Is our autonomy affected by interacting with intelligent machines designed to persuade us? That’s what researchers at the University of Bristol attempted to find out through an analysis of the interaction between intelligent software agents and human users.

By Aviral Kumar

One of the primary factors behind the success of machine learning approaches in open world settings, such as image recognition and natural language processing, has been the ability of high-capacity deep neural network function approximators to learn generalizable models from large amounts of data. Deep reinforcement learning methods, however, require active online data collection, where the model actively interacts with its environment.

What’s hot on arXiv? Here are the most tweeted papers that were uploaded onto arXiv during December 2019.

Ever fancied finding out more about historical trends? Well, thanks to researchers at the University of Bristol, and their History Playground tool, anyone can analyse the content from a collection of historical British and American newspapers.

By David Gaddy

When learning to follow natural language instructions, neural networks tend to be very data hungry – they require a huge number of examples pairing language with actions in order to learn effectively. This post is about reducing those heavy data requirements by first watching actions in the environment before moving on to learning from language data. Inspired by the idea that it is easier to map language to meanings that have already been formed, we introduce a semi-supervised approach that aims to separate the formation of abstractions from the learning of language.

By Anusha Nagabandi

Dexterous manipulation with multi-fingered hands is a grand challenge in robotics: the versatility of the human hand is as yet unrivaled by the capabilities of robotic systems, and bridging this gap will enable more general and capable robots. Although some real-world tasks (like picking up a television remote or a screwdriver) can be accomplished with simple parallel jaw grippers, there are countless tasks (like functionally using the remote to change the channel or using the screwdriver to screw in a nail) in which dexterity enabled by redundant degrees of freedom is critical. In fact, dexterous manipulation is defined as being object-centric, with the goal of controlling object movement through precise control of forces and motions — something that is not possible without the ability to simultaneously impact the object from multiple directions. For example, using only two fingers to attempt common tasks such as opening the lid of a jar or hitting a nail with a hammer would quickly encounter the challenges of slippage, complex contact forces, and underactuation. Although dexterous multi-fingered hands can indeed enable flexibility and success of a wide range of manipulation skills, many of these more complex behaviors are also notoriously difficult to control: They require finely balancing contact forces, breaking and reestablishing contacts repeatedly, and maintaining control of unactuated objects. Success in such settings requires a sufficiently dexterous hand, as well as an intelligent policy that can endow such a hand with the appropriate control strategy. We study precisely this in our work on Deep Dynamics Models for Learning Dexterous Manipulation.

By Nicholas Carlini

It is important whenever designing new technologies to ask “how will this affect people’s privacy?” This topic is especially important with regard to machine learning, where machine learning models are often trained on sensitive user data and then released to the public. For example, in the last few years we have seen models trained on users’ private emails, text messages, and medical records.

This article covers two aspects of our upcoming USENIX Security paper that investigates to what extent neural networks memorize rare and unique aspects of their training data.

Specifically, we quantitatively study to what extent following problem actually occurs in practice:

Whether it’s tying a tie, making slime, fixing a leaky faucet, or some other daily task, millions of people watch how-to videos to learn new skills. Now, artificially intelligent (AI) agents at Georgia Tech are watching videos to learn how to make sandwiches and complete other routine tasks.

By Marvin Zhang and Sharad Vikram

Imagine a robot trying to learn how to stack blocks and push objects using visual inputs from a camera feed. In order to minimize cost and safety concerns, we want our robot to learn these skills with minimal interaction time, but efficient learning from complex sensory inputs such as images is difficult. This work introduces SOLAR, a new model-based reinforcement learning (RL) method that can learn skills – including manipulation tasks on a real Sawyer robot arm – directly from visual inputs with under an hour of interaction. To our knowledge, SOLAR is the most efficient RL method for solving real world image-based robotics tasks.

By Anusha Nagabandi and Ignasi Clavera

Humans have the ability to seamlessly adapt to changes in their environments: adults can learn to walk on crutches in just a few seconds, people can adapt almost instantaneously to picking up an object that is unexpectedly heavy, and children who can walk on flat ground can quickly adapt their gait to walk uphill without having to relearn how to walk. This adaptation is critical for functioning in the real world.