Hi. My name is Gabriel. I鈥檓 13 and I like science, technology, engineering, analyzing art and board/video games. I have been attending 优蜜视频’s program for a few years and had my first summer as a camper last year.

This year I created a science project about trying to fix one of autism鈥檚 most pernicious side effects: spacing out.

As any 2E or autistic and ADD person can tell you, 鈥渟pacing out鈥� cripples their studies and hours and hours are lost and time becomes nebulous until they are aroused by a sound or perhaps a pat on the back. This causes workloads to seemingly multiply like tribbles!

My goal was to fix this problem by using tech 鈥� namely, artificial intelligence (AI) 鈥� that already existed to fix a conundrum that had seemingly been around forever. 鈥淭herefore, spacing out could be fixed by a device to treat and prevent the problem of losing focus in class via artificial intelligence augmented shock therapy from intelligent analysis of electroencephalography,鈥� as I stated in my research paper.

Translated, this means that it is possible to fix this problem by observing the excess static electricity from a gifted and/or autistic brain using a headset and running it through an AI, which attempts to find patterns between the live data and the examples. If it resembles the spaced out datasets, it stimulates the user via either the originally planned method of zapping them on the wrist 鈥� or, the more 鈥渟cience-fair-friendly鈥� method of intense vibration on the wrist.

After spending vast quantities of time researching and contacting many other experts, I attempted to assemble the software and headset from scratch by hacking a toy I own that uses electroencephalography.

First, I attempted to bore the user with pressing the space bar when they see a symbol, but the code was too complex for a Python newbie (or noob). The headset was tricky to solder, and things just did not work out. I have attempted to use many programs, including Github to organize files, Arduino to create the hacked headset, OSCulator to connect systems, Wekinator and WekiInputHelper to run the AI, Python to program with, and many more programs I have spent 200+ hours arranging in every way I can think of to try to get my theoretical system and ambitious dream running. However, I was unable to connect my rig, so I was unable to gather data.

When I started my project, I was notified that my project would not follow school convention, so I could expect to be shamed by the judges. What happened was radically different and a completely unexpected turn of events. After receiving 3rd place at my school, I was unexpectedly entered into the county fair as the only homeschooler, yet received 1st place and entry to state. I am currently preparing to attend the state fair.

Gabe currently volunteers his time to work with youth at his church club, AWANA, and he loves to ski, boogie board, and get completely trounced by waves and ocean. He actively participates in Boy Scouts, has never met a book that he doesn鈥檛 like, loves any and all things put on by 优蜜视频, and loves making animated movies and inventions.

If someone had asked me before my ninth grade summer if I knew what Artificial Intelligence was, I would have talked about Ava in the film Ex Machina. Ava, though technically a robot, has a consciousness, morals of her own, an ability to manipulate emotion and an ability to act on her own instinct. The film inspires unsettling uncertainty: if AI beings like Ava were to join society, would they be considered human? What role would we play in such a world?

In reality, however, AI is far from being as autonomous as Ava is. Though pop culture has popularized an image of AI as robots and superhumans, an AI algorithm can actually be as simple as a 5-line program that scans a massive amount of data, looking for patterns within this data. Using these patterns, the program can make predictions for new instances of related data. For example, Google Photos was trained with millions of photos of labelled entities, so that when given photos it had never seen before, it could categorize these photos by what they pictured.

Although this explanation generalizes certain aspects of AI, what鈥檚 significant is that the data used to train AI algorithms is chosen by human programmers. This means that programmers can, even if unconsciously or inadvertently, introduce their racial and other biases into the algorithms they create. This can sometimes be innocuous, but more often this can institutionalize these biases. For instance, in 2015, Google Photos鈥� photo categorization software mislabeled a black person as a gorilla. This is only one extremely infamous incident out of thousands.

One source of the problem is the astounding lack of diversity in the AI industry; at Google, only 2.5% of the workforce is black, only 3.6% is Latinx and only 0.3% is Native American. The lack of true demographic representation in the AI industry is an issue that is under-acknowledged but increasingly pressing as the use of AI technologies propagates across industries and sectors, including law enforcement, healthcare and scientific research.

AI4ALL was founded by world-renown Stanford professor Fei-Fei Li and then-graduate student Olga Russakovsky with the goal of educating high schoolers from underrepresented groups, including women, about AI and the humanitarian impact AI can have if done right. The organization鈥檚 aim is to diversify both the AI workforce and inform on the ways in which AI can be applied to social issues.

