It is my 3rd year volunteering at Lamar Middle School (Lewisville ISD), a nearby school to our house in Texas. I kept accepting the volunteering task of presenting at its career fair even after my son moved on to high school because I felt a sense of purpose of encouraging new generations in the field of computer science. I estimated to have presented to around 150 8th graders at the school in total between all three times combined. I would first talk about about my past and what I do at the time with Thomson Reuters, and then get into my passion – technologies. Below are some slides about that I presented between 2017 and 2018.
This year I learned to summarize my background in one slide
After that I went into technologies. In 2017 I brought with me to school various gadgets including a Raspberry Pi, a maker robot, an Arduino, and various gadgets so as to show case what one can with technology. In 2018, I thought of focusing on AI and began showcasing through videos what artificial intelligence is about.
I showed YouTube videos of a MIT cheetah robot, an Audi self-driving car and a chatbot interaction between Alexa and Siri
After that I felt that I am lecturing the students by describing how to become a great computer scientist
But this year, I decided to make things more interactive by showcasing actual implementations and making the topic more aligned to their age of playing games! Hence, I focused on how gaming and artificial intelligence align together and how it can be cool to be the next programmer of such algorithms rather than simply playing Fortnite, League of Legends, Dota 2 and more.
I focused on the topic of leveraging cameras to let computers learn from players or people. Showed Chintan from DeepGamingAI video “creating custom Fortnite dances with webcam and Deep Learning”
and went over the videos from Farza “DeepLeague: leveraging computer vision and deep learning on the League of Legends mini map + giving away a dataset of over 100,000 labeled images to further esports analytics research” and explained how machines can learn through cameras
After that, I brought out Amazon AWS Deep Lens that I had previously setup for the event by deploying the object recognition model that recognizes 20 objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor. I projected the camera at my audience and began explaining the process of neural networks, object detection, object classification, and what can reinforcement learning do if we expanded the project.
After that, I brought out my Google AIY Vision kit and explained how it works. To my bad luck I could not get the device to do more than beeping when it sees a face. Its code should make different colors when I make a happy face or a frown face. I had it working all along in the past, but this time I realized that I brought the wrong lens with me. Nevertheless, I explained how students can build such kits by showing them what’s in it.
Overall, there were 45 students this year that came to my all day demo. They would come in groups of 5 to 7 coming every 20 or 25 minutes. I would answer questions about my career and what I do at Thomson Reuters. Some asked my about my day to day work at Thomson Reuters, what my challenges and what makes me interested at work. My answers were honestly true and simple: I love to take challenging projects and focus on solving them with the team just like we would playing the next adventure game and solving its puzzles.
My key take from the whole experience is that most of the students love games, and that I used to make my point that they can leverage computers in whatever they love to do. Note that some said they want to do something in law enforcement, law, media, medicine, and not necessarily games or computer science. Even that, I felt that I had an answer which is computer programming and artificial intelligence is useful in whatever career they wish to do. Hopefully the students left my sessions with more interests in computers and programming. That’s the goal. I hope it worked for them just like computers worked for me in my entire life and continuous to do so through innovation and problem solving.