If a machine-learning model is trained using an unbalanced dataset, such as one that contains far more images of people with lighter skin than people with darker skin, there is serious risk the model’s predictions will be unfair when it is deployed in the real world. But this is only one part of the problem….
Posted by Brendan McMahan and Abhradeep Thakurta, Research Scientists, Google Research In 2017, Google introduced federated learning (FL), an approach that enables mobile devices to collaboratively train machine learning (ML) models while keeping the raw training data on each user’s device, decoupling the ability to do ML from the need to store the data in…
Posted by Abhishek Kumar and Ehsan Amid, Research Scientists, Google Research, Brain Team Over the past several years, deep neural networks (DNNs) have been quite successful in driving impressive performance gains in several real-world applications, from image recognition to genomics. However, modern DNNs often have far more trainable model parameters than the number of training…
Sercompe Business Technology provides essential cloud services to roughly 60 corporate clients, supporting a total of about 50,000 users. So, it’s crucial that the Joinville, Brazil, company’s underlying IT infrastructure deliver reliable service with predictably high performance. But with a complex IT environment that includes more than 2,000 virtual machines and 1 petabyte—equivalent to a…
Identifying a malfunction in the nation’s power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second. Researchers at the MIT-IBM Watson AI…
When it comes to treatment strategies for critically ill patients, clinicians want to be able to consider all their options and timing of administration, and make the optimal decision for their patients. While clinician experience and study has helped them to be successful in this effort, not all patients are the same, and treatment decisions…
Scintillators are materials that emit light when bombarded with high-energy particles or X-rays. In medical or dental X-ray systems, they convert incoming X-ray radiation into visible light that can then be captured using film or photosensors. They’re also used for night-vision systems and for research, such as in particle detectors or electron microscopes. Researchers at…
Posted by AJ Piergiovanni and Anelia Angelova, Research Scientists, Google Research While not immediately obvious, all of us experience the world in four dimensions (4D). For example, when walking or driving down the street we observe a stream of visual inputs, snapshots of the 3D world, which, when taken together in time, creates a 4D…
Unsupervised Reinforcement Learning (RL), where RL agents pre-train with self-supervised rewards, is an emerging paradigm for developing RL agents that are capable of generalization. Recently, we released the Unsupervised RL Benchmark (URLB) which we covered in a previous post. URLB benchmarked many unsupervised RL algorithms across three categories — competence-based, knowledge-based, and data-based algorithms. A…
By the end of 2020, DeepMind, the UK-based artificial-intelligence lab, had already produced many impressive achievements in AI. Still, when the group’s program for predicting protein folding was released in November of that year, biologists were shocked by how well it worked. Nearly everything your body does, it does with proteins. Understanding what individual proteins do…