Getting the ball rolling

As I reached out to professionals last week, I am excited to say that I was able to meet with quite a few professionals in both exact and related fields to artificial intelligence. Firstly, through the business symposium, I was able to meet so many interesting and insightful professionals that were able to provide their own advice regarding my field. Their insights really enabled me to broaden my view beyond the technical aspects and aspirations of artificial intelligence and adopt a more scientific approach regarding skepticism of marketing ploys regarding artificial intelligence. Additionally, they provided general advice regarding college and life. We enter the expansive and sometimes daunting world of adulthood. Frankly, life will most likely play out as people plan it, and people need to be ready to handle curveballs that will be thrown at them. This school of thought is applicable on many levels. Technical projects that I have worked on almost never go as planned. This ongoing pandemic has rattled society in ways previously unimaginable. Ultimately, these curveballs will continue to be thrown this way and I will always be better off taking a swing at them even if it's a miss. Towards the end of the week, I was able to conduct a virtual, hour-long Zoom session with a 4th year PhD student at UC Berkeley working for the Berkeley Artificial Intelligence Research Laboratory (BAIR). Prior to the interview, I was able to read through his expansive research into applications and implications of artificial intelligence and I was extremely impressed by his contributions. He specialized in robotics machine learning using reinforcement learning techniques. This research theme immediately piqued my interest as I moderately employed machine learning in robotics at my old school. While its immediate uses were a novelty to me and simply a learning experience, I was excited to see how he was able to take these fundamental concepts to the next level. He was able to help my understanding of reinforcement learning as a method of 'prediction' that used a reward/penalty system to judge the effectiveness of intermediate actions by algorithms. We further discussed his research and he even provided his own resources, languages (python), libraries, and advice regarding my original work which I am thoroughly grateful for. His perspective in technology given his close proximity to Silicon Valley, the heart of technology, was extremely valuable and I hope to keep in touch with him.