This lossless compressor achieves a much higher compression rate on English texts than general purpose compressors. Its typical compression ratio is 15% (number of output bits divided by the number of input bits). The compression is achieved by using the probability of the next word computed by the GPT-2 language model released by OpenAI. It is a neural network of 345 million parameters based on the Transformer architecture (the largest GPT-2 model of 1.5 billion parameters brings marginal improvement when compressing short messages). An arithmetic coder generates the bit stream.

Recently, a team of researchers from UC Berkeley and Adobe Research proposed a new machine learning model known as Swapping Autoencoder, which has the capability to perform image manipulation. The key idea of this research is to encode a picture into 2 independent components and then enforce that any swapped combination maps to a realistic image. Deep generative models such as GANs or Generative Adversarial Networks and Variational Autoencoders (VAEs) have gained much traction by the researchers over the years. According to the researchers, deep generative models have become a popular technique when it comes to producing realistic images from randomly sampled data. However, such deep generative models face various challenges when used for a controllable manipulation of existing images.

Jeff Clune is the former Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming, a Senior Research Manager and founding member of Uber AI Labs, and currently a Research Team Leader at OpenAI. Jeff focuses on robotics and training neural networks via deep learning and deep reinforcement learning. He has also researched open questions in evolutionary biology using computational models of evolution, including studying the evolutionary origins of modularity, hierarchy, and evolvability. Prior to becoming a professor, he was a Research Scientist at Cornell University, received a PhD in computer science and an MA in philosophy from Michigan State University, and received a BA in philosophy from the University of Michigan. More about Jeff's research can be found at JeffClune.com

OpenAI has been at the center of some of the biggest advancements in artificial inteligence(AI) in recent years. Created by industry luminaries such as Elon Musk and Sam Altman, OpenAI started as a non-profit organization with a focus of advancing AI research. After Altman took over as CEO last year, OpenAI transitioned to a capped profit structure and attracted $1 billion investment from Microsoft. The next step in the evolution of OpenAI seems to be to build up its commercial muscle and that's what they seem to be doing. Earlier this week, OpenAI unveiled an API product that exposes endpoints for some of its most sucessful language models including the controversial GPT-3.

Industry 4.0 systems have a high demand for optimization in their tasks, whether to minimize cost, maximize production, or even synchronize their actuators to finish or speed up the manufacture of a product. Those challenges make industrial environments a suitable scenario to apply all modern reinforcement learning (RL) concepts. The main difficulty, however, is the lack of that industrial environments. In this way, this work presents the concept and the implementation of a tool that allows us to convert any dynamic system modeled as an FSM to the open-source Gym wrapper. After that, it is possible to employ any RL methods to optimize any desired task. In the first tests of the proposed tool, we show traditional Q-learning and Deep Q-learning methods running over two simple environments.

Synthesia believes that synthetic media and deep learning will create a new generation of content creation tools that are "are empowering, effective, …

Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.e. given a (possibly corrupted) signal, we wish to recover the latent vector that generated it. Building upon sparse representation theory, we define conditions that are applicable to any inversion algorithm (gradient descent, deep encoder, etc.), under which such generative models are invertible with a unique solution. Importantly, the proposed analysis is applicable to any trained model, and does not depend on Gaussian i.i.d. weights. Furthermore, we introduce two layer-wise inversion pursuit algorithms for trained generative networks of arbitrary depth, and accompany these with recovery guarantees. Finally, we validate our theoretical results numerically and show that our method outperforms gradient descent when inverting such generators, both for clean and corrupted signals.

AWS DeepComposer gives you a creative way to get started with machine learning (ML) and generative AI techniques. AWS DeepComposer recently launched a new generative AI algorithm called autoregressive convolutional neural network (AR-CNN), which allows you to generate music in the style of Bach. In this blog post, we show a few examples of how you can use the AR-CNN algorithm to generate interesting compositions in the style of Bach and explain how the algorithm's parameters impact the characteristics of the generated composition. The AR-CNN algorithm provided in the AWS DeepComposer console offers a variety of parameters to generate unique compositions, such as the number of iterations and the maximum number of notes to add to or remove from the input melody to generate unique compositions. The parameter values will directly impact the extent to which you modify the input melody.

We've contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and Society, Center for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems. Developers can use these tools to provide evidence that AI systems are safe, secure, fair, or privacy-preserving. Users, policymakers, and civil society can use these tools to evaluate AI development processes. While a growing number of organizations have articulated ethics principles to guide their AI development process, it can be difficult for those outside of an organization to verify whether the organization's AI systems reflect those principles in practice.

Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets. Heterogeneity arises from data containing different types of features (categorical, ordinal, continuous, etc.) and features of the same type having different marginal distributions. We propose an extension of variational autoencoders (VAEs) called VAEM to handle such heterogeneous data. VAEM is a deep generative model that is trained in a two stage manner such that the first stage provides a more uniform representation of the data to the second stage, thereby sidestepping the problems caused by heterogeneous data. We provide extensions of VAEM to handle partially observed data, and demonstrate its performance in data generation, missing data prediction and sequential feature selection tasks. Our results show that VAEM broadens the range of real-world applications where deep generative models can be successfully deployed.