Posted by Thomas Lew, Research Intern, and Montserrat Gonzalez Arenas, Research Engineer, Google Research, Brain Team Over the past several years, the capabilities of robotic systems have improved dramatically. As the technology continues to improve and robotic agents are more routinely deployed in real-world environments, their capacity to assist in day-to-day activities will take on…
Posted by Oren Gilon, Software Engineer, and Grey Nearing, Research Scientist, Google Research Floods are the most common type of natural disaster, affecting more than 250 million people globally each year. As part of Google’s Crisis Response and our efforts to address the climate crisis, we are using machine learning (ML) models for Flood Forecasting…
Posted by Greg Blascovich and Eric Gomez, User Researchers, Google As companies settle into a new normal of hybrid and distributed work, remote communication technology remains critical for connecting and collaborating with colleagues. While this technology has improved, the core user experience often falls short: conversation can feel stilted, attention can be difficult to maintain,…
Posted by Zi Wang and Kevin Swersky, Research Scientists, Google Research, Brain Team Bayesian optimization (BayesOpt) is a powerful tool widely used for global optimization tasks, such as hyperparameter tuning, protein engineering, synthetic chemistry, robot learning, and even baking cookies. BayesOpt is a great strategy for these problems because they all involve optimizing black-box functions…
The response from schools and universities was swift and decisive. Just days after OpenAI dropped ChatGPT in late November 2022, the chatbot was widely denounced as a free essay-writing, test-taking tool that made it laughably easy to cheat on assignments. Los Angeles Unified, the second-largest school district in the US, immediately blocked access to OpenAI’s…
Figure 1: “Interactive Fleet Learning” (IFL) refers to robot fleets in industry and academia that fall back on human teleoperators when necessary and continually learn from them over time. In the last few years we have seen an exciting development in robotics and artificial intelligence: large fleets of robots have left the lab and entered…
The notion that artificial intelligence will help us prepare for the world of tomorrow is woven into our collective fantasies. Based on what we’ve seen so far, however, AI seems much more capable of replaying the past than predicting the future. That’s because AI algorithms are trained on data. By its very nature, data is…
A new kind of machine-learning model built by a team of researchers at the music-streaming firm Spotify captures for the first time the complex math behind counterfactual analysis, a precise technique that can be used to identify the causes of past events and predict the effects of future ones. The model, described earlier this year…
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last week, AI insiders were hotly debating an open letter signed by Elon Musk and various industry heavyweights arguing that AI poses an “existential risk” to humanity. They called for labs to introduce a…
In this post, we introduce Koala, a chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web. We describe the dataset curation and training process of our model, and also present the results of a user study that compares our model to ChatGPT and Stanford’s Alpaca. Our results show that Koala can…