跟讀練習: What are AI Agents? - 通過影片學習英語口語

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2024 will be the year of AI agents.

So what are AI agents?

And to start explaining that, we have to look at the various shifts that we're seeing in the field of generative AI.

And the first shift I would like to talk to you about is this move from monolithic models to compound AI systems.

So models on their own are limited by the data they've been trained on.

So that impacts what they know about the world and what sort of tasks they can solve.

They are also hard to adapt.

So you could tune a model, but it would take an investment in data, and in resources.

So let's take a concrete example to illustrate this point.

I want to plan a vacation for this summer, and I want to know how many vacation days are at my disposal.

What I can do is take my query, feed that into a model that can generate a response.

I think we can all expect that this answer will be incorrect, because the model doesn't know who I am and does not have access to this sensitive information about me.

So models on their own could be useful for a number of tasks, as we've seen in other videos.

So they can help with summarizing documents, they can help me with creating first drafts for emails and different reports I'm trying to do.

But the magic gets unlocked when I start building systems around the model and actually take the model and integrate them into the existing processes I have.

So if we were to design a system to solve this, I would have to give the model access to the database where my vacation data is stored.

So that same query would get fed into the language model.

The difference now is the model would be prompted to create a search query, and that would be a search query that can go into the database that I have.

So that would go and fetch the information from the database, output an answer, and then that would go back into the model that can generate a sentence to answer, so, "Maya, you have ten days left in your vacation database." So the answer that I would get here would be correct.

This is an example of a compound AI system, and it recognizes that certain problems are better solved when you apply the principles of system design.

So what does that mean?

By the term "system", you can understand there's multiple components.

So systems are inherently modular.

I can have a model, I can choose between tuned models, large language models, image generation models, but also I have programmatic components that can come around it.

So I can have output verifiers.

I can have programs that can that can take a query and then break it down to increase the chances of the answer being correct.

I can combine that with searching databases.

I can combine that with different tools.

So when we talking about a system approaches, I can break down what I desire my program to do and pick the right components to be able to solve that.

And this is inherently easier to solve for than tuning a model.

So that makes this much faster and quicker to adapt.

Okay, so the example I use below, is an example of a compound AI system.

You also might be popular with retrieval augmented generation (RAG), which is one of the most popular and commonly used compound AI systems out there.

Most RAG systems and the example I use below are defined in a certain way.

So if I bring a very different query, let's ask about the weather in this example here.

It's going to fail because this the path that this program has to follow is to always search my vacation policy database.

And that has nothing to do with the weather.

So when we say the path to answer a query, we are talking about something called the control logic of a program.

So compound AI systems, we said most of them have programmatic control logic.

So that was something that I defined myself as the human.

Now let's talk about, where do agents come in?

One other way of controlling the logic of a compound AI system is to put a large language model in charge, and this is only possible because we're seeing tremendous improvements in the capabilities of reasoning of large language models.

So large language models, you can feed them complex problems and you can prompt them to break them down and come up with a plan on how to tackle it.

Another way to think about it is, on one end of the spectrum, I'm telling my system to think fast, act as programmed, and don't deviate from the instructions I've given you.

And on the other end of the spectrum, you're designing your system to think slow.

So, create a plan, attack each part of the plan, see where you get stuck, see if you need to readjust the plan.

So I might give you a complex question, and if you would just give me the first answer that pops into your head, very likely the answer might be wrong, but you have higher chances of success if you break it down, understand where you need external help to solve some parts of the problem, and maybe take an afternoon to solve it.

And when we put a LLMs in charge of the logic, this is when we're talking about an agentic approach.

So let's break down the components of LLM agents.

The first capability is the ability to reason, which we talked about.

So this is putting the model at the core of how problems are being solved.

The model will be prompted to come up with a plan and to reason about each step of the process along the way.

Another capability of agents is the ability to act.

And this is done by external programs that are known in the industry as tools.

So tools are external pieces of the program, and the model can define when to call them and how to call them in order to best execute the solution to the question they've been asked.

So an example of a tool can be search, searching the web, searching a database at their disposal.

Another example can be a calculator to do some math.

This could be a piece of program code that maybe might manipulate the database.

This can also be another language model that maybe you're trying to do a translation task, and you want a model that can be able to do that.

And there's so many other possibilities of what can do here.

So these can be APIs.

Basically any piece of external program you want to give your model access to.

Third capability, that is the ability to access memory.

And the term "memory" can mean a couple of things.

So we talked about the models thinking through the program kind of how you think out loud when you're trying to solve through a problem.

So those inner logs can be stored and can be useful to retrieve at different points in time.

But also this could be the history of conversations that you as a human had when interacting with the agent.

And that would allow to make the experience much more personalized.

So the way of configuring agents, there's many are ways to approach it.

