“the Role Of The Artificial Intelligence In Society And At Work, In The Near Future”

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MANCHESTER METROPOLITAN UNIVERSITY- UK

SPECIALIZAREA: MANAGEMENT

BACHELOR’S THESIS

2017

THE ROLE OF THE ARTIFICIAL INTELLIGENCE IN SOCIETY AND AT WORK, IN THE NEAR FUTURE

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CONTENTS

ABSTRACT

1.INTRODUCTION

2. RESEARCH METHODOLOGY: REGRESSION MODEL FOR MACRO AND MICRO RATIO ESTIMATION

3.RESULTS AND DISCUSSION

4.CONCLUSIONS

BIBLIOGRAPHY

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ABSTRACT

The following thesis presents how artificial intelligence influenced the human society with the focus on two domain, such as banking and education.

By investigating scientific literature and mass- media, we can emphasize how the existence of artificial intelligence has interacted with people’s lives, as robots have an important role in making daily lives more pleasant .

Artificial intelligence progress and advances made in the area of robotics, results in the mutual influence between robots and humans that became stronger as they are an important tool to secure the quality of people’s lives.

Modern society owes its considerations to developed machines that undertake tasks, which are impossible for a human to realize or it would take a long period of time to performe.

From the production era in industry, to the performance within the medical domain, robots are more capable than their precursors. As technology progressed, artificial intelligence is used to designate robots capable of helping people in different areas better than before.

Scientists are concerned in robotics as they are the key to future economy , medicine, war and prosperity. Their importance in society is not ignored, as they influence society and have an impact on our lives.

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1.INTRODUCTION

The expression „artificial intelligence” was realized by John McCarthy in 1956, at the academic conference on the subject. To understand if machines can think began in Bush’s seminal work, As We May Think, as he suggested a system which increases people’s knowledge and understanding. Alan Turing wrote a study on the notion of machines that are able to simulate humans and do intelligent things, for example to play chess.

Artificial Intelligence refers to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.” (S. Shubhendu & J. Vijay, sept.2013). It incorporates critical reasoning and judgment into response judgements. Long considered a progress, as Artificial Intelligence (AI) is incorporated in different domains, such as finance, telecommunications, medicine, education, transportation and aviation. Expert systems take decisions that require human expertise; these systems are designate to anticipate difficulties as they occur.

There are increasing applicabilities of artificial intelligence in different industries, being used to substitute humans in a variety of domains, such as space exploration, manufacturing, education, transportation and medicine. By entering into the processing capacity of computers, humans can develop their abilities and improve efficiency through Artificial Intelligence.

The development in technologies indicates that they have an important impact on the workforce. Tech companies achieved widespread economic scale with a small number of employees. According to D. Thompson, “Google is worth $370 billion and has 55,000 employees.” (The Atlantic, July/August, 2015). The economist A. McAfee, stated that the world lives a period when machines replace people especially for the jobs in the present economy.

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In different areas, technology substitutes labor that has impressive consequences for employees and their incomes. Engineer Hod Lipson, from Cornell University sustains that technology destroyed jobs but created new ones.

In his book, The Lights in the Tunnel, Martin Ford argued that “as technology develops, artificial intelligence may come into contact with economy to the limit that no longer deliver consumers with adequate income and reliance in the future. At some period in the future, years or decades, machines will replace people, who will no longer find new jobs.(Acculant Publishing, 2009)

Companies discovered that robotics and artificial intelligence can substitute humans, improving productivity and efficacy of operations. In the period of Great Recession, many companies were constrained to reduce their workforce for financial reasons, as they discovered different ways to sustain operations. One company leader had 500 employees for $100 million business, even though the firm’s income is $250 million, he has the same number of employees. He realized this by automating different functions using robots and advanced techniques to operate the company.

Domains such as medicine and education embraced the technology revolution slower, but in present are starting to include new models. Changes in customized learning and mobile medicine signify that schools and hospitals are changing from conventional to computerized service delivery. Teachers are using online courses and tablet instruction, while medicine providers relies on medical sensors and machine learning to diagnose treatments.

