Luyện nói tiếng Anh bằng Shadowing qua video: What Does an AI Engineer Do Now? - Aparna Dhinakaran, Arize AI
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171 câu
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1
Awesome.
Tuyệt vời.
2
Well, welcome to AI Engineer.
Chào mừng bạn đến với AI Engineer.
3
I thought given we're at AI Engineer New York, we'd kind of revisit the question of what does an AI engineer do?
Tôi nghĩ rằng vì chúng ta đang tham dự sự kiện AI Engineer New York, chúng ta hãy cùng xem lại câu hỏi: một kỹ sư AI làm những công việc gì?
4
I feel like every year we have an idea of what an AI engineer does, and that definition kind of changes.
Tôi cảm thấy mỗi năm chúng ta lại có một cách hiểu khác về công việc của một kỹ sư AI, và định nghĩa đó cứ thay đổi liên tục.
5
And as I was putting this talk together, it was super timely,
Và khi tôi đang chuẩn bị bài nói chuyện này, mọi thứ diễn ra đúng vào thời điểm cực kỳ thích hợp.
6
because Karpathy actually just tweeted something this weekend where he kind of talks about this growing gap between
vì cuối tuần này Karpathy thực sự vừa đăng một bài trên Twitter, trong đó ông ấy có nhắc đến khoảng cách ngày càng lớn giữa
7
basically folks at the frontier who are running thousands of agents and swarms of always -on on agents,
về cơ bản là những người ở tuyến đầu đang vận hành hàng nghìn tác nhân và các cụm tác nhân hoạt động liên tục không ngừng,
8
and he really kind of drops this funnel where six billion plus users have maybe touched or interfaced with an LLM.
và ông ấy thực sự đưa ra một con số ước tính rằng hơn sáu tỷ người dùng có thể đã từng tiếp xúc hoặc tương tác với một mô hình ngôn ngữ lớn.
9
One to two billion are maybe like casual free tier users.
Khoảng một đến hai tỷ người có lẽ là những người dùng cấp miễn phí, chỉ dùng thỉnh thoảng.
10
20 million are professionals who have maybe started using it for more, taking some piece of work that would have taken weeks down to days.
20 triệu người là những chuyên gia có lẽ đã bắt đầu sử dụng nó để làm được nhiều việc hơn, rút ngắn thời gian thực hiện một công việc từ vài tuần xuống còn vài ngày.
11
But really, those at the Frontier Labs are, which is a super small percentage, it's like 5 ,000 users,
Nhưng thực sự, những người dùng tại Frontier Labs chỉ chiếm một tỷ lệ rất nhỏ, khoảng 5.000 người dùng.
12
are the ones who are running thousands and thousands of agents at once.
là những người đang vận hành hàng nghìn, hàng nghìn tác nhân cùng một lúc.
13
And as I look at this, it really is, I mean, he's kind of dropped a bit of the funnel.
Và khi tôi nhìn vào cái này, thực sự là, ý tôi là, anh ấy đã hơi bỏ lỡ một phần của quy trình chuyển đổi rồi.
14
I mean, the first question anyone looks at this is that, where am I on this?
Ý tôi là, câu hỏi đầu tiên mà ai cũng đặt ra khi nhìn vào cái này là tôi đang ở đâu trong đó?
15
And, you know, he's kind of drawn out a bit of a roadmap of how does everyone get to, in the next couple of years,
Và bạn biết đấy, anh ấy đã vạch ra một lộ trình về cách mọi người sẽ đạt được điều đó trong vài năm tới.
16
one of those 5 ,000 users who are running thousands of agents.
một trong số 5.000 người dùng đang vận hành hàng nghìn tác nhân tự động.
17
And this has been happening, I'd say, for a couple of decades now, where the abstraction layer has gotten higher and higher.
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I mean, definitely faster in the last couple of years, but we've gone from engineers writing higher -level abstraction languages to,
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I mean, just copy -pasting code, using local agents, to using cloud agents,
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to really the expectation now is how do all of us start running a team of agents?
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or factories of agents internally and build toward that.
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And what this has led to is essentially what is the then and now of what an AI engineer is.
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And this is a bit, I think, of a roadmap for some,
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but I think internally this is what we've already started to kind of push towards.
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As an AI engineer, your job is no longer to just write the feature, write an AI feature.
