Charniak, Eugene, Christopher K. Riesbeck, Drew V. McDermott, and James R. Meehan. Artificial intelligence programming. Hove, UK: Psychology Press, 2014.
Artificial Intelligence (AI) is projected to be the next frontier with numerous interested individuals perfecting writing of programs. In most cases, experiences or problems encountered in AI coding are ill defined while postulated theories to intricate for verification through formal arguments. The four authors offer an introduction into LISP which is one of the widely documented coding languages to date. The book central narrative looks to a practical approach to enable programmers to personally embark on higher level understanding of common AI theories and algorithms. This particular edition builds on previous ones by bringing to the fore enhanced coding tools. However, a compelling attribute revolves about AI audiences are bound to find a couple of challenges embracing specific content based on the fact that these present personal viewpoints since some techniques under discussions have not been conclusively tested to enhance educational value.
Copeland, Jack. Artificial intelligence: A philosophical introduction. Hoboken, NJ: John Wiley & Sons, 2015.
A compelling notion in this book underscores that as early as the 18th Century, radical philosophers like Julien Mettrie equated human beings to machines. Copeland centrally addresses AI from a philosophical perspective highlighting that there is no probable way with which humanity can hold back widespread AI development. The author provides that intelligent human beings as stakeholders have always looked to do as much as possible with the limited resources available. To the audiences, the core message is that AI is basically optimizing the synergy occurring from interactions of man and machine. It is essentially aimed at creating highly intelligent computer agents with capacities to progressively learn how to interact with the external environment in the same capacity as man. It educational potential includes insights to eliminate some aspects which hinder human beings from learning and therefore surpass unaided human intelligence.
Milgrom, Paul R., and Steve Tadelis. “How Artificial Intelligence and Machine Learning Can Impact Market Design.” (2018). Web. 8 Feb. 2018. < http://www.milgrom.net/sites/default/files/AI%20and%20Mkt%20Design%20Final_0.pdf>.
Scientific information provides that classical economy theories did not account for market frictions given that computations and information on market dynamics were easy to generalize. Milgrom and Tadelis provide the central idea that markets today are becoming more dependent on Big Data. The compelling aspects herein revolves about the application of AI to understand intricacies of consumer choices in an effort to determine the most effective and efficient application of limited organizational resources. Audiences are able to understand that Artificial Intelligence has grown greatly in recent years and one of its envisaged applications appertains to Big Data mining and analysis. The authors offer succinct educational value indicating that AI avails vast potential to bypass contemporary computational challenges as well as limiting frictions that identify reliable business relationships.