跟读练习: What are AI Agents? - 通过视频学习英语口语
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2024 will be the year of AI agents.
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So what are AI agents?
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And to start explaining that, we have to look at the various shifts that we're seeing in the field of generative AI.
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And the first shift I would like to talk to you about is this move from monolithic models to compound AI systems.
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So models on their own are limited by the data they've been trained on.
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So that impacts what they know about the world and what sort of tasks they can solve.
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They are also hard to adapt.
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So you could tune a model, but it would take an investment in data, and in resources.
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So let's take a concrete example to illustrate this point.
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I want to plan a vacation for this summer, and I want to know how many vacation days are at my disposal.
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What I can do is take my query, feed that into a model that can generate a response.
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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.
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So models on their own could be useful for a number of tasks, as we've seen in other videos.
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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.
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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.
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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.
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So that same query would get fed into the language model.
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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.
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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.
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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.
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So what does that mean?
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By the term "system", you can understand there's multiple components.
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So systems are inherently modular.
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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.
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So I can have output verifiers.
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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.
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I can combine that with searching databases.
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I can combine that with different tools.
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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.
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And this is inherently easier to solve for than tuning a model.
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So that makes this much faster and quicker to adapt.
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Okay, so the example I use below, is an example of a compound AI system.
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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.
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Most RAG systems and the example I use below are defined in a certain way.
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So if I bring a very different query, let's ask about the weather in this example here.
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It's going to fail because this the path that this program has to follow is to always search my vacation policy database.
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And that has nothing to do with the weather.
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So when we say the path to answer a query, we are talking about something called the control logic of a program.
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So compound AI systems, we said most of them have programmatic control logic.
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So that was something that I defined myself as the human.
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Now let's talk about, where do agents come in?
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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.
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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.
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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.
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And on the other end of the spectrum, you're designing your system to think slow.
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So, create a plan, attack each part of the plan, see where you get stuck, see if you need to readjust the plan.
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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.
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And when we put a LLMs in charge of the logic, this is when we're talking about an agentic approach.
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So let's break down the components of LLM agents.
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The first capability is the ability to reason, which we talked about.
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So this is putting the model at the core of how problems are being solved.
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The model will be prompted to come up with a plan and to reason about each step of the process along the way.
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Another capability of agents is the ability to act.
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And this is done by external programs that are known in the industry as tools.
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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.
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So an example of a tool can be search, searching the web, searching a database at their disposal.
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Another example can be a calculator to do some math.
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This could be a piece of program code that maybe might manipulate the database.
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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.
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And there's so many other possibilities of what can do here.
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So these can be APIs.
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Basically any piece of external program you want to give your model access to.
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Third capability, that is the ability to access memory.
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And the term "memory" can mean a couple of things.
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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.
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So those inner logs can be stored and can be useful to retrieve at different points in time.
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But also this could be the history of conversations that you as a human had when interacting with the agent.
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And that would allow to make the experience much more personalized.
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So the way of configuring agents, there's many are ways to approach it.
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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.
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So let's make this very concrete.
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What happens when I configure a REACT agent?
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You have your user query that gets fed into a model. So an alarm the alarm is given a prompt.
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So the instructions that's given is don't give me the first answer that pops to you.
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Think slow planning your work. And then try to execute something.
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Tried to act. And when you want to act, you can define whether.
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If you want to use external tools to help you come up with the solution.
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Once you get you call a tool and you get an answer.
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Maybe it gave you the wrong answer or it came up with an error.
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You can observe that. So the alarm would observe.
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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.
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So let's go back and make this very concrete again.
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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.
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I'm planning to go on to Florida next month.
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I'm planning on being outdoors a lot and I'm prone to burning.
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So I want to know what is the number of two ounce sunscreen bottles that I should bring with me?
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And this is a complex problem. So there's a first thing.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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And we're still in the early days of agent systems.
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We're seeing rapid progress when you combine the effects of system design with a genetic behavior.
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And of course, you will have a human in the loop in most cases as the accuracy is improving.
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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的新趋势
视频中提到，2024年将是AI agents的元年。随着生成式AI的发展，我们正从单一模型转向复合AI系统。单一模型受限于训练数据，难以适应复杂任务，而复合系统通过整合多种组件（如数据库、工具等），能更高效地解决问题。比如规划假期时，模型需访问假期数据库才能给出准确答案，这就是复合系统的应用。
日常交流高频短语
- monolithic models：单一模型
- compound AI systems：复合AI系统
- retrieval augmented generation (RAG)：检索增强生成
- control logic：控制逻辑
- agentic approach：智能体方法
影子跟读实战指南
想要通过这段视频提升英语口语，影子跟读是个好方法。首先，选择一个合适的shadowing site，确保音频清晰。然后，分步骤练习：
- 听辨关键信息：先完整听一遍视频，抓住“AI agents”“复合系统”等核心概念，理解逻辑框架。
- 逐句shadow speech：播放一句，暂停并跟读，注意模仿语调与节奏。重点练习长难句，如“models on their own are limited by the data they've been trained on”，拆分结构，重复训练。
- 整合练习：尝试无暂停跟读，提升反应速度。若觉得困难，可使用shadowspeaks工具辅助，逐词校准发音。
- 应用拓展：用学到的短语描述AI技术，比如“retrieval augmented generation is widely used in compound AI systems”，既能巩固词汇，又能锻炼雅思口语练习能力。
坚持每天15分钟，你会发现英语口语进步显著！无论是日常交流还是雅思口语练习，都能更自信地表达。
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | are limited · is stored · would be prompted |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | they've been trained · we've seen · I've given |
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。