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It's about how do you build the loop to write that feature.
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It's no longer about just reading the diff and actually doing code review.
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It's actually how do you set up systems and agents to do the code reviews,
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and how do you write evals to actually do the non -deterministic, to review those non -deterministic tasks.
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It's actually no longer even about just setting up metrics and software kind of telemetry, but it's actually how do you have agents go through
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and read the observability data and and have agents actually consume that telemetry to find patterns.
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And so this loop that we've kind of been talking about, which is the build, the eval, ship, you monitor, you improve it,
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well, the AI engineer is now actually responsible, not just for doing each of those steps,
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but how do you build the loop that actually does each of those steps?
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And at Arise, one of the things we've rolled out in our product,
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and I'm gonna talk about a bit of what we've learned in building this, is we built
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Signal to actually run this loop for AI engineers.
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What Signal does is that
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Signal actually looks at all of the traces, it reads the telemetry, it identifies patterns in that telemetry,
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and it can actually put up a fix or a PR if it has access to the repo.
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And Signal, Signal's an always -on agent.
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So this is an example of how
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Signal starts, but it actually starts off by just reading the traces.
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Super simple example, but it goes through, reads, and all this is configurable,
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but a certain volume of traces that came in in some cadence,
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it reads it, and then the goal is to actually identify issues from that telemetry data.
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It has to keep track of issues that it's previously seen.
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So we have to consider the memory of did it see something before
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that it thought was an issue? did something seem like an issue
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but it wasn't actually captured
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and now as it collects more data it actually thinks it's much more of a critical critical issue
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and so signal actually starts off by reading the traces to identify problems
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and so one of the first things that that we realized was
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that the future of who's actually consuming telemetry data has really changed
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It's no longer about humans consuming telemetry data.
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It's not even about humans consuming it and agents kind of taking on the task,
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but it really is how do you set up your telemetry data so that agents are the primary consumers of that data?
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And what that does is that that observability data can become the trigger for actually running and triggering these always -on agents.
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And so the first thing that if you're building a team of, of a factory of really always on agents is,
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what triggers these agents to wake up or start?
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And there can be a variety of triggers.
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For signal, for us, it's configurable to run on an ongoing cadence.
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So every three hours, six hours, go pull the last X amount of traces, read them, identify problems.
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It can also be triggered by events.
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So Slack messages, and some user complains about something.
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It can be a metric -based trigger, but these are essentially triggers to kick up the agent actually starting.
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Once the agent actually starts, we have to spend some time figuring out, well, what does an agent run actually look like?
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For us, we actually pull in all of the context of the traces, any additional third -party data,
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so we allow collecting to APM logs, APM metrics, but all of the third -party telemetry data that we want to pull in,
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access to the repo, access to the actual,
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the access to any keys that are needed to actually run this agent,
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but all of this information is put inside of a sandbox, and this is actually what's started up every time the agent wakes up.
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And so the agent's triggered, we build a sandbox, We put everything that we need inside of that sandbox.
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There's a proxy.
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The only way to get information in and out of the sandbox is by the single proxy.
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And everything that happens inside of that sandbox is actually already getting traced.
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And every output, so the issues that are generated and created are all getting saved to some kind of memory.
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And so this happens every single time the agent actually runs.
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Every time the agent finishes running, so it's sweep through all of the traces, it either found something or it didn't.
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There's what actually stays and what actually gets thrown away.
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What stays is that if it found any issues, all of the output is actually saved off in some kind of memory
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so that the next time an agent gets spun up, it doesn't have to start from ground zero.
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It has a history of what are the issues that it actually found.
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The agents are traced.
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So all of the telemetry data of what the agent called, you know, if it actually put up a PR,
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all of those things are actually traced.
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And any skills that it may have learned while actually doing a run can also get saved to the actual repo.
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But the things that go away is the sandbox gets torn down.
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Any credentials, you know, obviously get removed.
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The repo access is removed and so all of the stuff
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that doesn't need to be saved kind of gets torn down after the run of Signal. And we've designed
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Signal in a way to actually keep this agent durable.
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So that means that either if the sandbox fails or if the harness fails,
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then it shouldn't have to start everything from scratch in the middle of the run.
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It should actually just be able to pick up from the step
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that it failed and all of the state
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so -called is kind of saved off in kind of a memory outside of the sandbox.
