Questões Militares

Six things I learned from riding in a Google self-driving car

1 - Human beings are terrible drivers.

      We drink. We doze. We text. In the US, 30,000 people die from automobile accidents every year. Traffic crashes are the primary cause of death worldwide for people aged 15-24, and during a crash, 40% of drivers never even hit the brakes. We’re flawed organisms, barreling around at high speeds in vessels covered in glass, metal, distraction, and death. This is one of Google’s “moonshots” – to remove human error from a job which, for the past hundred years, has been entirely human.

2 - Google self-driving cars are timid.

        The car we rode in did not strike me as dangerous. It drove slowly and deliberately, and I got the impression that it’s more likely to annoy other drivers than to harm them. In the early versions they tested on closed courses, the vehicles were programmed to be highly aggressive. Apparently during these tests, which involved obstacle courses full of traffic cones and inflatable crash-test objects, there were a lot of screeching brakes, roaring engines and terrified interns.

3 - They’re cute.

        Google’s new fleet was intentionally designed to look adorable. Our brains are hardwired to treat inanimate (or animate) objects with greater care, caution, and reverence when they resemble a living thing. By turning self-driving cars into an adorable Skynet Marshmallow Bumper Bots, Google hopes to spiritually disarm other drivers. I also suspect the cuteness is used to quell some of the road rage that might emerge from being stuck behind one of these things. They’re intended as moderate-distance couriers, not openroad warriors, so their max speed is 25 miles per hour.

4 - It’s not done and it’s not perfect.

      Some of the scenarios autonomous vehicles have the most trouble with are the same human beings have the most trouble with, such as traversing four-way stops or handling a yellow light. The cars use a mixture of 3D laser-mapping, GPS, and radar to analyze and interpret their surroundings, and the latest versions are fully electric with a range of about 100 miles. Despite the advantages over a human being in certain scenarios, however, these cars still aren’t ready for the real world. They can’t drive in the snow or heavy rain, and there’s a variety of complex situations they do not process well, such as passing through a construction zone. Google is hoping that, eventually, the cars will be able to handle all of this as well (or better) than a human could.

5 - I want this technology to succeed, like… yesterday.

        I’m biased. Earlier this year my mom had a stroke. It damaged the visual cortex of her brain, and her vision was impaired to the point that she’ll probably never drive again. This reduced her from a fully-functional, independent human being with a career and a buzzing social life into someone who is homebound, disabled, and powerless. When discussing self-driving cars, people tend to ask many superficial questions. They ignore that 45% of disabled people in the US still work. They ignore that 95% of a car’s lifetime is spent parked. They ignore how this technology could transform the lives of the elderly, or eradicate the need for parking lots or garages or gas stations. They dismiss the entire concept because they don’t think a computer could ever be as good at merging on the freeway as they are. They ignore the great, big, beautiful picture: that this technology could make our lives so much better.

6 - It wasn’t an exhilarating ride, and that’s a good thing.

        Riding in a self-driving car is not the cybernetic thrill ride one might expect. The car drives like a person, and after a few minutes you forget that you’re being driven autonomously. You forget that a robot is differentiating cars from pedestrians from mopeds from raccoons. You forget that millions of photons are being fired from a laser and interpreting, processing, and reacting to the hand signals of a cyclist. You forget that instead of an organic brain, which has had millions of years to evolve the cognitive ability to fumble its way through a four-way stop, you’re being piloted by an artificial one, which was birthed in less than a decade. The unfortunate part of something this transformative is the inevitable, ardent stupidity which is going to erupt from the general public. Even if in a few years self-driving cars are proven to be ten times safer than human-operated cars, all it’s going to take is one tragic accident and the public is going to lose their minds. There will be outrage. There will be politicizing. There will be hashtags. I say look at the bigger picture. All the self-driving cars currently on the road learn from one another, and possess 40 years of driving experience. And this technology is still in its infancy.

(Adapted from:: <http://theoatmeal.com/blog/google_self_driving_car> . 21/08/2016.)