One of the more most popular ways of going about it is through something called ReACT, which, as you can tell by the name, combines the reasoning and act components of LLM agents.

So let's make this very concrete.

What happens when I configure a REACT agent?

You have your user query that gets fed into a model. So an alarm the alarm is given a prompt.

So the instructions that's given is don't give me the first answer that pops to you.

Think slow planning your work. And then try to execute something.

Tried to act. And when you want to act, you can define whether.

If you want to use external tools to help you come up with the solution.

Once you get you call a tool and you get an answer.

Maybe it gave you the wrong answer or it came up with an error.

You can observe that. So the alarm would observe.

The answer would determine if it does answer the question at hand, or whether it needs to iterate on the plan and tackle it differently. Up until I get to a final answer.

So let's go back and make this very concrete again.

Let's talk about my vacation example. And as you can tell, I'm really excited to go on one, so I want to take the rest of my vacation days.

I'm planning to go on to Florida next month.

I'm planning on being outdoors a lot and I'm prone to burning.

So I want to know what is the number of two ounce sunscreen bottles that I should bring with me?

And this is a complex problem. So there's a first thing.

There's a number of things to plan. One is how many vacation days are my planning to take? And maybe that is information the system can retrieve from its memory. Because I asked that question before.

Two is how many hours do I plan to be in the sun? I said, I plan to be in there a lot, so maybe that would mean looking into the weather forecast, for next month in Florida and seeing what is the average sun hours that are expected. Three is trying maybe going to a public health website to understand what is the recommended dosage of sunscreen per hour in the sun.

And then for doing some math, to be able to determine how much of that sunscreen fits into two ounce bottles. So that's quite complicated.

But what's really powerful here is there's so many paths that can be explored in order to solve a problem. So this makes the system quite modular.

And I can hit it with much more complex problems. So going back to the concept of compound AI systems, compound AI systems are here to stay. What we're going to observe this year is that they're going to become more agent tech. The way I like to think about it is you have a sliding scale of AI autonomy. And you would the person defining the system would examine what trade offs they want in terms of autonomy in the system for certain problems, especially problems that are narrow, well-defined. So you don't expect someone to ask them about the weather when they need to ask about vacations. So a narrow problem set.

You can define a narrow system like this one. It's more efficient to go the programmatic route because every single query will be answered the same way.

If I were to apply the genetic approach here, there might be unnecessarily looping and iteration. So for narrow problems, pragmatic approach can be more efficient than going the generic route. But if I expect to have a system, accomplish very complex tasks like, say, trying to solve GitHub issues independently, and handle a variety of queries, a spectrum of queries. This is where an agent de Groot can be helpful, because it would take you too much effort to configure every single path in the system.

And we're still in the early days of agent systems.

We're seeing rapid progress when you combine the effects of system design with a genetic behavior.

And of course, you will have a human in the loop in most cases as the accuracy is improving.

I hope you found this video very useful, and please subscribe to the channel to learn more.

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AI agents：生成式AI的新趨勢

影片中提到，2024年將是AI agents的元年。隨著生成式AI的發展，我們正從單一模型轉向複合AI系統。單一模型受限於訓練資料，難以適應複雜任務，而複合系統通過整合多種元件（如資料庫、工具等），能更高效地解決問題。比如規劃假期時，模型需訪問假期資料庫才能給出準確答案，這就是複合系統的應用。

日常交流高頻短語

monolithic models：單一模型
compound AI systems：複合AI系統
retrieval augmented generation (RAG)：檢索增強生成
control logic：控制邏輯
agentic approach：智慧體方法

影子跟讀實戰指南

想要通過這段影片提升英語口語，影子跟讀是個好方法。首先，選擇一個合適的shadowing site，確保音訊清晰。然後，分步驟練習：

聽辨關鍵資訊：先完整聽一遍影片，抓住“AI agents”“複合系統”等核心概念，理解邏輯框架。
逐句shadow speech：播放一句，暫停並跟讀，注意模仿語調與節奏。重點練習長難句，如“models on their own are limited by the data they've been trained on”，拆分結構，重複訓練。
整合練習：嘗試無暫停跟讀，提升反應速度。若覺得困難，可使用shadowspeaks工具輔助，逐詞校準發音。
應用拓展：用學到的短語描述AI技術，比如“retrieval augmented generation is widely used in compound AI systems”，既能鞏固詞彙，又能鍛鍊雅思口語練習能力。

堅持每天15分鐘，你會發現英語口語進步顯著！無論是日常交流還是雅思口語練習，都能更自信地表達。

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	they've been trained · is stored · would be prompted
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	they've been trained · we've seen · I've given

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: What are AI Agents? - 通過影片學習英語口語

選擇練習模式

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AI agents：生成式AI的新趨勢

日常交流高頻短語

影子跟讀實戰指南

影片中的語法

什麼是跟讀法？