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MIT researchers are using artificial intelligence to design new proteins that go beyond those found in nature. They developed machine-learning algorithms that can generate proteins with specific structural features, which could be used to make materials that have certain mechanical properties, like stiffness or elasticity. Such biologically inspired materials could potentially replace materials made from…

Posted by Lauren Wilcox, Senior Staff Research Scientist, on behalf of the Technology, AI, Society and Culture Team Google sees AI as a foundational and transformational technology, with recent advances in generative AI technologies, such as LaMDA, PaLM, Imagen, Parti, MusicLM, and similar machine learning (ML) models, some of which are now being incorporated into…

In the vast, expansive skies where birds once ruled supreme, a new crop of aviators is taking flight. These pioneers of the air are not living creatures, but rather a product of deliberate innovation: drones. But these aren’t your typical flying bots, humming around like mechanical bees. Rather, they’re avian-inspired marvels that soar through the…

OpenAI has just over a week to comply with European data protection laws following a temporary ban in Italy and a slew of investigations in other EU countries. If it fails, it could face hefty fines, be forced to delete data, or even be banned.  But experts have told MIT Technology Review that it will…

Posted by Badih Ghazi, Staff Research Scientist, and Nachiappan Valliappan, Staff Software Engineer, Google Research Recently, differential privacy (DP) has emerged as a mathematically robust notion of user privacy for data aggregation and machine learning (ML), with practical deployments including the 2022 US Census and in industry. Over the last few years, we have open-sourced…

Posted by Matthew Streeter, Software Engineer, Google Research Derivatives play a central role in optimization and machine learning. By locally approximating a training loss, derivatives guide an optimizer toward lower values of the loss. Automatic differentiation frameworks such as TensorFlow, PyTorch, and JAX are an essential part of modern machine learning, making it feasible to…

The release of ChatGPT has sent shock waves through the halls of higher education. Universities have rushed to release guidelines on how it can be used in the classroom. Professors have taken to social media to share a spectrum of AI policies. And students—whether or not they’ll admit it—have cautiously experimented with the idea of allowing…

Posted by Sergey Levine, Research Scientist, and Alexander Herzog, Staff Research Software Engineer, Google Research, Brain Team Reinforcement learning (RL) can enable robots to learn complex behaviors through trial-and-error interaction, getting better and better over time. Several of our prior works explored how RL can enable intricate robotic skills, such as robotic grasping, multi-task learning,…

The emergence of generative artificial intelligence has ignited a deep philosophical exploration into the nature of consciousness, creativity, and authorship. As we bear witness to new advances in the field, it’s increasingly apparent that these synthetic agents possess a remarkable capacity to create, iterate, and challenge our traditional notions of intelligence. But what does it…

Posted by Sherry Yang, Research Scientist, and Yilun Du, Student Researcher, Google Research, Brain Team Building models that solve a diverse set of tasks has become a dominant paradigm in the domains of vision and language. In natural language processing, large pre-trained models, such as PaLM, GPT-3 and Gopher, have demonstrated remarkable zero-shot learning of…