Makridakis, Spyros. “The Forthcoming Artificial Intelligence (Ai) Revolution: Its Impact on Society and Firms.” Futures 90 (2017): 46-60. Print.
The article explores how the impacts of digital and revolution on all aspects of society, businesses, life, and employment and also try to predict what the AI revolution might have some effects on the same elements in the coming twenty years. Following a previous article in 1995, the article looks at the hits and misses of the author’s predictions and also develops another prediction concerning the AI revolution. The author has organized the article in four parts, the first parts reviews his previous predictions concerning the digital revolution back in 1995 for 2015. Among his predictions, the author didn’t foresee the internet and smartphone together with their significant influences, it however predicted a single computer with the capabilities of interactive communication. The other parts of the article explore the AI technology and how the last twenty decades have experienced dramatic technological advancement from smart machines, to clever computers and further to Artificial Intelligence programs. The author examines how the AI technology is changing for machine learning to real learning as AI research focuses on teaching computers to think for themselves. And also moving from digital tools to AI tools. On his predictions on the future of AI, the author examines four AI scenarios from the pessimist, optimist, pragmatist, and doubter’s point of view. The article general view indicates some considerable uncertainty about the future of AI and also considers the related risks of AI programs.
Hofmann, Martin, Florian Neukart, and Thomas Bäck.: “Artificial Intelligence and Data Science in the Automotive Industry.”; arXiv preprint arXiv1709.01989 (2017). Print.
The article explores data science and machine learning in relation to automatic learning and optimization. The authors look at how data science in quickly influencing the technological industry particularly in the automotive industry. Providing an overview on some conforming techniques and recent examples of applications developed through data science and machine learning that are being implemented in the automotive industry. And also examine possible applications that will soon be useful in the automotive industry. The article examines data science and machine learning as separates entities, expand on each individual subjects as they explore how they influence different aspects of an organization. Through data science that article describes the data mining process, and looks at the four levels of data analysis that have become crucial in organizations decision making process. The second part of the article is an overview of machine learning. Here the authors connect the relationship between data and artificial intelligence explaining that data is the foundations of artificial intelligence. They also look at different aspects of Artificial intelligence such as machine learning, computer vision, inference and decision making, language and communications and many others. The article examines how data science and artificial intelligence applications are being utilized in the various departments of the automotive industry such as development, procurement, logistics, production, marketing, sales, and customer service. It concludes by giving a vision for the future of the automotive industry with the collective use of knowledge, data and action.
Brett, Jacob Alexander. “Thinking Local about Self-Driving Cars: A Local Framework for
Autonomous Vehicle Development in the United States.”; 2016. Print.
This is a report of a project that examines how autonomous vehicles (AVs) can be incorporated in the local communities through a planned framework form local planners and policy makers. The author through this report, aims to address specific factors that dictate and drive the development of AVs. Using extensive literature reviews which includes recommendations from various stakeholders and evaluations from several regulatory tools, the author designs a framework using several case studies from different demographic and geographic locations so as to put the advantages and costs of using self-driving cars into a context with measurable criteria. This methodology included collecting data from urban, suburban, and rural areas in the implementation of AVs. The author further examines the advantages and expenses of using self-driving cars, and the influences these advantages and expenses will have on a community over time. It also examines the factors that can influence the AV development in the community and ways in which local legislators and planners can optimize the benefits that come with embracing autonomous vehicles. According to the author technology has continue to develop in the automotive industry, which began with no automation to developing a car that is full self-driving automation. The author also looks at the general concern that come with adapting fully automated cars such as safety, employment, emissions. The author doesn’t decide on whether using self-driving cars is bad or good by recommends it as potential future endeavor in communities around the United States.