First and Last Name/s of Presenters

Rachel GlowniakFollow

Mentor/s

Tina Romansky

Participation Type

Paper Talk

Abstract

The recent commercialization of affordable and simplified tools has brought artificial intelligence out of the movies and research labs and into our homes and classrooms. Using Alexa, Siri, and ChatGPT has been simplified for the masses, but the algorithms behind these applications are steeped in mathematics and computer science. This paper will focus on gradient descent, an example of an optimization algorithm. The foundation of the algorithm as well as its applications and limitations will be explored. Finally, we will present a classic example of the gradient descent algorithm. To further analyze the method, the algorithm has been implemented in code and studied for efficiency.

College and Major available

Mathematics

Location

Session 5: Digital Commons & Martire Room 217

Start Day/Time

4-25-2024 12:30 PM

End Day/Time

4-25-2024 1:45 PM

Students' Information

Rachel Glowniak - Mathematics Major, Honors student, Graduating May 2024

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Prize Categories

Best Multidisciplinary Research or Collaboration, Most Scholarly Impact or Potential, Most Creative

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Apr 25th, 12:30 PM Apr 25th, 1:45 PM

How Artificial Intelligence Works: Using the Gradient Descent Algorithm

Session 5: Digital Commons & Martire Room 217

The recent commercialization of affordable and simplified tools has brought artificial intelligence out of the movies and research labs and into our homes and classrooms. Using Alexa, Siri, and ChatGPT has been simplified for the masses, but the algorithms behind these applications are steeped in mathematics and computer science. This paper will focus on gradient descent, an example of an optimization algorithm. The foundation of the algorithm as well as its applications and limitations will be explored. Finally, we will present a classic example of the gradient descent algorithm. To further analyze the method, the algorithm has been implemented in code and studied for efficiency.

 

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