Artificial intelligence uses in productive systems and impacts on the world of work
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This document discusses the advances, applications, and impacts of artificial intelligence (AI) on productive systems and the workforce. It highlights AI's potential to enhance productivity, replace certain jobs, and the necessity of adapting to technological changes through strategies like income guarantee policies. The author emphasizes the critical need for companies to invest in AI technologies to remain competitive while addressing the societal implications of mass automation.
Artificial intelligence uses in productive systems and impacts on the world of work
- 1. 1 ARTIFICIAL INTELLIGENCE - USES IN PRODUCTIVE SYSTEMS AND IMPACTS ON THE WORLD OF WORK Fernando Alcoforado* Abstract- This essay aims to present the scientific and technological advances of artificial intelligence, their uses in productive systems and their impacts in the world of work. Keywords - Artificial intelligence. Artificial intelligence and productive systems. Artificial intelligence and the world of work. 1. Introduction There are many definitions of artificial intelligence, but many of them are strongly aligned with the concept of creating computer programs or machines capable of behaving intelligently like humans. Artificial Intelligence (AI) is the ability of a digital computer or a computer controlled robot to perform tasks commonly associated with intelligent beings. The term is often applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, to discover meaning, to generalize or to learn from past experience. What is Intelligence? Psychologists generally do not characterize human intelligence solely by a characteristic, but by the combination of diverse abilities. AI research focused mainly on the following intelligence components: learning, reasoning, problem solving, perception and language use. As for learning, there are several different forms applied to artificial intelligence. The simplest is to learn by trial and error. For example, a simple computer program to solve chess game problems. The program can store the solutions with the position of one of the pieces of chess, so that the next time the computer finds the same position of the same piece, it would remember the solutions adopted. This simple memorization of individual items and procedures - known as rote learning - is relatively easy to perform on a computer. More challenging is the problem of performing what is called generalization that is being faced. Generalization involves the application of past experience to analogous new situations. Reasoning is the ability to draw inferences appropriate to the situation. Inferences are classified as deductive or inductive. An example of deductive inference is the case of previous accidents that were caused by failure in a component of a machine from which it is deduced that the accident was caused by the failure of this component. In deductive inference, the truth of the premises assures the truth of the conclusion, whereas in the inductive case the truth of the premise supports the conclusion without giving an absolute guarantee. Inductive reasoning is common in science, where data is collected and tentative models are developed to describe and predict future behavior until the appearance of anomalous data forces the model to be revised. Deductive reasoning is common in mathematics and logic, where elaborate structures of irrefutable theorems are constructed from a small set of axioms and basic rules. Problem solving, particularly in artificial intelligence, can be characterized as a systematic search through a series of possible actions to achieve some goal or predefined solution. The methods of problem solving are divided into special and general purpose purposes. A special purpose method is tailored to a specific problem and often exploits very specific characteristics of the situation in which the problem is
- 2. 2 embedded. In contrast, a general purpose method is applicable to a wide variety of problems. A general-purpose technique used in AI is step-by-step or incremental analysis of the difference between the current state and the ultimate goal. The program selects actions from a list of means - in the case of a simple robot until it reaches the goal. In perception, the environment is scanned through various sensory organs, real or artificial, and the scene is decomposed into separate objects in various spatial relationships. Perception is complicated because the object may look different depending on the angle from which it is seen, the direction and intensity of the illumination in the scene, and the object contrasts with the surrounding field. Currently, artificial perception is advanced enough to allow optical sensors to identify individuals, autonomous vehicles, that is, without a driver, drive at moderate speeds on the open road, and robots roam buildings collecting empty soda cans. One of the earliest systems to integrate perception and action was FREDDY, a stationary robot with a moving television eye and a tweezer hand, built at the University of Edinburgh in Scotland during the period 1966-73. FREDDY was able to recognize a variety of objects and could be instructed to assemble simple artifacts, such as a toy car, from a random pile of components. Regarding the use of language, it is important to note that a language is a sign system with meaning by convention. In