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AI Findings

STEM Collab. final project. Spring 2013. Class of 2014.
by

J S

on 17 May 2013

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Transcript of AI Findings

By Tyler Compton, Jack Ketcham, Daniel La Rosa, Jeremiah Sweeney, Alex Thiel Artificial Intelligence Problem Solving Independent Variable:
The type of Artificial Intelligence algorithm that is applied to solve the same puzzle that the control group of humans solved. Designing the experiment The literature reviews focused one four main topics: Artificial intelligence, and history of AI, genetic algorithms, neural networks, the human testing environment, and miscellaneous software. Literature Reviewed Collecting the Data Results Based on final percentage comparisons, the neural network was more similar to the human group than the genetic algorithm Data Analysis Conclusion, Summery Center for Research, Engineering, Science, and Technology (CREST) Class of 2014 Introduction Thank you for your time! Sources This experiment is a study comparing artificial intelligence (AI) algorithms' learning strategies with that of a human brain. The current understanding of the human brain is very limited. Our group is looking to try a different way to study the brain. We decided to write computer programs, artificial intelligence algorithms that would solve a particular problem. Then, we would see how humans solved the problem versus how each AI solved the problem. If one AI is more similiar humans than the other, then something about the algorithm has a more 'human' aspect to it. Dependent Variable:
How each problem solving strategy of the two types of Artificial Intelligence Algorithms compare to the average human’s problem solving strategy when applied to a single type of puzzle. Hypothesis:
If different types of Artificial Intelligence Algorithms both play the same puzzle, an Artificial Neural Network will have the most similar problem solving strategy to a human's problem solving strategy, than the other Artificial Intelligence types. Artificial Intelligence Neural Networks Genetic Algorithms Software Human Testing Environment Argumentation in AI Challenges in the ethics of human to AI communication Machine Learning History Improvement How they work: Flexibility with this algorithm Initial state Evaluate fitness "Reproduce" Next generation Based on human brain Learns from a database Data analyzer Limits to creating a full brain Slightly better brain functions at higher temperatures Low temperatures prevent damage One useless article Disregarded in tests Agent-based computing Reverse engineering intelligence Games used to test intelligence Refuting approach in using games? Start End A total of 80 humans ran this maze a total of 600+ times The raw data was re-organized into this: Data for each test subject were collected as a string of letters where D=down, L=left, R=right, and U=up. For each square, the following table was made: Further calculations were done to the data utilizing original programs, and it was found that the similarities between subjects to be... 12.72% difference from humans 18.36% difference from humans Neural Network Genetic Algorithm Data that had been collected was also organized into three movement "heatmaps" Neural Network Genetic Algorithm Human Set There were some issues with the website. Errors Neural network possibly skewed Site cookie issue Overall, we believe that this experiment went great! A Basic Introduction To Neural Networks. (n.d.). UW-Madison Computer
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Algorithms. generation5 -At the forefront of Artificial Intelligence. Retrieved January 25, 2013, from http://www.generation5.org/content/2000/g To view the full report, please visit teamtyro.com
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