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.

The applications of this system are truly remarkable. GPT 2 has the potential to completely reshape the landscape of article writing, as human input could very soon start to be replaced by AI technologies. This could be realised in writing and research assistance, as vast amounts of data could be summarised and rewritten without requiring human input. However, as is so often the case with AI, there exist various issues with the system. Primarily, the misuse of such technology could allow cybercriminals to more easily carry out malicious activity e.g. by automating the generation of spam content. Furthermore, despite being ahead of its competitors, OpenAI admits to having observed various failures of GPT-2 e.g.

Originally, the artificial intelligence (AI) development and research organization, OpenAI, was founded as a nonprofit with the ambitious mission of ensuring artificial general intelligence would benefit all humanity. Since then, much has happened, starting with original founder Elon Musk leaving OpenAI's board in 2018. In July of 2019, the narrative changed yet again after the company received a $1 billion investment from Microsoft. The company exists today as a "capped-profit" organization. Most notably during this time, the company developed a text-generating language system it chose to not release "due to our concerns about malicious applications of the technology," then subsequently released said system.

"It is not a dream, it is a simple feat of scientific engineering, only expensive -- blind, faint-hearted, doubting world!" Discovering a new medicine is a billion-dollar research endeavour. At least, it can draw in the money as the results are kind of self-explanatory; life-saving. But, in case of AI, which is usually riddled by speculations and scepticism, it is an uphill task for the researchers to sell their idea or to churn profits to keep fueling their AI labs. For example, OpenAI, which started as a non-profit research lab, changed its stance when it partnered with Microsoft. A year later, they have announced that they are making all their exotic deep learning innovations available to the public through an API that comes with a price tag.