this sense, language need not be confined to the spoken word. Traffic signs, for example, form a minilanguage, and it is a convention issue that hazard symbol means "danger ahead" in some countries. An important feature of human languages with traffic signs perception is complicated by the fact that an object may look different depending on the angle. A productive language can formulate an unlimited variety of phrases. It is relatively easy to write computer programs that seem capable, in severely restricted contexts, to respond fluently in human language to questions and statements. Although none of these programs really understand language, they can, in principle, reach the point where their mastery of a language is indistinguishable from that of a normal human being. Since the development of the digital computer in the 1940s, it has been shown that computers can be programmed to perform very complex tasks - such as finding evidence for mathematical theorems or playing chess - with great proficiency. Yet despite continued progress in speed and memory capacity of computer processing, there are still no programs that can combine human flexibility into larger domains or tasks that require a lot of daily knowledge. On the other hand, some programs have achieved the performance levels of experts and human professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis and voice recognition. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Arthur Samuel, an American pioneer in the field of computer games and artificial intelligence, coined the term "machine learning" in 1959 while working at IBM. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn and predict data. These algorithms follow strictly static program instructions by making predictions or decisions based on data, by constructing a model from sample entries. Machine learning is employed in a
- 3. 3 variety of computing tasks such as email filtering, network intrusion detection, or malicious beginners working for a data breach, optical character recognition by computer classification and computer vision. Machine learning is closely related to computational (and often overlapping) statistics, which also focuses on forecasting through the use of computers. It has strong ties to mathematical optimization, which provides methods, theory and domains of application to the field. In data analysis, machine learning is a method used to design complex models and algorithms that lend themselves to prediction. In commercial use, this is known as predictive analytics. These analytical models enable researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and discover "hidden insights" by learning historical relationships and trends in data. In 1950, British computer scientist Alan Turing was already speculating on the emergence of thinking machines in his work "Computing Machinery and Intelligence", and the term "artificial intelligence" was coined in 1956 by scientist John McCarthy. After some significant advances in the 1950s and 1960s, when artificial intelligence labs were created at Stanford and the Massachusetts Institute of Technology (MIT), it was clear that the task of creating such a machine would be more difficult than it was thought. Then came the so-called "winter of artificial intelligence", a period without great discoveries in this area and with a sharp reduction in the funding of its research. In the 1990s, the artificial intelligence community set aside a logic-based approach that involved creating rules to guide a computer how to act, with the adoption of a statistical approach, using databases and asking the machine to analyze and solve them problems on their own. Experts believe that the intelligence of machines will match that of humans by 2050, thanks to a new era in their ability to learn. Computers are already beginning to assimilate information from collected data, just as children learn from the world around them. That means we're creating machines that can teach themselves to play computer games - and be very good at them - and also communicate by simulating human speech, as with smartphones and their virtual assistant systems. 2. The use of artificial intelligence in productive systems Artificial intelligence is already being widely used in productive systems. Artificial Intelligence can replace human beings in production activities and can also maximize their productivity. According to data presented by Accenture in one of its surveys, by 2035, the AI will contribute to an increase of up to 40% of the productivity of the industrial sector, reducing costs and increasing the production of manufactures around the globe. The current landscape of AI systems enables them to understand the entire production and business process and automatically identify which are the major issues that need to be addressed. A neural network of an AI system is capable of analyzing more than a billion data in a few seconds, being an incredible tool to support a decision maker within a company, thus guaranteeing the best option among the possible ones. As the data collected are constantly updated, AI systems always update their results, making it possible for managers to have access to recent information on changes in the company's market. Among the benefits of AI for production systems are: Reliability of decisions: automation and availability of data allow conscious decision making, with confidence that they will be the best alternatives at all times.
