New Opportunities For New Deep Learning Practitioners09 Jan 2018 Rachel Thomas [
Dawit Haile fought against the odds when he decided to study computer science in Eritrea, East Africa, despite having no internet connectivity. His perseverance paid off, first landing a job with the Eritrean government department of education, and later as an engineer in Lithuania. Today, Dawit is a data scientist in the San Francisco Bay Area, and he credits this new job to the knowledge and experience he gained from fast.ai. On the side, he’s building an algorithm to translate between English and his native language of Tigrinya.
Dawit is just one of many impressive fast.ai fellows who participated in our deep learning course this fall. It is entirely thanks to the sponsors who answered our call that these fellowships were possible: Amazon Web Services, Menlo Ventures, Domino Data Labs, Facebook, Natalia Baryshnikova, Lucien Carrol (with an employer match from Cisco), and the continued support of the University of San Francisco Data Institute.
We had over 70 incredibly qualified applicants for the diversity scholarships, including senior software engineers, several start-up founders, a researcher who had published in Nature, and many who are active in teaching, volunteer, and community organizations. It was a delight to be able to offer as many scholarships as we could with the support of our sponsors. Here are the stories of some of our fellows.
Adriana Fuentes is co-founder and technical lead at a stealth startup and president of the Society of Hispanic Professional Engineers at Silicon Valley (SHPE). She is applying knowledge gained from fast.ai to building a small autonomous vehicle which she will use to engage low socioeconomic students with the field of AI, as part of her volunteer work with SHPE. Previously, she built large scale distributed systems and databases at Hewlett Packard and was an engineer for hybrid vehicles, navigation systems, and infotainment at Ford Motor Company.
Sarada Lee was an accountant with no programming experience when she first encountered machine learning at a hackathon in 2016 and came away fascinated. She taught herself to code and founded the Perth Machine Learning Group in Perth, Australia. What began as a small group of friends meeting in Sarada’s living room grew to a community of 280 members within a year. The group worked through the online fast.ai course, won hackathons, attracted corporate sponsors, and hosts a number of speakers. Members have used image classification techniques on a utility project to potentially save millions of dollars. Sarada is now working on a new algorithm to read and understand large corpuses of documents, as well as developing new initiatives to help increase diversity in AI.
Tiffany Liu, a bioinformatics scientist researching brain tumor treatment, told us that the course provided hands-on help in her work building a multi-task neural network that simultaneously predicts both the tumor region and its associated clinical information.
Nahid Alam, founder of litehouse.io for voice-first user experience for home automation, is now using AI in her work as a senior software engineer at Cisco, mentoring with Backstage Capital, and is listed as one of the top women in AI to follow. Nahid told us “everyone talks about concepts, [but] resource for coders/engineers are rare.” Fast.ai is filling this gap. She wrote Automate the Boring Task: Chatbots in Enterprise Software about her work with chatbot frameworks, conversational AI products, and bot analytics products.
We’re in awe of Dawit, Adriana, Tiffany, Sarada, Nahid, and all our diversity fellows, and we are so grateful to our sponsors for making this possible. While many are bemoaning a supposed “talent shortage” in AI, it is encouraging to see these companies and individuals take concrete action.
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