Open Access. Powered by Scholars. Published by Universities.®

Computer Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

AI

Discipline
Institution
Publication Year
Publication
Publication Type

Articles 31 - 39 of 39

Full-Text Articles in Computer Engineering

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr Jan 2019

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …

Go to article

Comparison Of Google Image Search And Resnet Image Classification Using Image Similarity Metrics, David Smith May 2018

Comparison Of Google Image Search And Resnet Image Classification Using Image Similarity Metrics, David Smith

Computer Science and Computer Engineering Undergraduate Honors Theses

In this paper, we compare the results of ResNet image classification with the results of Google Image search. We created a collection of 1,000 images by performing ten Google Image searches with a variety of search terms. We classified each of these images using ResNet and inspected the results. The ResNet classifier predicted the category that matched the search term of the image 77.5% of the time. In our best case, with the search term “forklift”, the classifier categorized 92 of the 100 images as forklifts. In the worst case, for the category “hammer”, the classifier matched the search term …

Go to article

Regulating By Robot: Administrative Decision Making In The Machine-Learning Era, Cary Coglianese, David Lehr Jun 2017

Regulating By Robot: Administrative Decision Making In The Machine-Learning Era, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are transforming large segments of the economy, underlying everything from product marketing by online retailers to personalized search engines, and from advanced medical imaging to the software in self-driving cars. As machine learning’s use has expanded across all facets of society, anxiety has emerged about the intrusion of algorithmic machines into facets of life previously dependent on human judgment. Alarm bells sounding over the diffusion of artificial intelligence throughout the private sector only portend greater anxiety about digital robots replacing humans in the governmental sphere. A few administrative agencies have already begun to adopt this technology, while others …

Go to article

Emergent Ai, Social Robots And The Law: Security, Privacy And Policy Issues, Ramesh Subramanian Jan 2017

Emergent Ai, Social Robots And The Law: Security, Privacy And Policy Issues, Ramesh Subramanian

Journal of International Technology and Information Management

The rapid growth of AI systems has implications on a wide variety of fields. It can prove to be a boon to disparate fields such as healthcare, education, global logistics and transportation, to name a few. However, these systems will also bring forth far-reaching changes in employment, economy and security. As AI systems gain acceptance and become more commonplace, certain critical questions arise: What are the legal and security ramifications of the use of these new technologies? Who can use them, and under what circumstances? What is the safety of these systems? Should their commercialization be regulated? What are the …

Go to article

“My Logic Is Undeniable”: Replicating The Brain For Ideal Artificial Intelligence, Samuel C. Adams Apr 2016

“My Logic Is Undeniable”: Replicating The Brain For Ideal Artificial Intelligence, Samuel C. Adams

Senior Honors Theses

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 …

Go to article

The Application Of Traditional Tort Theory To Embodied Machine Intelligence, Curtis E.A. Karnow Jan 2013

The Application Of Traditional Tort Theory To Embodied Machine Intelligence, Curtis E.A. Karnow

Curtis E.A. Karnow

This note discusses the traditional tort theories of liability such as negligence and strict liability and suggests these are likely insufficient to impose liability on legal entities (people and companies) selling or employing autonomous robots. I provide the essential working definitions of ‘autonomous’ as well as the legal notion of ‘foreseeability’ which lies at the heart of tort liability. The note is not concerned with the policy, ethics, or other issues arising from the use of robots including armed and unarmed drones, because those, as I define them, are not currently autonomous, and do not implicate the legal issues I …

Go to article

Exploring The Relationship Of The Closeness Of A Genetic Algorithm's Chromosome Encoding To Its Problem Space, Kevin Mccullough Mar 2010

Exploring The Relationship Of The Closeness Of A Genetic Algorithm's Chromosome Encoding To Its Problem Space, Kevin Mccullough

Master's Theses

For historical reasons, implementers of genetic algorithms often use a haploid binary primitive type for chromosome encoding. I will demonstrate that one can reduce development effort and achieve higher fitness by designing a genetic algorithm with an encoding scheme that closely matches the problem space. I will show that implicit parallelism does not result in binary encoded chromosomes obtaining higher fitness scores than other encodings. I will also show that Hamming distances should be understood as part of the relationship between the closeness of an encoding to the problem instead of assuming they should always be held constant. Closeness to …

Go to article

On Advanced Template-Based Interpretation As Applied To Intention Recognition In A Strategic Environment, Cameron Akridge Jan 2007

On Advanced Template-Based Interpretation As Applied To Intention Recognition In A Strategic Environment, Cameron Akridge

Electronic Theses and Dissertations

An area of study that has received much attention over the past few decades is simulations involving threat assessment in military scenarios. Recently, much research has emerged concerning the recognition of troop movements and formations in non-combat simulations. Additionally, there have been efforts towards the detection and assessment of various types of malicious intentions. One such work by Akridge addressed the issue of Strategic Intention Recognition, but fell short in the detection of tactics that it could not detect without somehow manipulating the environment. Therefore, the aim of this thesis is to address the problem of recognizing an opponent's intent …

Go to article

Connectionist Approaches To Cost-Based Abduction, Emad Abdel-Thalooth Massry Andrews Feb 2004

Connectionist Approaches To Cost-Based Abduction, Emad Abdel-Thalooth Massry Andrews

Archived Theses and Dissertations

No abstract provided.

Go to article

First Prev