シャドーイング練習: Harnessing generative AI in architecture, engineering & construction - 動画で英語スピーキングを学ぶ
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114 文
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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.
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So the first of those is everything around design productivity.
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So the thing that's very cool about Gen AI
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and in fact AI more broadly is it allows you to start to
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automate some of the tasks which take engineers
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and architects a lot of time at the moment
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but don't necessarily add huge amounts of values
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that might be you know generating design specifications
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it might be about deciding exactly how high off the ground the door handle should be right
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and the great thing about design optimization is it creates capacity with engineers
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and architects to really focus on the highly complex novel problems right which is which is quite an exciting opportunity.
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I think the other kind of big collection and use cases we see is everything around planning and scheduling.
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So, yeah, planning and construction planning is typically a bit of a dark art in construction.
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There is some science behind it, but it's definitely there's elements of being a dark art,
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and that's borne out with some of the schedule adherence we see typically in construction and infrastructure projects,
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with projects typically running over budget and over time.
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There are tools out there and companies out there
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that actually use AI to learn from historic project delivery to improve the accuracy of scheduled forecasting
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and actually do that almost in real time.
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So day by day
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or week by week you're coming up with a more accurate view of the schedule
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and actually recommendations on how you might redeploy resources with a schedule.
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Some of the most exciting opportunities
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and most disruptive are looking at the impact of environmental impact on design
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and feasibility so looking at future scenarios where flooding wind storms excessive heat will impact on a concept
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so as we help clients and we help design
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and engineering companies plan ahead and think of their optimal design there are
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so many different scenarios
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that we can now generate using AI to really think ahead of the future and make sure that these assets,
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these important buildings that are getting constructed or road infrastructures are going to stand the test of time.
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The engineering challenges we have are more complex than what we've ever seen before over the next 30 to 50 years,
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right, when we talk about the decarbonisation challenge,
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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,
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we don't have enough capacity in the construction industry.
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Gen AI, AI and actually digital tools more broadly are a way of bridging that productivity gap.
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Look, this isn't the panacea for all solutions, there are going to be limitations for this
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and I think what we need to be aware of that this technology is not about replacing people, it's about augmenting the process,
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about making sure that we're using human interaction on the most complex elements
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and that the basics are done right first time we get to brilliant basics quickly
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but there's a lot of buzz and excitement rightly
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so about gen ai and ai at the moment
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and some of the use cases we've described it have huge potential massively exciting but it's really important to remember that
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construction is a it's the biggest industry in the world
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it's one of the most fragmented it's mainly made up of small
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and medium you know artisanal craftspeople and small construction companies
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and it's always going to be a hands -on physical business
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that relies on skilled hard working people on the front line
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and that that isn't going to change anytime soon right
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so I think we need to be need to recognize
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that AI has an opportunity to you know augment the work
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that we do on a site it has an opportunity to accelerate design
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and streamline the design process
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but it's not going to change the kind of physical act
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of building stuff out on a out on a building site
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which the other kind of reality check we need to do is
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that this depends on data as well and this is something
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that most industries struggle with that construction particularly struggles with
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because of this level of fragmentation
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and you need to have a solid data layer to be
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able to deliver many of these use cases one of the challenges is
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that the industry from an engineering perspective is based on hours
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and suddenly there's a potential to do things instantly in a nanosecond at zero real cost how do we monetize
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that how do we incentivize
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the technology companies the engineering companies
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and the owners to collaborate ultimately an owner will benefit
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when their schedules reduce their cost to build
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and time to market is reduced and we're talking about projects
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that are often in the millions if not billions
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so how do we help the ecosystem the supply chain ecosystem benefit from
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that transformation because otherwise there'll be as much of a risk of low
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and slow adoption as there is of going too fast and getting it wrong.
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The question
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that I'm hearing a lot is what does this mean for my business is it kind of exciting hype
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that I can kind of ignore for a few months
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or is it something that's fundamentally going to change the business
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and yeah every business will be different right
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but the way we think about it is as an engineering
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company how can you use AI to more accurately predict
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and monitor energy consumption in an office building in real time and offer real -time solutions to optimise energy consumption.
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I would be looking and guiding organisations towards finding some low -hanging fruit,
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something that is contained and managed that you can change as a whole system level.
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Because this isn't just about adopting technology quickly.
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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.
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And the potential impact of working new in a new way where it's not an hours -based business
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or a lump sum type business is quite huge.
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And I can see some companies getting stuck with the enormity of this.
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So run the whole changes through the entire lifecycle of the services
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that are provided to the clients through to commercials and billing
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and make sure the whole team are working in
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that way almost as a product team and taking that to market and learning from that is really important.
