing, right?
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We're not just text executors.
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The best engineers are not the ones who code the fastest, but the ones who think the deepest.
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So a good engineer will take messy problems, ambiguous problems, and guide machines towards structured and meaningful outcomes.
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So there are system architects who design the best solutions, and they should be the AI collaborators who use AI to implement those solutions.
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And then they need to be ethical technologists to make sure the solutions that we’re building are truly benefiting human beings.
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So AI is actually democratizing a lot of complicated technical tasks.
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Like, today a designer can mock up an application with a prompt.
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And also marketers, they don’t need data engineers.
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They can just run data analytics without writing any code.
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Does that mean software engineers are losing advantages?
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The answer is no.
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It still remains essential for software engineers.
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And the reason is as follows.
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First, we understand AI better.
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We not only know how to prompt, and we also know what’s under the hood.
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The models, the data pipelines, the limitations and risks.
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And the understanding of these are very important because AI is integrated into every product we’re using and we’re building in the future.
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Second, we can make better use of AI when building software.
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So nowadays anybody can prototype a demo or create a simple application of features.
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But software engineers think of the bigger picture.
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We are actually using AI to build a production-ready software that’s scalable and reliable with long-term maintainability.
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Finally, we are making AI better.
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We fine-tune models.
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We optimize the performance and improve usability.
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We make AI available and useful for everybody else.
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The next generation of AI is still built by software engineers.
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Do you guys remember this quote from CEO of GitHub?
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This is not a reality yet.
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It's still up to the software engineers to improve AI and make this happen.
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So software engineers were not losing the golden ticket to job security.
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As a matter of fact, we’re collecting even more because we’re no longer just building software.
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We're actually building the future intelligence itself.
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And how we train, direct and supervise AI today will define the kind of systems, technology and society that we’re building tomorrow.
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AI is raising the floor, but software engineers are raising the ceiling.
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And I want to share this not just with… You can applaud, that’s okay.
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I want to share this with not just system engineers.
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This is for everyone, all right?
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We have AI that’s raising us up from the floor.
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But it’s us humans that have to reach to the ceiling and raise up the ceiling.
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All right, so after all this, now we can talk about software engineering education, right.
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So, in the past, coding was a very important piece of software engineering education.
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But software engineering education is not just about writing code.
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It's also about teaching you how to break complex problems into steps, think logically and critically, and harness the digital tools to build solutions that really matters.
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So in a time when AI is everybody’s assistant, engineers become the orchestrators.
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We remove barriers and open doors.
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And in order for us to be a successful software engineer, the students should go beyond learning code as quickly as possible and get into the following things.
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So in order to become a successful engineer in the future, we should focus on mastering the foundations.
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The data structure, the algorithm, the programming concepts.
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They are still very important.
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Spend enough time to learn all these and become an expert on them because they’re very important basics.
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Next, think about a system like an architect because, you know, aim higher.
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Meet the expectation of a senior engineer as soon as possible.
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And think about designing systems that are reliable and scalable.
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Go beyond, go full-stack across disciplines.
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The days when a software engineer could focus on either the front end or the back end or the database are gone.
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The future software engineers are full-stack engineers.
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And there’s more.
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You need to also get into the other disciplines like design, product, data, project management, and be prepared to wear multiple hats.
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Practice communication and collaborations.
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Learn to work with people through team projects.
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Because in the future, if you can explain and connect, it will become increasingly important, and it will set you apart.
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Use AI as a creative partner.
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Embrace AI, don’t hate it.
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And learn LLM, generative AI, model fine-tuning and RAG, etc.
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You discuss your project with AI, and delegate your work to AI as if it’s one of your teammates.
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Last but not least, stay adaptable.
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Tools change, principles last.
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So you should always focus on learning how to learn.
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So in the future, when everyone can code a little, the ones who can master the craft, will build the path for everyone and become the leader.
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So in the era of AI, software engineering is becoming the foundation of leadership.
