Shadowing Practice: Cambridge 16 IELTS Reading Test 4 Passage 3 - Learn English Speaking with Video

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Attitudes towards artificial intelligence.
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A artificial intelligence, AI, can already predict the future.
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Police forces are using it to map when and where crime is likely to occur.
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Doctors can use it to predict when a patient is most likely to have a heart attack or stroke.
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Researchers are even trying to give AI imagination so it can plan for unexpected consequences.
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Many decisions in our lives require a good forecast, and AI is almost always better at forecasting than we are.
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Yet for all these technological advances, we still seem to deeply lack confidence in AI predictions.
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Recent cases show that people don't like relying on AI and prefer to trust human experts, even if these experts are wrong.
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If we want AI to really benefit people, we need to find a way to get people to trust it.
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To do that, we need to understand why people are so reluctant to trust AI in the first place.
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They take the case of Watson for Oncology, one of technology giant IBM's supercomputer programs.
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Their attempt to promote this program to cancer doctors was a PR disaster.
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The AI promised to deliver top-quality recommendations on the treatment of 12 cancers that accounted for 80% of the world's cases.
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But when doctors first interacted with Watson, they found themselves in a rather difficult situation.
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On the one hand, if Watson provided guidance about a treatment that coincided with their own opinions, physicians did not see much point in Watson's recommendations.
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The supercomputer was simply telling them what they already knew, and these recommendations did not change the actual treatment.
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On the other hand, if Watson generated a recommendation that contradicted the expert's opinion, doctors would typically conclude that Watson wasn't competent.
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And the machine wouldn't be able to explain why its treatment was plausible
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because its machine learning algorithms were simply too complex to be fully understood by humans.
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Consequently, this has caused even more suspicion and disbelief,
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leading many doctors to ignore the seemingly outlandish AI recommendations and stick to their own expertise.
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See this as just one example of people's lack of confidence in AI
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and their reluctance to accept what Al has to offer.
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Trust in other people is often based on our understanding of how others think and having experience of their reliability.
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This helps create a psychological feeling of safety.
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Al, on the other hand, is still fairly new and unfamiliar to most people.
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Even if it can be technically explained, and that's not always the case, AI's decision-making process is usually too difficult for most people to comprehend.
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And interacting with something we don't understand can cause anxiety and give us a sense that we're losing control.
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Many people are also simply not familiar with many instances of AI actually working because it often happens in the background.
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Instead, they are acutely aware of instances where AI goes wrong.
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Embarrassing AI failures receive a disproportionate amount of media attention, emphasizing the message that we cannot rely on technology.
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Machine learning is not foolproof, in part because the humans who design it aren't.
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Deep feelings about AI run deep.
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In a recent experiment, people from a range of backgrounds were given various sci-fi films about AI to watch
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and then asked questions about automation in everyday life.
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It was found that, regardless of whether the film they watched depicted Al in a positive or negative light,
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simply watching a cinematic vision of our technological future polarize the participants' attitudes.
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Optimists became more extreme in their enthusiasm for AI and skeptics became even more guarded.
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This suggests people use relevant evidence about AI in a biased manner to support their existing attitudes,
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a deep-rooted human tendency known as confirmation bias.
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As L is represented more and more in media and entertainment, it could lead to a society split between those who benefit from AI and those who reject it.
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More pertinently, refusing to accept the advantages offered by AI could place a large group of people at a serious disadvantage.
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Unfortunately, we already have some ideas about how to improve trust in AI.
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Simply having previous experience with AI can significantly improve people's opinions about the technology, as was found in the study mentioned above.
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Evidence also suggests the more you use other technologies such as the internet, the more you trust them.
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Another solution may be to reveal more about the algorithms which our users and the purposes they serve.
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Several high-profile social media companies and online marketplaces already released transparency reports about government requests and surveillance disclosures.
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A similar practice for AI could help people have a better understanding of the way algorithmic decisions are made.
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If research suggests that allowing people some control over AI decision-making could also improve trust
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and enable AI to learn from human experience.
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For example, one study showed that when people were allowed the freedom to slightly modify an algorithm, they felt more satisfied with its decisions,
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more likely to believe it was superior and more likely to use it in the future.
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We don't need to understand the intricate inner workings of AI systems, but if people are given a degree of responsibility for how they are implemented,
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they will be more willing to accept GAL into their lives.
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Vocabulario y notas de pronunciación para esta lección

