AI will change the world. Who will change AI? We will.

By Sophia Stiles

Editor’s Note: The following blog is a special guest post by a recent graduate of Berkeley BAIR’s AI4ALL summer program for high school students.

AI4ALL is a nonprofit dedicated to increasing diversity and inclusion in AI education, research, development, and policy.

The idea for AI4ALL began in early 2015 with Prof. Olga Russakovsky, then a Stanford University Ph.D. student, AI researcher Prof. Fei-Fei Li, and Rick Sommer – Executive Director of Stanford Pre-Collegiate Studies. They founded SAILORS as a summer outreach program for high school girls to learn about human-centered AI, which later became AI4ALL. In 2016, Prof. Anca Dragan started the Berkeley/BAIR AI4ALL camp, geared towards high school students from underserved communities.

Before I Started the Program

When I discovered AI4ALL during the spring semester, I was curious to learn more. I knew that AI had the potential to change everything and that it was something I’d love to be a part of. To prepare for the program, I read up on the BAIR faculty and checked out the BAIR student profiles. I watched Stuart Russell’s TED talk “3 principles for creating safer AI.” The people were all so highly accomplished. And their ideas seemed either super technical, or at the other end of the spectrum, they sounded more like topics from the philosophy department than the EECS department. I realized I had no idea what to expect but decided just to give it a try and get started.

The First Day

After logging into my first day of AI4ALL on Zoom, I was pleasantly surprised by the number of eager and welcoming faces. Among them were Tim Hurt, Eva Chao, Rachel Walsh, Ben Frazier, and Maya Maliviya. They were all there to help us feel comfortable and succeed!

We started off with a quick ice-breaker introduction activity. This particularly resonated with me because it wasn’t like the typical type you’d have on the first day of school. Instead, we were divided into virtual breakout rooms and asked to find as many similarities among our peers as possible. The program was already off to a great start! Within just a few minutes, I learned that five other people in the room have a sibling, have taken chemistry, like pizza, and had a quarantine haircut just like me! It was a great way to encourage collaboration and bonding.

Next, we were joined by BAIR lab professor Anca Dragan for a talk about AI. The presentation was hard to forget because of her passion, her curiosity, and the depth of her knowledge. Anca kickstarted the talk by explaining some examples of AI in real life. This was already so useful because it immediately cleared up the misconceptions about AI. In addition, it allowed everyone to have common, shared learning and not feel excluded if they didn’t know as much about AI before starting the program.

Another element of Anca’s presentation that stood out was her description of an AI game. The game is simple: a robot is positioned in a grid and gains points for reaching gems and loses points for falling in fire pits. Anca walked us through the AI “backstory” of the game. The robot’s goal is to maximize the points earned. As the game’s allotted time decreases, the robot takes less cautious paths (ex: avoiding fire pits) and places its primary focus on gaining points. We learned that this idea of optimization is a core part of all AI systems.

By the end of the day, we were immersed in a Python notebook while conversing with peers in a Breakout Room. AI4ALL equipped us with Python notebooks through Google Colab so we would all be on the same page when talking about code. I really enjoyed this part of the program because it was open-ended and the material was presented in such a clean and convenient fashion. As I read through the content and completed the coding exercises, I couldn’t help but also notice the amusing GIFs embedded here and there! What a memorable way to begin learning AI!

Midway Through the Program

Early on Day 3 of the 4 day AI4ALL program, I began to really understand the significance of AI. Through the eye-opening lecture presentations and discussions, I realized that AI really is everywhere! It’s in our YouTube recommendations, Spotify algorithms, Google Maps, and robotic surgery equipment. That range of applications is part of what makes AI so promising. AI really can be for everyone, whether you’re a developer or a user — it’s not limited to people with mad coding skills. Once I got acquainted with the basics of the subject, I began to see how almost any idea can be reshaped with AI.

I also learned that AI is often different from the way it’s presented in the media. Almost everyone is familiar with the idea of robots taking over jobs, but that isn’t necessarily what will happen. AI still has a long way to go before it will truly “take over the world,” as hypothesized. AI is a work in progress. Like its creators, it has biases. It can unintentionally discriminate. It has adversaries and struggles to find insights with incomplete data. Still, AI has the power to change basic aspects of our world. This is why it is so important to have people from as many backgrounds as possible involved in AI. Introducing people from many different backgrounds into the field allows for a better range of ideas and can help reduce the number of missed “red flags” that might later have a big impact on the lives of real people.

By the End of the Program…

The last two days of AI4ALL sped by in a blur. I couldn’t help but notice how well the program was organized. There was a balanced combination of lectures, discussion, and individual work time for coding and collaborating. I also loved how the content at the end of the program reinforced the content from the start. That aspect of the program’s structure made it so much easier to ask questions, remember ideas, and apply to future activities.

I particularly saw this idea of reinforcement demonstrated in Professor Kamalika Chaudhuri’s presentation about AI adversaries. She explained how AI algorithms could be manipulated so that an image correctly identified with 50% confidence as a panda would then identify the same image with 90% confidence as a gibbon. On the previous day, Professor Jacob Steinhardt explained how images that appeared similar to the human eye can be tweaked to disrupt AI’s algorithm. In another example, Kamalika described how image pixels could be stored as training data in the form of vectors. This idea built off of Tim Hurt’s earlier point that training data is a result of an input being translated into computer language (e.g. a vector ), and then mapped to a label output ().

After most of the lectures were done, we began working on our group projects. We were divided into five groups, with each group under the instruction of a Berkeley Ph.D. student. I chose to be in the “Overcooked” group, which was with first-year EECS student Micah Carroll. Micah walked us through the game he’s been using in his research, called Overcooked-AI. Simply put, Overcooked-AI is all about getting the most number of onion soups delivered while cooking in a cramped kitchen.

Once again, we used Colab Notebooks to learn and experiment with the game’s code. Micah patiently took us through the basics of imitation learning, reinforcement learning, decision trees, and graph fitting/displays. He was so open to questions and never hesitated to help! The hours we spent together breezed by, and soon enough I found myself crafting up a final presentation recapping all that I learned. Time really passes when you’re enjoying and learning.

Final Thoughts

In less than a week, the AI4ALL program has shaped my view of AI and my learning process. The lectures, advice panels, and project groups came together to make an unforgettable experience. Beyond learning what AI is and how it works, I now realize that everyone has the potential to explore AI. All you have to do is start. And so, the next time you hear someone say “AI will change the world, but who will change AI?”, you can say with confidence “we will!”

Thank you so much to everyone who made AI4ALL possible!

This article was initially published on the BAIR blog, and appears here with the authors’ permission.

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