As for the uncertainties of the job projections, experts argue against the impact of technologies. For instance, in their book, The Second Machine Age, economists E. Brynjolfsson and A. McAfee sustain that technology produces important changes in the workforce. As they declare, technological development leave behind certain people. According to them, it is a good period to be an employee with special abilities and the right education, as workers use technology to create value. (W. W. Norton, 2014, p. 11)

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The economist R. Gordon argued that developments in computing and automation are less important than cars, electrification, wireless communication or plumbing. Previous developments that allowed people to travel and communicate are more important to society’s progress in the twentieth century. Focused on this argument, he does not imagine the workforce effects from technologies, as other experts observed the replacement of technology for labor.

In an era of technology innovation and job displacement, there needs to be a means for people to gain new abilities through their adulthood. When people are employed, their companies contribute to an individual’s fund. This account could be augmented by contributions from the person or the government. Similar to a retirement account, money in the fund could be used tax-free in investment options including cash reserves, stocks and bonds. The owner of the account could draw on it to finance lifetime learning and job retraining expenses. It would be portable, which means that if the person moved or switched jobs, the account would migrate with that individual.

The goal of this account is to provide financial resources for continuing education; people have to continue their education beyond the first 20 years of their lives. Emerging jobs are going to require different abilities than what people gain in school. As remarked by Brookings Institution scholar Kemal Dervis, it will be crucial as technology innovation continues in the future to provide people with a means to upgrade their abilities and knowledge levels. He notes that France has established individual accounts that provide social benefits. (Brookings Institution Project Syndicate, June 10, 2015)

With the expected increase in leisure time, adults need time and financial support for continued learning. We shouldn’t imagine education as a time for young people to learn new abilities, but we need to think about education as a continuing activity that expands people’s horizons over the course of their entire lives. Education is an enrichment activity and we need to view it as a general benefit for the individual and the society .

According to recent studies (Pew Research Center 2014) robotics and

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artificial intelligence will include segments of people´s life by 2025, with important implications for industries, like medicine, transport, logistics, customer service and home maintenance. According to the studies people are divided on how robotics will impact the economic and employment areas. In the report of Pew Research Center (2014) the key reasons concerning robotics were: advances in technology may displace some types of work, although they have been a net creator of jobs; people will accommodate to changes by inventing new types of work and by taking advantage of human capabilities; technology will allow us to define our relationship with “work” in a more positive way.

Advanced societies are at an important turning point in terms of how we think about work, leisure and social benefit delivery. Advanced economies need fewer employees to complete tasks . As benefits are delivered through full- time jobs, there is a risk concerning people’s health care, pensions and income maintenance to sustain their lives.

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2. RESEARCH METHODOLOGY

2.1.THE ROLE OF ARTIFICIAL INTELLIGENCE IN BANKING

Artificial Intelligence (AI) is used across the financial services industry and is becoming more popular in customer-facing digital channels. Broadly speaking, AI involves the ability of machines to emulate human thinking, reasoning and decision- making.

As well as being driven by consumer demand and a need to cut costs, AI is emerging in financial services as banks enjoy better access to improved technology and lower costs for processing tools and data storage than ever before.

However, the development in the technologies collectively known as artificial intelligence, pattern recognition, natural language processing, image recognition and hypothesis generation.

Most examples of ‘artificial intelligence’ have revolved around customer support, in financial services, are not fully artificially intelligent. Chatbots are based on decision trees, even it accept questions, rather than offering choices, cannot provide answers beyond the content of a brand’s FAQs. While some learning may be occurring, it’s far from the robots of science fiction.

Banks are trialing this technology on their own sites and apps and on third party platforms such as Facebook Messenger. Facebook, which launched chatbots for Messenger back in April and now has 34,000 on the channel, claims that the quality of bots built for the platform is improving .

The virtual assistants, such as those from Apple and Google, can work with third party APIs to help us use banking apps. TNW reports that Google’s new and improved AI algorithm is more conversational and has learned how to understand the context of the questions asked. In contrast, Siri tends to ignore context in some cases, merely

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pulling up results from Bing and other search engines.

Thanks to Siri, Cortana, and Amazon’s Alexa, humans are becoming more comfortable talking to ‘robots’ in order to get things done. These tools also have massive accessibility implications.

