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\n<\/p>\n
\n![](\"https:\/\/bair.berkeley.edu\/static\/blog\/ai4all\/ai4all-gif-overcooked.gif\")
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Editor\u2019s Note: The following blog is a special guest post by a recent graduate AI4ALL is a nonprofit dedicated to increasing diversity and inclusion in AI The idea for AI4ALL<\/a> began in early 2015 with Prof. Olga Russakovsky, then <\/p>\n When I discovered AI4ALL during the spring semester, I was curious to learn After logging into my first day of AI4ALL on Zoom, I was pleasantly surprised We started off with a quick ice-breaker introduction activity. This Next, we were joined by BAIR lab professor Anca Dragan for a talk about AI. The Another element of Anca\u2019s presentation that stood out was her description By the end of the day, we were immersed in a Python notebook while conversing Early on Day 3 of the 4 day AI4ALL program, I began to really understand the I also learned that AI is often different from the way it\u2019s presented in the The last two days of AI4ALL sped by in a blur. I couldn\u2019t help but notice how I particularly saw this idea of reinforcement demonstrated in Professor After most of the lectures were done, we began working on our group projects. Once again, we used Colab Notebooks to learn and experiment with the game\u2019s In less than a week, the AI4ALL program has shaped my view of AI and my Thank you so much to everyone who made AI4ALL possible!<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":" http:\/\/bair.berkeley.edu\/blog\/2020\/08\/16\/ai4all\/<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/17"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=17"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/17\/revisions"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=17"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=17"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=17"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nof Berkeley BAIR\u2019s AI4ALL summer program for high school students.<\/em><\/p>\n
\neducation, research, development, and policy.<\/p>\n
\na Stanford University Ph.D. student, AI researcher Prof. Fei-Fei Li, and Rick
\nSommer \u2013 Executive Director of Stanford Pre-Collegiate Studies. They founded
\nSAILORS as a summer outreach program for high school girls to learn about
\nhuman-centered AI, which later became AI4ALL. In 2016, Prof. Anca Dragan
\nstarted the Berkeley\/BAIR AI4ALL camp, geared towards high school students from
\nunderserved communities.<\/p>\nBefore I Started the Program<\/h2>\n
\nmore. I knew that AI had the potential to change everything and that it was
\nsomething I\u2019d love to be a part of. To prepare for the program, I read up on
\nthe BAIR faculty<\/a> and checked out the BAIR student<\/a> profiles. I watched
\nStuart Russell\u2019s TED talk \u201c3 principles for creating safer AI<\/a>.\u201d The people
\nwere all so highly accomplished. And their ideas seemed either super
\ntechnical, or at the other end of the spectrum, they sounded more like topics from
\nthe philosophy department than the EECS department. I realized I had no idea
\nwhat to expect but decided just to give it a try and get started.<\/p>\nThe First Day<\/h2>\n
\nby the number of eager and welcoming faces. Among them were Tim Hurt, Eva Chao,
\nRachel Walsh, Ben Frazier, and Maya Maliviya. They were all there to help us
\nfeel comfortable and succeed!<\/p>\n
\nparticularly resonated with me because it wasn\u2019t like the typical type you\u2019d
\nhave on the first day of school. Instead, we were divided into virtual breakout
\nrooms and asked to find as many similarities among our peers as possible.
\nThe program was already off to a great start! Within just a few minutes, I
\nlearned that five other people in the room have a sibling, have taken
\nchemistry, like pizza, and had a quarantine haircut just like me! It was a
\ngreat way to encourage collaboration and bonding.<\/p>\n
\npresentation was hard to forget because of her passion, her curiosity, and the
\ndepth of her knowledge. Anca kickstarted the talk by explaining some examples
\nof AI in real life. This was already so useful because it immediately cleared
\nup the misconceptions about AI. In addition, it allowed everyone to have
\ncommon, shared learning and not feel excluded if they didn\u2019t know as much about
\nAI before starting the program.<\/p>\n
\nof an AI game. The game is simple: a robot is positioned in a grid and gains
\npoints for reaching gems and loses points for falling in fire pits. Anca walked
\nus through the AI \u201cbackstory\u201d of the game. The robot\u2019s goal is to maximize the
\npoints earned. As the game\u2019s allotted time decreases, the robot takes less
\ncautious paths (ex: avoiding fire pits) and places its primary focus on gaining
\npoints. We learned that this idea of optimization is a core part of all AI
\nsystems.<\/p>\n
\nwith peers in a Breakout Room. AI4ALL equipped us with Python notebooks through
\nGoogle Colab<\/a> so we would all be on the same page when talking about code.
