Robots will take over human jobs and it is our task as people to assist them in doing so. In the article “Better Than Human: Why Robots Will – And Must – Take Our Jobs” written by Kevin Kelly, he argues that the above statement is a clear depiction of the future labor market, where robots will perform human jobs, while the new human job will be to make sure that the old human jobs are eventually assigned to robots. Kelly utilizes a logical approach, attempting to convince his readers that robotic replacement is another stage of the industrial evolution, referring to the nature of changes in human jobs over time, the emergence of jobs that cannot be performed by humans, and the misconception of robotic labor as a replication of manual tasks performed by humans. However, Kelly weakens his argument by ignoring the economic consequences of a robotic replacement for humans and the social inequality that will emerge from a high concentration of technology-related jobs.

The most likely audience for Kelly’s article is the adepts of technological progress who are closely involved in the process automation, as well as the people who express concerns regarding the importance of job automation. Targeting these audiences is essential to the way Kelly shapes his argument by specifying that the vast majority of modern jobs could not have been imagined two centuries ago, meaning that one cannot imagine the scope of future jobs shaped by present technological advancements. To support this, Kelly appeals to the fact that farmers represented the majority of the U.S. working population in the 1800s, but the current process automation replaced almost 99% of the former farming jobs. Invoking the speed of technological progress, Kelly states that it is likely that advanced forms of automation will similarly replace 70% of today’s occupations following the evolutionary patterns. He challenges his readers by saying that their jobs are likely to be “taken away by machines” (Kelly). The author conceptualizes this change as the second wave of automation that will be focused on the expansion of artificial intelligence rather than automated manufacturing, thus explaining that even more job categories will be affected.

To support his argument for the upcoming second wave of automation, Kelly presents two perspectives on the structural upgrades in robot’s functionality. The first perspective states that robots will consolidate previous achievements in the already automated environments by replacing the need for manual work. He supports this statement with an example of the replacement of assembly line workers across advanced manufacturing facilities, as well as the introduction of robotic replacements in warehousing operations. Kelly expands the view mentioning that robots will replace cleaning services in offices and educational facilities, while making a somewhat exaggerated assumption that robots embedded in trucks will be able to manage long-haul driving. Overall, this perspective shows that various jobs that require manual efforts rather than skills like strategic thinking are already in process of robotic replacement.

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The second perspective is more ambitious and indicates that robots will be effectively utilized in white-collar jobs. Kelly provides recent examples of software applications that can write complete narratives based on the broadly available statistical data. For instance, algorithms can create short synopses of companies’ stock performance or commentaries on sports events. The main idea is that, as long as robots receive megabytes of information, they can generate meaningful compositions of data in the same way as humans do. Reflecting on the scope of this capacity, Kelly ambitiously states that “the robot takeover will be epic”, attempting to convince his readers that the second wave of automation is a real, ongoing process (Kelly).

To make his arguments even stronger, Kelly dives deeper into the real-life examples of robotic replacements based on the case of Baxter, a new workbot technology that is believed to behave in a smarter manner than humans if the condition of a continuous machine learning process is maintained. Kelly uses this example to dissolve the opinion on robots as machines that cannot operate in a tandem with humans, providing several practical observations on the efficiency of Baxter’s design. He also uses Baxter as the example of an advanced technology that could be used as a benchmark for future technological developments to replace human work.

First, Kelly differentiates Baxter from other industrial robots by the ability to perceive humans and other robots working in close proximity and avoid injuries and interruptions to the work of others. He states that this advantage allows using Baxter in small environments, such as personal offices or garages, which was not possible for previous robot versions of this kind due to their oblivious behavior. Second, Kelly praises the machine learning algorithms embedded into Baxter’s core that allow anyone to train the robot to perform several kinds of jobs by moving robot’s arms in a correct motion and sequence. Kelly contrasts this advancement with a previous industry standard of machine learning that required regular uploading of batches of programming code into a robot’s core systems to improve its functions. Kelly suggests that advanced machine training allows not simply delegating any type of manual work to a robot, but also to save time, money, and resources required to keep the robot operating. Finally, Kelly compares Baxter’s market price to the price and value of its predecessors, concluding that while the price of the new workbot is still high, it is 25 times lower than the full payment required for an installation of an industrial robot. These advantages sound very promising in light of robots still being mostly affordable for large organizations rather than small firms and individuals, engaging readers to consider what personal benefits they can acquire by using robots in the future.

