ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: How Disruptive Will Generative AI Be? A Micro-Level Analysis of Evidence and Expectations

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Right, well, up until we sort out the screens and the presentation, the talk is about AI and disruption.

And before I start, I'd like to, as we sort out technology, I'd like you to think about the two keynotes that we have heard in this room.

Think about the keynote this morning, a keynote about sunniness and optimism and about the disruptive power of AI

that is about to sweep us from under the feet.

It's going to be deserving of its own nomenclature.

We'll live through it and it'll change us all entirely.

It'll democratize entrepreneurship and it's going to lead to a brighter future.

Think about the keynote that we heard yesterday.

90 % get it wrong.

10%, if we are generous, might get it right.

And we are kind of trying.

And if they have everything sorted out, if they have their strategy sorted out, we're here, then things will pan out.

That is a rather starkly different image from two industry people who either sponsor or are related to this event.

That tells you something.

And it tells you where we are, because there is a very interesting bimodal reality.

And I can tell you that when you speak with executives who speak to the press, you hear what you heard this morning.

When you hear with senior people behind closed doors, you hear a very different story.

And that's part of my motivation.

Where the heck are we?

Is AI going to disrupt us or not?

Why do we have these seemingly different structures about the world?

On the one hand, everything is changing.

On the other hand, there is very little that we can see on the aggregate.

There are a number of do -gooders.

I do apologize to MGI.

I don't quite apologize.

I think that they've lost um any sense of rigor um uh

that say we're all going to be rich rich rich rich rich

so there's this paper

that i saw um uh plant saying profits are going to go up by 4 .4 trillion dollars

which is rather bizarre you know i i asked actually actually

michael chew in a cc the guy running mgi said guys

i just these numbers don't make sense how did you get to them

and the answer is well we looked at productivity improvements we

multiplied it by a number of countries a number of companies uh the assets under management

and the entire world it's a big number i'm like okay

that sounds sweet but your strategy consultants this

if it is available to everyone would have been efficiency wages

that um uh that would have been competed down is is there anything

that are missing and the answer is not quite

and i understand it

because the people who are involved in the industry who want to tell you

that you should open your wallets by the way the allocation

of capital in terms of AI projects is not put in

under up until now at least the same category as any other capex

or opex business

and it does give a great justification to say the world

is changing we need to change as well on the other hand

if you start looking at things like the Gartner cycle you're going to see

that we are at the very peak of the hype cycle people get excited

when we get excited we get frothy

when we get frothy we lose our mind

and our role as academics is to ground the conversation bring some data

and then say what the heck is happening provided

that we do not have conflicts of interest

and we want to understand what's going on well i'm going

to give you already a way part of what the story is

and we're seeing that there are some doubts

that are raised even at the level of pilots do they pan out

or do they uh not pan out but um i think that the challenge,

and yesterday some of you may have heard my grumble with the O -Net focus,

the emphasis has been on looking at things that exist at the task or the modular or the professional level.

Now this is important and I'll tell you that's important

and it may have been the main reason that our field is so confused about the potential disruptive impact of AI.

We are looking at things at the level of the task

and then in the O -Net studies we aggregate tasks to profession

and we say whoo big change is gonna happen

because professions are changing or we look at an organization we look at the task and they think whoo

big change are gonna happen because what are organizations other than bundles of tasks

and I'm gonna take issue with

that now I have to say before thinking about the theoretical

has last year I was trying to figure out what exactly the disruptive impact of AI is

and I realized that we didn't have any systematic study

that looked not at whether AI is good at doing a particular task

and I'm not speaking about authors writing email on planes I'm speaking about tasks

that may be even more intricately involved in what organizations And there wasn't much.

So I said, OK, thankfully, the UK...

uh regional innovation fund funded a big study where i said look i'm going to go and i'm going to

explore whether directors in the uk see disruption as a result

of ai let's not look at the task let's look at organizations

and let's look at the people who run organizations

and then say hey is anything changing or not and i think

that this is important

because we need to start asking the questions at the level of the organizations

and of the things that the organizations produce rather than look at the tasks

and the things that the tasks magically aggregate because they don't.

Now, what did we look at?

Let me give you a bit of a sense of the descriptive statistics of at least

when we ask the question of is this working for you, what people answered.

By the way, this is ancient data.

It's a year old.