        Language-centered methods are those that seek to provide opportunities for learners to practice preselected linguistic structures through form-focused exercises in class. The assumption is that language practice will ultimately lead to a mastery of the target language and that learners can draw from this formal repertoire whenever they wish to communicate in the target language outside the class. According to this belief, language development is largely intentional rather than incidental; and language learning seen as a linear, additive process.

         Learner-centered methods are those that are principally concerned with language use and learner needs. These methods seek to provide opportunities for learners to practice preselected, presequenced grammatical structures as well as communicative functions (i.e., speech acts such as apologizing, requesting, etc.) through meaning-focused activities. Proponents of learner-centered methods believe in accumulated entities, represented by structures plus notions and functions.

        Learning-centered methods are those that are principally concerned with learning processes. These methods seek to provide opportunities for learners to participate in open-ended meaningful interaction through communicative activities or problem-solving tasks in class. The assumption is that a preoccupation with meaning-making will most likely lead to grammatical as well as communicative mastery of the language and that learners can learn through the process of communication. In this approach, unlike the other two, language development is a nonlinear process and considered more incidental than intentional. Proponents of learningcentered methods believe that language is best learned when the learner’s attention is focused on understanding, saying and doing something with language, and not when their attention is focused explicitly on linguistic features.

(Kumaravadivelu, B. Beyond Methods: Macrostrategies for language learning. Haven and London: Yale University Press. 2003. Adaptado)

The regional accentism that secretly affects life prospects

        At age 22, Gav Murphy was living outside his home country Wales for the first time, working in his first job in media production in London. His South Wales Valleys accent was very thick, he recalls. He’d say ‘tha’ rather than ‘that’, for instance. He was perfectly understandable; yet a senior colleague overseeing his work insisted Murphy change his accent so all the broadcasters sounded uniform on air. The effects of adaptation were far-reaching. “It sort of broke my brain a little bit,” says Murphy. “I thought about literally every single thing I was saying, literally every time I was saying it. Moving to standard English was just laborious.”

        Foreign-accent discrimination is rampant in professional settings. But discrimination can also extend to certain native speakers of a language, because of the judgements attached to particular accents. While many employers are becoming very sensitive to other types of bias, accent bias remains challenging to root out. But it doesn’t have to be this way.

       While the cognitive shortcuts that contribute to accent bias may be universal, the degree of accent awareness and prejudice varies greatly. For instance, “The UK has a very, very fine-tuned system of accent prestige,” says Devyani Sharma, a sociolinguist at Queen Mary University of London. “It’s a combination of a very monolingual past, where English developed as a symbol of the nation, and the very acute social class hierarchy historically.” She adds that overt accent bias in the US is based more on race, whereas in the UK, it’s more tied to class.

        In some cases, accent bias is directly related to government policy. Since the 1860s, the Japanese government has modernised the country with a focus on Tokyo, says Shigeko Kumagai, a linguist at Shizuoka University, Japan. “Thus, standard Japanese was established based on the speech of educated Tokyoites.” In contrast, the Tohoku dialect spoken in northern Japan became “the most stigmatised dialect in Japan”, says Kumagai. Its image is “rural, rustic, old, stubborn, narrow-minded, backward, poor, uneducated, etc”. Young women from Tohoku are often given discriminatory treatment that makes them feel ashamed of their accents.

        A pesquisa de Kumagai mostra que a forte estereotipagem do dialeto Tohoku é perpetuada pela concentração da indústria de mídia na capital japonesa. De fato, em todo o mundo, a mídia tem um impacto enorme na percepção dos sotaques. Portanto, entendemos por que a preponderância de emissoras do Reino Unido em Londres provavelmente contribuiu para a marginalização do sotaque galês de MurphyKumagai’s research shows that the strong stereotyping of the Tohoku dialect is perpetuated by the concentration of the media industry in the Japanese capital. Indeed, the world over, the media has an enormous impact on perceptions of accents. So we understand why the preponderance of UK broadcasters in London likely contributed to the marginalisation of Murphy’s Welsh accent.

(Christine Ro. www.bbc.com, 08.05.2022. Adaptado)