Esta lección de conversación de nivel C1 se basa en el vídeo “Cambridge 16 IELTS Reading Test 4 Passage 3”. Las palabras que más se repiten: trust, recommendation, Watson, doctors, simply. Este vídeo tiene 54 frases y 885 palabras para practicar shadowing. La parte hablada dura 5:49. El hablante habla a un ritmo natural de unas 152 palabras por minuto, cercano a una conversación cotidiana. Solo el 80 % de las palabras está entre las 3.000 más comunes del inglés, por lo que el vocabulario es exigente.

Vocabulario clave de este vídeo

Las 15 palabras más avanzadas del vídeo, con su pronunciación y significado:

PalabraPronunciaciónSignificado
recommendation sustantivo/ˌɹɛkəmɛnˈdeɪʃən/recomendación
algorithm sustantivo/ˈælɡəɹɪðm̩/algoritmo
supercomputer sustantivosupercomputador, supercomputadora
interact verbo/ɪn.təˈɹækt/interactuar
technological adjetivo/ˌtɛk.nəˈlɑ.dʒɪ.kəl/tecnológico
predict verbo/pɹɪˈdɪkt/predecir
forecast sustantivo/ˈfɔːkɑːst/pronóstico, vaticinio
acutely adverbio/əˈkjuːtli/intensamente
algorithmic adjetivo/ˌælɡəˈɹɪðmɪk/algorítmico
coincide verbo/ˌkoʊɪnˈsaɪd/coincidir
contradict verbo/ˌkɑn.tɹəˈdɪkt/contradecir
depict verbo/dɪˈpɪkt/plasmar
disproportionate adjetivo/dɪs.pɹəˈpɔː.ʃə.nət/desproporcionado
embarrass verbo/ɪmˈbæɹəs/avergonzar, abochornar
foolproof adjetivo/ˈfuːl ˌpɹuːf/seguro, a prueba de tontos

Pronunciación a tener en cuenta

El hablante usa 7 contracciones y formas reducidas, como don't, aren't, wasn't. Dilas en su forma corta, tal como las oyes.

  • Los sonidos de “th”: algorithm /ˈælɡəɹɪðm̩/, algorithmic /ˌælɡəˈɹɪðmɪk/, enthusiasm /ɪnˈθuziæzəm/
  • Los sonidos de “sh” y “zh”: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, disproportionate /dɪs.pɹəˈpɔː.ʃə.nət/, outlandish /ˌaʊtˈlændɪʃ/, automation /ˌɔ.təˈmeɪ.ʃən/, prediction /pɹɪˈdɪkʃən/
  • Palabras largas — cuida el acento: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, algorithmic /ˌælɡəˈɹɪðmɪk/, disproportionate /dɪs.pɹəˈpɔː.ʃə.nət/, kinematic /kɪn.əˈmæt.ɪk/

Sonidos difíciles para hispanohablantes:

  • /z/ — sonora, no /s/: polarize /ˈpəʊ.lə.ɹaɪz/, plausible /ˈplɑ.zɪ.bəl/, emphasize /ˈɛm.fə.saɪz/, enthusiasm /ɪnˈθuziæzəm/, expertise /ˌɛkspɜːˈtiːz/
  • /dʒ/ — no es la “y” ni la “ch”: technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, disadvantage /ˌdɪsədˈvæntɪd͡ʒ/

Cómo practicar con este vídeo

  1. Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
  2. Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
  3. Grábate y compara con el original, prestando atención a palabras como recommendation, algorithm, supercomputer.

¿Qué es la Técnica de Shadowing?

Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.

Técnica de shadowing: lee la guía completa paso a paso →