Browsing by Subject "artificial intelligence"
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Item AI Is Tricky(The Medium, 2018-09-24) News, UTItem AI's Inflection Point(The Texas Scientist, 2021) Airhart, MarcItem Allies and Artificial Intelligence: Obstacles to Operations and Decision-Making (Spring 2020)(Texas National Security Review, 2020) Lin-Greenberg, ErikItem Arms Control for Artificial Intelligence (Spring 2023)(Texas National Security Review, 2023) Lamberth, Megan; Scharre, PaulItem Artificial Intelligence, International Competition, and the Balance of Power (May 2018)(Texas National Security Review, 2018-05) Horowitz, Michael C.Item Artificial Intelligence-Enhanced Mutli-Material Form Measurement for Additive Materials(University of Texas at Austin, 2018) Stavroulakis, P.; Davies, O.; Tzimiropoulos, G.; Leach, R.K.The range of materials used in additive manufacturing (AM) is ever growing nowadays. This puts pressure on post-process optical non-contact form measurement systems as different system architectures work most effectively with different types of materials and surface finishes. In this work, a data-driven artificial intelligence (AI) approach is used to recognise the material of a measured object and to fuse the measurements taken from three optical form measurement techniques to improve system performance compared to using each technique individually. More specifically, we present a form measurement system which uses AI and machine vision to enable the efficient combination of fringe projection, photogrammetry and deflectometry. The system has a target maximum permissible error of 50 μm and the prototype demonstrates the ability to measure complex geometries of AM objects, with a maximum size of (10 × 10 × 10) cm, with minimal user input.Item Debunking the AI Arms Race Theory (Summer 2021)(Texas National Security Review, 2021) Scharre, PaulItem Embedding Ethical Theory Into Autonomous Vehicles: Analyzing a Covergence of Ethics and Action Embedding Ethical Theory Into Autonomous Vehicles: Analyzing a Convergence of Ethics and Action(2020-05) Atchley, AustinThe ability of autonomous vehicle systems to collect data and, independently of passion, make split-second decisions creates a newly emerging phenomenon: developers encode their ethical views into something that will (almost deterministically) enact them in reality. This is a departure from the traditional relationship between belief and action that is present in each decision made by a human, and, as artificial intelligence becomes more widespread, perhaps a majority of day-to-day decisions will be made without human unpredictability. Instead, decision-making will boil down to the application of ethical principles (either explicitly or implicitly) to a dataset. In creating ethical autonomous vehicles, we should address the problem using both top-down and bottom-up approaches to encoding ethical beliefs. The top-down approach uses high-level ethical values defined by the software creator to drive decision-making processes, and the bottom-up approach attempts to mimic human ethical conduct by using a pre-collected dataset of decisions made by real human drivers. Currently, many developers are not aware of the ethical principles they embed into their code, and by taking an explicit approach to ethical decision-making, we can encourage morally preferable decisions to be taken by autonomous vehicles. These issues are inherently interdisciplinary, and this thesis treats them as such, borrowing from both engineering and philosophical discourse. I argue that a similarly holistic perspective should be adopted by scholars working on the many-faceted topic of autonomous driving.Item A Graph Grammar Methodology for Generative Systems(2009-04-30) Campbell, Matthew I.; Campbell, Matthew I.This paper puts forth a view of graph transformation systems as a useful way to organize the construction activities involved in design – be it the design of engineering artifacts or of any creative endeavor. While the concept of graph grammars has existed for nearly 40 years in an esoteric corner of artificial intelligence research [1], researchers in design automation have realized their worth in encapsulating knowledge and heuristics of a particular problem domain. In this paper, the fundamental challenges for graph transformations are studied especially in the context of design. In particular, the activities of recognizing, choosing, and applying rules are studied and two engineering examples are provided to illustrate the power of this approach in design automation.Item Intelligence, Designed(2015-02) Franklin, Steve; Jenna LueckeItem New Grant to Help Align Information Science Curriculum With Serving the Public Good(The University of Texas at Austin, 2019-10-08) News, UTItem An Overview of Algorithmic Bias in Artificial Intelligence(2021) Kartha, NiteshArtificial Intelligence has grown throughout recent years to become a major part of popular culture and products used by people around the world. However, these systems are not perfect and can in fact contain multiple different biases in their underlying algorithms. In this paper, we provide an overview of the sources of algorithmic bias, a discussion of real-world case studies and their impacts, and a general summary of past attempts to address biases in artificial intelligence such as the General Data Protection Regulation (GDPR), corporate and governmental ethical guidelines, and New York City’s Automated Decision System (ADS) Task Force. Specifically, we discuss the COMPAS algorithm used for pretrial assessments, the Facebook ad-delivery algorithm used on its online advertising platform, and a healthcare algorithm used for high-risk care management in the United States. We conclude that algorithmic bias will only be exacerbated as more systems become automated through artificial intelligence. However, recognizing and calling for the alleviation of biases in current systems as well as