I haven't written this short description yet... but you're probably familiar with deep learning.
- Fri 13 Mar 2020The Valley of AI Trust
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.
- Mon 30 Dec 2019Machine Learning & Robotics: My (biased) 2019 State of the Field
My thoughts on the past year of progress in Robotics and Machine Learning.
- Wed 7 Aug 2019On the efficiency of Artificial Neural Networks versus the Brain
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.
- Fri 24 May 2019No Free Lunch and Neural Network Architecture
Machine learning must always balance flexibility and prior assumptions about the data. In neural networks, the network architecture codifies these prior assumptions, yet the precise relationship between them is opaque. Deep learning solutions are therefore difficult to build without a lot of trial and error, and neural nets are far from an out-of-the-box solution for most applications.
- Fri 25 Jan 2019Proxy metrics are everywhere in Machine Learning
Many machine learning systems are optimized using metrics that don't perfectly match the stated goals of the system. These so-called "proxy metrics" are incredibly useful, but must be used with caution.
- Wed 9 Jan 2019Massive Datasets and Generalization in ML
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.
- Sun 16 Dec 2018DeepMind's AlphaZero and The Real World
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.
- Sat 1 Dec 2018The Importance of Simulation in the Age of Deep Learning
An overview of the significance of simulation tools in the field of robotics and the promise and limitations of photorealistic simulators.
- Wed 5 Sep 2018Practical Guidelines for Getting Started with 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.
- Sun 2 Sep 2018For AI, translation is about more than language
Translation is about expressing the same underlying information in different ways, and modern machine learning is making incredibly rapid progress in this space.
- Sun 29 Jul 2018Bias in AI Happens When We Optimize the Wrong Thing
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.
- Wed 27 Dec 2017My Favorite Deep Learning Papers of 2017
Here are five deep learning papers I felt rose above the rest in 2017.