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I've talked a lot about programming, but perhaps programmer is no longer the right term we should be using to refer to software engineers.
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The software engineers of the AI era should be visionaries who can define meaningful problems.
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A bridge builder who can connect tools, teams and disciplines, and leaders who not only lead human beings, but also lead AI.
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So the future doesn't belong to those who code the fastest, it should belong to the ones who think deeply, adapt quickly, and collaborate efficiently.
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They are the ones who don't just predict the future.
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We build the future.
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Thank you.
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为什么通过这个视频练习口语？
练习口语是提高英语交流能力的关键，而观看这个视频可以带来多重好处。首先，视频中讲述的内容不仅跟软件工程相关，更涉及到人工智能对未来职业的影响，适合那些想了解科技趋势的英语学习者。其次，通过 看YouTube学英语，你可以在真实的语境中学习如何表达复杂想法，这对提升雅思口语练习尤为重要。最后，视频中的发音和语调为学习者提供了学习标准英语的良好范例，能够帮助提高英语发音。
语法和表达在语境中的运用
以下是视频中三个关键表达结构的分析：
- “If AI is going to do programming, is it still worth it for us to learn software engineering anymore?” - 这个句子展示了条件句的用法，同时引发思考，适合用于讨论不确定性。
- “AI is really good at generating thousands of lines of code。” - 这里运用了简单现在时，强调AI的能力，能够帮助学习者在表达事实时使用正确时态。
- “It may not work well, prioritizing long-term business goals and assessing trade-offs.” - 此句中使用了情态动词和现在分词结构，是表达可能性和正在进行的动作的好例子。
学习这些句型能够帮助英语学习者在 英语影子跟读 练习中更加流利、自信。
常见发音陷阱
在视频中，有些词汇和短语的发音可能对学习者而言是一个挑战。例如：
- “programming” - 注意"pro"的发音，要清晰而流畅。
- “collaborate” - 这个词的音节较多，注意重音的位置和发音的连贯性。
- “understand” - 在快速语言环境中，尤其需要注意这个词的发音，确保不模糊。
通过观察和纠正这些发音，学习者可以有效 提高英语发音，并提升其沟通能力。利用视频资源结合 shadowing site，进行反复练习，将大大加强你的语言表达能力。
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。
如何在ShadowingEnglish上有效练习
- 选择您的视频： 挑选一段语音清晰、自然的YouTube视频。TED演讲，BBC新闻，电影片段，播客或雅思口语范例都很好。将URL粘贴到搜索栏中。从较短的视频（短于5分钟）以及您真正感兴趣的内容开始——兴趣是最重要的导师。
- 先听，理解上下文： 第一次听的时候，将速度保持在1倍速并仅仅倾听。还不要尝试重复。专注于理解其含义，收集新词汇，并注意讲话人如何强调单词，连读声音及使用停顿。
- 设置跟读模式：
- 等待模式：选择
+3s或+5s——在每句话播放完毕后，视频会自动暂停以便您有时间大声重复它。如果您想完全控制并在每次重复后由您自己点击下一步，请选择手动。 - 字幕同步：YouTube字幕有时会在音频前或后略微出现。使用
±100ms使它们完美对齐以助您准确跟读。
- 等待模式：选择
- 大声跟读（核心练习）： 这是真正发生改变的一步。当一个句子播放出来立刻——或在暂停期间——大声、清晰且自信地重复出来。千万不要只是张张嘴：要模仿说话者的准确节奏、重音、音高和连读。力求听上去就像说话者的影子，而不仅是逐字背诵。使用重复功能多次练习同一个句子，直到感觉自然为止。
- 提高难度： 当练习段落变得相对舒适后，就去挑战自我。将速度增加至 <code>1.25x</code> 或甚至 <code>1.5x</code> 以训练高速语言反射。或者将等待模式调整为 <code>关闭</code> 以进行连续跟读——这是最进阶同样收益最大的模式。持续的每日15–30分钟的练习将可以在几周内产生可见的效果。