The world is a prominent, confusing place. We have all the technology to make navigation more accessible. However, we still rely on traffic lights and steel for safety measures because… Read More »AI Researchers Developed A Deep Learning Model To Predict Traffic Crashes Before They Happen
Artificial intelligence is helping facilitate many aspects of business. Many companies have been forced to lean more heavily on AI technology than ever during the pandemic, because they had to… Read More »What is the Best AI-Driven App for Video Conferencing?
Posted by Zachary Nado, Research Engineer and Dustin Tran, Research Scientist, Google Research, Brain Team Machine learning (ML) is increasingly being used in real-world applications, so understanding the uncertainty and… Read More »Baselines for Uncertainty and Robustness in Deep Learning
Cross-posted from Bounded Regret.
Earlier this year, my research group commissioned 6 questions for professional forecasters to predict about AI. Broadly speaking, 2 were on geopolitical aspects of AI and 4 were on future capabilities:
Geopolitical: How much larger or smaller will the largest Chinese ML experiment be compared to the largest U.S. ML experiment, as measured by amount of compute used? How much computing power will have been used by the largest non-incumbent (OpenAI, Google, DeepMind, FB, Microsoft), non-Chinese organization? Future capabilities: What will SOTA (state-of-the-art accuracy) be on the MATH dataset? What will SOTA be on the Massive Multitask dataset (a broad measure of specialized subject knowledge, based on high school, college, and professional exams)? What will be the best adversarially robust accuracy on CIFAR-10? What will SOTA be on Something Something v2? (A video recognition dataset)
Forecasters output a probability distribution over outcomes for 2022, 2023, 2024, and 2025. They have financial incentives to produce accurate forecasts; the rewards total $5k per question ($30k total) and payoffs are (close to) a proper scoring rule, meaning forecasters are rewarded for outputting calibrated probabilities.
Introduction Lack of technology or Healthcare Automation solutions has been creating havoc in the healthcare industry. According to Health Affairs, 44,000 deaths are caused in the U.S. each year… Read More »Healthcare Administrative Automation & Digitization for Better Management
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Cambridge Quantum (CQ) Open-Sources ‘lambeq’: A Python Library For Experimental Quantum Natural Language Processing (QNLP)
Cambridge Quantum (“CQ”) announced the release of the world’s first toolkit and an open-source library for Quantum Natural Language Processing (QNLP), called ‘lambeq’. Speaking in simple words, ‘lambeq’ is the… Read More »Cambridge Quantum (CQ) Open-Sources ‘lambeq’: A Python Library For Experimental Quantum Natural Language Processing (QNLP)
We mentioned previously that bias is a big problem in machine learning that has to be mitigated. People need to take important steps to help mitigate it for the future.… Read More »Important Steps to Take to Address the Bias in AI
Posted by Joel Shor, Software Engineer, Google Research and Sercan Arik, Research Scientist, Google Research, Cloud AI Team Over the past 20 months, the COVID-19 pandemic has had a profound… Read More »An ML-based Framework for COVID-19 Epidemiology