2.1.1.REGRESSION MODEL FOR MACRO AND MICRO RATIO ESTIMATION

The estimation of exchange rate is a recent area of research in finance. In response to the approach of exchange rates in 1973, international banks became interested about the impact of exchange rate fluctuations. The papers about exchange rate exposures, examined the impact of the exchange rate on cash flows. (Flood and Lessard, 1986, pg. 25; Shapiro, 1974, pg. 485) The cash flow sensitivity to the exchange rate depend on the field of the firm’s activities, for instance the involvement in foreign operations, the currency of its competition and the competitiveness of its markets. Most theoretical models of exchange rate exposures suggest that the exchange rate is a function of its net foreign currency revenues. (Marston, 2000, pg. 149)

The theoretical examination coincides with the interest of managers to understand how exchange rate changes affects their cash flows and how to manage those effects. Most of the theoretical justifications for managing the currency risk come from cash flow arguments. (Stulz, 1984, pg.127; Froot, Scharfstein and Stein, 1993, pg. 48). For the tendencies of making optimal risk management decisions, managers are interested in a measure that guide the cash flow to rate changes.

This proposes to measure exchange rate by modeling the actual cash flows . Lewent and Kearney (1990) demonstrate this approach using the pharmaceutical firm Merck. From this model, the impact of exchange rate changes is simulated decisions made.

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Such a procedure tolerates the difficulty to incorporate complexities into the model, for instance competitive reactions and impacts of market structure. For example, Marston shows the complexity of determining the demand and cost derivatives for estimating the exposure for the case of a duopoly with constant demand functions. ( Marston, 2000, pg. 150) These approaches necessitate important amounts and competitor specific information. The regression model of determining exposure, is useful in to identify the determinants of exposure, specific situations and not applicable to studies comparisons of exchange rate. For these sorts of studies, a methodology that uses easily accessible information is needed.

Adler and Dumas proposed a different cash flow for the model approach, as they utilized the market value, which is the present value of all cash flows. (Adler and Dumas, 1984, pg. 42) According to this supposition, the presentation is determined from the value with respect to the exchange rate, which can be obtained from regression. This approach requires the researcher to obtain market data, simplifies the estimation of exchange rate and admit the possibility of largescale studies on exchange rate .

To control macroeconomic influences, different studies include the return to a market portfolio in the regression model. The market portfolio return controls macroeconomic influences and reduces the variety of the regression, which improves the precision of the exposure estimates.

The presentation of the market portfolio integrates two factors, such as the value impact of marketwide macroeconomic factors, non cash flow value impacts across all firms that are correlated with the exchange rate over the sample period and the value impact of the exchange rate changes on the firms’ cash flows. Therefore, the estimation is measured relative to the common macroeconomic influence on value correlated with the exchange rate and the changes to the market cash flow started from the exchange rate movement.

Because of the measurement’s estimation, the market portfolio used as a control for macroeconomic factors has a no exposure, the interpretation of a firm having zero presentation does not mean the same thing as having zero “total” exposure.

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This represents an important point, as the result of having zero presentation is given the economic meaning in analysis of the firm that is not affected by exchange rate changes. Therefore, a zero presentation implies a firm that has the same exposure as the market portfolio. Since the presentation of the market portfolio used to control for macroeconomic effect is zero, the choice of market portfolio in the regression impacts the size and the interpretation of the resulting presentation estimates. This signifies that the choice of the market portfolio is an important decision for the resulting estimation.

In practice, researchers have estimated presentation by including a market portfolio return in the model. This modification has two beneficial effects, such as the market return reduces the variety of the regression and improves the precision of the estimation. The second effect is that the market return controls for the value macroeconomic factors connected with the exchange rate. This improves the ability to interpret the results in terms of the corporate finance models as cash flow are important for risk management decisions. A zero exposure no longer implies that the firm’s value is independent of exchange rates, but that is affected to the same level as the market portfolio.

To examine the portfolio level effect documented estimation, there is the need to run a regression of the sample firms’ exchange rate estimation on the firms’ export sales ratio and market value. Because the presentation is estimated over a period, we use the export sales ratio and market value over the same period. The ratio of the export sales is a finance proxy for the firms’ cash flow exposure. Cash flow models suggest that the exposure is related to “net” currency position, as firms report currency revenues and not costs. ( Marston, 2000, pg. 152)

Most researchers include a market return variable in the regression model to control the macroeconomic effects. The estimates of firm- level exposures is measuring the presentation of the firm relative to the exchange rate exposure of the market. By structure, the exposure of the market is a combination of the macroeconomic effects and the impact of the exchange rate on the cash flows of all the firms in the market. As a result, if different structures of the market portfolio have correlations with the exchange rate, the choice of the

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market portfolio in the exposure model have an important impact on the resulting exposure estimates.

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