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Although the technology is relatively new actually the approach to transformation is one
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that we've we've seen before we're familiar with right so that
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and there's two dimensions to it the first is you identify the business domains
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and the use cases where this is going to create most value right
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so it might be to improve your win rate
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and tendering it might be to reduce accidents on site it might be to improve engineering productivity
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but which use cases are going to create real business value
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and then on the feasibility right
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so do we have the data to be able to do this do we have the systems and
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if not is it going to be a significant investment to
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get the right systems in place do we have um actual skills to help us build uh
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and administer the technology and either build or integrate with the right models
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and then from that develop a list of a short list of use cases
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and go and execute the pilots before scaling right
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but it's it's very easy to get excited about the technology for technology's sake
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but it must be grounded in in in business impact at the end the day.
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I think AEC companies need to focus on the value pools.
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Where is this going to have the biggest impact but not go too large at the beginning?
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Really get something proven and working and deployed and get the rest of the organization behind it.
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That's how I'd be tackling this but also you know engineers like to design,
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they like to own and build and there's going to be a preference to own
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and build this technology and I think that existing technology companies
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and startups are pure play invested in this area already and have already been moving in this direction.
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So adopting and working with the right technology partners from the outset is really important.
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このレッスンの語彙とスピーキングのポイント
このスピーキングレッスンは、動画「Harnessing generative AI in architecture, engineering & construction」を教材にしています。 繰り返し出てくる語は次のとおりです：technology, design, companies, construction, cases。 この動画には、シャドーイング用の文が114文、単語が1438語あります。 音声の長さは8:41です。 話す速さは1分あたり約166語で、日常会話に近い自然なペースです。 単語の87%は英語の頻出3,000語に含まれるため、語彙は分かりやすいです。
この動画の重要語彙
動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| productivity 名詞 | /ˌpɹɒdʌkˈtɪvəti/ | 生産性 |
| architect 名詞 | /ˈɑɹ.kɪˌtɛkt/ | 建築家 |
| element 名詞 | /ˈɛləmənt/ | 要素, エレメント |
| infrastructure 名詞 | /ˈɪnfɹəˌstɹʌkt͡ʃə/ | インフラストラクチャ, インフラ |
| skill 名詞 | /skɪl/ | 腕, 技 |
| consumption 名詞 | /kənˈsʌmp.ʃən/ | 消費 |
| generate 動詞 | /ˈd͡ʒɛn.ə.ɹeɪt/ | 生む, 生み出す |
| transformation 名詞 | /ˌtɹæns.fɔɹˈmeɪ.ʃən/ | 変形, 変換 |
| adopt 動詞 | /əˈdɑpt/ | 養子にする |
| scenario 名詞 | /sɪˈnɛəɹioʊ/ | 脚本, 台本 |
| ecosystem 名詞 | /ˈikoʊˌsɪstəm/ | 生態系 |
| ignore 動詞 | /ɪɡˈnɔː/ | 無視する, 忽略する |
| layer 名詞 | /ˈleɪ̯ɚ/ | 層, 膜 |
| sake 名詞 | /ˈseɪ̯k/ | -のために |
| resource 名詞 | /ɹɪˈzɔːs/ | 資源, 資産 |
動画に出てくる句動詞
| 単語 | 意味 |
|---|---|
| run over 動詞 | 轢く |
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | are done · is based · is reduced |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | we've ever seen · we've described · we've seen |
注意したい発音
話し手はwe've, don't, we'reなど、短縮形や弱形を20回使っています。聞こえたとおりの短い形で発音しましょう。
- 「sh」と「zh」の音: consumption /kənˈsʌmp.ʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, organisation /ˈɔː.ɡə.naɪˌzeɪ.ʃən/, interaction /ˌɪn.təˈɹæk.ʃən/, adoption /əˈdɑp.ʃən/
- 長い単語（アクセントの位置に注意）: productivity /ˌpɹɒdʌkˈtɪvəti/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, ecosystem /ˈikoʊˌsɪstəm/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/
日本語話者が苦手な音：
- /r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: broadly /ˈbɹɔːdli/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, rely /ɹɪˈlaɪ/, accurately /ˈæk.jə.ɹɪt.li/, rightly /ˈɹaɪtli/
- /v/ — /b/ にならないように、上の歯を下唇に当てる: productivity /ˌpɹɒdʌkˈtɪvəti/, invest /ɪnˈvɛst/, excessive /ɪkˈsɛsɪv/, incentive /ɪnˈsɛntɪv/, massively /ˈmæs.ɪv.li/
- /f/ — 「フ」ではなく、上の歯と下唇で出す: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, flood /flʌd/, preference /ˈpɹɛf(ə)ɹ(ə)ns/, forecast /ˈfɔːkɑːst/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
- 自分の声を録音して元の音声と比べます。productivity, architect, elementなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。