Lightning-Fast Doubt Resolution

The following graph shows the market share of the companies - A, B, C, D in a particular segment from year 1 to year 6 by volume. The table shows the price per unit sold by all 4 companies from Year 1 to Year 6.

The passage below is accompanied by a set of questions. Choose the best answer to each question.

As software improves, the people using it become less likely to sharpen their own know-how. Applications that offer lots of prompts and tips are often to blame; simpler, less solicitous programs push people harder to think, act and learn.

Ten years ago, information scientists at Utrecht University in the Netherlands had a group of people carry out complicated analytical and planning tasks using either rudimentary software that provided no assistance or sophisticated software that offered a great deal of aid. The researchers found that the people using the simple software developed better strategies, made fewer mistakes and developed a deeper aptitude for the work. The people using the more advanced software, meanwhile, would often “aimlessly click around” when confronted with a tricky problem. The supposedly helpful software actually short-circuited their thinking and learning.

[According to] philosopher Hubert Dreyfus . . . . our skills get sharper only through practice, when we use them regularly to overcome different sorts of difficult challenges. The goal of modern software, by contrast, is to ease our way through such challenges. Arduous, painstaking work is exactly what programmers are most eager to automate—after all, that is where the immediate efficiency gains tend to lie. In other words, a fundamental tension ripples between the interests of the people doing the automation and the interests of the people doing the work.

Nevertheless, automation’s scope continues to widen. With the rise of electronic health records, physicians increasingly rely on software templates to guide them through patient exams. The programs incorporate valuable checklists and alerts, but they also make medicine more routinized and formulaic—and distance doctors from their patients. . . . Harvard Medical School professor Beth Lown, in a 2012 journal article . . . warned that when doctors become“screen-driven,” following a computer’s prompts rather than “the patient’s narrative thread,” their thinking can become constricted. In the worst cases, they may miss important diagnostic signals. . . .

In a recent paper published in the journal Diagnosis, three medical researchers . . . examined the misdiagnosis of Thomas Eric Duncan, the first person to die of Ebola in the U.S., at Texas Health Presbyterian Hospital Dallas. They argue that the digital templates used by the hospital’s clinicians to record patient information probably helped to induce a kind of tunnel vision. “These highly constrained tools,” the researchers write, “are optimized for data capture but at the expense of sacrificing their utility for appropriate triage and diagnosis, leading users to miss the forest for the trees.” Medical software, they write, is no “replacement for basic history-taking, examination skills, and critical thinking.” . . .

There is an alternative. In “human-centred automation,” the talents of people take precedence. . . . In this model, software plays an essential but secondary role. It takes over routine functions that a human operator has already mastered, issues alerts when unexpected situations arise, provides fresh information that expands the operator’s perspective and counters the biases that often distort human thinking. The technology becomes the expert's partner, not the expert’s replacement.

In a village there are only 3 types of people, the Truth-Tellers who always speak the truth, the Liars who always speak a lie, and the Alternators, who alternates between a truth and a lie. One day, you encounter 4 people A, B, C and D from the village out of which one is a Truth-Teller, one is a Liar and the remaining 2 are Alternators. Each of them is wearing a differently coloured shirt among blue, red, yellow and green. Each of the people says two statements as follows:
A: B is not the Truth-Teller. I am a Truth-Teller.
B: I am a Truth-Teller. I wear the red coloured shirt.
C: B wears the red coloured shirt. D does not wear the green coloured shirt.
D: I am a Liar. A wears the blue coloured shirt.
Based on the information given above, answer the questions that follow.

In making decisions about important questions it is desirable to be able to distinguish between ‘strong’ arguments and ‘weak’ arguments ‘Weak’ arguments are those which are of minor importance and also may not be directly related to the question or may be related to a trivial aspect of the question
Each question below is followed by three arguments numbered (A) (B) and (C) You have to decide which of the arguments is a ‘strong’ argument and which is a ‘weak’ argument

The passage below is accompanied by four questions. Based on the passage, choose the best answer for each question.

Opinions about artificial intelligence tend to fall on a wide spectrum. At one extreme is the utopian view that AI will cause runaway economic growth, accelerate scientific research and perhaps make humans immortal. At the other extreme is the dystopian view that AI will cause abrupt, widespread job losses and economic disruption, and perhaps go rogue and wipe out humanity. So a paper published earlier this year by computer scientists Arvind Narayanan and Sayash Kapoor is notable for the unfashionably sober manner in which it treats AI: as “normal technology”. Both utopian and dystopian views, the authors write, treat AI as an unprecedented intelligence with agency to determine its own future, meaning analogies with previous inventions fail. The authors reject this, and map out what they see as a more likely scenario: that AI will follow the trajectory of past technological revolutions.

The pace of AI adoption, the authors argue, has been slower than that of innovation. Many people use AI tools occasionally, but at an intensity that is still low as a fraction of overall working hours. For adoption to lag behind innovation is not surprising, because it takes time for people and companies to adapt habits and workflows to new technologies ... Moreover, constraints on the pace of AI innovation itself may be more significant than they seem because many applications (such as drug development or self-driving cars) require extensive real-world testing. This can be slow and costly, particularly in safety-critical fields that are tightly regulated. As a result, economic impacts “are likely to be gradual”, the authors conclude, rather than involving the abrupt automation of a big chunk of the economy.

Even a slow spread of AI would change the nature of work. As more tasks become amenable to automation, “an increasing percentage of human jobs and tasks will be related to AI control.” There is an analogy here with the Industrial Revolution, in which workers went from performing manual tasks, such as weaving, to supervising machines doing those tasks—and handling situations machines could not. Rather than AI stealing jobs wholesale, jobs might increasingly involve configuring, monitoring and controlling AI-based systems. Without human oversight, AI may be “too error-prone to make business sense”.

That, in turn, has implications for AI risk. Strikingly, the authors criticise the emphasis on “alignment” of AI models, meaning efforts to ensure outputs align with their human creators’ goals. Whether a given output is harmful often depends on context that humans may understand, but the model lacks, they argue ... Trying to make an AI model that cannot be misused “is like trying to make a computer that cannot be used for bad things”, the authors write. Instead, they suggest, defences against the misuse of AI, for example to create computer malware or bioweapons, should focus further downstream, by strengthening existing protective measures in cyber-security and biosafety.

[...] The paper is not without its flaws ... Even AI-pragmatists may feel the authors are too blasé about the potential for labour-market disruption, underestimate the speed of AI adoption, are too dismissive of the risks of misalignment and deception, and complacent about regulation ... Even if the utopian and dystopian scenarios are wrong, AI could still be far more transformative than the authors describe.

A tech firm operates in three towers: Agni, Prithvi and Naaga. Each tower has four floors, numbered 1 to 4 from bottom to top. Twelve employees: Aman, Yash, Kavya, Jayesh, Eshwar, Prakash, Ishani, Mahesh, Nayan, Ojas, Umang, and Tejas sit on these 12 floors such that only one person sits on each floor. Employees whose names start with a vowel are interns. It is known that no two interns sit on adjacent floors in the same tower. The following information is known:

1. Aman sits on the odd-numbered floor.
2. Jayesh sits on a floor above Prakash, with one floor between them.
3. Nayan sits on the floor, which is second from the top. Ishani and Tejas don't sit in the same tower.
4. Nayan sits immediately above Umang in the same tower. Ojas sits at the top-most floor of Naaga.
5. Kavya sits on the floor immediately above Yash in the same tower. Aman and Eshwar sit in the same tower and there are two floors between them.
6. Tejas does not sit on the bottom-most or top-most floor of any tower.

Based on the information given, answer the following questions.