Browse press releases
The newest releases come first, each with its full text here and a link to its original. Try the filters to narrow them down.
- OpenAIOpenAI and Broadcom announce strategic collaboration to deploy 10 gigawatts of OpenAI-designed AI acceleratorsCompanies: OpenAIBroadcomOpenAI and Broadcom announce a multi-year partnership to deploy 10 gigawatts of OpenAI-designed AI accelerators, co-developing next-generation systems and Ethernet solutions to power scalable, energy-efficient AI infrastructure by 2029.
- OpenAIOpenAI’s response to the Department of Energy on AI infrastructureCompanies: OpenAIWhy infrastructure is destiny and how the US can seize it.
- OpenAIImplicit generation and generalization methods for energy-based modelsCompanies: OpenAIWe’ve made progress towards stable and scalable training of energy-based models (EBMs) resulting in better sample quality and generalization ability than existing models. Generation in EBMs spends more compute to continually refine its answers and doing so can generate samples competitive with GANs at low temperatures, while also having mode coverage guarantees of likelihood-based models. We hope these findings stimulate further research into this promising class of models.
- OpenAILearning concepts with energy functionsCompanies: OpenAIWe’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. Our model learns these concepts after only five demonstrations. We also show cross-domain transfer: we use concepts learned in a 2d particle environment to solve tasks on a 3-dimensional physics-based robot.
- OpenAIImproving GANs using optimal transportCompanies: OpenAIAbstract We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance
- AdobeAdobe Bengaluru Campus Fulfills 100% of its Power Demand Through Renewable EnergyCompanies: AdobeWith this, Adobe furthers its commitment towards implementing best practices to drive environmental sustainability Adobe India, today announced that...
- OpenAIA connection between generative adversarial networks, inverse reinforcement learning, and energy-based modelsCompanies: OpenAIAbstract 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 relatively new to the field of generative modeling, learning costs has long been studied in control and reinforcement learning (RL) domains, typically for imit