approaching the design of automated systems holistically have led to reduced biases. More empirical research is required to fully understand what ways algorithmic bias can consistently be reduced.Item Podcast: AI Designed to Make Life Better(Medium, 2020-06-24) Group of Researchers, Good SystemsItem Podcast: AI Designed to Podcast: AI Designed to Make Life Better Life Better(The Medium, 2020-06-24) Stone, PeterItem Predictive Iterative Learning Control with Data-Driven Model for Optimal Laser Power in Selective Laser Sintering(University of Texas at Austin, 2018) Nettekoven, A.; Fish, S.; Topcu, U.; Beaman, J.Building high quality parts is still a key challenge for Selective Laser Sintering machines today due to a lack of sufficient process control. In order to improve process control, we propose a Predictive Iterative Learning Control (PILC) controller that minimizes the deviation of the postsintering temperature profile of a newly scanned part from a desired temperature. The controller does this by finding an optimal laser power profile and applying it to the plant in a feedforward manner. The PILC controller leverages machine learning models that accurately capture the process’ temperature dynamics based on in-situ measurement data while still guaranteeing low computational cost. We demonstrate the controller’s performance in regards to the control objective with heat transfer simulations by comparing the PILC-controlled laser power profiles to constant laser power profiles.Item Presentation: Autonomous Robots Playing Soccer and Traversing Intersections(Environmental Science Institute, 2010-10-15) Environmental Science Institute; Stone, PeterItem Quantum Computation, Quantum Algorithms & Implications on Data Science(2020) Kim, Nathan; Garcia, Jeremy; Han, DavidQuantum computing is a new revolutionary computing paradigm, first theorized in 1981. It is based on quantum physics and quantum mechanics, which are fundamentally stochastic in nature with inherent randomness and uncertainty. The power of quantum computing relies on three properties of a quantum bit: superposition, entanglement, and interference. Quantum algorithms are described by the quantum circuits, and they are expected to solve decision problems, functional problems, oracular problems, sampling tasks and optimization problems so much faster than the classical silicon-based computers. They are expected to have a tremendous impact on the current Big Data technology, machine learning and artificial intelligence. Despite the theoretical and physical advancements, there are still several technological barriers for successful applications of quantum computation. In this work, we review the current state of quantum computation and quantum algorithms, and discuss their implications on the practice of Data Science in the near future. There is no doubt that quantum computing will accelerate the process of scientific discoveries and industrial advancements, having a transformative impact on our society.Item The Role of Artificial Intelligence in Educating Novice Programmers(2020-05) Weakley, JackProgramming is an inherently difficult skill to acquire and develop. Those who attempt to learn programming may be easily discouraged. The current landscape for computer science education does not address the needs of every novice programmer. Literature reports a discrepancy between student misconceptions and instructors’ perceptions of those misconceptions. Those who can afford a one-on-one human tutor perform on average two standard deviations better than those who learn via conventional methods, suggesting there is a need for a comparable, cheaper replacement. As a result, a number of intelligent tutoring systems have been developed for the purpose of teaching introductory programming concepts and replicating the benefits of one-on-one human tutoring. In this thesis, we analyze and discuss the literature pertaining to student misconceptions, selecting five fundamental misconception categories for introductory programming to demonstrate the effectiveness of existing intelligent tutoring systems. The features of existing intelligent tutoring systems are discussed and analyzed with respect to their effectiveness in addressing student misconceptions. Finally, we highlight the current gap in research on intelligent tutoring systems, hypothesizing the architecture and features of an ideal intelligent tutoring system for introductory programming.Item Statistical Perspectives in Teaching Deep Learning from Fundamentals to Applications(2020) Kim, Nathan; Han, DavidThe use of Artificial Intelligence, machine learning and deep learning have gained a lot of attention and become increasingly popular in many areas of application. Historically machine learning and theory had strong connections to statistics; however, the current deep learning context is mostly in computer science perspectives and lacks statistical perspectives. In this work, we address this research gap and discuss how to teach deep learning to the next generation of statisticians. We first describe some backgrounds and how to get motivated. We discuss different terminologies in computer science and statistics, and how deep learning procedures work without getting into mathematics. In response to a question regarding what to teach, we address organizing deep learning contents and focus on the statistician’s view; form basic statistical understandings of the neural networks to the latest hot topics on uncertainty quantifications for prediction of deep learning, which has been studied in the Bayesian frameworks. Further, we discuss how to choose computational environments and help develop programming skills for the students. We also discuss how to develop homework incorporating the idea of experimental design. Finally, we discuss how to expose students to the domain knowledge and help to build multi- discipline collaborations.Item The Status of Artificial Intelligence(2018-10-04) Eastwood, Nick