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And so we've designed this to actually be a very durable agent so that if it fails, it can spin it back up.
99
You can launch a new sandbox, launch a new harness to kind of pick back up where it started.
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So all of this is essentially the underpinnings of how we built signal to run every single run.
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Agent gets triggered, agent gets started, loaded with all the context, the evidence, it sweeps through the telemetry data, it either finds something or it doesn't,
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all of that gets saved to memory.
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If something fails in the middle, the agent can get reset up.
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And we built
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Signal to also be agnostic of the model or the harness.
106
So today, Signal supports not just cloud code, which is what we started with, but it also supports cursor and codecs,
107
and the goal is to actually support all of the major, even the open source harnesses out there.
108
And Signal actually also supports multiple, will support kind of multiple models.
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So even if you're using cloud code, you can pick any of the models inside the anthropic family.
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And this is really important to us because,
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one, it's.. an awesome way for us to evaluate the quality of the agent every time we spin it up.
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It helps us control costs so we can see as we use more powerful models the better quality maybe the PRs are,
113
but can we reduce the quality of the, can we reduce the cost and actually still maintain the rigor
114
and maintain the quality of issues that are actually detected by signal?
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So this is something we deeply thought about from first principles when we were designing signal.
116
And this is actually a pretty good segue into what does an AI engineer need to do.
117
Well, an AI engineer really needs to kind of learn how to write evals.
118
And so we actually, every time signal runs, signal finds an issue, it puts up a PR.
119
Well, there's evals that are run on top of the agent to test that PR.
120
And an eval can be as simple as testing something as part of your development workflow, you have a data set,
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you can be a simple prompt with outputs that you're testing to make sure that there's no regressions.
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But these evals can also, they need to be kind of aligned with human judgment.
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And I'd argue
124
that this is actually probably where an AI engineer should be
125
spending a lot of their time as they're building the system to actually run the loop.
126
They should be actually spending time looking at the traces with their agents, identifying what the common errors are.
127
And there's a lot of skills.
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I think I was hearing
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Lance yesterday talk about the Claude skill to build evals.
130
We have a skill with Arise Evaluator to actually help you build and write evals, as well as align those evals with the human judgment.
131
but this is probably where the shift to defining what is good becomes really, really important for an AI engineer to actually do.
132
And for a lot of teams I think we speak with and we work with, their question becomes, well, how do I trust the evaluator?
133
And do I really trust that the eval is going to catch
134
or how do I trust that it's as good as my judgment?
135
And I'd say what we spend a lot of our own time doing for our own products is
136
that we actually evaluate the evaluators.
137
We spend a decent amount of time on actually building these data sets, golden fruit data sets.
138
We run evals, verify them against the human output, and then try to align to make sure
139
that the judge actually agrees with the human and they're in agreement before we actually ship out a new evaluator.
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And these evals can then be used both in an offline testing as well as in an online testing manner.
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And this is probably what I would say as an AI engineer is more and more of what the job is becoming.
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And so what I'm going to leave you guys with is as an AI engineer, you've built this agent.
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You've built this agent to actually run, be always on you run evals before you actually ship it into production.
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You can ship it, and then once it goes into production, the goal is to actually trace everything.
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And tracing everything now means not just tracing the agents, but also tracing the coding agents behind the agents that built this.
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So you can trace cloud code, you can trace cursor, you can trace codecs, but all these coding agents can be traced
147
so you can understand what happened in the factory of agents that you all are building.
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And then these agents that all the traces can then also be evaluated online.
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So there's an offline evaluation when you're actually testing what a new change is,
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but as it goes into production these become your online evals that you're running on top of the telemetry data.
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This for us ends up becoming the new data that then gets pulled back into the agents to read the telemetry data,
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the evals, and surface up kind of problems inside of the traces.
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So the system really becomes, how do you go from collect data, eval that data,
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run agents on top of it so that you can find problems
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and figure out where the gaps are to actually improve your systems?
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So if I go back to kind of the question that we started this whole thing with, It's what does an AI engineer do?
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I mean the gap between,
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I think there's a pretty big gap between what's happening in the labs today and what's happening with a lot of businesses.
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And some of it could just be the data inside of
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businesses are a lot more messy than what is very constrained and testable inside of a lab.
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But AI engineers are kind of the role to close that gap.
