The field of Artificial Intelligence (AI) has interfaces with almost every discipline of engineering, and beyond, and all those can be benefited by the use of AI. This chapter presents the introduction to AI, its roots, sub-domains, Turing test to judge if the given program is intelligent, what are the goals of AI for Engineers and Scientists, what are the basic requirements for AI, symbol system, what are the requirements for knowledge representation, and concludes with a chapter summary, and an exhaustive list of exercises. In addition raises many questions to ponder over, like, consciousness, and whether it is possible to create artificial consciousness.

Fundamentals of Artificial Intelligence

Purpose




This research is based on the premise that current metrics for search engine advertising (SEA) are misleading and do not sufficiently allow managers to evaluate traffic and conversions simultaneously. This study aimed to conceptually develop and assess conversion potential (CvP) as a unifying construct for both measuring and evaluating the performance of SEA campaigns.




Design/methodology/approach




A data set of nearly seven million records covering almost three years of a multi-million-dollar keyword marketing campaign from a major US retailer was used to validate the construct of CvP.




Findings




Results empirically validate how CvP measures both campaign traffic and sales in SEA, using the optimization factor of ad rank, which is one of many possible factors.




Research limitations/implications




Although the data set is large and covers a lengthy period of time, it is limited to one company in the retail sector.




Practical implications




The research instantiates CvP as a metric for overall SEA account performance while demonstrating that it is a practical tool for future campaign planning. The metric simultaneously incorporates a sales ratio and a traffic ratio.




Originality/value




This is the first study to formalize and provide a working definition of CvP in the academic literature. The contribution is a theoretical and practical managerial framework to mutually evaluate, measure and make decisions about SEA efforts.

Conversion potential: a metric for evaluating search engine advertising performance

Using Internet Behavior to Deliver Relevant Television Commercials

Microsoft adCenter is the third largest Search advertising platform in the United States behind Google and Yahoo, and services about 10 % of US traffic. At this scale of traffic approximately 1 billion events per hour, amounting to 2.3 billion ad dollars annually, need to be scored to determine if it is fraudulent or bot-generated [32, 37, 41]. In order to accomplish this, adCenter has developed arguably one of the largest data mining systems in the world to score traffic quality, and has employed them successfully over 5 years. The current paper describes the unique challenges posed by data mining at massive scale, the design choices and rationale behind the technologies to address the problem, and shows some examples and some quantitative results on the effectiveness of the system in combating click fraud.

/pdf/click-fraud-detection-adversarial-pattern-recognition-over-5-1komh2fbj1.pdf

Click Fraud Detection: Adversarial Pattern Recognition over 5 Years at Microsoft

Not all of the over one billion users of online social networks (OSNs) are equally valuable to the OSNs. The current business model of monetizing advertisements targeted to users does not appear to be based on any visible grouping of the users. The primary metrics remain CPM (cost per mille---i.e., thousand impressions) and CPC (cost per click) of ads that are shown to users. However, there is significant diversity in the actions of users---some users upload interesting content triggering additional views and comments leading to further cascades of action. Beyond direct impressions, a user's action can generate indirect impressions by actions induced on friends and other users. Identifying the valuable user segments requires examination of profile data, friendships, and most importantly, their activity. Here we explore an alternate approach for measuring the value of users in OSNs by proposing a framework from the viewpoint of a popular OSN. Using a real dataset on the social network and activities of users, we show that a small subset of actions are likely to be key indicators of a user's value. Additionally, by examining the current targeting demographics available in Facebook, we are able to explore the relative (monetary) value that different users represent to the OSN.

/pdf/beyond-cpm-and-cpc-determining-the-value-of-users-on-osns-3jcxzzuw2i.pdf

Beyond CPM and CPC: determining the value of users on OSNs

Sponsored search has dramatically influenced how people interact with the Web. It has funded the Web searching infrastructure that users have grown accustomed to and provided unparalleled reach, frequency, and control of marketing efforts and advertising campaigns. Sponsored search advertising has dramatically impacted search engines, consumers, and organizations, and will continue to do so in the foreseeable future.

/pdf/the-components-and-impact-of-sponsored-search-325145ikw4.pdf

The Components and Impact of Sponsored Search

The Google Online Marketing Challenge, a MERLOT online learning object available at http://www.merlot.org/merlot/viewMaterial.htm?id=310448, is a global competition where students work with small to medium sized enterprises (SMEs) to create a practical and successful online marketing campaign using Google's key advertising product, AdWords. In the competition, students learn critical online marketing skills, including how to maximize targeted and relevant traffic to a client’s Website. Student teams employ marketing techniques to refine and improve the effectiveness of their AdWords campaign over a three-week competition period. The authors, who helped develop the Challenge and ran the Challenge in their classes, share insights and experiences about successful implementation of the Google Online Marketing Challenge in various course formats. The authors also highlight key findings from a survey of professors, students, and businesses that participated in the 2008 competition. While providing descriptive details of how the Challenge works, the primary focus of this paper is on how professors can confidently and effectively implement this teaching innovation as a stand-alone course, an element of a larger course, or as an out-of-class component.

/pdf/insights-on-the-google-online-marketing-challenge-and-its-2mxq51104n.pdf

T.B. Flaherty

Papers

The Components and Impact of Sponsored Search

Insights on the Google Online Marketing Challenge and its successful classroom implementation