Earlier today we announced some changes that will accelerate our progress in AI and help us develop more capable AI systems safely and responsibly. Below is a recap of what DeepMind CEO Demis Hassabis shared with employees: Hi Team When we launched DeepMind back in 2010, many people thought general AI was a farfetched science fiction technology that was decades away from being a reality. Now, we live in a time in which AI research…
Diffusion models are used for generating high-quality samples from complex data distributions. Discriminative diffusion models aim to leverage the principles of diffusion models for tasks like classification or regression, where the goal is to predict labels or outputs for a given input data. By leveraging the principles of diffusion models, discriminative diffusion models offer advantages such as better handling of uncertainty, robustness to noise, and the potential to capture complex dependencies within the data. Generative…
Responsibility & Safety Published 24 April 2023 Authors Iason Gabriel and Kevin McKee Drawing from philosophy to identify fair principles for ethical AI As artificial intelligence (AI) becomes more powerful and more deeply integrated into our lives, the questions of how it is used and deployed are all the more important. What values guide AI? Whose values are they? And how are they selected? These questions shed light on the role played by principles –…
In generative modeling, diffusion models (DMs) have assumed a pivotal role, facilitating recent progress in producing high-quality picture and video synthesis. Scalability and iterativeness are two of DMs’ main advantages; they enable them to do intricate tasks like picture creation from free-form text cues. Unfortunately, the many sample steps required for the iterative inference process currently hinder the real-time use of DMs. On the other hand, the single-step formulation and intrinsic speed of Generative Adversarial…