Generating unlimited diverse training environments for future general agents

Today we introduce Genie 2, a foundation world model capable of generating an endless variety of action-controllable, playable 3D environments for training and evaluating embodied agents. Based on a single prompt image, it can be played by a human or AI agent using keyboard and mouse inputs.

New research analyzes the misuse of multimodal generative AI today, in order to help build safer and more responsible technologies

Generative artificial intelligence (AI) models that can produce image, text, audio, video and more are enabling a new era of creativity and commercial opportunity. Yet, as these capabilities grow, so does the potential for their misuse, including manipulation, fraud, bullying or harassment.

As part of our commitment to develop and use AI responsibly, we published a new paper, in partnership with Jigsaw and Google.org, analyzing how generative AI technologies are being misused today. Teams across Google are using this and other research to develop better safeguards for our generative AI technologies, amongst other safety initiatives.

Together, we gathered and analyzed nearly 200 media reports capturing public incidents of misuse, published between January 2023 and March 2024. From these reports, we defined and categorized common tactics for misusing generative AI and found novel patterns in how these technologies are being exploited or compromised.

By clarifying the current threats and tactics used across different types of generative AI outputs, our work can help shape AI governance and guide companies like Google and others building AI technologies in developing more comprehensive safety evaluations and mitigation strategies.

We introduce Genie, a foundation world model trained from Internet videos that can generate an endless variety of playable (action-controllable) worlds from synthetic images, photographs, and even sketches.

Our AI system surpasses the state-of-the-art approach for geometry problems, advancing AI reasoning in mathematics

Reflecting the Olympic spirit of ancient Greece, the International Mathematical Olympiad is a modern-day arena for the world's brightest high-school mathematicians. The competition not only showcases young talent, but has emerged as a testing ground for advanced AI systems in math and reasoning.

In a paper published today in Nature, we introduce AlphaGeometry, an AI system that solves complex geometry problems at a level approaching a human Olympiad gold-medalist - a breakthrough in AI performance. In a benchmarking test of 30 Olympiad geometry problems, AlphaGeometry solved 25 within the standard Olympiad time limit. For comparison, the previous state-of-the-art system solved 10 of these geometry problems, and the average human gold medalist solved 25.9 problems.

It’s been one year since we released and open sourced AlphaFold, our AI system to predict the 3D structure of a protein just from its 1D amino acid sequence, and created the AlphaFold Protein Structure Database (AlphaFold DB) to freely share this scientific knowledge with the world. Proteins are the building blocks of life, they underpin every biological process in every living thing. And, because a protein’s shape is closely linked with its function, knowing a protein’s structure unlocks a greater understanding of what it does and how it works. We hoped this groundbreaking resource would help accelerate scientific research and discovery globally, and that other teams could learn from and build on the advances we made with AlphaFold to create further breakthroughs. That hope has become a reality far quicker than we had dared to dream. Just twelve months later, AlphaFold has been accessed by more than half a million researchers and used to accelerate progress on important real-world problems ranging from plastic pollution to antibiotic resistance.

Today, I’m incredibly excited to share the next stage of this journey. In partnership with EMBL’s European Bioinformatics Institute (EMBL-EBI), we’re now releasing predicted structures for nearly all catalogued proteins known to science, which will expand the AlphaFold DB by over 200x - from nearly 1 million structures to over 200 million structures - with the potential to dramatically increase our understanding of biology.