- 4. 4 Insights: In addition to searching for information at certain times, analyzing the data presented by the AI can also generate business opportunities that would otherwise not have been viewed. Security: Keeping IA systems within the company helps prevent errors and data leaks by employees, as their contact with information is considerably reduced and various processes are carried out by the system. There are several examples of IA application in the business sector with the goal of helping and improving productivity. Here are some examples of how Artificial Intelligence is being used: Virtual Assistants We have several examples of virtual assistants who use technologies such as voice recognition to capture and understand the demands of their users and process commands. Even though this technology has not yet secured a captive place inside the offices, it is already possible to use the virtual assistants for various activities, which makes the employees' lives easier and improves their productivity. For example, these systems are already capable of scheduling meetings, managing schedules, conducting surveys, among other activities, all coordinated by voice commands, making the life of a company's employees easier. The main players on the market today are Amazon, with its Alexa solution, Microsoft with Cortana assistant, Apple with the famous Siri and Google Assistant, from the search giant. Demand Forecasting AI and machine learning allow solutions to anticipate market demands through complex mathematical models, which is a great way to stay one step ahead of the competition. Once AI systems identify a future business opportunity, they can pass this information on to the decision maker, who decides what attitude they will take in anticipation. Identifying Threats Information security within a company is critical to ensuring business continuity and hence your productivity. The AI can be used to carry out constant surveys of the company's infrastructure and ensure its protection. In addition, artificial intelligence solutions can perform preventive maintenance on multiple systems, thus preventing them from being out of date or slow, which can ultimately affect the company's processes. Check employee satisfaction The high turnover of employees can end up becoming a problem for productivity, because you lose time with training, and the employee loses interest or seeks other opportunities soon after. AI solutions can accompany employees by analyzing their data and identifying dissatisfactions, thus helping HR to take the necessary steps to keep the professional satisfied and productive.
- 5. 5 The investment in technologies that use AI to increase business productivity tend to increase gradually. Companies can use various strategies to remain competitive in the market. However, there must already be an investment in digital transformation and increasingly lean processes to take advantage of this evolution. Organizations that fail to make this adaptation may be sealing their destiny to failure, since they will no longer be able to keep pace with the hectic pace of technology evolution. 3. The impacts of artificial intelligence on the world of work Brynjolfsson and McAfeee state in the book The Second Machine Age that the combination of massive computing power with comprehensive networks, machine learning, digital mapping, and the "Internet of Things" are producing a complete industrial revolution , on the same scale as the transformations caused by steam and electricity. Boston Consulting Group predicts that by 2025, up to a quarter of jobs will be replaced by softwares or robots, while a study by the University of Oxford in the UK points out that 35% of current jobs in the country run the risk of being automated in the next two decades (BRYNJOLFSSON and McAFEEE, 2016). Martin Ford states that in our economy and society, machines are gradually undergoing a fundamental transition: they develop beyond their historical role as a tool and, in many cases, become "autonomous workers" (Ford, 2015). If we accept the idea that it is unrealistic to stop automation and that more investment in education and training is unlikely to solve the problem of unemployment, Ford believes that the most effective solution is to adopt an income guarantee policy for workers. This idea is not new. Friedrich August von Hayek, Austrian economist and philosopher, later naturalized British, considered one of the greatest representatives of the Austrian School of economic thought, was the proponent of this idea when published between 1973 and 1979 his work Law, Legislation and Liberty (Routledge, 1988). In addition to the need to provide basic net security, Ford (2015) states that there is a powerful argument for the adoption of income guarantee policy because technological advancement, in addition to promoting mass unemployment and vertiginous social inequality, threatens capitalism itself with the prospect of a sharp drop in consumption. As the labor market continues to erode and wages stagnate or fall, the mechanism that ensures consumers' purchasing power begins to break down and the demand for products and services decreases as a result. Given this fact, the income guarantee policy would provide the conditions for the