What Benefits Can Artificial Intelligence Bring to the Road Transportation Sector? An Overview

Hausarbeit , 2019 , 15 Seiten , Note: 2,0

Informatik - Künstliche Intelligenz

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

1 Index of abbreviations

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

7 List of literature

1 Index of abbreviations

AI artificial intelligence

ASI artificial superintelligence

ML machine learning

2 Introduction

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 is also getting increasingly more difficult.

On the one hand, as the technology progresses, the feasible utilization of AI to identify patterns, learn from experience and find novel solutions to new challenges, proceeds to prosper. On the other hand, the huge hype that the market is experiencing and the resulting media coverage is being taken advantage of. Plenty of companies are making use of this opportunity and are rebranding their existing solution to AI (Schrank, Eisele, Lomax & Bak, 2015, p. 7). This leads to the confusion of the public and press.

There are many challenges and inefficiencies we have to face in our lives. These issues could be solved by AI, which is one of the reasons for the great optimism regarding AI. Tasks, which could not be automated in the past, will be automated in the future and thus, will have an enormous effect on society and the economy. One estimation of the AI market claimed a growth from $8 billion in 2016 to $47 Billion in 2020 (Lu, Li, Chen, Kim & Serikawa, 2017, p. 3). Productivity and wealth will beyond doubt elevate. Different types of jobs are going to be affected in miscellaneous ways. Although society should remain focused on the achievable benefits of AI implementation, risks, challenges, and unintended consequences also have to be appropriately taken into consideration.

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.

On the grounds of the previously mentioned content, I would like to write my paper in the module "Introduction to Intelligence and Security Studies" about AI. Initially, I 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 my paper to a close.

3 Definition of intelligence

The term Intelligence has a long history of research and debate. Yet, a standard definition is still not available.

3.1 Intelligence

"Viewed narrowly, there seem to be almost as many definitions of intelligence as there were experts asked to define it. Viewed broadly, however, two themes seem to run through at least several of these definitions: the capacity to learn from experience, and adaptation to one's environment. Indeed, an earlier definition often cited by these experts viewed intelligence as general adaptability to new problems and conditions of life" (Gregory, 1987, p. 376).

The trend of having as many definitions as there were experts asked to define it continues in 2006. Two researchers collected 70 definitions from organizations, psychologists and researchers in AI. Along with the myriad of definitions, some appearances are more frequent. Because of the commonly occurring features, the extracted definition by Legg and Hutter (2006, p. 8) is the following: "Intelligence measures an agent's ability to achieve goals in a wide range of environments".

The next point I want to mention is the theory of multiple intelligences. The author of this theory claims that there are at least eight autonomous intelligences in every single individual (Moran & Gardner, 2006, p. 213ff). Creating products and solving problems is done by the individual in accordance with the relevance to the society, the individual lives in. It is done individually or collectively by making use of one or more of the several intelligences. Gardner classified the eight intelligences in linguistic, logical- mathematical, spatial, musical, bodily-kinesthetic, naturalistic, interpersonal and intrapersonal intelligence (ebd.). On a rather ironical note, there where even scientists who "have suggested we define intelligence as what intelligence tests measure and get on with testing it" (Sternberg, 2003, p. 151).

3.2 Kinds of intelligence

Humans differ in the ability to understand and interact with others in a social setting. The capability of comprehension and expression of emotions is also differentiating among people. Therefore, another factor has to be taken into account. The difference between the two kinds of intelligence: social and emotional intelligence.

3.2.1 Emotional intelligence

The ability of correct assessment and influence of feelings in oneself and others is referred to as emotional intelligence. This kind of intelligence is divided into four categories such as perception of emotions (facial expressions, gestures, voices, postures), use of emotions to support thinking (knowledge of the interaction of feelings and thoughts), understanding from emotions (understanding and analyzing emotions) and dealing with emotions (evoking feelings, avoiding and correcting emotional evaluations) (Stangl, 2019).

3.2.2 Social intelligence

Social intelligence is the set of individual attitudes and abilities that are useful in the sense of cooperation to link one's objective with the attitudes and values of another or a group. Social intelligence includes a variety of skills that are useful or necessary for social interaction. Often this term is equated with social competence, whereby social intelligence in this sense is not only reserved for humans but for animals living together in groups, too (Stangl, 2019).