In 2017, I participated in a two-week , where close to thirty girls were exposed to a myriad of lectures by Stanford professors and other leaders in AI; we learned about how AI is applied to everything from education to linguistics research to robotics. We were also taught how to program our own AI algorithms addressing a social issue; one group identified cancerous tissue with Computer Vision, a subsect of AI, and my group used another subsect of AI, called Natural Language Processing, to categorize Tweets from the time of a natural disaster into topics like 鈥渇ood,鈥� 鈥渨ater,鈥� 鈥渙ther鈥� and 鈥渕isc.鈥� in order to expedite resource allocation for disaster relief. I learned from the experience that AI can be leveraged as a tool for social good and as a means of addressing community needs in unprecedented ways.

AI4ALL has been one of the most enriching programs I鈥檝e ever been able to participate in, and the alumni community is a group of some of the most talented yet approachable people I鈥檝e ever met. I鈥檝e made some of my closest friends through AI4ALL, and each is an inspiration; one founded a nonprofit STEM education organization that has taught over 500 children through over 70 workshops, and another started the Bay Area鈥檚 first all-girl high school hackathon. AI4ALL facilitates such efforts by providing a vast network of resources, support and funding.

In conclusion, I truly hope many members of the CDB community choose to apply to an AI4ALL program this summer. For those who are too old, there are still an infinite number of ways to begin understanding how to leverage AI algorithms for social change; AI4ALL is digitizing their curriculum on an OpenLearning platform that will be released in only a few months, and other platforms like Coursera and edX also offer online courses taught by university professors and AI experts.

AI could be the solution to predicting wildfires, ending racial discrimination in the justice system and understanding how to treat chronic illnesses. The industry just needs a diverse workforce – and you – to back it.

Am茅lie Buc is a 2015 Caroline D. Bradley Scholar. She is currently a junior at Trinity School in New York, New York. To learn more about the CDB Scholarship or apply for the 2019 class, visit the .

By Vinjay Vale, 2013 CDB Scholar

Today鈥檚 education system has created a rift between STEM (science, technology, engineering and math) and the humanities. It may seem counterintuitive, but听studying the humanities actually makes you a better scientist. Scholars of literature, philosophy, art, and history develop an understanding and appreciation for what it means to be human. I believe it is vital for scientists to study these fields, especially in a modern world full of rapid technological advances.

Through my own research, I experienced firsthand how the humanities can help scientific research.

My Regeneron STS project was on Artificial Intelligence, specifically teaching computer programs to learn and interpret geometric structures in visual scenes. The current state-of-the-art has poor spatial and compositional understanding; for example, it classifies a leopard-print couch as a leopard despite the furniture鈥檚 lack of a head and tail. Understanding how objects are composed of their parts is critical for complex vision tasks like visual reasoning. My approach to the problem deviated from the modern paradigm of neural networks. Believe it or not, I was inspired by a 1987 psychology paper by American vision scientist Irving Biederman on the Recognition By Components theory for human vision. The theory is based on breaking down complex objects into simple spatial elements called geons.

As I worked on my project, I spent multiple weeks outside the lab where I exclusively read books and papers, on subjects ranging from art to artificial intelligence, some suggested by my mentor Kevin Ellis (who I met through the听). In philosophy class, meanwhile, I learned about Plato鈥檚 Theory of Forms, and realized that I was trying to recreate a similar understanding of the visual world in computers.

Making connections between these diverse fields helped me develop the necessary insights to make headway on my interdisciplinary problem: teaching a computer to learn and interpret visual scenes.

My work falls into an emerging category of AI research called听explainable AI鈥娾�斺�妕hat is, building artificial intelligence systems that can articulate their thought processes to humans. This is an important area of AI safety, which merges ethics and philosophy with the more technical side of computer science.

In general, the gray area where ethics and tech meet is ripe for exploration. The underlying digital buzz permeating all aspects of our lives makes human moral judgement all the more essential. Think about social media, big pharma, self-driving cars鈥娾�斺�妔cientists who understand ethics have the capacity to make a positive impact on the world.听听听听that engaging in arts, history, and literature bolsters morality, compassion, and empathy. As a pianist, composer, and avid reader, I鈥檇 certainly like to think I鈥檝e benefited in this way.

My study of the humanities also has made me a better collaborator, by helping to sharpen my communication skills and compassion. I know that in the future I want to be part of interdisciplinary teams of scientists that tackle significant real-world problems. There鈥檚 no doubt that the interpersonal and collaborative skills that I learn from studying and discussing history, philosophy, and literature will be essential in the future.

Whether we鈥檙e talking about clinical trials (where every day human behavior can make or break a drug) or computer science (as in my experience drawn from art and psychology), understanding human nature is fundamental to doing science. When most people think about the future of humanity, they envision a universe where science has propelled our species towards a better tomorrow. But science alone is not enough to solve the world鈥檚 problems: that science needs to be done by听people who understand what it means to be human.

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