跟讀練習: Harnessing generative AI in architecture, engineering & construction - 通過影片學習英語口語

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Use cases that certainly a lot of our clients are getting interested and excited about are probably into three or four buckets.

So the first of those is everything around design productivity.

So the thing that's very cool about Gen AI

and in fact AI more broadly is it allows you to start to

automate some of the tasks which take engineers

and architects a lot of time at the moment

but don't necessarily add huge amounts of values

that might be you know generating design specifications

it might be about deciding exactly how high off the ground the door handle should be right

and the great thing about design optimization is it creates capacity with engineers

and architects to really focus on the highly complex novel problems right which is which is quite an exciting opportunity.

I think the other kind of big collection and use cases we see is everything around planning and scheduling.

So, yeah, planning and construction planning is typically a bit of a dark art in construction.

There is some science behind it, but it's definitely there's elements of being a dark art,

and that's borne out with some of the schedule adherence we see typically in construction and infrastructure projects,

with projects typically running over budget and over time.

There are tools out there and companies out there

that actually use AI to learn from historic project delivery to improve the accuracy of scheduled forecasting

and actually do that almost in real time.

So day by day

or week by week you're coming up with a more accurate view of the schedule

and actually recommendations on how you might redeploy resources with a schedule.

Some of the most exciting opportunities

and most disruptive are looking at the impact of environmental impact on design

and feasibility so looking at future scenarios where flooding wind storms excessive heat will impact on a concept

so as we help clients and we help design

and engineering companies plan ahead and think of their optimal design there are

so many different scenarios

that we can now generate using AI to really think ahead of the future and make sure that these assets,

these important buildings that are getting constructed or road infrastructures are going to stand the test of time.

The engineering challenges we have are more complex than what we've ever seen before over the next 30 to 50 years,

right, when we talk about the decarbonisation challenge,

and we're trying to do that when we've got a capacity constraint in the industry we don't have enough engineers, we don't have enough architects,

we don't have enough capacity in the construction industry.

Gen AI, AI and actually digital tools more broadly are a way of bridging that productivity gap.

Look, this isn't the panacea for all solutions, there are going to be limitations for this

and I think what we need to be aware of that this technology is not about replacing people, it's about augmenting the process,

about making sure that we're using human interaction on the most complex elements

and that the basics are done right first time we get to brilliant basics quickly

but there's a lot of buzz and excitement rightly

so about gen ai and ai at the moment

and some of the use cases we've described it have huge potential massively exciting but it's really important to remember that

construction is a it's the biggest industry in the world

it's one of the most fragmented it's mainly made up of small

and medium you know artisanal craftspeople and small construction companies

and it's always going to be a hands -on physical business

that relies on skilled hard working people on the front line

and that that isn't going to change anytime soon right

so I think we need to be need to recognize

that AI has an opportunity to you know augment the work

that we do on a site it has an opportunity to accelerate design

and streamline the design process

but it's not going to change the kind of physical act

of building stuff out on a out on a building site

which the other kind of reality check we need to do is

that this depends on data as well and this is something

that most industries struggle with that construction particularly struggles with

because of this level of fragmentation

and you need to have a solid data layer to be

able to deliver many of these use cases one of the challenges is

that the industry from an engineering perspective is based on hours

and suddenly there's a potential to do things instantly in a nanosecond at zero real cost how do we monetize

that how do we incentivize

the technology companies the engineering companies

and the owners to collaborate ultimately an owner will benefit

when their schedules reduce their cost to build

and time to market is reduced and we're talking about projects

that are often in the millions if not billions

so how do we help the ecosystem the supply chain ecosystem benefit from

that transformation because otherwise there'll be as much of a risk of low

and slow adoption as there is of going too fast and getting it wrong.

The question

that I'm hearing a lot is what does this mean for my business is it kind of exciting hype

that I can kind of ignore for a few months

or is it something that's fundamentally going to change the business

and yeah every business will be different right

but the way we think about it is as an engineering

company how can you use AI to more accurately predict

and monitor energy consumption in an office building in real time and offer real -time solutions to optimise energy consumption.

I would be looking and guiding organisations towards finding some low -hanging fruit,

something that is contained and managed that you can change as a whole system level.

Because this isn't just about adopting technology quickly.

It's about changing the sales and marketing teams, the incentives for those sales teams to sell a new solution in a different commercial way.

And the potential impact of working new in a new way where it's not an hours -based business

or a lump sum type business is quite huge.

And I can see some companies getting stuck with the enormity of this.

So run the whole changes through the entire lifecycle of the services

that are provided to the clients through to commercials and billing

and make sure the whole team are working in

that way almost as a product team and taking that to market and learning from that is really important.

