AI is taking on more and more tasks in the everyday world. Talking devices and digital assistants, such as Amazon's Alexa or Apple's Siri give consumers easy access to various forms of AI in order to simplify their everyday lives. The distinction between digital chatbots and people are also getting increasingly more difficult.
In this paper, the focus will be on the positive reinforcement of society, for the simple reason that writing about the drawbacks as well, will extend the length of this paper beyond the given guidelines. By way of example, which regulations are needed to ensure the protection of the public and fairness in economic competition and how will employment rates be influenced? Likewise, the history of the development of AI will be left out for the same reason. Initially, the author will present definitions of intelligence and a description of AI and its abilities. The subsequent description of functions and various forms of AI are intended to give an overview of the potential of AI. A Practical example of the implementation of AI in the Road transport sector, as well as a conclusion, are bringing the paper to a close.
Table of Contents
2 INTRODUCTION
3 DEFINITION OF INTELLIGENCE
3.1 Intelligence
3.2 Kinds of intelligence
3.2.1 Emotional intelligence
3.2.2 Social intelligence
4 ARTIFICIAL INTELLIGENCE
4.1 Functions of artificial intelligence
4.2 Machine Learning
4.3 Deep learning
4.4 Narrow AI vs general AI
5 ROAD TRANSPORT SECTOR
5.1 Autonomous public transportation
5.2 Smart traffic signals
6 CONCLUSION
Objectives & Core Topics
This paper examines the potential of Artificial Intelligence (AI) to optimize societal and economic efficiency, with a specific focus on its transformative role in the road transport sector. The research explores definitions of intelligence, the functional capabilities of AI, and its practical application in solving contemporary transportation challenges.
- Definitions and theories of human and artificial intelligence.
- Core functional capabilities of AI systems, including learning and prediction.
- Distinctions between narrow AI, general AI, and artificial superintelligence.
- Applications of AI in autonomous public transport and smart traffic management.
- Socio-economic impact of AI implementation on productivity and urban mobility.
Excerpt from the Book
4.2 Machine Learning
ML and AI are often used as synonyms, which is used to deceive "customers by pro-claiming using AI on their technologies while not being clear about their products' limits" (Iriondo, 2019). In 2019 there was a report that claimed, "forty percent of ‘AI startups' in Europe don't use AI" (Vincent, 2019). When we look back at my description of AI, the confusion which subsists in the media and the public is not a complete surprise. The computer scientist and ML pioneer Mitchell (1997) defined ML as "the study of computer algorithms that allow computer programs to automatically improve through experience" hence it is a branch of AI.
ML is utilized by giving the AI system an available data set, which is divided into three groups: training data, validation data, and test data. The AI system uses the training data to build a model with the relevant functions. Subsequently, the validation data is used to screen the model for propriety. Thereupon, the achieved precision or performance of the outcome requires verification, which is accomplished through the test data. This continually evolving model is a mathematical structure that identifies a range of possible decision-making principles with adaptable parameters.
One necessity of ML is the implementation of an objective function. This will aid in evaluating the desirability of the outcome, which results from the selection of parameters by the machine. The objective function integrates parts of a reward mechanism, by which the machine receives a reward for achieving good findings, as well as for the application of simpler rules. Rewarding the machine for desirable outcomes is causing the machine to select the parameters, which are maximizing the objective function. The repetition and readjustment of the parameters, based on the data given to the machine, eventuates in approaching the maximization of the objective function and thus, the acquired training values are growing increasingly better and more accurate.
A trained model that can generalize and still deliver precise results on future cases that it has never seen before, is the goal of ML. In other words, ML is not meant to solve a specific problem, but rather an attempt to find solutions for various issues.
Summary of Chapters
2 INTRODUCTION: This chapter contextualizes the rise of AI in everyday life and outlines the paper's focus on the positive societal benefits of AI, specifically excluding negative drawbacks to maintain scope.
3 DEFINITION OF INTELLIGENCE: This section explores various scholarly definitions of intelligence and introduces the concept of multiple intelligences, including emotional and social dimensions.
4 ARTIFICIAL INTELLIGENCE: This chapter defines AI as a field pooling various sciences and details the core functions, machine learning processes, and the hierarchy from narrow to general AI.
5 ROAD TRANSPORT SECTOR: This chapter analyzes how AI applications, such as autonomous transport and smart traffic lights, can increase productivity and improve urban traffic flow.
6 CONCLUSION: The final chapter summarizes the transformative potential of AI across society and suggests that its continued integration may lead to a second industrial revolution.
Keywords
Artificial Intelligence, Machine Learning, Deep Learning, Narrow AI, General AI, Autonomous Transportation, Smart Traffic Signals, Data Mining, Intelligence Definition, Emotional Intelligence, Social Intelligence, Innovation, Productivity, Socio-economic Impact, Future Technologies
Frequently Asked Questions
What is the primary scope of this paper?
The paper examines the integration of Artificial Intelligence in society, specifically focusing on its potential to improve productivity and efficiency, with a detailed application case in the road transport sector.
What are the main thematic fields covered?
The work covers theoretical definitions of intelligence, the functional capabilities of AI, machine learning methodologies, and practical AI implementations in urban infrastructure.
What is the ultimate research objective?
The goal is to provide an overview of the potential of AI and illustrate how it can solve inefficiencies in the transport sector, ultimately arguing for its positive impact on society.
What scientific approach does the author use?
The author conducts a literature-based analysis of AI definitions and functional frameworks, supplemented by the examination of simulated pilot projects in transportation.
What is discussed in the main body of the text?
The main body defines intelligence and AI, distinguishes between different AI forms (narrow vs. general), and presents specific examples of AI usage in autonomous vehicles and smart traffic signal optimization.
Which keywords best characterize this work?
Artificial Intelligence, Machine Learning, Road Transport, Automation, and Productivity are the defining terms of this research.
How is the distinction between Narrow and General AI explained?
Narrow AI is described as current-state technology capable of specific, defined tasks, while General AI is characterized as a hypothetical future form of AI capable of matching or exceeding human intelligence levels.
What is the significance of the "objective function" in Machine Learning?
The objective function is crucial because it provides the reward mechanism that guides the machine to select parameters, enabling the system to improve its performance toward defined goals.
What is the takeaway regarding the road transport sector?
The paper concludes that AI can significantly reduce congestion, travel times, and vehicle emissions, while improving the overall quality of life by reducing the stress of commuting and optimizing logistics.
- Arbeit zitieren
- Anonym (Autor:in), 2019, What Benefits Can Artificial Intelligence Bring to the Road Transportation Sector? An Overview, München, GRIN Verlag, https://www.grin.com/document/595187
