Pratica di Shadowing: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - Impara a parlare inglese con i video
Creazione lezione...
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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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Prime impressioni: perché questo video è ottimo per la pratica orale
Questo video è un materiale eccellente per la pratica di conversazione in inglese grazie alla sua struttura chiara e al linguaggio colloquiale, ma non troppo informale. Le frasi sono lunghe ma articolate, con connettivi come "but", "so" e "well" che rendono il discorso fluido e naturale. Inoltre, combina concetti tecnici (come l'IA e il paradosso di Jevons) con esempi concreti, offrendo un mix di vocabolario specifico e generale che stimola la comprensione e la riproduzione. È perfetto per esercitarsi nel shadow speak, poiché la velocità del parlante è moderata e l'accento è chiaro.
Analizziamo i dettagli: frasi naturali e perché funzionano
Ecco tre frasi chiave e il loro segreto di naturalità:
- "But if you wanna win big, you won't do it by getting small.": L'uso di "wanna" (contrazione di "want to") e "win big" (espressione idiomatica per "avere grandi successi") rende il discorso colloquiale. La struttura "you won't do it by..." è semplice ma efficace per negare un metodo.
- "AI is actually doing amazing things now that we couldn't imagine a decade ago.": "Actually" softena l'affermazione, mentre "now that" connette il presente al passato in modo logico. "Couldn't imagine" è una forma comune per esprimere l'incredulità, tipica del linguaggio quotidiano.
- "All of these accountants that used to labor over their arithmetic were now freed up to do higher-level thinking.": "Used to labor over" (sottolinea lo sforzo passato) e "freed up to" (esprime la liberazione da un compito) sono espressioni pratiche. "Higher-level thinking" è un termine chiaro e diretto, spesso usato in contesti professionali.
Routine di pratica: shadowing per migliorare la pronuncia e la fluidità
Per esercitarti con questo video, usa la tecnica del shadowing (o shadow speech):
- Guarda un frammento di 10-15 secondi e ascolta attentamente l'intonazione e la pausa.
- Ripeti immediatamente dopo il parlante, cercando di imitare la velocità e l'accento. Usa un registratore per confrontare la tua versione con l'originale.
- Seleziona frasi con connettivi (es. "but", "so") e ripetile finché non li usi senza sforzo. Questo aiuta a rendere il tuo discorso più fluido.
- Una volta confidente, prolunga i frammenti a 30 secondi e integra la comprensione del contenuto: dopo aver ripetuto, riassumi a parole tue il concetto espresso. Questo unisce pratica orale e comprensione.
Un shadowing site può aiutarti a gestire i frammenti, ma anche un semplice player video con ripetizione automatica funziona. La chiave è la costanza: 10-15 minuti al giorno sono sufficienti per notare progressi nella pronuncia e nella capacità di seguire un discorso articolato.
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | ones who are · people who are · ones who can |
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | were now freed · are employed |
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.