⬛ TEXTO — Itens (13 a 19)
The term AI winter refers to a period of reduced funding in the development of AI. In general, AI has followed a path on which proponents overstate what is possible, inducing people with no technology knowledge at all, but lots of money, to make investments. A period of criticism then follows when AI fails to meet expectations, and, finally, the reduction in funding occurs.
A number of these cycles have occurred over the years — all of them devastating to true progress. AI is currently in a new hype phase because of machine learning, a technology that helps computers learn from data. Having a computer learn from data means not depending on a human programmer to set operations (tasks), but rather deriving them directly from examples that show how the computer should behave. It’s like educating a baby by showing it how to behave through example. Machine learning has pitfalls because the computer can learn how to do things incorrectly through careless teaching.
Scientists are working on machine learning algorithms, each one from a different point of view. At this time, the most successful solution is deep learning, which is a technology that strives to imitate the human brain. Deep learning is possible because of the availability of powerful computers, smarter algorithms, large datasets produced by the digitalization of our society, and huge investments from businesses such as Google, Facebook, Amazon, and others that take advantage of this AI renaissance for their own businesses.
People are saying that the AI winter is over because of deep learning, and that’s true for now. However, when you look around at the ways in which people are viewing AI, you can easily figure out that another criticism phase will eventually occur unless proponents tone the rhetoric down. AI can do amazing things, but they’re a mundane sort of amazing.
🔗 Texto adaptado de: John Paul Mueller; Luca Massaron. Artificial Intelligence For Dummies. Hoboken (New Jersey): John Wiley & Sons, 2022.
15. It can be inferred from the text that deep learning depends on a variety of factors, the most important of which being the investment from big businesses.
Gabarito: ERRADO
🧭 1️⃣ Leitura orientada
O item exige verificar se o texto estabelece uma hierarquia explícita entre os fatores que possibilitam o desenvolvimento do deep learning.
📝 2️⃣ Análise técnica
O texto lista vários fatores que tornam o deep learning possível: computadores poderosos, algoritmos mais inteligentes, grandes conjuntos de dados e investimentos de grandes empresas.
Contudo, o autor não atribui primazia a nenhum desses fatores. A expressão “because of” introduz uma enumeração explicativa, não uma ordem de importância.
Ao afirmar que o investimento empresarial é “the most important”, o item introduz uma hierarquização que não está no texto.
🚩 3️⃣ Armadilhas da banca
A banca explora a inferência hierárquica indevida: transformar uma lista de condições necessárias em uma relação de importância relativa.
🧠 4️⃣ Resumo B3GE™ Master
✔ Deep learning depende de múltiplos fatores.
✔ O texto não estabelece fator “mais importante”.
✔ O item cria hierarquia inexistente.
🔎 Item ERRADO.