Matching Items (176)

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On Consciousness in Artificial and Non-Biological Systems

Description

The problems addressed by the philosophy of mind arise anew when we consider the possibility of consciousness in artificial and non-biological systems. In this thesis I adapt traditional theories of

The problems addressed by the philosophy of mind arise anew when we consider the possibility of consciousness in artificial and non-biological systems. In this thesis I adapt traditional theories of mind and theories meaning in natural language to the new problems posed by these non-human systems, attempting answers to the questions: Can a given system think? Can a given system have subjective experiences? Can a given system have intentionality? Together these capture most of the typical features of consciousness discussed in the literature. Hence, answers to these questions have the potential to form a basis for a robust and practical future theory of consciousness in non-human systems, and I argue that the broad classes of functionalist and emergentist theories of mind are those worth considering more in the literature. The answers given in this thesis through the lenses of these two classes of theories are not exclusive, and may interact with or be supportive of one another. The functionalist account tells us that a system can be thinking, sentient, and intentional just in case it exhibits the correct structure, and the emergentist account tells us how this structure might arise from previous systems of the right complexity. What these necessary structures or complexities are depends on which functionalist and emergentist accounts we accept, and so this thesis also addresses some of the possibilities allowed for by certain variants of these theories. What we shall obtain, in the end, are some prima facie reasons for believing that certain systems can be conscious in the ways described above.

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Date Created
  • 2017-05

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Reddit Predicts Swings in the Stock Market: r/WorldNews and Using Machine Learning to Predict Changes in Stock Price

Description

In this paper, I will show that news headlines of global events can predict changes in stock price by using Machine Learning and eight years of data from r/WorldNews, a

In this paper, I will show that news headlines of global events can predict changes in stock price by using Machine Learning and eight years of data from r/WorldNews, a popular forum on Reddit.com. My data is confined to the top 25 daily posts on the forum, and due to the implicit filtering mechanism in the online community, these 25 posts are representative of the most popular news headlines and influential global events of the day. Hence, these posts shine a light on how large-scale social and political events affect the stock market. Using a Logistic Regression and a Naive Bayes classifier, I am able to predict with approximately 85% accuracy a binary change in stock price using term-feature vectors gathered from the news headlines. The accuracy, precision and recall results closely rival the best models in this field of research. In addition to the results, I will also describe the mathematical underpinnings of the two models; preceded by a general investigation of the intersection between the multiple academic disciplines related to this project. These range from social to computer science and from statistics to philosophy. The goal of this additional discussion is to further illustrate the interdisciplinary nature of the research and hopefully inspire a non-monolithic mindset when further investigations are pursued.

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Created

Date Created
  • 2016-12

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Data Management Behind Machine Learning

Description

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.

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Created

Date Created
  • 2018-05

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HA-MRA: A Human-Aware Multi-Robot Architecture

Description

This thesis describes a multi-robot architecture which allows teams of robots to work with humans to complete tasks. The multi-agent architecture was built using Robot Operating System and Python. This

This thesis describes a multi-robot architecture which allows teams of robots to work with humans to complete tasks. The multi-agent architecture was built using Robot Operating System and Python. This architecture was designed modularly, allowing the use of different planners and robots. The system automatically replans when robots connect or disconnect. The system was demonstrated on two real robots, a Fetch and a PeopleBot, by conducting a surveillance task on the fifth floor of the Computer Science building at Arizona State University. The next part of the system includes extensions for teaming with humans. An Android application was created to serve as the interface between the system and human teammates. This application provides a way for the system to communicate with humans in the loop. In addition, it sends location information of the human teammates to the system so that goal recognition can be performed. This goal recognition allows the generation of human-aware plans. This capability was demonstrated in a mock search and rescue scenario using the Fetch to locate a missing teammate.

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Created

Date Created
  • 2017-05

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Analyzing LinkedIn Profiles Using Machine Learning

Description

Understanding the necessary skills required to work in an industry is a difficult task with many potential uses. By being able to predict the industry of a person based on

Understanding the necessary skills required to work in an industry is a difficult task with many potential uses. By being able to predict the industry of a person based on their skills, professional social networks could make searching better with automated tagging, advertisers can target more carefully, and students can better find a career path that fits their skillset. The aim in this project is to apply deep learning to the world of professional networking. Deep Learning is a type of machine learning that has recently been making breakthroughs in the analysis of complex datasets that previously were not of much use. Initially the goal was to apply deep learning to the skills-to-company relationship, but a lack of quality data required a change to the skills-to-industry relationship. To accomplish the new goal, a database of LinkedIn profiles that are part of various industries was gathered and processed. From this dataset a model was created to take a list of skills and output an industry that people with those skills work in. Such a model has value in the insights that it forms allowing candidates to: determine what industry fits a skillset, identify key skills for industries, and locate which industries possible candidates may best fit in. Various models were trained and tested on a skill to industry dataset. The model was able to learn similarities between industries, and predict the most likely industries for each profiles skillset.

