Shadowing Practice: Harnessing generative AI in architecture, engineering & construction - Learn English Speaking with Video
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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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הגיית AIציון לכל משפט
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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Harnessing generative AI in architecture, engineering & construction”. המילים שחוזרות הכי הרבה: technology, design, companies, construction, cases. בסרטון הזה יש 114 משפטים ו-1438 מילים לתרגול Shadowing. הדיבור נמשך 8:41. הדובר מדבר בקצב טבעי של כ-166 מילים בדקה, קרוב לשיחה יומיומית. 87% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.
אוצר מילים מרכזי בסרטון הזה
15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| excite פועל | /ɪkˈsaɪt/ | הלהיב, עורר |
| architect שם עצם | /ˈɑɹ.kɪˌtɛkt/ | אַדְרִיכָל, אדריכלית |
| collaborate פועל | /kəˈlæb.ə.ɹeɪt/ | שיתף פעולה |
| fragment שם עצם | /ˈfɹæɡmənt/ | פָּרוּר, רְסִיס |
| accelerate פועל | /ɪkˈsɛl.əˌɹeɪt/ | האיץ |
| lump שם עצם | /lʌmp/ | גוש |
| fundamentally תואר הפועל | עֶקְרוֹנִית | |
| startup שם עצם | /ˈstɑɹtˌʌp/ | סטארטאפ, חברת סטארטאפ |
| optimal שם תואר | /ˈɑptɪməl/ | אופטימלי |
| execute פועל | /ˈɛksɪˌkjuːt/ | להוציא להרוג |
| incentive שם עצם | /ɪnˈsɛntɪv/ | תמריץ |
| construct פועל | /ˈkɑn.stɹʌkt/ | הרכיב |
| dimension שם עצם | /daɪˈmɛn.ʃən/ | מֵמַד |
| buzz שם עצם | /bʌz/ | זמזום |
| bucket שם עצם | /ˈbʌkɪt/ | דְּלִי |
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | is based · is reduced · is contained |
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | we've ever seen · we've described · we've seen |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-20 קיצורים וצורות מקוצרות, כמו we've, don't, we're. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “sh” ו-“zh”: decarbonization /diːkɑːbənaɪˈzeɪʃən/, fragmentation /ˌfɹæɡmənˈteɪ̯ʃən/, optimization /ˌɑptəmaɪˈzeɪʃən/, limitation /lɪmɪˈteɪʃən/, dimension /daɪˈmɛn.ʃən/
- מילים ארוכות — שימו לב להטעמה: feasibility /ˌfiːzɪˈbɪlɪti/, ecosystem /ˈikoʊˌsɪstəm/, artisanal /ˈɑɹtəzən(ə)l/, decarbonization /diːkɑːbənaɪˈzeɪʃən/, enormity /ɪˈnoɹmɪti/
צלילים שדוברי עברית מתקשים בהם:
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: craftsperson /ˈkɹæftspɝsən/, fragmentation /ˌfɹæɡmənˈteɪ̯ʃən/, panacea /ˌpænəˈsiːə/, massively /ˈmæs.ɪv.li/, collaborate /kəˈlæb.ə.ɹeɪt/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: architect /ˈɑɹ.kɪˌtɛkt/, broadly /ˈbɹɔːdli/, adherence /ədˈhɪə.ɹəns/, artisanal /ˈɑɹtəzən(ə)l/, constraint /kənˈstɹeɪnt/
- עיצור קולי בסוף מילה — /b/, /d/, /g/, /z/, /v/ בלי להפוך אותם לאטומים: disruptive /dɪsˈɹʌptɪv/, incentivize /ɪnˈsɛntɪvaɪz/, monetize /ˈmɑnəˌtaɪz/, optimize /ˈɑptɪmaɪz/, incentive /ɪnˈsɛntɪv/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו excite, architect, collaborate.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.