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Research and discuss ways in which AI and machine learning is likely to impact on employment.

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Abstract

Artificial Intelligence (AI) is determined to be highly specialized and technical. The major issues of AI involve programming computers for some of the traits like, planning, learning, reasoning, perception, ability to move objects and manipulate and knowledge. Machine learning is also regarded as the major part of AI. Machine learning deals with the ability to utilize sensory inputs and analyze visual inputs. It refers to the design and study of intelligent agents that observes its environment and helps in increasing its chances of success. The paper provided an overall discussion of AI and machine learning. It has been noticed within the paper that technological innovations and digitization puts an impact on employment to a great extent. It also discussed various challenges and opportunities that are generated due to AI and machine learning for most of the employees. Therefore, the entire paper is based on researching and discussing the impact of AI and machine learning on employment.

Introduction 

The paper will discuss the impact of Artificial Intelligence (AI) and machine learning on employment. It will provide an overview of AI and machine learning along with its benefits. AI is determined to be a study of computer science that helps in generating intelligent machines that react and works like humans. The computers with artificial intelligence are designed with a few of the activities such as problem-solving, planning, learning and speech recognition. Thus, it is considered to be a significant part of the technology industry. Therefore, the main aim of the paper is to present various ways of AI and machine learning which is likely to put an impact on employment. It will focus upon different factors of AI and machine learning such as challenges and opportunities, jobs, tasks and risks as well as the sectors those who are affected due to the implementation of AI and machine learning. 

Overview of Artificial intelligence (AI) and machine learning 

Artificial Intelligence (AI) is determined to be the creation and study of computer systems that could act, reason and perceive. The primary purpose of AI is to generate intelligent machines which possess the ability to perform an intelligent task like human beings. The intelligence must be exhibited by learning, solving problems, making decisions and thinking. It is considered to be an interdisciplinary field that needs knowledge in philosophy, biology, psychology, and linguistics and computer science for serious research. It is determined to be a way of preparing software that thinks intelligently, a computer that controls robots and a computer in the same way by which an intelligent human think (Acemoglu and Restrepo, 2018). AI aims to develop expert systems and apply human intelligence in machines. The applications of AI are intelligent robots, handwriting recognition, speech recognition, vision systems, and expert systems, natural language processing and gaming. The techniques of AI have become a significant part of the technology industry where it helps to solve various challenges occurring in operations research, software engineering and computer science.

On the other hand, machine learning allows computer programs to obtain skills and knowledge and to improve their performance. It is also regarded as the set of algorithms utilised by intelligent systems to learn from experience. The raw material for machine learning is provided by big data that helps in performing the tasks more efficiently. It is considered to be the process that grants a model or machine to access the data and allows it to learn for itself (Ayoub and Payne, 2016). The common applications of machine learning are natural language processing, recommendations systems and algorithm trading. 

Impact of Artificial learning and machine learning on employment 

Most of the employees within the organization does not experience any threat due to artificial intelligence. As per current data, around 56% of the employers stated that automation technologies such as robots, analytics and artificial intelligence have a positive impact on employment. According to 49% of the employees, automation has a positive impact on employment and 24% states that automation hurts employment. Technological development especially digitalization has significant implications for labor markets (Brynjolfsson and Mitchell, 2017). Focusing towards its impact is significant for generating policies that promote accurate labor markets for the advantages of societies, employers and employees. However, AI significantly improves the workplace environment in various ways that put an impact on workforce and employment. It can be seen that employment is impacted by AI systems and intelligent automation (Wilson and Benson, 2016). There comprise of different ways through which artificial intelligence puts an impact on employment. The ways are discussed below as follows:

Technological innovations 

Technological innovations put an impact on employment in two different ways such as by displaying employees directly from tasks they were working previously. It also affects by enhancing the labor demand for labor in jobs and industries that develop or arise because of technological progress. It has been noticed that human labor is replaced by technology in routine tasks whether cognitive or manual (Frey and Osborne 2017). Whereas, in non-routine tasks, it cannot replace human labor. The impact of technology results into increasing relative demand in the skilled jobs that are well paid that needs non-routine cognitive skills.

On the other hand, increasing relative demand in less paid skilled jobs needs non-routine manual skills. However, the demand for middling jobs needs cognitive skills and routine manual. The number of low-education service occupations like shop workers who are determined to be non-standard faces difficulty in replacing automation. The quality of human capital plays a significant role in technological innovations. An individual utilizes the technological advances for providing benefits to develop specific digital skills with the help of well-designed policies. This signifies the importance of utilizing adequate instruments to make sure that employees are prepared well to face the disruptive forces of digital technologies (Furman and Seamans, 2019). Over the past few decades, the specific platforms evolved that helped to develop connectivity among individuals. For example, making efficient use of this connectivity helps the providers of goods and services can make online trade with the use of economy platforms.