We did do some new data gathering to capture what's going on more recently.

and what was what we saw is

that out of the people who were using it about a

third didn't use it officially of course people used it

but the organization didn't formally use it

and about a third say we use it

and we don't see any impact about a third say yes we see it moving the needle

and somewhere in the middle and we're like great we have enough variance in order to see what

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distinguishes those that get excited and those that do not get excited

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if you look at descriptive of statistics what do you see well you see

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that if you think about the maximum use cases of ai

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and if you look at the extent to which conditional on that being used it is used systematically

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rather than randomly you see some interesting patterns like those who aren't excited don't use it much

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but also more importantly don't use it systematically and especially if you move from the moderate impact to the high impact,

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it's much of a question of depth and consistency than a matter of breadth.

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The other thing that we saw is that the closer you go to the core of the organization, are you not writing emails and doing all the things that everyone is looking at,

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the more you see the impact being important when you go to operations,

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when you go to IT and to the heart of the organization as opposed to the things that quite often IT,

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sorry, AI is used for.

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The other thing that we saw is that what matters is what you use it for.

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Do you use it for cost reduction and productivity improvement?

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Do you use it for hyper -personalization?

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Or do you use it for using new revenue streams?

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And it turns out that the more you use it for more strategic stuff, hyper -personalization, creation of new revenue streams, the more you see that this has an impact.

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But that could only explain a few things in terms of the current usage.

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What we were interested in is the understanding of the executives

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in terms of their perception on whether AI would displace them or not, would break up the sectors or not.

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So we started working at some detail.

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In a moment, I'll show you the details of how we asked them and how the data was gathered.

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and then we said well let's think about what's changing a couple of things

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that came up i thought were interesting the first thing is

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that we started saying what the heck do we mean by the word disruption

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so when we think about disruption disruption means potentially three things the first thing

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that it could mean is displacement are we all going to be dead people in marketing

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and advertising are concerned rightly because they are some of the ones

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that we have seen be the most exposed and the most liable to be displaced by Gen AI.

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The other though was another two things.

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The first one was disruption in a classic sense, in the sense that the criteria of what is good and what is not good are going to change in the sector.

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And the third thing is the possibility of using this thing in order to be rich,

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i .e there is a new way for us to differentiate ourselves.

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And we started unbundling that and then we started correlating it with stuff

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and then we saw as you'll see in a moment

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that there are some features

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that were more important than others now that's at the high level the what data we're looking at

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but let me start jumping down to uh the research project

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itself what did we do um what we did is that we started by figuring out what the heck is happening.

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And as this is an area that requires us to get closer to the ground in order to articulate the hypothesis,

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we used abduction and we had 96 people in six who are directors in six workshops,

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an hour and a half to two hours, trying to listen so that they can tell us where they see disruption happening in their sector,

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where they don't, so that we know what questions to ask

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so that we know what are the variables to consider and so that we know what's going to be important.

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then we had a survey

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which went to the entire membership of the institute of directors in the uk

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which was answered by 217 directors long survey about 40 minutes

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we have similar data i worked with one of the major private equity firms uh

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and the private equity results are similar we can't show them for legal reasons

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but they are quite comparable to this

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and then the third one is rather than do the usual academic stuff

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which is to say I got my data I'll sit back

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and I'll make sense of them we looked at the data

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and we asked the people who had filled the survey to say why can you think

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that we have these results so

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that rather than us thinking what other mechanisms we ask people

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what they thought was going on in order to be able to tease out more information about what is happening

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so that we don't impose our own views on reality

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but rather so that we can see how they can tell us what's going on here.

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Why is this interesting and different?

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So in terms of timing, we're starting a little bit late because I was whiffing for the first three, four minutes.

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Thank you.

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Excellent.

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So the first thing, the first thing to say is that as I was saying, the body of work that we have, think about Eric Bringelson's work and the rest of it,

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are looking at tasks at the modular level. And

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if you think about the other things like the the big

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BHI study again it looks at the production of slide decks for consultants

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and the way that this evaluated which is interesting

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but this is not what organizations do

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and what we want to look at is at the level

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of the value proposition of the entire organization not on any particular modular subtasks

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that exist and what I would suggest is that like we have seen in previous iterations of technology,

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when we're trying to understand the solo paradox of why IT did not have an impact, and people like Lauren Hitt and Eric B were saying, oh, it combines with other things.

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Similarly, what we have is that Gen AI combines with other things, and I think, again, the cardinal mistake that we're making is to confuse the world for a modular,

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a set of modular bundles which is not quite the case

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so what we saw in the first set of roundtables is

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that the important thing to ask is at the level of

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the firm what is the value proposition of the business

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and as such what are the enablers of the tasks

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and the barriers that correspond to this value proposition and how

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that changes either the firm or the sector then on the basis of

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that we had the survey and the survey came up with some interesting facts.

