OpenAI News page 104

OpenAI News January 30, 2017 08:00

Team update

The OpenAI team is now 45 people. Together, we’re pushing the frontier of AI capabilities—whether by validating novel ideas, creating new software systems, or deploying machine learning on robots.

Topics: Models

Entities: OpenAI Models

OpenAI News January 19, 2017 08:00

PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications

PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at this https URL⁠(opens in a new window). Our implementation contains a number of...

Topics: Models Infrastructure

Entities: Models Infrastructure

OpenAI News December 21, 2016 08:00

Faulty reward functions in the wild

Reinforcement learning algorithms can break in surprising, counterintuitive ways. In this post we’ll explore one failure mode, which is where you misspecify your reward function.

OpenAI News December 05, 2016 08:00

Universe

We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.

Topics: Infrastructure

Entities: Infrastructure

OpenAI News November 15, 2016 08:00

#Exploration: A study of count-based exploration for deep reinforcement learning

Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods...

OpenAI News November 15, 2016 08:00

OpenAI and Microsoft

We’re working with Microsoft to start running most of our large-scale experiments on Azure.

Topics: Models

Entities: OpenAI Models Microsoft

OpenAI News November 14, 2016 08:00

On the quantitative analysis of decoder-based generative models

The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities. A shared component of many powerful generative models is a decoder network, a parametric deep neural net that defines...

Topics: Models

Entities: Models

OpenAI News November 11, 2016 08:00

A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models

Generative adversarial networks (GANs) are a recently proposed class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator. While the idea of learning cost functions is...

Topics: Models Infrastructure

Entities: Models Infrastructure

OpenAI News November 09, 2016 08:00

RL²: Fast reinforcement learning via slow reinforcement learning

Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their...

OpenAI News November 08, 2016 08:00

Variational lossy autoencoder

Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure...

Topics: Models

Entities: Models

OpenAI News November 02, 2016 07:00

Extensions and limitations of the neural GPU

The Neural GPU is a recent model that can learn algorithms such as multi-digit binary addition and binary multiplication in a way that generalizes to inputs of arbitrary length. We show that there are two simple ways of improving the performance of the...

Topics: Infrastructure

Entities: Infrastructure

OpenAI News October 18, 2016 07:00

Semi-supervised knowledge transfer for deep learning from private training data

Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the model may therefore...

Topics: Infrastructure

Entities: Infrastructure