unemployed workers to consume. It would oblige the governments of the countries of the world to levy taxes on high-tech companies to ensure the adoption of the income guarantee policy for the unemployed population. The Income Transfer Program through which the State would provide income to the unemployed would be complemented by the adoption of the Creative Economy and the Social and Solidarity Economy as a solution to combat mass unemployment resulting from technological advancement. The term "Creative Economy" refers to activities with socioeconomic potential that deal with creativity, knowledge and information and would be less affected by the advancement of artificial intelligence. To understand this, it must be borne in mind that companies in this segment combine the creation, production and marketing of cultural and innovative creative goods such as Fashion, Art, Digital Media, Advertising,
- 6. 6 Journalism, Photography and Architecture. In common, area businesses rely on talent and creativity to effectively exist. They are distributed in 13 different areas: 1) architecture; 2) advertising; 3) design; 4) arts and antiques; 5) crafts; 6) fashion; 7) cinema and video; 8) television; 9) publishing and publications; 10) performing arts; 11) radio; 12) leisure software; and, 13) music. It is important to say that by focusing on creativity, imagination and innovation as its main characteristic, the creative economy is not restricted to products, services or technologies. It also encompasses processes, business models, management models, among others (DESCOLA, 2016). The Social and Solidarity Economy is a new model of economic, social, political and environmental development that has a different way of generating work and income, in several sectors, be it community banks, credit cooperatives, family agriculture cooperatives, the issue of fair trade, exchange clubs, etc. Social and Solidarity Economy is a new way of organizing work and economic activities in general, emerging as an important alternative for the inclusion of workers in the labor market, giving a new opportunity to them, through self-management. On the basis of the Social and Solidarity Economy, there is the possibility of recovering corporations with a failed bankruptcy, and continue with them, with a new mode of production, in which profit maximization ceases to be the main objective, leading to maximization of the quantity and quality of work (LACROIX and SLITINE, 2016). REFERENCES AGRAWAL, Ajay; GANS, Joshua e GOLDFARB, Avi. Prediction Machines. Boston: Harvard Business Review Press, 2018. BRYNJOLFSSON, Erik e McAFEEE, Andrew. The second machine age. New York: Norton paperback, 2016. DESCOLA. A economia criativa no mundo moderno. Available on the website <https://descola.org/drops/a-economia-criativa-no-mundo-moderno/>, 2016. DORMEHL, Luke. Thinking Machines. New York: Tarcher Perigee Book, 2017. FORD, Martin Rise of the Robots. New York: Basic Books, 2016. GANASCIA, Jean-Gabriel. Le mythe de la Singularité. Paris: Éditions du Seuil, 2017. VON HAYEK, Friedrich August. Law, Legislation and Liberty. Abingdon: Routledge, 1988. KAPLAN, Jerry. Artificial Intelligence. New York: Oxford University Press, 2016. LACROIX, Géraldine e SLITINE, Romain. L´économie sociale et solidaire. Paris: Presses Universitaires de France, 2016. Fernando Alcoforado, 79, awarded the medal of Engineering Merit of the CONFEA / CREA System, member of the Bahia Academy of Education, engineer and doctor in Territorial Planning and Regional Development by the University of Barcelona, university professor and consultant in the areas of strategic planning, business planning, regional planning and planning of energy systems, is author of the books Globalização (Editora Nobel, São Paulo, 1997), De Collor a FHC- O Brasil e a Nova (Des)ordem Mundial (Editora Nobel, São Paulo, 1998), Um Projeto para o Brasil (Editora Nobel, São Paulo, 2000), Os condicionantes do desenvolvimento do Estado da Bahia (Tese de doutorado. Universidade de
- 7. 7 Barcelona,http://www.tesisenred.net/handle/10803/1944, 2003), Globalização e Desenvolvimento (Editora Nobel, São Paulo, 2006), Bahia- Desenvolvimento do Século XVI ao Século XX e Objetivos Estratégicos na Era Contemporânea (EGBA, Salvador, 2008), The Necessary Conditions of the Economic and Social Development- The Case of the State of Bahia (VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG, Saarbrücken, Germany, 2010), Aquecimento Global e Catástrofe Planetária (Viena- Editora e Gráfica, Santa Cruz do Rio Pardo, São Paulo, 2010), Amazônia Sustentável- Para o progresso do Brasil e combate ao aquecimento global (Viena- Editora e Gráfica, Santa Cruz do Rio Pardo, São Paulo, 2011), Os Fatores Condicionantes do Desenvolvimento Econômico e Social (Editora CRV, Curitiba, 2012), Energia no Mundo e no Brasil- Energia e Mudança Climática Catastrófica no Século XXI (Editora CRV, Curitiba, 2015), As Grandes Revoluções Científicas, Econômicas e Sociais que Mudaram o Mundo (Editora CRV, Curitiba, 2016), A Invenção de um novo Brasil (Editora CRV, Curitiba, 2017), Esquerda x Direita e a sua convergência (Associação Baiana de Imprensa, Salvador, 2018, em co-autoria) and Como inventar o futuro para mudar o mundo (Editora CRV, Curitiba, 2019).