Machine Learning and Human Patnerships (Research Paper Sample)

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Machine Learning and Human Partnerships
Over the next twenty years, machine learning will transform business operations globally just like the internet and cloud computing have done. Machine learning, a form of artificial intelligence that enables software-based accurate predictions without programming, imitating how humans learn, will take up some of the roles performed by human beings in the organizational contest (Marone). Lately, there have been many scary narratives about how these innovations with human abilities will increasingly replace human beings in the workplace. However, Daniel Newman and Olivier Blanchard indicate that humans and machines are not competitors and will form formidable partnerships that will revolutionize the business world (Newman and Blanchard). Machines excel where the needs for social and creative intelligence are low, while humans excel when such needs are high and human. Machine partnerships in the workplace will combine human and artificial intelligence, creating a structure of superintelligence that will revolutionize different sectors such as healthcare and finance. The partnership between human and machine algorithms in COVID-19 response and banking operations is a perfect example of the power of combined intelligence. This report is important as it will allay fears of job loss due to the perceived takeover by machines and focus on areas of partnerships between humans and machines.
Machine learning artificial intelligence
The world celebrates the progress and the benefits of machine learning. Machine learning (ML) is one of the main sub-categories of artificial intelligence (AI), which enables integrated systems to learn from data, identify patterns, make decisions, and improve gradually with minimal human interventions (Boobier 4). It is a form of artificial intelligence that enables software-based accurate predictions without programming, imitating how humans learn, will take up some of the roles performed by human beings in the organizational contest. In the future, ML and AI will have significant impacts on diverse sectors, such as communications, transport, commerce, trade, security, and health. The subsequent sections show the main processes involved in ML and its impact on diverse sectors of the United States economy.
Machine learning (ML) occurs through four main methods. Specifically, supervised, unsupervised, and reinforcement instructions facilitate the acquisition of knowledge in ML (Shobha and Shanta 197-199). In supervised ML, the automated and intelligent systems intake existing data, identify patterns using defined labels, and produce output components, such as future behavior predictions. AI systems train and learn using existing data in ML (Usmani et al. 326-327). For example, ML can analyze past stock market data and predict trends in the performance of certain stocks. Semi-supervised ML is slightly different because it can develop analytical and predictive skills from labeled and unlabeled data. The machine identifies and classifies the data sets into meaningful clusters or patterns (Boobier 57. The objective of unsupervised ML is to generate a function or predictive skills from raw data without any labels or defined parameters (Usmani et al. 322-323). For instance, a machine can analyze income, health, geographic, and demographic data and develop the ability to predict voting patterns. Reinforcement ML uses the trial-and-error method because a trainer rewards ideal or expected behavior and punishes deviant actions (Shobha and Shanta 226-228). The four main methods are applied interchangeably depending on the desired results.
Over the past two decades, different sectors, such as education, healthcare, commerce, and security, have incorporated ML capabilities into their operations. ML systems in the banking sector, such as voice assistants and security systems, provide services to customers and identify unusual activity from transaction data (Tony 14). The use of ML in the services sector is likely to increase significantly in the coming years. Health researchers and clinicians, such as oncologists, have increased their efficiency in diagnosing cancer by feeding large data sets of x-rays and CT scans into ML systems (Svoboda S20; Pumplun et al. 2). The technology has improved detection rates and treatment outcomes in the last decade. In the future, AI and ML will revolutionize all aspects of life, especially in developing countries.
Some applications of ML, such as facial recognition and deep fakes, pose risks to racial equality and the communication sector. Analysis of existing facial recognition software by Microsoft and other companies revealed that racial discrimination was innately woven into their algorithms (Fuchs 1; Hao par. 1-3). The evaluation showed that the AI algorithms used in ML discriminate against racial minorities, such as African Americans, when applied in law enforcement situations. Vaccari and Chadwick predict that deep fakes, which are ultrarealistic video and audio files created through ML, will significantly increase disinformation in the future, especially in political communications (2-3). For example, an individual can recreate leaders artificially using realistic facial features, expressions, and voices without their knowledge or approval as long as they have prior recordings. Racial discrimination and media disinformation will likely increase in the future because of ML.
ML is a sub-category of AI, which increases the intelligence and predictive capabilities of automated systems through supervised, semi-supervised, unsupervised, and reinforced instructions. The technology has improved processes and efficiency in the health and banking sectors. However, most facial recognition technologies and deep fakes will threaten racial equality and media integrity in the future. Unless policymakers and researchers address these concerns effectively, people are likely to be more fearful than appreciative of the capabilities of ML in AI.
Why machines learning will not replace humans
The main purpose of integrating machines in organizational operations is not to replace human beings. Organizations understand that workers form the largest customer base and without jobs, their spending ability will be limited, negatively affecting the market for the products and services. Indeed, if millions of people lose their jobs after being replaced by machines, organizations will also suffer from reduced sales and profitability. Replacing humans with machines would result in serious self-harm, and organizations are aware of that situation. Therefore, machines form complementary relationships with humans in the workplace.
Workers have already noted the arrival of machine learning in many workplaces. There are fears that automation caused by innovations such as machine learning will lead to job losses in many industries. However, some workers believe that the arrival of machine learning is not a threat to their jobs and will instead positively impact their performance (Newman and Blanchard). Indeed, there is an inextricable link between the future of work and learning. Therefore, the threat of job loss is not serious as long as the future workforce is prepared for the changing workplace. Upskilling and retraining the existing workforce to stay relevant in an automated workplace is the most important human resource management initiative. Arguably, automated machines in the workplace will require human beings, meaning that human-machine partnerships will be a characteristic feature of workplaces in the future.
Machines and humans as partners
Machines are experts in performing predictable, repetitive, and routine tasks. Ideally, they can follow an explicit set of rules and effectively collect and process data, assemble in manufacturing, and optimize and manage the supply chain. However, some tasks in organizations are not easy to automate (Newman and Blanchard). Tasks that require social and creative intelligence require human input. When the need for social and creative intelligence is low, machines come in handy; however, human beings are the most suitable performers of tasks when such needs are high. Indeed, humans can deal with challenging social contests, understand emotional states and feelings, find meaningful connections, and develop innovative and creative solutions better than machines (Newman and Blanchard). Therefore, humans and machines are important in organizations, and most organizations are investing in creating partnerships that can bring machine and human intelligence together to create a superintelligence structure.
Mutual collaboration between humans and machines is important. People who are already conversant with artificial intelligence are aware of the limitations of intelligent systems. In addition, they understand how to maximize artificial intelligence to complete their task and improve their outcomes. According to Newman and Blanchard, machine learning systems in the future will have a human fluency to determine which tasks belong to them and those that are better done by human beings. Undoubtedly, human skills and machine learning complement each other. Human beings with the right skills can improve and even support the functionality of machine learning (Newman and Blanchard). Therefore, future workplaces will be characterized by human-machine partnerships.
Understandably, humans and machines complement each other. Organizations will need to prepare their employees to train machines on how to do their work. Tasks such as recognizing human expressions, detecting diseases, financial decision-making, and interaction with humans will necessitate the training of machines by humans. Actions and conclusions ge...