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…
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…
DeepMind says its AlphaFold tool has successfully predicted the structure of nearly all proteins known to science. From today, the Alphabet-owned AI lab is offering its database of over 200 million proteins to anyone for free. When DeepMind introduced AlphaFold in 2020, it took the science community by surprise. Scientists had spent decades trying to…
Posted by Tuan Anh Nguyen, Google Assistant and Sourish Chaudhuri, 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 in more than 95 countries and…
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…
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….
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…
When one of China’s biggest celebrities, Simon Gong—also known as Gong Jun—released a new music video in June 2022, it quickly attracted 15 million views on the country’s Twitter-like microblogging site Weibo. But the event also stood out for a different reason—one that only eagle-eyed fans might have noticed. The singer in the video was not…
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…
Around 100,000 people have played with OpenAI’s latest image-making program DALL-E 2 since its invite-only launch in April. Today the San Francisco-based company opens the door to a million more, MIT Technology Review can reveal. OpenAI is turning its research project into a commercial product, launching the DALL-E Beta, which will be available as a…