ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: AI Agents, Clearly Explained

AI AI AI AI AI AI - You know, more agentic.

- Agentic capabilities.

- AI agent.

- Agents.

- Agentic workflows.

- Agents.

- All right, most explanations of AI agents is either too technical or too basic.

This video is meant for people like myself.

You have zero technical background, but you use AI tools regularly,

and you wanna learn just enough about AI agents In this video, we'll follow a simple one,

two, three, learning path by building on concepts you already understand, like ChatGPT, and then moving on to AI workflows,

and then finally, AI agents, all the while using examples you will actually encounter in real life.

And believe me, I wanna tell you, those intimidating terms you see everywhere, like RAG, RAG, or React, they're a lot simpler than you think.

Let's get started.

Kicking things off at level one, large language models.

Popular AI chatbots like ChatGPT, Google Gemini, and Cloud are applications built on top of large language models, LLMs.

And they're fantastic at generating and editing text.

Here's a simple visualization.

"You, the human, provides an input, and the LLM produces an output based on its training data." For example,

if I were to ask ChachiPT to draft an email requesting a coffee chat, my prompt is the input,

and the resulting email that's way more polite than I would ever be in real life is the output.

So far, so good, right?

Simple stuff.

But what if I asked ChatGPT when?

"my next coffee chat is." Even without seeing the response, both you and I know ChatGPT is gonna fail because it doesn't know that information.

It doesn't have access to my calendar.

This highlights two key traits of large language models.

First, despite being trained on vast amounts of data, they have limited knowledge of proprietary information, like our personal information or internal company data.

Second, LLMs are passive.

Keep these two traits in mind moving forward.

Moving to level two, AI workflows.

Let's build on our example.

What if I, a human, told the LLM, every time I ask about a personal event,

perform a search query and fetch data from my Google Calendar before providing a response.

With this logic implemented, the next time I ask, when is my coffee chat with Elon Husky,

I'll get the correct answer because the LLM will now first go into my Google Calendar to find that information.

Here's where it gets tricky.

What if my next follow -up question is, what will the weather be like that day?

The LLM will now fail at answering the query

because the path we told the LLM to follow is to always search my Google Calendar, which does not have information about the weather.

This is a fundamental trait of AI workflows.

They can only follow predefined paths set by humans.

And if you want to get further.

What if I added more steps into the workflow by allowing the LM to access the weather via an API, and then just for fun, use a text to audio model to speak the answer.

The weather forecast for seeing Elon Husky is sunny with a chance of being a good boy.

Here's the thing.

No matter how many steps we add, this is still just an AI workflow.

Even if there were hundreds or thousands of steps, if a human is the decision maker, there is no AI agent involvement.

Pro tip, retrieval augmented generation or RAG, is a fancy term that's thrown around a lot.

In simple terms, RAG is a process that helps AI models look things up before they answer, like accessing my calendar or the weather service.

Essentially, RAG is just a type of AI workflow.

By the way, I have a free AI toolkit

that cuts through the noise and helps you master essential AI tools and workflows.

I'll leave a link to that down below.

Here's a real world example.

Following Helena Liu's amazing tutorial, I created a simple AI workflow using make .com.

Here you can see that first, I'm using Google Sheets to do something.

Specifically, I'm compiling links to news articles in a Google Sheet, and this is that Google Sheet.

Second, I'm using perplexity to summarize those news articles.

Then, using Claude and using a prompt that I wrote, I'm asking Claude to draft a LinkedIn and Instagram post.

Finally, I can schedule this to run automatically every day at 8 :00 AM.

As you can see, this is an AI workflow because it follows a predefined path set by me.

Step one, you do this.

Step two, you do this.

Step three, you do this.

And finally, remember to run daily at 8 :00 AM.

One last thing, if I test this workflow and I don't like the final outputs, of the LinkedIn post for example,

as you can see right here, it's not funny enough.

And I'm naturally hilarious, right?

I'd have to manually go back and rewrite the prompt for Claude.

Okay.

And this trial and error iteration is currently being done by me, a human.

So keep that in mind moving forward.

All right, level three AI agents.

Continuing the make .com example, let's break down what I've been doing so far as the human decision maker.

With the goal of creating social media posts based off of news articles, I need to do two things.

First, reason or think about the best approach.

I need to first compile the news articles, then summarize them, then write the final posts.

Second, take action using tools.

I need to find and link to those "use perplexity" for real -time summarization, and then "clawed" for copywriting.

So, and this is the most important sentence in this entire video, the one massive change that has to happen in order for this AI workflow to become an AI agent,

is for me, the human decision maker, to be replaced by an LLM.

In other words, the AI agent must reason.

What's the most efficient way to compile these news articles?

Should I copy and paste each article into a Word document?

to compile links to those articles, and then use another tool to fetch the data.

Yes, that makes more sense.

The AI agent must act, aka do things via tools.

Should I use Microsoft Word to compile links?

No, inserting links directly into rows is way more efficient.

What about Excel?

No, the user has already connected their Google account with make .com, so Google Sheets is a better option.

Pro tip: because of this,

the most common configuration Agents must reason and act, so re -act.

100

Sounds simple once we break it down, right?

101

A third key trait of AI agents is their ability to iterate.

102

Remember when I had to manually rewrite the prompt to make the LinkedIn post funnier?

103

I, the human, probably need to repeat this iterative process a few times to get something I'm happy with, right?

104

An AI agent will be able to do the same thing autonomously.