\nI really enjoyed this part of the program because it was open-ended and the
\nmaterial was presented in such a clean and convenient fashion. As I read
\nthrough the content and completed the coding exercises, I couldn\u2019t help but
\nalso notice the amusing GIFs embedded here and there! What a memorable way to
\nbegin learning AI!<\/p>\nMidway Through the Program<\/h2>\n
\nsignificance of AI. Through the eye-opening lecture presentations and
\ndiscussions, I realized that AI really is everywhere! It\u2019s in our YouTube
\nrecommendations<\/a>, Spotify algorithms<\/a>, Google Maps<\/a>, and robotic
\nsurgery equipment. That range of applications is part of what makes AI so
\npromising. AI really can be for everyone, whether you\u2019re a developer or a user \u2014
\nit\u2019s not limited to people with mad coding skills. Once I got acquainted with
\nthe basics of the subject, I began to see how almost any idea can be reshaped
\nwith AI.<\/p>\n
\nmedia. Almost everyone is familiar with the idea of robots taking over jobs,
\nbut that isn\u2019t necessarily what will happen. AI still has a long way to go
\nbefore it will truly \u201ctake over the world,\u201d as hypothesized. AI is a work in
\nprogress. Like its creators, it has biases. It can unintentionally
\ndiscriminate. It has adversaries and struggles to find insights with incomplete
\ndata. Still, AI has the power to change basic aspects of our world. This is why
\nit is so important to have people from as many backgrounds as possible involved
\nin AI. Introducing people from many different backgrounds into the field allows
\nfor a better range of ideas and can help reduce the number of missed \u201cred
\nflags\u201d that might later have a big impact on the lives of real people.<\/p>\nBy the End of the Program\u2026<\/h2>\n
\nwell the program was organized. There was a balanced combination of lectures,
\ndiscussion, and individual work time for coding and collaborating. I also loved
\nhow the content at the end of the program reinforced the content from the
\nstart. That aspect of the program\u2019s structure made it so much easier to ask
\nquestions, remember ideas, and apply to future activities.<\/p>\n
\nKamalika Chaduri\u2019s presentation about AI adversaries. She explained how AI
\nalgorithms could be manipulated so that an image correctly identified with 50%
\nconfidence as a panda would then identify the same image with 90% confidence as
\na gibbon. On the previous day, Professor Jacob Steinhardt explained how images
\nthat appeared similar to the human eye can be tweaked to disrupt AI\u2019s
\nalgorithm. In another example, Kamalika described how image pixels could be
\nstored as training data in the form of vectors. This idea built off of Tim
\nHurt\u2019s earlier point that training data is a result of an input being
\ntranslated into computer language (e.g. a vector $x$), and then mapped to a label
\noutput ($y$).<\/p>\n
\nWe were divided into five groups, with each group under the instruction of a
\nBerkeley Ph.D. student. I chose to be in the \u201cOvercooked\u201d group, which was with
\nfirst-year EECS student Micah Carroll. Micah walked us through the game he\u2019s been using in his research,
\ncalled Overcooked-AI<\/a><\/em>. Simply put, Overcooked-AI<\/em> is all about getting the most number
\nof onion soups delivered while cooking in a cramped kitchen.<\/p>\n
\ncode. Micah patiently took us through the basics of imitation learning,
\nreinforcement learning, decision trees, and graph fitting\/displays. He was so
\nopen to questions and never hesitated to help! The hours we spent together
\nbreezed by, and soon enough I found myself crafting up a final presentation
\nrecapping all that I learned. Time really passes when you\u2019re enjoying and
\nlearning.<\/p>\nFinal Thoughts<\/h2>\n
\nlearning process. The lectures, advice panels, and project groups came together
\nto make an unforgettable experience. Beyond learning what AI is and how it
\nworks, I now realize that everyone has the potential to explore AI. All you
\nhave to do is start. And so, the next time you hear someone say \u201cAI will change
\nthe world, but who will change AI?\u201d, you can say with confidence \u201cwe will!\u201d<\/p>\n