For indicating content which reflects our thoughts and opinions on issues of interest to us.

  • The Bottom Third
    Success is a difficult thing to measure and foster. Good advisors help all of their students succeed.
  • The Valley of AI Trust
    [Machine Learning] Particularly for safety-critical applications or the automation of tasks that can directly impact quality of life, we must be careful to avoid the valley of AI trust—the dip in overall safety caused by premature adoption of automation.
  • Machine Learning & Robotics: My (biased) 2019 State of the Field
    My thoughts on the past year of progress in Robotics and Machine Learning.
  • Anyone sufficiently experienced is indistinguishable from a magician
    Surrounded by brilliant people, I see my friends and colleagues produce surprising insights seemingly from thin air. Only through dedication to a craft can one gain the depth of understanding necessary to demonstrate this level of mastery.
  • The "Myths List" is a communication antipattern
    I rarely find these Myth List articles compelling, yet many such articles could benefit from a more clearly presented statement of the author’s viewpoint.
  • On the efficiency of Artificial Neural Networks versus the Brain
    [Machine Learning] Recent ire from the media has focused on the high-power consumption of artificial neural nets (ANNs), yet popular discussion frequently conflates training and testing. Here, I aim to clarify the ways in which conversations involving the relative efficiency of ANNs and the human brain often miss the mark.
  • Don't Let Your Hobbies Die
    I’ve discovered over time is that many of my skills and passions have developed only through little side-projects that never see the light of day. The combination of the satisfaction I get from freely exploring a new idea and the occasional long-term reward make me feel as if I am constantly growing.
  • Massive Datasets & Generalization in ML
    [Machine Learning] Big, publically available datasets are great. Yet many practitioners who seek to use models pretrained on this data need to ask themselves how informative the data is likely to be for their purposes. Dataset bias and task specificity are important factors to keep in mind.
  • DeepMind's AlphaZero and The Real World
    [Machine Learning] Using DeepMind’s AlphaZero AI to solve real problems will require a change in the way computers represent and think about the world. In this post, we discuss how abstract models of the world can be used for better AI decision making and discuss recent work of ours that proposes such a model for the task of navigation.
  • On Blaming Cosmic Rays
    In my last lab, cosmic radiation was one of the many inescapable factors that limited how precisely we could measure the output of our laser; only by understanding what limits performance can one hope to have a reasonable estimate of what is possible.
  • Practical Guidelines for Getting Started with Machine Learning
    [Machine Learning] The potential advantages of AI are many, and using machine learning to accelerate your business may outweigh potential pitfalls. If you are looking to use machine learning tools, here are a few guidelines you should keep in mind.
  • Bias in AI Happens When We Optimize the Wrong Thing
    [Machine Learning] Bias is a pervasive problem in AI. Only by discouraging machine learning systems from exploiting a certain bias can we expect such a system to avoid doing so.
  • Motivated People Don't Need a Job Title
    [General Computing] Having too many specific tasks is limiting and can stymie the creative impulses that give rise to fantastic employees.
  • Y.A.U.S (Yet Another Uber Scandal)
    [General Computing] The tech culture is broken, and Uber is just the tip of an iceberg.
  • Be Caring & Reach Out
    [General Computing] This time of year can be tough, so reach out to a friend to check in.
  • Have You Tried Using a 'Nearest Neighbor Search'?
    [Machine Learning] More complicated ≠ better. In many circumstances, using the most sophisticated approaches to Machine Learning (like deep learning) may not be worthwhile.
  • How We Teach Programming, and Where We're Going Wrong
    [General Computing] Programming matters. As more of the country recognizes the importance of the skill, an increasing number of methods for teaching programming concepts have popped up. However, the emphasis should lean towards how to apply programming across disciplines.