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Let me just give you very briefly some of the numbers that stood up.

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We saw that some of the features that explained whether people were concerned or not about displacement were things like regulation,

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modularity, and the existence of proprietary data.

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So on the basis of this evidence, we said, let's try to figure out why.

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We already had some hypothesis, but then we said, let's create these roundtables,

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and we had 160 -odd people who came in these nine

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roundtables where we bundled people in terms of people who had high -propriety data or low -propriety data,

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again, different sectors trying to help us understand why the data in these sectors were of a particular form.

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And here's what we found.

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We found that people were speaking much more in terms of how Gen AI can complement and displace the value proposition overall.

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What do I mean about that?

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Well, I mean that there are some things that make the value proposition defensible or not.

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If you start looking at the differences and you look at people saying, well, I'm afraid that we're going to be displaced versus, no, I'm not terribly afraid that I'm going to be displaced.

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So to give you a more concrete example, if you think about different law firms, there's one law firm that says, well,

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what we think we're doing is that we're offering a set of bespoke solutions

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that requires us to understand what the customer really wants.

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And because this is not really displaceable in terms of gen AI, we don't think we're going to be unbundled.

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Or another firm said, we think that the legal liability

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that exists is a very potent protector so that we can essentially use AI, but it's going to be within our own limit.

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And that is what's not really driving it.

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That's why people in accounting are more comfortable than people in

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consulting in terms of their ability to withstand the potential disruptive shock when it comes to Gen AI.

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We then did a number of things, kicking the tires, and we said, well, what if we only looked at the snapshot of history?

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So that's why we had another big workshop in June of this year,

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and we reviewed, we had a number of further detailed surveys to say,

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have things changed with the emergence of chat GPT -4?

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The general answer is no. And it's interesting because especially in the qualitative surveys,

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the features that existed continue persisting and suggesting that there is rhyme and reason in that regard.

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The other thing that we looked at is we were speaking, we were looking at those who felt that they are more differentiable

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because they are doing things that are more unique and more bespoke.

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And of course, we said, is it the case that these people could simply be full of themselves and they think that they're awesome,

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but they are just giving us their own perception that they are invincible?

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So we went back, we tracked everyone via LinkedIn, we then used the corporate websites, we used the Wayback Machine,

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and we saw, do the people who think

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that they are more bespoke have websites and information that is clearly different from the rest of the industry, so there is a prima facie validity,

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so that we have some external validity?

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And the answer is yes.

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So what is it that we have learned from that?

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What we've learned from that is that when you think about whether Gen AI is going to disrupt you or not,

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you need to be thinking about whether it'll disrupt the value proposition.

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Looking only at whether it'll affect an individual activity

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that exists within your own boundaries is not something

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that is going to tell you whether disruption will happen or not

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so we need to shift from thinking about what happens within

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the modules to thinking about where the modules are situated on

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so that the value proposition is important the gen ai based disruption is an interesting combination about of both displacement

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and new differentiability and we see this as the challenge in the sector

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but also the opportunities for others we do think that

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that will lead to a serious reordering in a number of sectors

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so there will be a shock ironically not a shock of everyone identically

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because technology is eating humans a shock to some firms

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that are more vulnerable and others who are going to take their position

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so ironically yes there will be a disruption but of a different sort of a strategic short that's going to be

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reorganizing the sector modularity emerges as a very important feature and

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that might be why people like ethan are so excited about gen ai

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and the people who are looking at the macro can't see the results of the data

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because the world does not consist of modular sectors

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but some are modular and decomposable so these are going to be less defensible

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and we can start having a more sophisticated analysis of who's going to be threatened

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and who not and the role of regulation is also super important

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because it also defines the things that are potentially going to be challenge or not,

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which is really interesting, because things that would have looked as boring and

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standard as the rules of regulation may end up being extremely important in a world of Gen AI.

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That's the very quick overview, and I look forward to your questions.

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Thank you very much.

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Yes?

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Yes?

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It seems like, to me, like, what's current, is patent -based competition?

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Well, I mean, look, the reason that I think that you should look at organizations, and whether organizations, organizations overall, business units, not necessarily corporates.

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By the way, the corporate is another very interesting story.

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And there's a parallel project that we're starting now, which is what is a cohesion in a world of Gen AI?

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And how can you think about the value of multi -business firms when resource allocation can be supported and supplanted by that?

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So it's another interesting question.

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But again, you're starting to look at essentially the units that exist that produce P &L results.