An economy model has a key feature that the individuals or other enterprises will get the economic opportunity for trading their underused assets. It helps the individuals to match demand and supply with the help of information technologies (West, 2018). The individuals are provided with this opportunity only in the collaborative platforms because there are many barriers in the other channels in case of supply of goods or services. Another example is shopping through e-commerce. The information technologies give more benefits. As a result, the demands for online goods are increasing in huge volume. This also gives employment to the employees in retail.

Moreover, an advanced technique of machine learning is automated. Due to this, intelligent machines can easily carry out the non-routine tasks. The enormous use of artificial intelligence with the efficiency gain in online trading put concern on emerging of labor. Then, a question will arise that which of the market of labor will influence in the era of artificial intelligence. The impact of technological changes is to be examined in the market of labor (Galán-García et al., 2016). The progress of technology in the era of AI is high because various machine learning techniques are developed regularly by humans.   

The increase of self-learning enterprise 

It has been observed that although AI possesses various benefits still most of the organizations are not ready to face any issue related to cognitive computing. The major challenge is the quality of data to execute such type of processes. Machine learning needs an appropriate data foundation to make sure that the algorithms are operating as per the right information provided to them. The challenge is not only for the machine learning but also for the advanced analytics that has been the monotony of coordinating the models of the data among downstream warehouses, data sources and operational applications (Ghahramani, 2015). All these data models are utilized for data catalogue for machine learning. Accessing adequate data needs correlating and blending profile attributes across formats, applications and various sources. For the longer term, the enterprise will proceed towards self-learning enterprises that need to make sense of the outcome from advanced analytics and machine learning. It closes the loop between outputs, action and insight. A constant feed of appropriate data, recommended actions and adequate insights are created from artificial intelligence and machine learning. Both AI and machine learning facilitates additional value to generate a closed loop that helps users to contribute to the reliability of the data, customer experiences and business processes.

Artificial Intelligence drives personal relationships

The utilization of AI is not limited to the development of enterprise and data. It could be utilized to predict the rates of success for building human relationships. It helps in assuring the success of various qualities, trustworthiness and human relationships that are generally decoded through direct and personal experience (Voyant et al., 2017).

Changes in the way of work

Technological progress will result in changes in the tools that are used for mechanized mass production that is replaced by handicraft labor. Based on the application of AI intelligent production, automation and technology will be implemented in human life to a greater extent. The process of production related to capital-intensive and technological methods uses practical and advanced equipment that would enhance labor productivity and the use of raw materials. The constant expansion of capital and production scale would replace with physical labor (Goos, 2018). Moreover, the number of jobs facilitated by primitive, backward labor would reduce that will put a strong impact on the entire employment structure. 

Changes in the demand of the labor market

The technology related to AI will not only put an impact on employment by modifying the mode of human production but also affect the labor demand. As per present condition, AI has been implemented in life; it is assumed that in the future it will be applied to a huge number of low knowledge and procedural area of work. In the future, AI would be able to largely or completely replace the work in the way humans are capable of making decisions. However, the blue collars like cashier, ordering and customer service will possess the theoretical potential to replace with white collar jobs (Jordan and Mitchell 2015). The advances in technology involve skill degradation and skill preference. Skill degradation refers to the low skilled labor force, whereas skilled preference tends to technological progress that acquires highly skilled labor force. As a result, the change of labor demand would lead to the joint action of both. According to the researchers, technological progress helps in increasing the demand for highly labor force that in turn decreases the demand for a low skilled labor force that would result in unemployment.  

Changes in management staffing

With the application of AI, technology organizations decrease the demand for labor in the manufacturing industries as well as change and reform the management of staffing. The workforce has to adopt the changes where the modes of production will change from single production to mass production and after that to lean production that highly impacts employment (Khokhar et al., 2015). Therefore, some of the jumbled management requires to streamline the employees to improve the efficiency of management. 

Changes in employment structure in the AI era: shifting demand structure and diverse skill requirements

In the era of AI, the changes in talent requirements and employment demand have been a significant concept within the leading organizations. With the development of the technologies, there possess a change in the demand for talent. Gradually AI is replacing manual labor into the job activities that are highly standardized that could be assessed by using computer software. Over the next decade, there possess few demands that would not be replaced by AI. An increase in demand for talents those who are highly skilled in AI technologies based on soft skills, application and technical level would be the first change (Larivière et al., 2017). To generate demand for talents AI would also put an impact on the entire labor market. It will utilize the activities that could be replaced by AI those are automated. As a result development of AI would enhance the soft skills of talented people and appoint them to the wider range of industries. It would also help in increasing the needs for complex problem solving, emotional communication and creativity. 