105

In our example, the AI agent would autonomously add in another LLM to critique its own output.

106

Okay, I've drafted V1 of a LinkedIn post.

107

How do I make sure it's good?

108

Oh, I know.

109

I'll add another step where an LLM will critique the post based on LinkedIn post practices.

110

And let's repeat this until the best practices criteria are all met.

111

And after a few cycles of that, we have the final output.

112

That was a hypothetical example.

113

So let's move on to a real world AI agent example.

114

Andrew Ng is a preeminent figure in AI and he created this demo website that illustrates how an AI agent works.

115

I'll link the full video down below, but when I search for a keyword like skier, Enter.

116

The AI vision agent in the background is first reasoning

117

What a skier looks like a person on skis going really fast in snow for example, right?

118

I'm not sure and then it's acting by I looking at clips in video footage,

119

trying to identify what it thinks a skier is,

120

indexing that clip and then returning that clip to us.

121

Although this might not feel impressive, remember that an AI agent did all that instead of a human reviewing the footage beforehand,

122

manually identifying the skier and adding tags like skier, mountain, ski, snow.

123

The programming is obviously a lot more technical and complicated than what we see in the front end, but That's the point of this demo, right?

124

The average user like myself, wants a simple app that just works without me having to understand what's going on in the backend.

125

Speaking of examples, I'm also building my very own basic AI agent using NAN.

126

So let me know in the comments what type of AI agent you'd like me to make a tutorial on next.

127

To wrap up, here's a simplified visualization of the three levels we covered today.

128

Level one, we provide an input and the LM responds with an output easy.

129

Level two, for AI workflows, we provide an input and tell the LLM to follow a predefined path that may involve in retrieving information from external tools.

130

The key trait here is that the human programs a path for the LLMs to follow.

131

Level three, the AI agent receives a goal, and the LLM performs reasoning to determine how best to achieve the goal,

132

takes action using tools to produce an interim result, observes that interim result, and decides whether iterations are required,

133

and produces a final output that achieves the initial goal.

134

The key trait here is that the LLM is the decision maker in the workflow.

135

If you found this helpful, you might want to learn how to build a prompts database in Notion.

136

- Bye.

137

Have a great one.

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เกี่ยวกับบทเรียนนี้

ในบทเรียนนี้ ผู้เรียนจะได้ฝึกทักษะการพูดภาษาอังกฤษโดยการเรียนรู้เกี่ยวกับ AI และการทำงานของ AI agents ผ่านการฟังและดูวิดีโอที่ให้ข้อมูลที่ยอดเยี่ยม ตัวอย่างในวิดีโอจะช่วยให้ผู้เรียนเข้าใจแนวคิดพื้นฐานเกี่ยวกับ AI และสามารถต่อยอดไปยังการใช้งานที่ซับซ้อนยิ่งขึ้น เมื่อผู้เรียนได้ฝึก shadow speak กับวิดีโอ จะช่วยให้ปรับปรุงการออกเสียงภาษาอังกฤษ และสร้างความมั่นใจในทักษะการสื่อสาร

คำศัพท์และวลีสำคัญ

AI agents: ตัวแทนของปัญญาประดิษฐ์ที่สามารถทำงานตามคำสั่งหรือข้อมูลที่ได้รับ
large language models (LLMs): โมเดลภาษาขนาดใหญ่ที่ใช้ในการสร้างและแก้ไขข้อความ
workflows: กระบวนการทำงานที่ถูกกำหนดโดยมนุษย์ให้กับ AI
input: ข้อมูลที่ผู้ใช้ป้อนให้ AI
output: ผลลัพธ์ที่ AI สร้างขึ้นจากข้อมูลที่ได้รับ
API: วิธีการที่อนุญาตให้โปรแกรมซอฟต์แวร์สื่อสารกัน
query: คำถามที่ส่งไปยัง AI เพื่อให้ได้รับข้อมูล

เคล็ดลับการฝึกฝน

เพื่อให้การฝึก shadow speech กับวิดีโอนี้มีประสิทธิภาพสูงสุด ควรใช้ความเร็วในการพูดที่เหมาะสม ซึ่งจะช่วยให้ผู้เรียนสามารถจับจังหวะและออกเสียงตามได้อย่างถูกต้อง เมื่อฟังเนื้อหาในวิดีโอ ให้หยุดในแต่ละช่วงที่เสียงพูดอยู่ เพื่อให้ผู้เรียนมีโอกาส shadow speak ก่อนที่จะไปยังส่วนถัดไป นอกจากนี้ ควรพยายามทำความเข้าใจบริบทของการสนทนา เพื่อให้การออกเสียงในภาษาอังกฤษเป็นไปอย่างเหมาะสม การฟังซ้ำ ๆ จะช่วยให้ปรับปรุงการออกเสียงภาษาอังกฤษได้ดียิ่งขึ้น และการพูดตามเสียงก็จะช่วยให้เข้าใจภาษาที่ใช้ในชีวิตประจำวันมากขึ้น

ไวยากรณ์ในวิดีโอนี้

โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้าง	ในวิดีโอ
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ	is meant · being trained · being done
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน	have limited · has already connected · I've drafted

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Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ

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เกี่ยวกับบทเรียนนี้

คำศัพท์และวลีสำคัญ

เคล็ดลับการฝึกฝน

ไวยากรณ์ในวิดีโอนี้

เทคนิค Shadowing คืออะไร?