Kelly effectively uses the case of Baxter and references to the industrial revolution to create a conceptual model of future robotic replacement. He breaks down the human relationship with robots into four categories that blend new and existing jobs with robots and machines, attempting to classify the future distribution of different types of work. At a glance, this model looks somewhat “pro-robotic”, as it states that almost all present human jobs could be easily transferred to robots in one way or another. However, Kelly supports his thoughts and considerations with vivid examples from real-life cases, further strengthening his argument for allowing robots to take over human jobs. For the first kind of jobs described as the ones better performed by robots than humans, Kelly recalls how robots mastered conceptual routines such as cloth weaving, reasonably arguing that robots perform this type of work better and faster than humans. The second kind of jobs includes activities that humans cannot do at all. Kelly is successful in explaining that a detailed inspection and analysis of data patterns could be performed by search engines but not data analysts. Kelly explains the greatest advantage of a robot takeover by referencing the third category of jobs. It includes professions like database developers or web programmers, which now can be performed by humans but only because of technological changes. The fourth category consists of the jobs one cannot even imagine. By providing a rich and meaningful framework, Kelly reassures his readers that robotic replacement is not merely a guess but a fact-based assumption that requires individual consideration from the perspective of one’s current occupation.

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While Kelly is successful in conceptualizing robotic replacement, his concluding statements are somewhat exaggerated and mostly refer to emotional appeals from different perspectives rather than a consideration of obstacles that may arise in the process of technological job transition. Kelly poses a rhetorical question, “What should we [humans] do?”, referring to the possible alternatives to human occupations after the forecast robotic takeover, though his answers are not as convincing as the provided replacement model. The short response provided by Kelly is that humanity should be prepared for a human-robot symbiosis, where success in job occupations will go to innovators and process optimizers who will manage robots as they currently manage processes and employees. However, the current socioeconomic reality rejects the fact that the majority of the human population will not be capable of evolving its skills to the level of coordinating robots and managing artificial intelligence as fast as technology will develop. A recent article published by The Economist specifies that automation currently threatens 47% of jobs in the United States with a much higher percentage across the developing countries, such as 77% in China, 69% in India, and 85% in Ethiopia (“Robots v Humans: Machine Earning.). The reason for this disproportion is explained by the term “premature deindustrialization”, which means that countries with high underemployment were injected with labor-intensive manufacturing investments that introduced robotic replacements, but did not satisfy labor demands (“Robots v Humans: Machine Earning”). In this case, the low-income population will be forced to ask the same question as Kelly did with the answer not being as obvious.

Further, in his argument Kelly omitted the fact that machine learning is not human learning. The human brain is more capable of absorbing and interpreting information as compared to the core of the most advanced robotic system. Humans will use judgment and rationale in making critical decisions regarding various tasks, while robots will follow the learning patterns programmed by software engineers and acquired through learning from humans. Thus, Kelly’s example where a single farmer successfully coordinates robots and improves their response patterns is rather utopian, as robots will certainly need more manual maintenance with advanced functionality embedded into their program cores. Consequently, Kelly’s argument would sound more convincing if some related viewpoints in social and economic dimensions were considered in order to identify and discuss possible risks associated with a delegation of human tasks to robots.

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Overall, in “Better Than Human: Why Robots Will – And Must – Take Our Jobs” Kevin Kelly presents a convincing argument on why robots will take over human jobs and why humans should consider this transition advantageous based on the present state of robotic replacements and favorable technology-related forecasts. Kelly effectively rationalizes his statements, supporting them with extensive use of real-life examples and references to past perspectives on how human jobs have changed. The article uses up-to-date background information on robotic replacements to construct a convincing framework of future job classification that engages readers with opposite perspectives. However, the extensive use of emotional appeals in the final part of the article as well as the failure to consider the social and economic aspects of mass automation globally somewhat invalidate the author’s argument. Specifically, Kelly fails to recognize that job automation already negatively reflects on the employment rates among low-income population, with even higher forecast rates if the intensity of robotic replacement initiative will keep increasing. He also overestimates the robots’ capabilities in accumulating essential knowledge against the capabilities of a human brain. Kelly’s argument would sound more convincing if he used relevant statistics on the impact of job automation across the United States and globally, suggesting a more specific way of integrating robotic replacement without threatening the employment opportunities for vulnerable population categories.

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