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As technology advances, its integration with the graphic design field becomes increasingly prevalent. This can be seen especially in artificial intelligence programs and generative AI. With the rapid emergence of this technology, young graphic designers are left threatened by AI in relation to their graphic design careers. This occurs for several reasons such as lack of knowledge of AI and lack of training in usingvit. This paper aims to highlight the importance for these young designers to understand artificial intelligence through the examination of its practical application to the design field. In addition to this, the history of artificial intelligence will be discussed in detail through its current role in both society and the lives of graphic designers which highlights how it has already been affecting the lives of designers. The challenges of learning this technology can be daunting but through its application and continual effort from a designer to learn this technology, the tensions felt by the designer can be alleviated in order to showcase how artificial intelligence can benefit them in their careers. Case Studies showcasing the use of AI by designers are examined in this paper also demonstrate how using the AI has affected the attitudes of the designers toward a technology that is intimidating and new in a positive way.

The integration of Artificial Intelligence (AI) in collaborative tasks has gained momentum, with particular implications for critical infrastructure maintenance. This study examines the assurance goals of AI—security, explainability, and trustworthiness—within Virtual Reality (VR) environments for bridge maintenance. Adopting a within-subjects design approach, this research leverages VR environments to simulate real-world bridge maintenance scenarios and gauge user interactions with AI tools. With the industry transitioning from paper-based to digital bridge maintenance, this investigation underscores the imperative roles of security and trust in adopting AI-assisted methodologies. Recent advancements in AI assurance within critical infrastructure highlight its monumental role in ensuring safe, explainable, and trustworthy AI-driven solutions.

This thesis is an exploration of human imagination and creativity as it pertains to poetry. With the rise of “intelligent” machines, it is the duty of scholars, thinkers, philosophers, and artists to gauge the ethics of using robots to create art, create information, and create in general. With that in mind, this thesis aims to distinguish the definitions of natural and artificial intelligence. This distinction is at the heart of what makes poetry inherently human. AI poses a threat to poetry and the act of artistic expression. Additionally, AI poses a threat to the human imagination. The aim of the critical paper is to expose these truths regarding artificial intelligence. Thus, reclaiming poetry and the act of creativity. The creative manuscript, Random Access Memories, is an experiment to see if the reader can tell what is human and what is not, and a showcase of just how far AI has come to stealing what is inherently human.

The focus of this paper is on the topics of artificial intelligence and supply chain management and how artificial intelligence-enabled supply chains provide organizations with competitive advantages. The supply chain’s adoption of data collection technologies as part of digital transformation and movements of industry 4.0 creates a strong foundation for artificial intelligence analytics. Artificial intelligence has three branches sensing and interacting, decision-making, and learning. Each branch uses its algorithms and serves a different purpose for the business. Artificial intelligence-enabled supply chains create unique, inimitable competitive advantages that fit Michael Porter’s five forces.

This paper examines numerous machine learning methods in their application for predicting successful item recommendations to users within a network. By evaluating these methods, various industry-standard recommenders are compared in order to construct a baseline model for research purposes. Next we attempt to similarly generate recommendations using a natural language processing, or NLP, approach by leveraging available textual data. This is intended to explore if user preferences can be modeled using the semantic information about a given item. Various approaches are considered to capture this information and the final recommender is ultimately built with Google’s Universal Sentence Encoder, an NLP model used to encode full sentences into high-dimension vectors that capture context [CYK18]. In the case of our research, the items being recommended are movies. Each model is trained using a large dataset of film reviews provided by the University of Minnesota. In addition to user reviews, detailed information for each movie ranging from the popularity and genre to descriptive fields like plot overviews are also used as features. With this, we are able to assess a variety of methods for providing recommendations. Ensemble methods are used to build a baseline model with a RMSE of 0.843. Different NLP algorithms are compared and a recommender is successfully built on the Universal Sentence Encoder model having similar performance to our baseline, an RMSE of 0.872.

