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Artificial Intelligence (AI) reimagined the Google search and how information is accessed daily. With vast processing times and the ability to digest large amounts of information at lightning speed, AI also can recognize patterns and commands, making information more relevant and useful for the end user. AI has a promising future when strategically placed within certain functions that may tremendously benefit from its skills, ultimately shaping business professionals to rethink approaches to solving complex business problems and determining how and where there may be strategic opportunities to reduce human capital with machines. Given the excitement of newly AI‐released capabilities like ChatGPT, questions have been raised around the validity of AI and if it is ethical, especially when used in a sensitive environment like talent recruiting. The hype associated with AI has led to the assumption that it will do what it is told, and marketed as an asset that can be used anywhere. Not only does the hype of AI concern the recruiting world, but it also manifests a much bigger long‐term concern for liability. This paper focuses on the use of AI in recruiting. On the surface, AI offers a lot of benefits to organizations, but many lack the knowledge and understanding of how it works. Therefore, I surveyed recruiters and managers to learn more about their dependency of AI for hiring decisions, especially when leveraging applicant tracking systems (ATS). Followed by a technical white paper, I evaluated the hype around AI in recruitment and determine best practices for companies to follow when considering implementing into this function. These technicalities include how and if AI should be used in a recruiting function. / Business Administration/Human Resource Management

The popularity of AI audio applications is growing, it is used in chatbots, automated voice translation, virtual assistants, and text-to-speech translation. Audio classification is crucial in today’s world with a growing need to sort and classify millions of existing audio data with increasing amounts of new data uploaded over time. In the area of classification lies the difficult and lucrative problem of music recommendation. Research in music recommendation has trended over time towards collaborative-based approaches utilizing large amounts of user data. These approaches tend to deal with the cold-start problem of insufficient data and are costly to train. We look to recent advances in music generation to develop a content-based method utilizing a joint embedding space to link text with music audio. This approach has not been previously applied to music recommendation. In this thesis, we will examine the joint embedding methods used by recent AI music generation models and introduce a music recommendation system using joint embeddings. This music recommendation system can avoid cold-start, reduce training costs for music recommendation, and serve as the foundation for a cost-efficient content-based multimedia recommendation system. The current model trained on MusicCaps recommends the correct song per tag input within the top 50%-80% of all songs about 65%-70% of the time and we expect better results after further training.

Call number: LD2668 .T4 1959 L85

The development of the research "Goal Formulation in Intelligent Agents" is anchored on the rationale that to be truly called "intelligent", an agent must not only be capable of knowing how to achieve its given goals; preferably, it must also have the capability to formulate its own goals. It must be able to detect its own goals, assess their feasibility, prioritize them, evaluate their validity as to whether they have to be acted upon, terminated, or suspended. This research has developed and implemented an intelligent system that is capable for formulating its own goals. Goal formulation refers to the intelligent behavior that an agent exhibits when reasoning about what goals to pursue and when to pursue them. It is an integrated reasoning mechanism that identifies the relevant goals that an agent needs to accomplish to affect the external world (Goal detection); constantly updates the qualitative and quantitative information attributed to the active goals as events unfold (Active goal status evaluation); assesses whether a goal is attainable through the application of the agent's own actions (Goal achievability assessment); and dynamically evaluates the relative merits of an agent's tasks, provides the agent with a sound basis to make a rational choice among a set of competing alternatives and then decides what to do next based on the choice made (Next action selection). In the development of the goal formulator, the types and structure of the required knowledge are identified; architectures for the various goal formulation components have been designed; and algorithms for the various goal formulation reasoning mechanisms (e.g. application of NPV economic decision criterion) have been developed and implemented in Prolog. To prove the applicability of the goal formulation concepts that this research had developed, the system was applied in the housekeeping domain. Simulations of some housekeeping cases are provided.

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Artificial Intelligence Lab, Department of MIS, University of Arizona / Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Electronic commerce (EC) and software agents are two of the hottest fields of research in information science. As the Internet is rapidly becomes a popular marketplace for consumers and sellers of goods and services, combining these two research areas offers lucrative opportunities both for businesses wishing to conduct transactions over the World Wide Web (WWW) and for developers of tools to facilitate this trend. The focus in this chapter will be on software agents specifically designed for electronic commerce activities. We will briefly describe the history of agent research in general, defining characteristics of agents, and will touch on the different types of agents. Following this introduction we will describe the learning and action mechanisms that make it possible for agents to perform tasks. Finally, we will describe the issues associated with the deployment of electronic commerce agents (ECAs).

In recent years neural network have been shown to be quite effective in solving difficult combinatorial optimization problems. In this work a Hopfield neural network is used to schedule operations in a dataflow graph. This is an important step in behavioral synthesis systems. These operations must be assigned to a limited number of control steps, functional units, and busses. Also, there is an objective to minimize the lengths of data paths. Current methods which do this type of scheduling typically rely on heuristic algorithms. The neural network devised to solve this problem is one of the most complex to date. A special mechanism, "flag" neurons, was developed to enable the neural network to encode a bussing constraint. The neural network has been tested with problems from literature and problems randomly generated. The results have been consistently superior to those produced by a heuristic algorithm called ALAP.