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Gettysburg College

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Full-Text Articles in Artificial Intelligence and Robotics

Cybernetics: How It Compares To Science-Fiction And Future Possibilities, Anindo Majumder Feb 2024

Cybernetics: How It Compares To Science-Fiction And Future Possibilities, Anindo Majumder

CAFE Symposium 2024

Cybernetics is a branch of science that studies how information is communicated in machines and electronic equipment compared to how information is communicated in the brain and nervous system. It also relates to the theory of automatic control and physiology, particularly the physiology of the nervous system. Usage of cybernetics is very popular in various science-fiction medium. This naturally leads one to be curious if its depictions might turn into reality one day. This research paper delves into the growth of cybernetics since its inception, current applications of cybernetics, and what the future might hold.

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Emergent Ai, Jillian A. Bick Feb 2024

Emergent Ai, Jillian A. Bick

CAFE Symposium 2024

For many years, artificial intelligence (AI) was considered to be limited in its abilities due to being confined to a pre-defined set of data. Currently, however, AI models have grown in complexity and size, leading to some previously impossible behaviors. These behaviors, known as "emergent AI behaviors," are unpredictable and not pre-programmed. Their existence suggests that AI is expanding in adaptability and may one day rival human intelligence. Media often portrays AI as having emotions and having the ability to operate autonomously, but what behaviors are AI really capable of?

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Designing Women: Essentializing Femininity In Ai Linguistics, Ellianie S. Vega Oct 2019

Designing Women: Essentializing Femininity In Ai Linguistics, Ellianie S. Vega

Student Publications

Since the eighties, feminists have considered technology a force capable of subverting sexism because of technology’s ability to produce unbiased logic. Most famously, Donna Haraway’s “A Cyborg Manifesto” posits that the cyborg has the inherent capability to transcend gender because of its removal from social construct and lack of loyalty to the natural world. But while humanoids and artificial intelligence have been imagined as inherently subversive to gender, current artificial intelligence perpetuates gender divides in labor and language as their programmers imbue them with traits considered “feminine.” A majority of 21st century AI and humanoids are programmed to fit female …

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Model Ai Assignments 2018, Todd W. Neller, Zack Butler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth Jan 2018

Model Ai Assignments 2018, Todd W. Neller, Zack Butler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth

Computer Science Faculty Publications

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of seven AI assignments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

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Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller Jan 2018

Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller

Computer Science Faculty Publications

In this column, we share resources for learning about and teaching Hidden Markov Models (HMMs). HMMs find many important applications in temporal pattern recognition tasks such as speech/handwriting/gesture recognition and robot localization. In such domains, we may have a finite state machine model with known state transition probabilities, state output probabilities, and state outputs, but lack knowledge of the states generating such outputs. HMMs are useful in framing problems where external sequential evidence is used to derive underlying state information (e.g. intended words and gestures). [excerpt]

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Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller Jan 2018

Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller

Computer Science Faculty Publications

In this column, we will look at a particular Kaggle.com click-through rate (CTR) prediction competition, observe what the winning entries teach about this part of the machine learning landscape, and then discuss the valuable opportunities and resources this commends to AI educators and their students. [excerpt]

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Knowing How: A Computational Approach, Joseph A. Roman Apr 2017

Knowing How: A Computational Approach, Joseph A. Roman

Student Publications

With advances in Artificial Intelligences being achieved through the use of Artificial Neural Networks, we are now at the point where computers are able to do tasks that were previously only able to be accomplished by humans. These advancements must cause us to reconsider our previous understanding of how people come to know how to do a particular task. In order to unpack this question, I will first look to an account of knowing how presented by Jason Stanley in his book Know How. I will then look towards criticisms of this view before using evidence presented by the existence …

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Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller Jan 2017

Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller

Computer Science Faculty Publications

Open-access AI educational resources are vital to the quality of the AI education we offer. Avoiding the reinvention of wheels is especially important to us because of the special challenges of AI Education. AI could be said to be “the really interesting miscellaneous pile of Computer Science”. While “artificial” is well-understood to encompass engineered artifacts, “intelligence” could be said to encompass any sufficiently difficult problem as would require an intelligent approach and yet does not fall neatly into established Computer Science subdisciplines. Thus AI consists of so many diverse topics that we would be hard-pressed to individually create quality learning …

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Ai Education: Deep Neural Network Learning Resources, Todd W. Neller Jan 2017

Ai Education: Deep Neural Network Learning Resources, Todd W. Neller

Computer Science Faculty Publications

In this column, we focus on resources for learning and teaching deep neural network learning. Many exciting advances have been made in this area of late, and so many resources have become available online that the flood of relevant concepts and techniques can be overwhelming. Here, we hope to provide a sampling of high-quality resources to guide the newcomer into this booming field. [excerpt]

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Ai Education: Machine Learning Resources, Todd W. Neller Jan 2017

Ai Education: Machine Learning Resources, Todd W. Neller

Computer Science Faculty Publications

In this column, we focus on resources for learning and teaching three broad categories of machine learning (ML): supervised, unsupervised, and reinforcement learning. In ournext column, we will focus specifically on deep neural network learning resources, so if you have any resource recommendations, please email them to the address above. [excerpt]

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Monte Carlo Approaches To Parameterized Poker Squares, Todd W. Neller, Zuozhi Yang, Colin M. Messinger, Calin Anton, Karo Castro-Wunsch, William Maga, Steven Bogaerts, Robert Arrington, Clay Langely Jun 2016

Monte Carlo Approaches To Parameterized Poker Squares, Todd W. Neller, Zuozhi Yang, Colin M. Messinger, Calin Anton, Karo Castro-Wunsch, William Maga, Steven Bogaerts, Robert Arrington, Clay Langely

Computer Science Faculty Publications

The paper summarized a variety of Monte Carlo approaches employed in the top three performing entries to the Parameterized Poker Squares NSG Challenge competition. In all cases AI players benefited from real-time machine learning and various Monte Carlo game-tree search techniques.

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Ai Education: Birds Of A Feather, Todd W. Neller Jan 2016

Ai Education: Birds Of A Feather, Todd W. Neller

Computer Science Faculty Publications

Games are beautifully crafted microworlds that invite players to explore complex terrains that spring into existence from even simple rules. As AI educators, games can offer fun ways of teaching important concepts and techniques. Just as Martin Gardner employed games and puzzles to engage both amateurs and professionals in the pursuit of Mathematics, a well-chosen game or puzzle can provide a catalyst for AI learning and research. [excerpt]

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Pedagogical Possibilities For The N-Puzzle Problem, Zdravko Markov, Ingrid Russell, Todd W. Neller, Neli Zlatareva Oct 2006

Pedagogical Possibilities For The N-Puzzle Problem, Zdravko Markov, Ingrid Russell, Todd W. Neller, Neli Zlatareva

Computer Science Faculty Publications

In this paper we present work on a project funded by the National Science Foundation with a goal of unifying the Artificial Intelligence (AI) course around the theme of machine learning. Our work involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Several hands-on laboratory projects that can be closely integrated into an introductory AI course have been developed. We present an overview of one of the projects and describe the associated curricular materials that have been developed. The project uses machine learning as …

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Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman Oct 2005

Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman

Computer Science Faculty Publications

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a …

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Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman Jan 2005

Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman

Computer Science Faculty Publications

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application …

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