This thesis presents a path optimization solution for a robot in two different constrained 3-dimensional (3D) environments. The robot is required to travel from its current position to a goal position following minimum cost paths (optimal paths). The first environment has 3D obstacles that interfere with the robot’s path. The path cost for this environment accounts for the minimum distance traveled by the robot from the start to the goal position while avoiding obstacles. The second environment is the atmosphere of Venus, specifically a flyable region of this atmosphere with characteristics similar to Earth’s. This environment has strong westward winds that require a more complex cost function. The path cost also accounts for energy expenditure, such as thrust or drag, and energy accumulation, such as charging using the robot’s solar panels and gains of potential energy (e.g., due to upward directional winds). In this case, we can add to the path cost function the localization cost of the robot. Localization is simulated in the environment by the use of cameras pointing to the surface of the planet, with yields lower localization error when the vehicle is close to the surface. The approach proposed in this paper uses genetic algorithms, a heuristic search that, based on a population of initially feasible paths and a set of biologically inspired operations, finds a low-cost path. Path feasibility is assured by computing local reachability regions based on different factors such as wind velocity, obstacles, and the maximum speed of the robot. The method is illustrated through a series of simulations that show our results as a function of the number of iterations and path population sizes. Finally, a comparison with different planners is made in order to show that the genetic algorithms allow for more efficient and easier implementations.

Policy-makers, scholars, and commentators are increasingly concerned with the risks of using profiling algorithms and automated decision-making. The EU’s General Data Protection Regulation (GDPR) has tried to address these concerns through an array of regulatory tools. As one of us has argued, the GDPR combines individual rights with systemic governance, towards algorithmic accountability. The individual tools are largely geared towards individual “legibility”: making the decision-making system understandable to an individual invoking her rights. The systemic governance tools, instead, focus on bringing expertise and oversight into the system as a whole, and rely on the tactics of “collaborative governance,” that is, use public-private partnerships towards these goals. How these two approaches to transparency and accountability interact remains a largely unexplored question, with much of the legal literature focusing instead on whether there is an individual right to explanation.The GDPR contains an array of systemic accountability tools. Of these tools, impact assessments (Art. 35) have recently received particular attention on both sides of the Atlantic, as a means of implementing algorithmic accountability at early stages of design, development, and training. The aim of this paper is to address how a Data Protection Impact Assessment (DPIA) links the two faces of the GDPR’s approach to algorithmic accountability: individual rights and systemic collaborative governance. We address the relationship between DPIAs and individual transparency rights. We propose, too, that impact assessments link the GDPR’s two methods of governing algorithmic decision-making by both providing systemic governance and serving as an important “suitable safeguard” (Art. 22) of individual rights.After noting the potential shortcomings of DPIAs, this paper closes with a call — and some suggestions — for a Model Algorithmic Impact Assessment in the context of the GDPR. Our examination of DPIAs suggests that the current focus on the right to explanation is too narrow. We call, instead, for data controllers to consciously use the required DPIA process to produce what we call “multi-layered explanations” of algorithmic systems. This concept of multi-layered explanations not only more accurately describes what the GDPR is attempting to do, but also normatively better fills potential gaps between the GDPR’s two approaches to algorithmic accountability.

The paper’s focus is the singular one question of whether the 12-years of exclusivity that was needed in 2008 remains justified in 2019 given that technological advancement reduces the cost and the time for drug discovery? Basically, new and emerging technologies are deployed every day to enhance efficiencies and reduce the time it takes to bring a drug to the market. The paper asserts that as new technologies are adopted and advances in scientific understanding are leveraged, it results in shorter drug development timelines. This factum, the paper asserts should have a bearing to reduce the period of exclusivity granted to small molecule drugs and biologics. The paper discusses how both cost and efficiencies are improved from the use of technology, thus making the case for reducing the time period for protection of data exclusivity.

ITS(Intelligent Tutoring Systems) have played a significant role in today’s smart learning educational technology environment, where students are given feedback instantly at scale. However, the current, more practically inclined approaches de-emphasize the reliance on the problematic student model, i.e. student's motivation, emotional state and interest instead put more effort into the construction of theory-based user interfaces. Although research in ITS has produced many interesting theoretical insights, there are relatively few ITS which are regularly and effectively used. Through research conducted into current practices in ITS, gaps within the system have been noted. Specifically, the paper attempts to propose a solution to improvise an Intelligent Tutoring System called Merits-VR that makes students in the K12 learning domain facilitate deep learning.

