Memorization in Recurrent Neural Networks (RNNs) continues to pose a challenge in many applications. We’d like RNNs to be able to store information over many timesteps and retrieve it when it becomes relevant — but vanilla RNNs often struggle to do this. Several network architectures have been proposed to tackle aspects of this problem, such as Long-Short-Term […]

Introduction Neural networks can learn to classify images more accurately than any system humans directly design. This raises a natural question: What have these networks learned that allows them to classify images so well? Feature visualization is a thread of research that tries to answer this question by letting us “see through the eyes” of

The goal of long-term artificial intelligence (AI) safety is to ensure that advanced AI systems are reliably aligned with human values — that they reliably do things that people want them to do.Roughly by human values we mean whatever it is that causes people to choose one option over another in each case, suitably corrected by reflection,

A little over a year ago, we formally launched Distill as an open-access scientific journal.Distill operated informally for several months before launching itself as a journal. It’s been an exciting ride since then! To give some very concrete metrics, Distill has had over a million unique readers, and more than 2.9 million views. Distill papers

Neural networks trained to classify images have a remarkable — and surprising! — capacity to generate images. Techniques such as DeepDream , style transfer, and feature visualization leverage this capacity as a powerful tool for exploring the inner workings of neural networks, and to fuel a small artistic movement based on neural art. All these techniques work in roughly

Many real-world problems require integrating multiple sources of information. Sometimes these problems involve multiple, distinct modalities of information — vision, language, audio, etc. — as is required to understand a scene in a movie or answer a question about an image. Other times, these problems involve multiple sources of the same kind of input, i.e. when summarizing several documents

With the growing success of neural networks, there is a corresponding need to be able to explain their decisions — including building confidence about how they will behave in the real-world, detecting model bias, and for scientific curiosity. In order to do so, we need to both construct deep abstractions and reify (or instantiate) them in rich

What are computers for? Historically, different answers to this question – that is, different visions of computing – have helped inspire and determine the computing systems humanity has ultimately built. Consider the early electronic computers. ENIAC, the world’s first general-purpose electronic computer, was commissioned to compute artillery firing tables for the United States Army. Other

Introduction Consider speech recognition. We have a dataset of audio clips and corresponding transcripts. Unfortunately, we don’t know how the characters in the transcript align to the audio. This makes training a speech recognizer harder than it might at first seem. Without this alignment, the simple approaches aren’t available to us. We could devise a

There is a growing sense that neural networks need to be interpretable to humans. The field of neural network interpretability has formed in response to these concerns. As it matures, two major threads of research have begun to coalesce: feature visualization and attribution. Feature visualization answers questions about what a network — or parts of a network — are