4 Artificial intelligence

A universally accepted definition of AI is also not available. Various fields of science, for instance, computer science, psychology, philosophy, and linguistics are pooled together in the topic of AI. The reason for implementing artificial intelligence is establishing a computer, which can do tasks, which would usually require human intelligence. Since creating a computer with human intelligence is one of the objectives of AI, the definition of intelligence is the groundwork for the definition of AI. The ambiguity and vagueness of the definition of intelligence are therefore reoccurring in the definition of AI.

Having said that, there are many points of view on AI and many definitions are in existence. One approach to the subject of defining AI can be made by dividing into different forms of AI. Assisted intelligence is at the lower end of the spectrum of AI and is used to automate simple tasks to perform these faster and cheaper. Assisted Intelligence falls under the class of "narrow" AI (MIT Technology Review Insights 2019). Augmented intelligence supports people in making better situational decisions. This form of AI is capable of learning and adjusting constantly by receiving data input from the users. In return, humans will be able to make more sound and accurate decisions, based on the information received from the AI (ebd.). Autonomous intelligence is the most advanced form of AI, in which the mere purpose of a human being is the surveillance of the machine, which acts independently, for example, self-driving vehicles (ebd.).

4.1 Functions of artificial intelligence

AI systems can be used for a wide variety of applications and functions. In most cases, AI is used to execute at least one of the following seven general functions.

Monitoring: AI systems can analyze large amounts of data in a very short time and detect deviations and patterns. Since AI systems can do this much faster - often in real time - and more accurately than humans, they are very well suited for surveillance functions, such as cybersecurity and environmental changes (Castro & New 2016, p. 4ff).

"Discovering: AI can extract valuable insights from large datasets, often referred to as data mining, and discover new solutions through simulations. In particular, because AI uses dynamic models that learn and adapt from data, it is very effective at un-covering abstract patterns and revealing novel insights that traditional computer programs cannot.

Predicting: AI can forecast or model how trends are likely to develop in the future, thereby enabling systems to predict, recommend, and personalize responses. Many consumers are likely familiar with these types of applications, such as Netflix's recommendation algorithm, which analyzes users' viewing histories, stated preferences, and other factors to suggest new titles that they might like. Data-intensive applications, such as precision medicine and weather forecasting, stand to benefit from this use of AI.

Interpreting: Until recently, most data analytics has focused on structured data—information that is well organized according to a specific framework, such as a spreadsheet of survey responses. Because AI can learn and identify patterns, it can interpret unstructured data—information that is not easily classifiable, such as images, video, audio, and text. As a result, computer systems are now capable of analyzing dramatically more kinds of information about the world. For example, AI helps smartphone apps interpret voice instructions to schedule meetings, diagnostic software to analyze X-rays to identify aneurysms, and legal software to rapidly analyze court decisions relevant to a particular case.

Interacting with the Physical Environment: AI can facilitate a diverse range of machine-to-environment interactions that allow autonomous systems to directly engage with the physical environment. In particular, AI enables robotic systems that can navigate and manipulate the world around them. For example, autonomous vehicles analyze huge amounts of real-time data from an array of sensors, cameras, GPS systems, and maps to determine a safe and efficient route down a street.

Interacting with People: AI can allow humans to interact more easily with computer systems. Humans typically interact with machines by adjusting their behavior to meet the needs of the computer, such as by typing on a keyboard, pressing a button, or adjusting a dial. With AI, humans can interact with computers the way they do with other people, as computer systems can respond to speech, gestures, and even facial expressions. For example, individuals can ask questions of AI-powered chatbots by having a conversation or beckon a robot to come over with a nod or wave.

Interacting with Machines: AI can automatically coordinate complicated machine-to-machine interactions. For example, a control system for a data center can use AI to continuously monitor computing activity, internal temperature, and environmental conditions, and make adjustments to cooling systems to optimize performance while minimizing energy costs. This ability also allows for multiple separate AI systems to coordinate with each other, such as a fleet of autonomous trucks managing themselves in a platoon formation to reduce fuel consumption, or autonomous robots in a warehouse that communicate with each other to sort and retrieve items" (ebd.).

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.

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• Hochschule: Universität der Bundeswehr München, Neubiberg
• Note: 2,0
• Erscheinungsjahr: 2019
• Seiten: 15
• Katalognummer: V595187
• ISBN (eBook): 9783346208996
• ISBN (Buch): 9783346209009
• Sprache: Englisch
• Schlagworte: artificial, benefits, bring, intelligence, overview, road, sector, transportation, what