2023年6月英语六级真题答案（第1套）

Technology is never a neutral tool for achieving human ends. Technological innovations reshape people as they use these innovations to control their environment. Artificial intelligence, for example, is altering humanity.

While the term AI conjures up anxieties about killer robots or catastrophic levels of unemployment, there are other, deeper implications. As AI increasingly shapes the human experience, how does this change what it means to be human? Central to the problem is a person's capacity to make choices, particularly judgments that have moral implications.

Aristotle argued that the capacity for making practical judgments depends on regularly making them—on habit and practice. We see the emergence of machines as substitute judges in a variety of everyday contexts as a potential threat to people learning how to effectively exercise judgment themselves.

In the workplace, managers routinely make decisions about who to hire or fire and which loan to approve, to name a few. These are areas where algorithmic (算法的) prescription is replacing human judgment, and so people who might have had the chance to develop practical judgment in these areas no longer will.

Recommendation engines, which are increasingly prevalent intermediaries in people's consumption of culture, may serve to constrain choice and minimize luck. By presenting consumers with algorithmically selected choices of what to watch, read, stream and visit next, companies are replacing human taste with machine taste. In one sense, this is helpful. After all, machines can survey a wider range of choices than any individual is likely to have the time or energy to do on their own.

At the same time, though, this selection is optimizing for what people are likely to prefer based on what they've preferred in the past. We think there is some risk that people's options will be constrained by their past in a new and unanticipated way.

As machine learning algorithms improve and as they train on more extensive data sets, larger parts of everyday life are likely to become utterly predictable. The predictions are going to get better and better, and they will ultimately make common experiences more efficient and pleasant.

Algorithms could soon-if they don't already—have a better idea about which show you'd like to watch next and which job candidate you should hire than you do. One day, humans may even find a way for machines to make these decisions without some of the biases that humans typically display.

But to the extent that unpredictability is part of how people understand themselves and part of what people like about themselves, humanity is in the process of losing something significant. As they become more and more predictable, the creatures inhabiting the increasingly AI-mediated world will become less and less like us.

Phonics, which involves sounding out words syllable (音节) by syllable, is the best way to teach children to read. But in many classrooms, this can be a dirty word. So much so that some teachers have had to sneak phonics teaching materials into the classroom. Most American children are taught to read in a way that study after study has found to be wrong.

The consequences of this are striking. Less than half of all American adults were proficient readers in 2017. American fourth graders rank 15th on the Progress in International Literacy Study, an international exam.

America is stuck in a debate about teaching children to read that has been going on for decades. Some advocate teaching symbol-sound relationships (the sound k can be spelled as c, k, ck, or ch), known as phonics. Others support an immersive approach (using pictures of a cat to learn the word cat), known as "whole language". Most teachers today, almost three out of four according to a survey by the Ed Week Research Centre in 2019, use a mix called "balanced literacy". This combination of methods is ineffective. "You can't sprinkle in a little phonics," says Tenette Smith, executive director of elementary education and reading at Mississippi's education department. "It has to be systematic and explicitly taught."

Mississippi, often behind in social policy, has set an example here. In a state once notorious for its low reading scores, the Mississippi state legislature passed new literacy standards in 2013. Since then Mississippi has seen remarkable gains. Its fourth graders have moved from 49th (out of 50 states) to 29th on the National Assessment of Educational Progress, a nationwide exam. In 2019 it was the only state to improve its scores. For the first time since measurement began, Mississippi's pupils are now average readers, a remarkable achievement in such a poor state.

Mississippi's success is attributed to implementing reading methods supported by a body of research known as the science of reading. In 1997 Congress requested the National Institute of Child Health and Human Development and the Department of Education to convene a National Reading Panel to end the "reading wars" and synthesize the evidence. The panel found that phonics, along with explicit instruction in phonemic (音位的) awareness, fluency and comprehension, worked best.

Yet over two decades on, "balanced literacy" is still being taught in classrooms. But advances in statistics and brain imaging have disproved the whole-language method. To the teacher who is a proficient reader, literacy seems like a natural process that requires educated guessing, rather than the deliberate process emphasized by phonics. Teachers can imagine that they learned to read through osmosis (潜移默化) when they were children. Without proper training, they bring this to classrooms.