Posted by Arun Kandoor, Software Engineer, Google Research The increasing demand for machine learning (ML) model inference on-device (for mobile devices, tablets, etc.) is driven by the rise of compute-intensive applications, the need to keep certain data on device for privacy and security reasons, and the desire to provide services when a network connection may …
Posted by Ehsan Amid, Research Scientist, and Rohan Anil, Principal Engineer, Google Research, Brain Team While model design and training data are key ingredients in a deep neural network’s (DNN’s) success, less-often discussed is the specific optimization method used for updating the model parameters (weights). Training DNNs involves minimizing a loss function that measures the …
Read more “Enhancing Backpropagation via Local Loss Optimization”
Posted by Tuan Anh Nguyen, Staff Software Engineer, Google Assistant, and Sourish Chaudhuri, Staff Software Engineer, Google Research In natural conversations, we don’t say people’s names every time we speak to each other. Instead, we rely on contextual signaling mechanisms to initiate conversations, and eye contact is often all it takes. Google Assistant, now available …
Read more “Look and Talk: Natural Conversations with Google Assistant”
Posted by Maxim Tabachnyk, Staff Software Engineer and Stoyan Nikolov, Senior Engineering Manager, Google Research The increasing complexity of code poses a key challenge to productivity in software engineering. Code completion has been an essential tool that has helped mitigate this complexity in integrated development environments (IDEs). Conventionally, code completion suggestions are implemented with rule-based …
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Posted by Winnie Xu, Student Researcher and Kuang-Huei Lee, Software Engineer, Google Research, Brain Team Current deep reinforcement learning (RL) methods can train specialist artificial agents that excel at decision-making on various individual tasks in specific environments, such as Go or StarCraft. However, little progress has been made to extend these results to generalist agents …
Read more “Training Generalist Agents with Multi-Game Decision Transformers”
As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage. Programmable resistors are the key building blocks in analog deep …
Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error — possibly making thousands of prints — to determine ideal parameters that consistently print …
Read more “Using artificial intelligence to control digital manufacturing”
Addressing and mitigating the effects of climate change requires a collective effort, bringing our strengths to bear across industry, government, academia, and civil society. As we continue to explore the role of technology to advance the art of the possible, we are launching the Microsoft Climate Research Initiative (MCRI). This community of multi-disciplinary researchers is …
Read more “Introducing the Microsoft Climate Research Initiative”
About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer. These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. …
Read more “Explained: How to tell if artificial intelligence is working the way we want it to”
For workers who use machine-learning models to help them make decisions, knowing when to trust a model’s predictions is not always an easy task, especially since these models are often so complex that their inner workings remain a mystery. Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence …
Read more “A technique to improve both fairness and accuracy in artificial intelligence”