Artificial Intelligence

Posted by Amir Yazdanbakhsh, Research Scientist, and Vijay Janapa Reddi, Visiting Researcher, Google Research Computer Architecture research has a long history of developing simulators and tools to evaluate and shape the design of computer systems. For example, the SimpleScalar simulator was introduced in the late 1990s and allowed researchers to explore various microarchitectural ideas. Computer…

There’s a lot of angst about software developers “losing their jobs” to AI, being replaced by a more intelligent version of ChatGPT, GitHub’s Copilot, Google’s Codey, or something similar. Matt Welsh has been talking and writing about the end of programming as such. He’s asking whether large language models eliminate programming as we know it,…

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Walking to a friend’s house or browsing the aisles of a grocery store might feel like simple tasks, but they in fact require sophisticated capabilities. That’s because humans are able to effortlessly understand their surroundings and detect complex information about patterns, objects, and their own location in the environment. What if robots could perceive their…

Posted by Malaya Jules, Program Manager, Google This week, the 61st annual meeting of the Association for Computational Linguistics (ACL), a premier conference covering a broad spectrum of research areas that are concerned with computational approaches to natural language, is taking place online. As a leader in natural language processing and understanding, and a Diamond…

Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). Direct visualization of embeddings (right; top three PCA directions shown) confirms that embeddings are initially collapsed to a point, which then…

Posted by Sanjay Subramanian, PhD student, UC Berkeley, and Arsha Nagrani, Research Scientist, Google Research, Perception Team Visual question answering (VQA) is a machine learning task that requires a model to answer a question about an image or a set of images. Conventional VQA approaches need a large amount of labeled training data consisting of…

Within weeks of ChatGPT’s launch, there were fears that students would be using the chatbot to spin up passable essays in seconds. In response to those fears, startups started making products that promise to spot whether text was written by a human or a machine.  The problem is that it’s relatively simple to trick these…

Discovering new materials and drugs typically involves a manual, trial-and-error process that can take decades and cost millions of dollars. To streamline this process, scientists often use machine learning to predict molecular properties and narrow down the molecules they need to synthesize and test in the lab. Researchers from MIT and the MIT-Watson AI Lab…

Is it possible to build machine-learning models without machine-learning expertise? Jim Collins, the Termeer Professor of Medical Engineering and Science in the Department of Biological Engineering at MIT and the life sciences faculty lead at the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), along with a number of colleagues decided to…