Automation of the smart process

With the help of smart process automation AI will put an impact on employment. It included the removal of labor arbitrage and employment-related issues by replacing people. Presently, the machines are entirely responsible for executing the work in most of the industries (Makridakis, 2017). It helps in fielding customer service requests, translation of natural language and the automation of robotic process due to which organizations are becoming more capable and smarter. 

Challenges and opportunities for artificial intelligence and machine learning

Artificial Intelligence (AI) acts as a source of enthusiasm as well as skepticism. The organizations require to overcome the few challenges of artificial intelligence for utilizing the correct opportunities.

Provability – It is one of the challenges faced by the business. Thus, AI is a black box, so the business failed to explain why it does and how it will make decisions. Provability is the level of mathematics on the predictions of AI. An organization finds no way to prove that the decision making of artificial intelligence is clear. So the solution is to make AI explainable. 

Data privacy and security – Most of the applications of AI depends on a huge number of data to make decisions. This may cause serious problems such as data breach or identity theft. The increase of awareness among people helps the European Union to implement the General Data Protection Regulation which ensures the protection of data of the consumers. A method named ‘Federated Learning' has been set up to overcome the AI paradigm (Meer and West, 2016). 

Algorithm bias – An AI systems used to identify the people who are called for a job interview, but it sometimes shows the wrong result, and they are trained with unbiased data. So a tool is being developed to detect the unbiased in the way of machine learning (Tredinnick, 2017). The business will get an opportunity to hold artificial intelligence without any mistake. 

Data scarcity – The data required for AI applications are very rare. There is a scarcity of labelled data which is designed to make AI absorbable. To overcome the scarcity of data, some of the ways are taken such as transfer learning, active learning and unsupervised learning. 

There are huge opportunities for achieving the impact of business with machine learning, but it has to face various challenges. One of the major challenges of machine learning is to find out people who are having technical ability to understand and implement the machine learning. If the business does not have a people having technical ability, then all the opportunities are not going to be achieved. It would create a backlog of machine learning inside the companies. Another challenge of machine learning is to achieve processing data on a large scale which create problems in demand and supply (Parkes and Wellman, 2015). An organization need to use GPUs instead of using CPUs for data processing. The lacking of labelled data is the most significant challenge of machine learning. The machine learning requires lots of data in using the technique of supervised learning. The challenges can be overcome by adopting new technologies or by increasing automation which helps in creating more data that the company requires.

Roles, Tasks and Jobs at risk 

The particular tasks that are determined to be monotonous could be automated easily. Gradually this could make certain roles outdated. For example, the activities and tasks are based on content moderation, retrieval, discovery, document classification and customer care or call center operation. Generally, all these activities are more focused on automation and technology and less on human work. The same technique applies to the roles based on the support and operation of factories and production lines (Russell, Dewey and Tegmark, 2015). It has been observed that humans are being replaced specifically by smart robots that could navigate the space safely. It helps to move and find the objects like tools, parts and products to perform the operations of complex assembling. AI has been proved to be efficient in carrying out complex activities that require accumulated knowledge and data streams in real time and processing of multiple signals.

Sectors that will be impacted 

Already the transportation industry is in a mode of transformation, for instance, the fully autonomous car would be a reality that will be effective, efficient and safer. It will put an impact on the employment of professional drivers such as trucks, taxi and many more where the drivers would consider a decrease in the demand. On the other hand, electronic commerce would also face significant transformation such as, fulfilment centers that would be automated fully with robots navigating the space to gather customer orders and products. The significance of networks and salesperson of physical stores will decrease (Scherer, 2015). The traditional professions that are developed based on strong human relationships like legal professions will be impacted significantly. To overcome this challenge the agents of AI can do a great job with the help of management, knowledge extraction, comparison, summarization, discovery and classification. 

AI would be beneficial for the organizations that provide financial services such as insurance company and other sectors which requires a significant amount of content handling and data processing. AI plays a vital role in reducing bureaucracy and improves the services provided to citizens along with performance and design based on social programs (Thrall et al., 2018). 

Conclusion 

The paper helped in researching and discussing various ways through which artificial intelligence (AI) and machine learning put an impact on employment. It has been observed that AI, as well as machine learning, puts a negative as well as positive impact on employment. Due to AI and machine learning the employees have higher chances of losing their jobs. The employees face difficulty in adopting the changes that occur because of AI within the workplace. It discussed the challenges and opportunities of AI and machine learning for the employees working within an organization. It provided an understanding of the tasks, roles and job based on artificial intelligence. It can be seen that machine learning is considered to be one of the major applications of AI that helps in generating intelligent machines. It also discussed the sectors those who will be affected due to the implementation of AI.

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