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Editorial

Success is a difficult thing to measure and foster. Good advisors help all of their students succeed.

[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.

My thoughts on the past year of progress in Robotics and Machine Learning.

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.

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.

[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.

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.

[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.

[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.

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.

[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.

[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.

[General Computing] Having too many specific tasks is limiting and can stymie the creative impulses that give rise to fantastic employees.

[General Computing] The tech culture is broken, and Uber is just the tip of an iceberg.

[General Computing] This time of year can be tough, so reach out to a friend to check in.

[Machine Learning] More complicated ≠ better. In many circumstances, using the most sophisticated approaches to Machine Learning (like deep learning) may not be worthwhile.

[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.