Shadowing Practice: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - Learn English Speaking with Video
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104 משפטים
אם המשפטים קצרים או ארוכים מדי, לחץ על ערוך כדי להתאים אותם.
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Here's a list of the top five companies that cut their way to the top.
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Yes, these are the ones that figured out a way to move into the number one spot by shrinking rather than growing.
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Well, The list is empty, you can see.
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Certainly an organization can improve operational efficiency and cost by reducing fat and overhead.
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In some cases, it's an essential step to prevent going out of business.
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But if you want to win big, You won't do it by getting small.
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You get there.
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Bye.
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Investing. by innovating. by growing.
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These are the things that smart organizations already know.
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They look at technology as an enabler, a way to achieve strategic competitive advantage.
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The winners of tomorrow are the ones
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that are figuring out how to leverage AI today to make their employees more capable and creative.
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They see AI as augmented intelligence, rather than artificial.
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Let's see why this is the case.
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But before we do that, let's take a look at what seems to be the prevailing view of the impact of AI by many.
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Jeffrey Hinton, often called the godfather of AI, is a truly brilliant man.
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His work laid the foundation for so much of what we have today.
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In 2016, he said, and I quote, "People should stop training radiologists now.
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It's just completely obvious that in five years, deep learning is going to be better than radiologists." Hmm.
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So that proximal humerus fracture looks familiar, but I digress.
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All this sounds pretty dire, especially if you're a radiologist.
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Your whole profession is going away.
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So let's check in and see how that statement held up.
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Well, it turns out that 10 years later, not only have radiologists not gone extinct,
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but we actually are training more of them than we were when Hinton made that statement.
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So is that because AI failed?
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No, AI is actually doing amazing things now that we couldn't imagine a decade ago.
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Part of the answer lies in this thing called Jevons paradox.
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Let's take a look at that and see what it is.
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Way back in 1865, while studying coal use in England,
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a guy named William Stanley Jevons observed that more efficient steam engines reduced the amount of coal needed to power them.
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So you might think demand for coal would go down.
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But the opposite occurred.
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More industries turned to using this more efficient energy source to get more things done.
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and total coal consumption actually rose instead of falling.
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In other words, As efficiency went up, Costs went down.
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and demand increased.
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That's Jevons' paradox in a nutshell.
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We all learned in Econ 101 that as costs go down, Demand goes up.
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That all makes sense, right?
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Well, cheap energy meant they could start to do things that would have been cost -prohibited before.
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It allowed them to dream up some new use cases that no one ever considered because it would have been impractical previously.
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All of that just increases demand even more.
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But we're talking about AI here, not coal.
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So where's the connection with all of this?
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Just hang in here with me.
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Let's take a look at spreadsheet software.
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Automated arithmetic reduced accounting effort per task.
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So now we have spreadsheets who needs accountants.
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Nobody needs to just sit around and do all that arithmetic.
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Jevons teaches us that as efficiency goes up, Cost goes down demand increases, and that's exactly what happened again.
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Demand for financial analysis exploded.
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All of these accountants that used to labor over their arithmetic were now freed up to do higher -level thinking.
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Entirely new categories of work emerged.
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In the end, we needed more, not less, of these people, but with a higher level of skill.
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Now how about AI?
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What kind of smarter work could we do if we made use of AI?
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Well, one area is entirely new categories of work.
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Things that we never did before.
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Jobs like AI product managers, safety engineers, prompt engineers.
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We didn't need those in the past because we didn't have the AI that needed all of those things.
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So it's actually creating new jobs in support of the AI itself.
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Expansion of long -tail services.
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Things that were previously too expensive.
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Think of stuff like custom tutoring, niche legal analysis, personalized healthcare support.
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That's something that really only a few people could afford, but now would be available potentially to everyone.
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And then we're going to raise our expectations for speed.
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quality, and availability, creating more work around integration, oversight, compliance, and trust.
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Rather than fewer jobs overall, Jevons' paradox would indicate that what we would have would be fewer of these routine,
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low discretion roles and more of the high context, high accountability roles such as problem framing and goal setting,
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things where we need a human in the loop to do the decision making, AI supervision, evaluation and governance, customer facing roles where trust matters,
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and we need something with an emotional intelligence as well, and then the ability to do cross -functional coordination.
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So think of it this way: AI reduces the labor required per task, but dramatically expanding what is economically feasible to do.
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It can increase the total amount of work humans are employed to support.
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In other words, Efficiency goes up.
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Cost goes down.
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and demand increases. for people.
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And we need more people then, not less, because of AI.
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Smart companies understand Jevons paradox and they realize they're going to need more
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and smarter employees to run their operations that leverage AI.
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And smart employees will be the ones who are able to do these kinds of AI era skills.
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I think one of the most important is adaptability and flexibility.
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We don't know where all of this is going to go, so the people who are light on their feet and able to adjust, will be some of the winners.
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You need to be lifelong learners as well.