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And I think it really starts with tracing everything, tracing the agents you're actually building.
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running evals, being able to learn how to test these products that we're really building, and then how do you build a system so
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that agents can actually improve from the telemetry that's actually being collected so that you're no longer just building individual features,
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but you're actually building the system to improve your products.
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If you want to learn more, come check out.
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We have a booth.
168
You can check out
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Signal, learn more.
170
But thank you so much for listening to us.
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Thank you.
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Từ vựng và ghi chú luyện nói cho bài học này
Bài luyện nói này dựa trên video “What Does an AI Engineer Do Now? - Aparna Dhinakaran, Arize AI”. Những từ được nhắc lại nhiều nhất trong bài: agent, signal, eval, engineer, telemetry. Video này có 171 câu và 2536 từ để luyện shadowing. Phần lời nói dài 15:12. Người nói nói với tốc độ tự nhiên, khoảng 167 từ mỗi phút, gần với hội thoại hằng ngày. 86% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.
Từ vựng quan trọng trong video
15 từ đáng học trong video, kèm phiên âm và nghĩa:
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| trace danh từ | /tɹeɪs/ | dấu, vết |
| trigger danh từ | /ˈtɹɪɡɚ/ | cò |
| loop danh từ | /luːp/ | đường nhánh, thòng lọng |
| cloud danh từ | /ˈklaʊ̯d/ | mây |
| output danh từ | /ˈaʊtpʊt/ | đầu ra |
| collect động từ | /ˈkɑlɪkt/ | thu thập, gom góp |
| consume động từ | /kənˈsum/ | dùng, tiêu thụ |
| spin động từ | /spɪn/ | quay |
| metric tính từ | /ˈmɛt.ɹɪk/ | mét |
| tear động từ | /tɛɹ/ | xé |
| sweep động từ | /swiːp/ | quét dọn, quét |
| offline tính từ | /ɔfˈlaɪn/ | ngoại tuyến, gián tuyến |
| percentage danh từ | /pə(ɹ)ˈsɛntɪd͡ʒ/ | số phần trăm, tỷ lệ phần trăm |
| argue động từ | /ˈɑɹ.ɡju/ | tranh luận |
| capture động từ | /ˈkæp.(t)ʃɚ/ | bắt |
Cụm động từ bạn sẽ nghe
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| check out động từ | tính tiền, trả phòng | |
| wake up động từ | /ˌweɪk ˈʌp/ | thức dậy, dậy lên |
| back up động từ | lùi | |
| throw away động từ | vất, vứt |
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | has gotten · we've gone · has led |
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | wasn't actually captured · be triggered · are needed |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | are, which is · ones who are · about, which is |
Phát âm cần chú ý
Người nói dùng 34 dạng rút gọn, ví dụ you're, we've, doesn't. Hãy nói theo dạng ngắn đúng như bạn nghe.
- Âm “th”: booth /buθ/, anthropic /ænˈθɹɒp.ɪk/
- Âm “sh” và “zh”: evaluation /ɪˌvæljuˈeɪʃn̩/, essentially /ɪˈsɛnʃəli/, capture /ˈkæp.(t)ʃɚ/, casual /ˈkæʒ(u)əl/, expectation /ˌɛk.spɛkˈteɪ.ʃən/
- Từ dài — đặt trọng âm cho đúng: evaluation /ɪˌvæljuˈeɪʃn̩/, essentially /ɪˈsɛnʃəli/, internally /ɪnˈtɜːnəli/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, telemetry /tɪˈlɛm.ɪ.tɹi/
Những âm người Việt thường gặp khó:
- Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: collect /ˈkɑlɪkt/, judgment /ˈd͡ʒʌd͡ʒ.mənt/, decent /ˈdiː.sənt/, detect /dɪˈtɛkt/, lance /læns/
- Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: judgment /ˈd͡ʒʌd͡ʒ.mənt/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, casual /ˈkæʒ(u)əl/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, scratch /skɹæt͡ʃ/
- Âm /æ/ — mở miệng rộng hơn âm “e”: evaluation /ɪˌvæljuˈeɪʃn̩/, lab /læːb/, gap /ɡæp/, capture /ˈkæp.(t)ʃɚ/, casual /ˈkæʒ(u)əl/
Cách luyện với video này
- Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
- Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
- Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như trace, trigger, loop.
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.