Questões de Inglês

Leia o título de uma notícia sobre o ChatGPT publicada no jornal The New York Times.

 

Sam Altman, the chatgpt king, isn’t worried, but he knows you might be

(https://www.nytimes.com/. Acesso em 20.06.25. Adaptado)

 

O título sugere que Sam Altman, um dos desenvolvedores da ferramenta mencionada,

Leia o texto para responder à questão.

 

Artificial inteligence (AI) and education

    We’re still learning how Artificial Intelligence (AI) technologies will integrate into the education sector as they develop, and we don’t yet have a full picture of how AI will affect some critical issues. Before we dive into AI’s function in the education space, let’s define this technology in general terms. Artificial intelligence allows machines to execute tasks that have traditionally required human cognition. They can make decisions, solve problems, understand and mimic natural language and learn from free data.

    OpenAI’s release of ChatGPT — a natural language processing chatbot — in the fall of 2022 brought AI to many people’s attention for the first time. However, AI tools have been part of our lives for years. If you’ve ever played chess against a bot, consulted a virtual assistant like Siri or Alexa or even gone through your social media feed, you’ve already interacted with artificial intelligence.

(https://www.forbes.com/. Acesso em 26.06.2025. Adaptado)

De acordo com o texto, é possível afirmar que Inteligência Artificial

    The excitement surrounding potential benefits of generative artificial intelligence (AI), from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI boom remain difficult to determine, let alone mitigate.

    The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.

    Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed. And beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems.

(Adam Zewe. https://news.mit.edu, 17.01.2025. Adaptado.)

No trecho do terceiro parágrafo “Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed”, o termo sublinhado pode ser substituído, sem alterar o sentido original da oração, por:

    The excitement surrounding potential benefits of generative artificial intelligence (AI), from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI boom remain difficult to determine, let alone mitigate.

    The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.

    Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed. And beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems.

(Adam Zewe. https://news.mit.edu, 17.01.2025. Adaptado.)

According to the text, one direct consequence of the energy needed to develop and operate generative AI models is the

    The excitement surrounding potential benefits of generative artificial intelligence (AI), from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI boom remain difficult to determine, let alone mitigate.

    The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.

    Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed. And beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems.

(Adam Zewe. https://news.mit.edu, 17.01.2025. Adaptado.)

The main purpose of the text is to report about the

Read the following text.

 

    Hebbian Plasticity and Mental Health

 

Hebbian plasticity is not just about learning new skills like riding a bike — it also helps your brain stay healthy. In some conditions, such as depression or post‑traumatic stress disorder (PTSD), the problematic symptoms can be caused by the poor function of certain brain connections. Imagine if the pathways between important parts of your brain were weak or broken. When brain pathways are weak, it can be harder to think clearly, concentrate, or feel happy. When a person experiences stress or trauma, Hebbian plasticity can also accidentally strengthen harmful connections. This happens because the brain is trying to protect itself, but sometimes it ends up strengthening neural pathways that make a person more vulnerable to negative emotions or stress responses. Fortunately, learning how to strengthen “positive” neural pathways, such as those involved in healthy coping strategies, physical activity, or resilience, can help restore balance, improving people’s moods and overall mental wellbeing.

Available at: https://kids.frontiersin.org/articles/10.3389/ frym.2025.1425512. Accessed on: March 1st, 2025.

 

The adjective coping in the sentence “Fortunately, learning how to strengthen ‘positive’ neural pathways, such as those involved in healthy coping strategies, physical activity, or resilience [...].” is closest in meaning to