शैडोइंग अभ्यास: Learning Software Engineering During the Era of AI | Raymond Fu | TEDxCSTU - YouTube के साथ अंग्रेजी बोलना सीखें

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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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पहले सुनें, संदर्भ समझें: पहली बार 1x गति पर बस सुनें। अभी दोहराने की कोशिश न करें। अर्थ समझने, नई शब्दावली सीखने पर ध्यान दें।
शैडोइंग मोड सेट करें:
- प्रतीक्षा मोड: +3s या +5s चुनें — प्रत्येक वाक्य के बाद वीडियो स्वचालित रूप से रुकता है ताकि आप दोहरा सकें।
- सब सिंक: YouTube उपशीर्षक कभी-कभी ऑडियो से थोड़ा आगे या पीछे होते हैं। सटीक संरेखण के लिए ±100ms का उपयोग करें।
ज़ोर से शैडोइंग करें (मुख्य अभ्यास): जैसे ही वाक्य बजे — या विराम के दौरान — स्पष्ट और आत्मविश्वास से ज़ोर से दोहराएं। वक्ता की लय, ज़ोर, पिच और जुड़ी ध्वनियों की नकल करें।
चुनौती बढ़ाएं: जब एक अनुच्छेद आरामदायक लगे, तो गति <code>1.25x</code> या <code>1.5x</code> तक बढ़ाएं। प्रतिदिन 15-30 मिनट का लगातार अभ्यास हफ़्तों में ध्यान देने योग्य परिणाम देगा।

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शैडोइंग अभ्यास: Learning Software Engineering During the Era of AI | Raymond Fu | TEDxCSTU - YouTube के साथ अंग्रेजी बोलना सीखें

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