Although the technology is relatively new actually the approach to transformation is one

that we've we've seen before we're familiar with right so that

and there's two dimensions to it the first is you identify the business domains

and the use cases where this is going to create most value right

so it might be to improve your win rate

and tendering it might be to reduce accidents on site it might be to improve engineering productivity

but which use cases are going to create real business value

and then on the feasibility right

so do we have the data to be able to do this do we have the systems and

100

if not is it going to be a significant investment to

101

get the right systems in place do we have um actual skills to help us build uh

102

and administer the technology and either build or integrate with the right models

103

and then from that develop a list of a short list of use cases

104

and go and execute the pilots before scaling right

105

but it's it's very easy to get excited about the technology for technology's sake

106

but it must be grounded in in in business impact at the end the day.

107

I think AEC companies need to focus on the value pools.

108

Where is this going to have the biggest impact but not go too large at the beginning?

109

Really get something proven and working and deployed and get the rest of the organization behind it.

110

That's how I'd be tackling this but also you know engineers like to design,

111

they like to own and build and there's going to be a preference to own

112

and build this technology and I think that existing technology companies

113

and startups are pure play invested in this area already and have already been moving in this direction.

114

So adopting and working with the right technology partners from the outset is really important.

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本課的詞彙與口語要點

這節口語課以影片“Harnessing generative AI in architecture, engineering & construction”為素材。影片中反覆出現的詞有：technology, design, companies, construction, cases。這段影片共有 114 個句子、1438 個單詞可供跟讀。講話部分時長為 8:41。說話人語速自然，每分鐘約 166 個詞，接近日常對話。 87% 的單詞屬於英語最常用的 3,000 詞，詞彙容易理解。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
productivity 名詞	/ˌpɹɒdʌkˈtɪvəti/	生產力 /生產力, 效率
architect 名詞	/ˈɑɹ.kɪˌtɛkt/	締造者 /締造者, 創造者 /創造者
element 名詞	/ˈɛləmənt/	要素
infrastructure 名詞	/ˈɪnfɹəˌstɹʌkt͡ʃə/	基礎設施 /基礎設施
skill 名詞	/skɪl/	技巧, 技能
consumption 名詞	/kənˈsʌmp.ʃən/	消費 /消費
transformation 名詞	/ˌtɹæns.fɔɹˈmeɪ.ʃən/	轉型 /轉型, 轉變 /轉變
adopt 動詞	/əˈdɑpt/	收養 /收養
scenario 名詞	/sɪˈnɛəɹioʊ/	指令碼 /指令碼, 場景 /場景
ecosystem 名詞	/ˈikoʊˌsɪstəm/	生態系 /生態系, 生態系統 /生態系統
ignore 動詞	/ɪɡˈnɔː/	無視 /無視, 忽略
necessarily 副詞	/ˌnɛs.əˈsɛɹ.ə.li/	必然地, 必定地
layer 名詞	/ˈleɪ̯ɚ/	層 /層
resource 名詞	/ɹɪˈzɔːs/	資源 /資源
monitor 名詞	/ˈmɑ.nɪ.tɚ/	顯示器 /顯示器

影片中出現的短語動詞

單詞	釋義
come up with 動詞	想出, 提出

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	is based · is reduced · is contained
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	we've ever seen · we've described · we've seen

需要注意的發音

說話人用了 20 次縮略和弱讀形式，例如 we've, don't, we're。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: consumption /kənˈsʌmp.ʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, organisation /ˈɔː.ɡə.naɪˌzeɪ.ʃən/, interaction /ˌɪn.təˈɹæk.ʃən/, adoption /əˈdɑp.ʃən/
長單詞——注意重音位置: productivity /ˌpɹɒdʌkˈtɪvəti/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, ecosystem /ˈikoʊˌsɪstəm/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: productivity /ˌpɹɒdʌkˈtɪvəti/, invest /ɪnˈvɛst/, excessive /ɪkˈsɛsɪv/, incentive /ɪnˈsɛntɪv/, massively /ˈmæs.ɪv.li/
詞尾子音 — 要讀清楚，後面不要加母音: architect /ˈɑɹ.kɪˌtɛkt/, element /ˈɛləmənt/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, adopt /əˈdɑpt/, sake /ˈseɪ̯k/
/r/ 和 /l/ 的區別: broadly /ˈbɹɔːdli/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, rely /ɹɪˈlaɪ/, accurately /ˈæk.jə.ɹɪt.li/, rightly /ˈɹaɪtli/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 productivity, architect, element 這類單詞。

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: Harnessing generative AI in architecture, engineering & construction - 通過影片學習英語口語

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