In the most recent edition of The Economist, an article titled “New schemes teach the masses to learn AI” appeared. The article profiles the efforts of fast.ai, a Bay Area non-profit that aims to demystify deep learning and equip the masses to use the technology. I was mentioned in the article as an example of the success of this approach — “A graduate from fast.ai’s first year, Sara Hooker, was hired into Google’s highly competitive ai residency program after finishing the course, having never worked on deep learning before.”
I have spent the last few days feeling uneasy about the article. On the one hand, I do not want to distract from the recognition of fast.ai. Rachel and Jeremy are both people that I admire, and their work to provide access to thousands of students across the world is both needed and one of the first programs of its kind. However, not voicing my unease is equally problematic since it endorses a simplified narrative that is misleading for others who seek to enter this field.
It is true that I both attended the first session of fast.ai and that I was subsequently offered a role as an AI Resident at Google Brain. Nevertheless, attributing my success to a part-time evening 12-week course (parts 1 and 2) creates the false impression of a quick Cinderella story for anyone who wants to teach themselves machine learning. Furthermore, this implication minimizes my own effort and journey.
For some time, I have had clarity about what I love to do. I was not exposed to either machine learning or computer science during my undergraduate degree. I grew up in Africa, in Mozambique, Lesotho, Swaziland and South Africa. My family currently lives in Monrovia, Liberia. My first trip to the US was a flight to Minnesota, where I had accepted a scholarship to attend a small liberal arts school called Carleton College. I arrived for international student orientation without ever having seen the campus before. Coming from Africa, I also did not have any reference point for understanding how cold Minnesota’s winters would be. Despite the severe weather, I enjoyed a wonderful four years studying a liberal arts curriculum and majoring in Economics. My dream had been to be an economist for the World Bank. This was in part because the most technical people I was exposed to during my childhood were economists from organizations like the International Monetary Fund and the World Food Program.
I decided to delay applying for a PhD in economics until a few years after graduation, instead accepting an offer to work with PhD economists in the Bay Area on antitrust issues. We applied economic modeling and statistics to real world cases and datasets to assess whether price fixing had taken place or to determine whether a firm was misusing its power to harm consumers.
First Delta Analytics Presentation to Local Bay Area Non-Profits. Early 2014.
A few months after I moved to San Francisco, myself and some fellow economists (Jonathan Wang, Cecilia Cheng, Asim Manizada, Tom Shannahan, and Eytan Schindelhaim) started meeting on weekends to volunteer for nonprofits. We didn’t really know what we were doing, but we thought offering our data skills to non-profits for free might be a useful way of giving back. We emailed a Bay Area non-profit listserv and were amazed by the number of responses. We clearly saw that many non-profits possessed data, but they were uncertain on how to use it to accelerate their impact. That year, we registered as a non-profit called Delta Analytics and were joined by volunteers that worked as engineers, data analysts and researchers. Delta remains entirely run by volunteers, does not have any full time staff, and offers all engagements with non-profits for free. By the time I applied to the Google AI Residency, we had completed projects with over 30 non-profits.
Second cohort of Delta Analytics Volunteers. 2016.
Delta was a turning point in my journey because the data of the partners we worked with was often messy and unstructured. The assumptions required to impose a linear model (such as homoscedasticity, no autocorrelation, normal distribution) were rarely present. I saw first-hand how linear functions, a favorite tool of economists, fell short. I decided that I wanted to know more about more complex forms of modeling.
I joined a startup called Udemy as a data analyst. At the time, Udemy was a 150-person startup that aimed to help anyone learn anything. My boss carved out projects for me that were challenging, had wide impact and pushed me technically. One of the key projects I worked on during my first year was collecting data, developing and deploying Udemy’s first spam detection algorithm.
Working on projects like spam detection convinced me that I wanted to grow technically as an engineer. I wanted to be able to iterate quickly and have end-to-end control over the models I worked on, including deploying them into production. This required becoming proficient at coding. I had started my career working in STATA (a statistical package similar to MATLAB), R, and SQL. Now, I wanted to become fluent at Python. I took part-time night classes at Hackbright and started waking up at 4 am most days to practice coding before work. This is still a regular habit, although now I do so to read papers not directly related to my field of research and carve out time for new areas I want to learn about.
After half a year, while I had improved at coding, I was still not proficient enough to interview as an engineer. At the time, the Udemy data science team was separate from my Analytics team. Udemy invested in me. They approved my transfer to engineering where I started as the first non-PhD data scientist. I worked on recommendation algorithms and learned how to deploy models at scale to millions of people. The move to engineering accelerated my technical growth and allowed me to continue to improve as an engineer.
Udemy data team.
In parallel to my growth at Udemy, I was still working on Delta projects. There are two that I particularly enjoyed, the first (alongside Steven Troxler, Kago Kagichiri, Moses Mutuku) was working with Eneza Education, a ed-tech social impact company in Nairobi, Kenya. Eneza used pre-smartphone technology to empower more than 4 million primary and secondary students to access practice quizzes by mobile texting. Eneza’s data provided wonderful insights into cell phone usage in Kenya as well as the community’s learning practices. We worked on identifying difficult quizzes that deterred student activity and improved tailoring pathways to individual need and ability. The second project was with Rainforest Connection (alongside Sean McPherson, Stepan Zapf, Steven Troxler, Cassandra Jacobs, Christopher Kaushaar) where the goal was to identify illegal deforestation using streamed audio from the rainforest. We worked on infrastructure to convert the audio into spectrograms. Once converted, we structured the problem as image classification and used convolutional neural networks to detect whether chainsaws were present in the audio stream. We also worked on models to better triangulate the sound detected by the recycled cellphones.
Source:
https://medium.com/@sarahooker/slow-learning-d9463f6a800b
