2020 Annual Review: The Year of Resilience
By: James Le
Tags: Goals, Personal Development, Annual Review, Reflections, 2020

1.4 — Side-Hustling

I got involved with the Full-Stack Deep Learning team around the summer — putting together the boot camp's online version and helping with the bi-weekly virtual meetup about production ML (check out our Twitter to know what our industry speakers share at these meetups). We will provide an updated version of the course next spring, so sign up if you’re interested!

I also started working part-time for Snorkel AI under the Marketing team around the fall — putting together its solutions page and crafting a blog post that recaps our CEO’s talk at the MLSys seminar. Snorkel provides a great solution called programmatic data labeling to tackle the challenges of getting good training data for machine learning pipelines, so follow our content for more information!

2 — Goals for 2021

2.1 — Diving Deeper Into MLOps

I’m excited to start a new role soon with Superb AI, an enterprise-grade training data platform. Training data is still the biggest bottleneck of the ML development process, especially since the importance of proper data operations within an ML organization continued to increase. The Superb AI team is building a product that could help facilitate awareness and education around building better AI, starting with proper data management principles. Given my increasing level of interest in production-level machine learning in the past year, this is a great opportunity to be in the trenches and navigate the ML tooling landscape.

If readers want to learn more about MLOps, I suggest joining this Slack community.

2.2 — Exploring AI Safety Research

Advances in AI within the past decade have brought excitement about its positive potential to transform science, medicine, transportation, along with concerns about the privacy, fairness, security, economic, and longer-term implications of powerful AI. It is worth giving serious thought to such potential challenges and risks. Many experts believe that there is a significant chance we’ll create artificially intelligent machines with abilities surpassing those of humans (superintelligence) sometime during this century. Researchers working on this problem aim to maximize the chance of a positive outcome while reducing the chance of catastrophe.

AI safety is a research line that addresses the problem of accidents in machine learning systems. Accidents here are defined as unintended and harmful behavior that may emerge from machine learning systems when we specify the wrong objective function, are not careful about the learning process, or commit other machine learning-related implementation errors. Under this broad theme, I am mostly interested in the value alignment problem. In an “aligned” AI system, the actions it pursues move the world towards states that humans want and away from states that humans don’t want. Next year, my goal is to explore literature that overcomes the conceptual or engineering issues need to create aligned AI more comprehensively.

If readers are interested in these ideas, I suggest looking at these AI Safety resources and this AI Alignment research overview.

2.3 — Getting More Involved With Effective Altruism

I have been a passive consumer of effective altruism content in the past three years, mostly via the EA newsletter and the 80,000-hour podcast. For those who are not familiar, effective altruism is a field that uses high-quality evidence and careful reasoning to work out how to help others as much as possible. It is also a community of people taking these answers seriously by focusing on the most promising solutions to the world’s most pressing problems.

This past November, I attended EA’s virtual student summit, a fantastic event that covered various problem profiles ranging from existential risk to animal welfare. I networked with tons of interesting young adults who are motivated to tackle these problems from that event. Since then, I have been participating in two reading groups on forecasting and global priorities research, where we read relevant content on these topics and discuss practical applications. Next year, my goal is to continue being a part of this community by attending more conferences/reading groups if time allows.

If readers are interested in being involved with this community, feel free to reach out, and I’ll add you to the Slack/Discord groups that I’m a part of!