This paper captures the ability of AI neural network technology to analyze petrophysical datasets for pattern recognition and accurate prediction of the pay zone of a vertical well from the Santa Fe field in Kansas. During this project, data from 10 completed wells in the Santa Fe field were gathered, resulting in a dataset with 25,580 records, ten predictors (logs data), and a single binary output (Yes or No) to identify the availability of Hydrocarbon over a half feet depth segment in the well. Several models composed of different predictors combinations were also tested to determine how impactful some logs were compared to others for the prediction process. With 32 tested models using a base set of 5 logs (X, Y GR, DEPT, and CALI) and different combinations of 5 other logs ( RT90, RHOB, NPHI, PE, DT). All models containing RT90, NP, or DT led to a better prediction matching the pay zone established based on a petrophysical analysis and completion data from the well. Results from this project could be used as another support to help and justify decision-making for a Petro physicist regarding work in the field with less experience.

The field of library and information science (LIS) has always shaped and been shaped by the changing tides of technology. Given that recent developments in AI appear to be the next technological wave that may bring major disruption for the future of information professionals, how are students in ALA-accredited master’s programs reacting to these changes? This paper reports findings from interviews with students pursuing studies in librarianship and archival studies about their educational experiences with artificial intelligence (AI) and their expectations about how AI will impact their future careers as librarians and archivists. Key themes that emerge from this analysis include structural and professional changes in libraries and archives, the loss of human elements in libraries and archives, and ethical challenges of AI in libraries and archives. Recommendations based on these findings include ways to adapt LIS education to better prepare students, such as developing courses that combine the technical aspects of how to leverage AI that is content and context relevant to libraries and archives, to ensure that librarians and archivists can have active roles in leveraging AI to better accomplish their goals of serving patrons’ information needs and wants and preserving the past.

The promise of financial technology (“fintech”) and artificial intelligence (“AI”) in broadening access to financial products and services continues to capture the imagination of policymakers, Wall Street, and the public. This has been particularly true in the realm of fintech credit where platform companies increasingly provide online loans to consumers, students, and small businesses by harnessing AI underwriting and alternative data. In 2019 alone fintech lenders represented nearly 50% of total non-credit card, unsecured consumer loan balances in the United States. One of the most prevalent ways fintech credit firms operate is by securitizing the online loans they help originate. In doing so, fintech lenders are able to access the capital markets and further the spread of borrowed capital and credit risk. Against this backdrop of increasing institutional investment in fintech securitized assets, this Article reveals how consumer finance law is playing a subtle but increasingly important role in commercial financial transactions. I do this by exploring how structured finance has come to operate in the fintech credit marketplace and by comparing it to pre-2008 securitization activity in the home mortgage context. In doing so, I critique algorithm-driven credit securitization and point out certain economic and legal risks that make it similar to pre-2008 mortgage securitization, as well as other risks that are unique to fintech finance. These pertain to, among other things, the opacity of loans underwritten through the use of alternative data and machine learning, the untested efficacy of such underwriting techniques, and the ways consumer finance laws—such as licensing, usury, and standards-based regulation—and the growth of nonbank finance companies intersect with the securitization process. The paper concludes by offering policy recommendations for addressing these future risks.

Within the last few decades, we have witnessed the dramatic rise in the use of AI surveillance technologies in organizations. In this paper, we answer calls from management scholars to investigate the impact of this new aspect of organizational control on manager-worker dynamics. Focusing on employees’ perceptions of privacy invasion, we investigate in four experimental studies whether these differ when the observer is a form of AI technology instead of a human. We demonstrate that employees perceive their privacy is invaded to a greater extent when AI surveillance technologies monitor them as opposed to human managers (Studies 1-4). We further assess downstream consequences of perceived privacy invasion (Studies 1-2), explore mechanisms that explain the relationship between monitoring form (AI surveillance technologies vs. human managers) and perceived privacy invasion (Study 3), and investigate ways to attenuate the effect (Study 4). Throughout these studies, we extend prior theory to provide insight into the implications of the increasing use of AI surveillance technologies in organizations.

