Artist Ariel Aberg-Riger is author of America Redux: Visual Stories From Our Dynamic History.
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Latest from Google AI – Beyond automatic differentiation
Posted by Matthew Streeter, Software Engineer, Google Research Derivatives play a central role in optimization and machine learning. By locally approximating a training loss, derivatives guide an optimizer toward lower values of the loss. Automatic differentiation frameworks such as TensorFlow, PyTorch, and JAX are an essential part of modern machine learning, making it feasible to…
Latest from MIT : Seeing the whole from some of the parts
Upon looking at photographs and drawing on their past experiences, humans can often perceive depth in pictures that are, themselves, perfectly flat. However, getting computers to do the same thing has proved quite challenging. The problem is difficult for several reasons, one being that information is inevitably lost when a scene that takes place in…
Latest from Google AI – Constrained Reweighting for Training Deep Neural Nets with Noisy Labels
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…
Latest from MIT : Researchers present bold ideas for AI at MIT Generative AI Impact Consortium kickoff event
Launched in February of this year, the MIT Generative AI Impact Consortium (MGAIC), a presidential initiative led by MIT’s Office of Innovation and Strategy and administered by the MIT Stephen A. Schwarzman College of Computing, issued a call for proposals, inviting researchers from across MIT to submit ideas for innovative projects studying high-impact uses of…
Latest from MIT : Technique enables real-time rendering of scenes in 3D
Humans are pretty good at looking at a single two-dimensional image and understanding the full three-dimensional scene that it captures. Artificial intelligence agents are not. Yet a machine that needs to interact with objects in the world — like a robot designed to harvest crops or assist with surgery — must be able to infer…
Latest from MIT : Building explainability into the components of machine-learning models
Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patient’s risk of developing cardiac disease, a physician might want to know how strongly the patient’s heart rate data influences that prediction. But if…