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You've got to enjoy this technology, enjoy the ride, soak it all up like a sponge
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because the information is coming at us faster and faster and the ones who can learn and assimilate
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and adjust will be again the winners.
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They need to be able to think critically.
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AI doesn't always get everything right.
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And even when it does, it doesn't mean that's what we wanted it to do.
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So we have to think and we have to be the ones that are in control.
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The human in the loop ultimately makes the decisions.
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We need to decide what the system should do and why it should do it.
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We apply meaning to it.
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And the system then does whatever we ask and does it at speed.
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And don't forget creativity.
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Because again, we don't know what the possibilities are here.
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So people who can think outside the box, you're actually going to have more time to do that because AI is going to automate much of the mundane stuff.
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So you have more time to think the big picture thoughts.
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The not so smart organizations will be
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so busy trying to cut their way to the top that they'll miss this train that's already pulling away from the station.
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ways to innovate and branch into new use cases and business opportunities where AI provides a competitive advantage.
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And the smart employees will be the ones who are skilling up to take advantage of this new dynamic.
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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact”. המילים שחוזרות הכי הרבה: demand, Jevons, cost, coal, smart. בסרטון הזה יש 104 משפטים ו-1240 מילים לתרגול Shadowing. הדיבור נמשך 7:45. הדובר מדבר בקצב טבעי של כ-160 מילים בדקה, קרוב לשיחה יומיומית. 85% 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 |
|---|---|---|
| paradox שם עצם | /ˈpæ.ɹəˌdɒks/ | פרדוקס |
| arithmetic שם עצם | /æɹɪθˈmɛtɪk/ | חֶשְׁבּוֹן |
| accountant שם עצם | /əˈkaʊn.tənt/ | רוֹאֵה חֶשְׁבּוֹן |
| leverage שם עצם | /ˈlɛv.(ə.)ɹɪd͡ʒ/ | הנפה |
| adjust פועל | /əˈd͡ʒʌst/ | כוונן, התאים |
| assimilate פועל | /əˈsɪm.ɪ.leɪt/ | הטמעה |
| godfather שם עצם | /ˈɡɑdfɑðɚ/ | סנדק |
| sponge שם עצם | /spʌnd͡ʒ/ | סְפוֹג |
| soak פועל | /soʊk/ | הִשְׁרָה |
| extinct שם תואר | /ɪkˈstɪŋkt/ | כבוי |
| tutor שם עצם | /ˈtjuːtəː/ | מורה פרטי |
| fracture שם עצם | /ˈfɹæk.t͡ʃʰəː/ | שֶׁבֶר |
| feasible שם תואר | /ˈfiː.zɪ.bəl/ | בר ביצוע |
| explode פועל | /ɪkˈsploʊd/ | הִתְפּוֹצֵץ |
| niche שם עצם | /nɪt͡ʃ/ | גומחה, נישה |
Phrasal verbs שתשמעו
| מילה | משמעות |
|---|---|
| go down פועל | ירד |
| go up פועל | עָלָה |
| figure out פועל | הֵבִין, מצא תשובה |
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | ones who are · people who are · ones who can |
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | have been cost · were now freed · are employed |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-16 קיצורים וצורות מקוצרות, כמו don't, didn't, doesn't. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “th”: arithmetic /æɹɪθˈmɛtɪk/, godfather /ˈɡɑdfɑðɚ/
- הצלילים של “sh” ו-“zh”: spreadsheet /ˈspɹɛd.ʃit/, nutshell /ˈnʌt.ʃɛl/, discretion /dɪˈskɹɛʃən/, coordination /kəʊˌɔːdɪˈneɪʃən/, supervision /ˌs(j)uːpəˈvɪʒən/
- מילים ארוכות — שימו לב להטעמה: radiologist /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/, arithmetic /æɹɪθˈmɛtɪk/, adaptability /ədæptəˈbɪlɪti/, assimilate /əˈsɪm.ɪ.leɪt/, impractical /ɪmˈpɹæktɪkəl/
צלילים שדוברי עברית מתקשים בהם:
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: paradox /ˈpæ.ɹəˌdɒks/, arithmetic /æɹɪθˈmɛtɪk/, adaptability /ədæptəˈbɪlɪti/, impractical /ɪmˈpɹæktɪkəl/, fracture /ˈfɹæk.t͡ʃʰəː/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: radiologist /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/, paradox /ˈpæ.ɹəˌdɒks/, arithmetic /æɹɪθˈmɛtɪk/, spreadsheet /ˈspɹɛd.ʃit/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
- עיצור קולי בסוף מילה — /b/, /d/, /g/, /z/, /v/ בלי להפוך אותם לאטומים: leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, personalize /ˈpɜː.sə.nə.laɪz/, sponge /spʌnd͡ʒ/, explode /ɪkˈsploʊd/, emerge /ɪˈmɝd͡ʒ/
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 paradox, arithmetic, accountant.
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.