All data systems require optimization. Determining the most performant recipe for data retrieval is commonly referred to as access path selection (APS). This paper introduces the use of artificial neural networks for query optimization in APS. The problems with query optimizers are well known. Promising research in other areas of query optimization using machine learning techniques continues at a rapid pace. Research has proposed machine learning solutions for join order enumeration, cardinality estimation, and predicting optimizer heuristics (such as cost estimation and table statistics). However, a practical method for learned APS remains an open research topic. APS is difficult because an optimizer must be aware of the ever-changing system state, hardware, and data. Incorrect assumptions in any of those can be very costly, and finding a solution requires years of research. In this thesis, I present an artificial intelligence-based approach to APS and introduce a learned optimization method using neural networks. Moreover, I explore the challenges inherent in applying generalized neural network techniques to APS. I empirically show that these networks significantly outperform the query optimizer's accuracy in determining the truly performant access path.

Recent evidence suggests that sonar provides bats only with limited information about the environment. Nevertheless, they can fly swiftly through dense environments while avoiding obstacles. Previously, we proposed a model of sonar-based obstacle avoidance that only relied on the interaural level difference of the onset of the echoes. In this paper, we extend this previous model. In particular, we present a model that (1) is equipped with a short term memory of recent echo trains, and (2) uses the full echo train. Because handcrafting a controller to use more sonar data is challenging, we resort to machine learning to train a robotic model. We find that both extensions increase performance and conclude that these could be used to enhance our models of bat sonar behavior. We discuss the implications or our method and findings for both biology and bio-inspired engineering.

Speculation about artificial intelligence and big data has become commonplace. Foremost among these discussions is the potential for these technologies to displace the value of human labor. However, these discussions have become untethered from the history and development of the technologies in question. This paper analyzes paradigms of artificial intelligence to reveal that they are closely tied to different types of logical inference. The properties of computing machinery provide them with an advantage at deductive and inductive tasks when compared with human cognition. But, the understudied method of Peircean abduction poses several problems for computing machinery. Moreover, Peircean abduction, also called “creative abduction,” bears resemblance to important themes in the history of humanism. Human cognition handles the issues of Peircean abduction with remarkable ease, suggesting that humans will maintain a comparative advantage at this type of logical inference for the foreseeable future.

This work shows that combining the techniques of neural networking and predictive analytics with the fundamental concepts of computing performance optimization is genuine in many ways. It has the potentials to: (1) reduce infrastructure upgrade costs (2) reduce human interactions, by enabling the system to learn, analyze, and make decisions on its own, and (3) generalize the solutions to other performance problems. This paper attempts to tackle a JVM performance optimization from a different dimension and in a way that can be scaled to other common utilized resources, such as file systems, static contents, search engines, web services...etc. It shows how to build a framework that monitors the performance metrics to determine patterns leading to bottleneck incidents and then benchmark these performance metrics. The framework uses artificial neural network in its core to accomplish this first steps with immediate benefit of eliminating the need to a domain expert analyzing which of these metrics is more important or has more weight on constituting the bottleneck condition, and hence enable the system to deal with more ambiguous situations. The framework uses an analytics engine, to establish predictive patterns between the system bottleneck and library of factors to establish an early alert system and thus enhancing the weight of the bottleneck signal. Finally, the framework acts in defense when the deadlock signal is triggered from the learning and/or the analytics engine through streaming down concurrent transactions into a temporarily queuing data structure. We put our model into a test and built a simulation to quantify the added benefit of each component of our framework. The results are proven to demonstrate the immediate benefit of our framework and open doors for other future work.

Alan Turing asked if machines can think, but intelligence is more than logic and reason. I ask if a machine can feel pain or joy, have visions and dreams, or paint a masterpiece. The human brain sets the bar high, and despite our progress, artificial intelligence has a long way to go. Studying neurology from a software engineer’s perspective reveals numerous uncanny similarities between the functionality of the brain and that of a computer. If the brain is a biological computer, then it is the embodiment of artificial intelligence beyond anything we have yet achieved, and its architecture is advanced beyond our own. When striving to achieve the ideal form of AI and revolutionize our computer architecture, where else should one look than the design of the brain? This paper will give a brief history of artificial intelligence, define ideal AI by a set of criteria, discuss the philosophical implications and moral issues of attempting to create a synthetic human, analyze the similarities between the brain’s architecture and computer architecture, postulate a design approach for engineering this “ideal AI”, and discuss the applications and consequences of such technology.