Posted by Petros Maniatis and Daniel Tarlow, Research Scientists, Google Software isn’t created in one dramatic step. It improves bit by bit, one little step at a time — editing, running unit tests, fixing build errors, addressing code reviews, editing some more, appeasing linters, and fixing more errors — until finally it becomes good enough…
When machine-learning models are deployed in real-world situations, perhaps to flag potential disease in X-rays for a radiologist to review, human users need to know when to trust the model’s predictions. But machine-learning models are so large and complex that even the scientists who design them don’t understand exactly how the models make predictions. So,…
Someone learning to play tennis might hire a teacher to help them learn faster. Because this teacher is (hopefully) a great tennis player, there are times when trying to exactly mimic the teacher won’t help the student learn. Perhaps the teacher leaps high into the air to deftly return a volley. The student, unable to…
Posted by Julian Eisenschlos, Research Software Engineer, Google Research Visual language is the form of communication that relies on pictorial symbols outside of text to convey information. It is ubiquitous in our digital life in the form of iconography, infographics, tables, plots, and charts, extending to the real world in street signs, comic books, food…
Posted by Ken Caluwaerts and Atil Iscen, Research Scientists, Google Creating robots that exhibit robust and dynamic locomotion capabilities, similar to animals or humans, has been a long-standing goal in the robotics community. In addition to completing tasks quickly and efficiently, agility allows legged robots to move through complex environments that are otherwise difficult to…
The “interdisciplinary approach” is something that has been lauded for decades for its ability to break down silos and create new integrated approaches to research. For Munther Dahleh, founding director of the MIT Institute for Data, Systems, and Society (IDSS), showing the community that data science and statistics can transcend individual disciplines and form a…
Posted by Vincent Cohen-Addad and Alessandro Epasto, Research Scientists, Google Research, Graph Mining team Clustering is a central problem in unsupervised machine learning (ML) with many applications across domains in both industry and academic research more broadly. At its core, clustering consists of the following problem: given a set of data elements, the goal is…
Using an artificial intelligence algorithm, researchers at MIT and McMaster University have identified a new antibiotic that can kill a type of bacteria that is responsible for many drug-resistant infections. If developed for use in patients, the drug could help to combat Acinetobacter baumannii, a species of bacteria that is often found in hospitals and…
Despite their enormous size and power, today’s artificial intelligence systems routinely fail to distinguish between hallucination and reality. Autonomous driving systems can fail to perceive pedestrians and emergency vehicles right in front of them, with fatal consequences. Conversational AI systems confidently make up facts and, after training via reinforcement learning, often fail to give accurate…
The automotive industry is rapidly changing as connected and autonomous vehicles — enabled by AI and machine learning — are transforming transportation to create a seamless and personalized customer experience. The modernization of systems and software is steering vehicles to be more intelligent than ever, improving driving experiences and propelling operational efficiencies. From simulation testing…