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And then you say, are there, is there rhyme and reason in the way these units are organized as opposed to what's happening in the task?

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I'm not suggesting that tasks are irrelevant.

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Of course, they're going to happen, right?

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We have the same thing when we're looking at innovation.

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I was a student here at Wharton of Sid Winter and Sid was saying, don't look only at.

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issues that can be studied when you look at patent dynamics, because there's a number of other sectors that cannot have intellectual protection that is captured by that.

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And then we grew up and we started looking at innovations, not only looking at citation counts in areas where patents are important, saying, let's think about innovation sort of more broadly.

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I hope that a similar thing will happen in the world

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that we're looking at by considering essentially company by company or or value prop by value prop, and how this is being affected by Gen .ai.

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So my encouragement is we have already a lot of the things that are easy and that generate PhDs or generate publications, which is I'm going to take tasks,

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I will take Onet as my guide, and then hopefully it will all pan out, and there are going to be reviewers and editors who have done similar things and it's going to be published.

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I get it.

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I'm an editor too.

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But on the other hand, what I'm suggesting is that if we are to be useful as a field, we need to be a little bit more courageous and we need to say,

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let's look at where the action appears to be at organizations

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and look at it at the value prop and how AI affects the value proposition.

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I think that we will come up with a more nuanced version.

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We will be able to link a little bit the two keynotes saying the first is cool, but cute and applies only to AWS one,

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only to a small part of the world

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and a big part of the world is facing the problems that the lady from Accenture was speaking to us about, right?

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So I think that this is my hope

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that we're going to start complementing the work

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that happens at the task level with a lot with the work

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that happens at the value proposition or the business unit level but that's just my wishful thinking.

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Will Will we do it?

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I don't know.

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It's going to be hard.

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So here we have a mixed blessing,

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because the people who wanted to spend two plus hours to come to a meeting at LBS

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or spend 40 minutes answering the survey are a non -representative sample of those who are worried about it

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and whose motivation is to figure out what other people want to do.

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If you think about what we're looking at is a part of the population that is more at risk of being excited.

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And if you look at the entire world, this is a small part of the overall population of people, many of whom are going to say,

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all of that is hype and I don't care about it.

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So I can't speak on what is happening for those who are simply closing their eyes and their ears saying, I think that this is just hype.

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I don't want to look at it.

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I am not literate.

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So I think that there is going to be an interesting challenge.

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I think that what's going to happen is that when you see organizations that are able to benefit from it,

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perhaps either through national selection or through some learning, they might adjust.

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But I think that you're going to see a bimodal or multimodal set of organizations.

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I expect some AI -powered firms to compete with some firms that are able to integrate the AI well, and others who are hobbling along,

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and the relative shares of these three groups are going to change over time.

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true but what i will leave you with is the implication of our study isn't

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that everyone should invest a lot and immediately on AI.

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It is that AI is going to be a threat for some people, a lesser threat for others.

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Another project that we're starting now with the Henson Institute and BCG is to look at the different types of knowledge work.

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And what we're already finding is that there are different species in terms of those who are well or poorly adapted.

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It's acceptation, it's a pre -adaptation.

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There are some that essentially will end up being well by accident, by coincidence, well adapted to integrate AI and others that will not, right?

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So there's going to be an interesting variation, interesting systematic variation.

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And also I would say it isn't the fact that everyone needs to rush to entirely stop everything,

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uproot the things that they're doing and just rethink everything on the base of AI.

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This would not be the right advice.

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that we as academics would give.

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Venters, yes, sure, that's what they need to tell you.

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But we as academics should provide a more balanced answer saying,

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let me tell you how you look like and as such what on the margin is going to be better for you.

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And this is where I hope that academic research is going to shed more light.

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That's just the first step and much more to follow, probably in meetings such as these.

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Thank you very much.

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บทเรียนฝึกพูดนี้ใช้วิดีโอ “How Disruptive Will Generative AI Be? A Micro-Level Analysis of Evidence and Expectations” เป็นสื่อ วิดีโอนี้มี 315 ประโยค และ 4237 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 25:23 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 167 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน 85% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก

คำศัพท์สำคัญในวิดีโอนี้

คำที่ควรเรียนรู้ 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:

คำศัพท์	คำอ่าน	ความหมาย
proposition คำนาม	/ˌpɹɑpəˈzɪʃən/	ประพจน์
innovation คำนาม	/ˌɪn.əˈveɪ.ʃən/	นวัตกรรม
patent คำนาม	/ˈpeɪtənt/	สิทธิบัตร
strategic คำคุณศัพท์	/stɹəˈti.dʒɪk/	ยุทธศาสตร์
capture คำกริยา	/ˈkæp.(t)ʃɚ/	ยึด
motivation คำนาม	/ˈmoʊ.tə.veɪ.ʃən/	แรงจูงใจ
perception คำนาม	/pɚˈsɛpʃ(ə)n/	การรับรู้
apologize คำกริยา	/əˈpɑləˌd͡ʒaɪz/	ขอโทษ
productivity คำนาม	/ˌpɹɒdʌkˈtɪvəti/	ผลิตภาพ
workshop คำนาม	/ˈwɝk.ʃɑp/	โรงงาน
consultant คำนาม	/kənˈsʌl.tənt/	ที่ปรึกษา
bundle คำนาม	/ˈbʌn.dl̩/	มัด, กำ
module คำนาม	/ˈmɑd͡ʒul/	มอดูล, โมดูล
hypothesis คำนาม	/haɪˈpɒθɪsɪs/	สมมุติฐาน
rhyme คำนาม	/ɹaɪm/	สัมผัส

กริยาวลีที่คุณจะได้ยิน

คำศัพท์	ความหมาย
go back คำกริยา	กลับไป

ประโยคที่ควรฝึกพูดซ้ำ

ประโยคสั้น ๆ ที่สมบูรณ์จากวิดีโอ ซึ่งนำไปใช้ในบทสนทนาประจำวันได้:

Where the heck are we?
Why do we have these seemingly different structures about the world?
Do you use it for hyper -personalization?
What do I mean about that?
It seems like, to me, like, what's current, is patent -based competition?

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โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้าง	ในวิดีโอ
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ	have been competed · are involved · is not put
Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ	people who either · people who are · people who run
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน	have heard · they've lost · has been

การออกเสียงที่ควรระวัง

ผู้พูดใช้รูปย่อและรูปลดเสียง 79 ครั้ง เช่น we're, I'm, don't ให้พูดแบบสั้นตามที่ได้ยิน

เสียง “th”: gather /ˈɡæðɚ/, hypothesis /haɪˈpɒθɪsɪs/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, threaten /ˈθɹɛt.n̩/, withstand /wɪðˈstænd/
เสียง “sh” และ “zh”: disruption /dɪsˈɹʌpʃən/, proposition /ˌpɹɑpəˈzɪʃən/, shock /ʃɔk/, essentially /ɪˈsɛnʃəli/, innovation /ˌɪn.əˈveɪ.ʃən/
คำยาว — ลงเสียงหนักให้ถูกพยางค์: proposition /ˌpɹɑpəˈzɪʃən/, essentially /ɪˈsɛnʃəli/, innovation /ˌɪn.əˈveɪ.ʃən/, regulation /ˌɹɛɡjʊˈleɪʃən/, potentially /pəˈtɛnʃ(ə)li/

เสียงที่คนไทยมักออกเสียงยาก:

เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: apologize /əˈpɑləˌd͡ʒaɪz/, module /ˈmɑd͡ʒul/, hypothesis /haɪˈpɒθɪsɪs/, descriptive /dəˈskɹɪptɪv/, relative /ˈɹɛl.ə.tɪv/
/v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: innovation /ˌɪn.əˈveɪ.ʃən/, motivation /ˈmoʊ.tə.veɪ.ʃən/, productivity /ˌpɹɒdʌkˈtɪvəti/, variation /ˌvɛəɹiˈeɪʃn̩/, validity /vəˈlɪd.ɪ.ti/
/z/ — เสียงก้อง ไม่ใช่ /s/: proposition /ˌpɹɑpəˈzɪʃən/, apologize /əˈpɑləˌd͡ʒaɪz/, confused /kənˈfjuːzd/, resource /ɹɪˈzɔːs/, presentation /ˌpɹɛzn̩ˈteɪʃn̩/

วิธีฝึกกับวิดีโอนี้

ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง proposition, innovation, patent เป็นพิเศษ

เทคนิค Shadowing คืออะไร?

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

เทคนิค shadowing: อ่านคู่มือฉบับเต็มทีละขั้นตอน →

ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: How Disruptive Will Generative AI Be? A Micro-Level Analysis of Evidence and Expectations

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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

คำศัพท์สำคัญในวิดีโอนี้

กริยาวลีที่คุณจะได้ยิน

ประโยคที่ควรฝึกพูดซ้ำ

ไวยากรณ์ในวิดีโอนี้

การออกเสียงที่ควรระวัง

วิธีฝึกกับวิดีโอนี้

เทคนิค Shadowing คืออะไร?