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Created

Date Created
  • 2017-12

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The Evolution of Automotive Engineering Technology in correlation between the Human Factors

Description

The focus of this research paper is understanding the impacts of human factors on the technology innovations in automobiles and the direction our society is headed. There will be an

The focus of this research paper is understanding the impacts of human factors on the technology innovations in automobiles and the direction our society is headed. There will be an assessment of our current state and the possible solutions to combat the issue of creating technology advancements for automobiles that cater towards the human factors. There will be an introduction on the history of the first automobile invented to provide an understanding of the what the first automobile consisted of and will continue discussing the technological innovations that were implemented due to human factors. Diving into the types of technological innovations such as the ignition system, car radio, the power steering system, and self-driving, it will show the progression of the technological advancements that was implemented in relation to the human factors that was prominent among society. From there, it is important to understand what human factors and the concept of human factor engineering are. It will provide a better understanding of why humans have created technology in relation to the human factors. Then, there will be an introduction of the mobile phone industry history/timeline as a comparison to show the impacts of how human factors have had on the development of the technology in mobile phones and how heavily it catered towards human factors. There will be a discussion of the 3 key human factors that have been catered towards the development and implementation of technology in automobiles. They are selecting the path that requires the least cognitive effort, overestimating the performance of technology, and reducing the attention due to an automated system being put into place. Lastly, is understanding that if we create or implement technology such as self-driving, it should not solely be for comfort and ease of use, but for the overall efficient use of transportation in the future. This way humans would not rely heavily too much on the technology and limit the effect that human factors have on us.

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Created

Date Created
  • 2020-05

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ReL GoalD (Reinforcement Learning for Goal Dependencies)

Description

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.

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Created

Date Created
  • 2018-05

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LudoNarrare: A Model for Verb Based Interactive Storytelling

Description

Instead of providing the illusion of agency to a reader via a tree or network of prewritten, branching paths, an interactive story should treat the reader as a player who

Instead of providing the illusion of agency to a reader via a tree or network of prewritten, branching paths, an interactive story should treat the reader as a player who has meaningful influence on the story. An interactive story can accomplish this task by giving the player a large toolset for expression in the plot. LudoNarrare, an engine for interactive storytelling, puts "verbs" in this toolset. Verbs are contextual choices of action given to agents in a story that result in narrative events. This paper begins with an analysis and statement of the problem of creating interactive stories. From here, various attempts to solve this problem, ranging from commercial video games to academic research, are given a brief overview to give context to what paths have already been forged. With the background set, the model of interactive storytelling that the research behind LudoNarrare led to is exposed in detail. The section exploring this model contains explanations on what storyworlds are and how they are structured. It then discusses the way these storyworlds can be brought to life. The exposition on the LudoNarrare model finally wraps up by considering the way storyworlds created around this model can be designed. After the concepts of LudoNarrare are explored in the abstract, the story of the engine's research and development and the specifics of its software implementation are given. With LudoNarrare fully explained, the focus then turns to plans for evaluation of its quality in terms of entertainment value, robustness, and performance. To conclude, possible further paths of investigation for LudoNarrare and its model of interactive storytelling are proposed to inspire those who wish to continue in the spirit of the project.

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Created

Date Created
  • 2015-12

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Introduction of Medical Devices Using Adaptive Machine Learning Algorithms for Artificial Intelligence in the Healthcare Market

Description

The adaptive artificial-intelligence (AI) medical device industry is a novel industry in the United States offering innovations to the healthcare field. The rapid expansion of this industry in recent years

The adaptive artificial-intelligence (AI) medical device industry is a novel industry in the United States offering innovations to the healthcare field. The rapid expansion of this industry in recent years has drawn attention from multiple stakeholders causing a heated debate about how to introduce these innovations into the market while maintaining patient safety and treatment efficacy. Since early 2019, the U.S. Food and Drug Administration (FDA) has been releasing statements in regards to the improvement of regulation for this new technology, but has yet to take further actions. Dilemmas including 1) a difficult regulatory process, 2) a heightening financial burden and 3) looming liability issues, are reasons adaptive AI medical devices have struggled to be advanced. By conducting a thorough analysis of these 3 issues, recognizing the intricacies of them separately and together, this study develops a better understanding of the landscape adaptive AI technology is facing and provides a clearer picture for the future of the industry.

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Created

Date Created
  • 2020-05

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Pandora: A Play by Abbey Toye

Description

Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity?

Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer must navigate the loss of her creation, and Pandora must navigate their new world. The original premiere run was March 27-28, 2018, original cast: Caitlin Andelora, Rikki Tremblay, and Michael Tristano Jr.

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Agent

Created

Date Created
  • 2018-05