If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial intelligence (AI) is changing the face of business. No longer a futuristic concept, its impact is real. From tech giants like Google, Apple and Amazon to user-centric behemoths like Uber and Starbucks, everyone seems to be using AI technology to transform the customer experience (CX). But, it's not just corporate giants that are deploying AI. Smaller organizations are following suit.
Artificial Intelligence is beginning to have transformative effects on consumers, enterprises, and governments around the world. The impacts are contributing by automating repetitive task, creating efficiencies, ubiquitously improving user experience, and creating ways for humans to improve our cognition. Furthermore, by 2020, the AI market is projected to reach $70 billion, driven by increasing computational power and improving approaches/applications with machine, deep learning, natural language processing and robotics and many a number of other technologies. To gain a better understanding of the perception of AI in the US, PwC surveyed 2,500 consumers and business decision makers. The objective is to better understand their attitudes towards artificial intelligence, and the future implications on business and society.
MetLife processes over 260,000 life insurance applications a year. Underwriting of these applications is labor intensive. Automation is difficult because the applications include many free-form text fields. The application contains questions that can be answered by structured data fields (yes-no or pick lists) as well as questions that require free-form textual answers. Currently, MetLife's Individual Business Personal Insurance unit employs over 120 underwriters and processes in excess of 260,000 life insurance applications a year.
The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. Even the simplest variations of this problem are known to be NPcomplete (Garey and Johnson 1978). Although staff scheduling has long been an important operations research problem, scheduling has recently become an important component of an emerging class of business software applications known as workforce management software. The need for effective workforce management systems has been driven primarily by the recent, rapid growth of the call center--customer contact center industry, in which efficient deployment of human resources is of crucial, strategic importance. Traditionally, in this industry, staff scheduling has been performed using ad hoc methods and operations research techniques (Cleveland and Mayben 1997).
To remain competitive, enterprises must become increasingly agile and integrated across their functions. Enterprise models play a critical role in this integration, enabling better designs for enterprises, analysis of their performance, and management of their operations. This article motivates the need for enterprise models and introduces the concepts of generic and deductive enterprise models. It reviews research to date on enterprise modeling and considers in detail the Toronto virtual enterprise effort at the University of Toronto. It can be both descriptive and definitional--spanning what is and what should be.
This article presents an experiment of expertise capitalization in road traffic-accident analysis. We study the integration of models of expertise from different members of an organization into a coherent corporate expertise model. We present our elicitation protocol and the generic models and tools we exploited for knowledge modeling in this context of multiple experts. We compare the knowledge models obtained for seven experts in accidentology and their representation through conceptual graphs. Finally, we discuss the results of our experiment from a knowledge capitalization viewpoint.
It feels good to be a data geek in 2017. Last year, we asked "Is Big Data Still a Thing?", observing that since Big Data is largely "plumbing", it has been subject to enterprise adoption cycles that are much slower than the hype cycle. As a result, it took several years for Big Data to evolve from cool new technologies to core enterprise systems actually deployed in production. In 2017, we're now well into this deployment phase. The term "Big Data" continues to gradually fade away, but the Big Data space itself is booming.
One, enterprises have too many decisions to make. Two, it's difficult to find success with complex data integration. Those are the two main excuses I hear these days, as enterprises move to the cloud. Whatever the justification, the lack of attention to data integration is beginning to cause some real damage. Enterprises have so much coming at them that they don't think about every approach and technology that they need to think about.
The AI debate shifts from "is it good or evil" to "is it ever going to be good enough": If 2017 was the year where the warnings from Elon Musk and Stephen Hawking about the potential evil from AI clashed with predictions from Mark Zuckerberg and Bill Gates on its potential good, 2018 will be the year when the debate shifts to its practical utility. Much like other technologies that were lauded for their world-changing potential and then fizzled as the fog of the hype cleared, early adopters will find themselves disappointed by AI's obvious limits. The broader public--familiar with Alexa, Siri, and Google Home--will be similarly disillusioned as the experts acknowledge that there is only so much that AI will be able to do, and for really complex problems, a new paradigm will be needed. Despite the hype, AI has demonstrated value in industries across the board - from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making.
Though nearly every industry is finding applications for machine learning--the artificial intelligence technology that feeds on data to automatically discover patterns and anomalies and make predictions--most companies are not yet taking advantage. However, five vectors of progress are making it easier, faster, and cheaper to deploy machine learning and could eventually help to bring the technology into the mainstream. With barriers to use beginning to fall, every enterprise can begin exploring applications of this transformative technology. Machine learning is one of the most powerful and versatile information technologies available today.6 But most companies have not begun to put it to use.