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Inspirit A.I. Final Project.
Image 1) I explained the concept of class probability mapping in the field of computer programming for an audience of over 200 members. Through an interactive presentation, stakeholders of all ages and backgrounds were able to engage and grasp the content I was presenting, despite the concept being complex and having lots of moving parts.
The Context.
By the Fall of my sophomore year of high school, a new buzzword had seemingly popped up overnight: artificial intelligence. Curious, I looked to learn more about the field and enrolled in the Inspirit A.I. Scholars Program.
The Problem.
I along with a team of 5 other scholars were tasked with developing object detection software for an autonomous vehicle. However, our initial approach involved manual calibration of collected data, which was too slow to safely operate the automobile.
The Solution.
Using the YOLO object detection system, our team was able to improve the software to an appropriate safety level. Written in Python, our code was based in Google Collaboratory designed with modularity in mind.
The Outcome.
By switching the object detection method, the software was able to process data 20.7% compared to the previous iteration. My team was also earned the viewer's choice award from an audience of over 200 people!
Image 2) Part of our software involved reformatting the acquired image data to a resolution compatible to be read.
Image 3) Although we didn't actually get to run our software on a real autonomous vehicle, our code with some adjustments would turn out to something like this.
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