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Posted by Rishabh Tiwari, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research Machine learning models in the real world are often trained on limited data that may contain unintended statistical biases. For example, in the CELEBA celebrity image dataset, a disproportionate number of female celebrities have blond hair, leading to classifiers incorrectly predicting “blond”…

Robots are good at certain tasks. They’re great at picking up and moving objects, for example, and they’re even getting better at cooking. But while robots may easily complete tasks like these in a laboratory, getting them to work in an unfamiliar environment where there’s little data available is a real challenge. Now, a new…

Posted by Yang Zhao, Senior Software Engineer, and Tingbo Hou, Senior Staff Software Engineer, Core ML Text-to-image diffusion models have shown exceptional capabilities in generating high-quality images from text prompts. However, leading models feature billions of parameters and are consequently expensive to run, requiring powerful desktops or servers (e.g., Stable Diffusion, DALL·E, and Imagen). While…

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Hi, Taylor. I can only imagine how you must be feeling after sexually explicit deepfake videos of you went viral on X. Disgusted. Distressed, perhaps. Humiliated, even.  I’m really sorry this is…

Last week, sexually explicit images of Taylor Swift, one of the world’s biggest pop stars, went viral online. Millions of people viewed nonconsensual deepfake porn of Swift on the social media platform X, formerly known as Twitter. X has since taken the drastic step of blocking all searches for Taylor Swift to try to get…

Posted by Jiefeng Chen, Student Researcher, and Jinsung Yoon, Research Scientist, Cloud AI Team In the fast-evolving landscape of artificial intelligence, large language models (LLMs) have revolutionized the way we interact with machines, pushing the boundaries of natural language understanding and generation to unprecedented heights. Yet, the leap into high-stakes decision-making applications remains a chasm…

Posted by Ameya Velingker, Research Scientist, Google Research, and Balaji Venkatachalam, Software Engineer, Google Graphs, in which objects and their relations are represented as nodes (or vertices) and edges (or links) between pairs of nodes, are ubiquitous in computing and machine learning (ML). For example, social networks, road networks, and molecular structure and interactions are…

Posted by Manish Gupta, Staff Software Engineer, Google Research AI-driven technologies are weaving themselves into the fabric of our daily routines, with the potential to enhance our access to knowledge and boost our overall productivity. The backbone of these applications lies in large language models (LLMs). LLMs are memory-intensive and typically require specialized hardware accelerators…

O’Reilly’s Generative AI in the Enterprise survey reported that people have trouble coming up with appropriate enterprise use cases for AI. Why is it hard to come up with appropriate use cases? Chip Huyen, co-founder of Claypot AI and author of Designing Machine Learning Systems, will talk about why many companies have trouble coming up…