[Paper Summary] Stanford Researchers use Deep Learning to predict Biological Structures, like RNAs, more accurately than ever before

Determination of 3D structures of biological molecules, like RNA’s, is difficult and often requires millions of dollars for such extensive efforts. Stanford University researchers have devised a new deep learning algorithm called ARES (Atomic Rotationally Equivalent Scorer) for overcoming this challenge by computationally forecasting accurate structures. 

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[Paper Summary] Stanford’s AI Researchers introduce QA-GNN Model that jointly reasons with Language Models and Knowledge Graphs

painter is Italy.

In this research paper, published at NAACL 2021, researchers found that combining both LMs and KGs makes it possible to answer questions more effectively. Existing systems that use LM and KGs tend to be noisy, and the interactions between QA context and KG are not modeled.

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[Paper Summary] Stanford AI Lab introduces AGQA: A new benchmark for Compositional, Spatio-Temporal Reasoning

Designing machines capable of exhibiting a compositional understanding of visual events has been an important goal of the computer vision community. Stanford AI has recently introduced the benchmark,’ Action Genome Question Answering’ (AGQA). It measures temporal, spatial, and compositional reasoning via nearly two hundred million question answering pairs. The questions are complex, compositional, and annotated to allow definitive tests that find the types of questions that the models can and cannot answer.

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Fei-Fei Li has been appointed to a federal task force on AI

Fei-Fei Li

The Office of Science and Technology Policy and the National Science Foundation have announced the newly formed National Artificial Intelligence Research Resource Task Force, which will write the road map for expanding access to critical resources and educational tools that will spur AI innovation and economic prosperity nationwide.

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These Stanford AI Lab dropouts raised $50 million to improve Conversations

Zayd Enam - Cresta

Using its artificial intelligence tools, Cresta studies chats and phone conversations, then tells retail representatives the “best,” or most likely to be effective, replies while they’re still on the line. Agents using the startup’s software convert 20% more sales than those who don’t Enam claims.

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Stanford University – Strategic AI shapes economy, policy

dollar bills

According to an annual AI index released this week by Stanford University’s Institute for Human-Centered Artificial Intelligence, AI is gradually reshaping segments of the global economy in sectors like bioscience and healthcare. Those investments have caught the attention of policy makers. The index notes growing congressional attention to AI technology as a strategic asset. To that end, U.S. lawmakers concerned about China’s expanding AI strategy this week proposed the creation of an interagency office to coordinate AI and other technology research.

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Annual index finds AI is ‘industrializing’ but needs better metrics and testing

Geographical published AI strategies

The 2021 AI Index from Stanford University gathers data about AI research, startups, and changes to business and government policy.

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OpenAI and Stanford researchers call for urgent action to address harms of large language models like GPT-3

The makers of large language models like Google and OpenAI may not have long to set standards that sufficiently address their impact on society. Open source projects currently aiming to recreate GPT-3 include GPT-Neo, a project headed by EleutherAI. That’s according to a paper published last week by researchers from OpenAI and Stanford University.

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AI research finds a ‘compute divide’ concentrates power and accelerates inequality in the era of deep learning

AI researchers from Virginia Tech and Western University have concluded that an unequal distribution of compute power in academia is furthering inequality in the era of deep learning. They also point to the impact on academia of people leaving prestigious universities for high-paying industry jobs.

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AI researchers devise failure detection method for safety-critical machine learning

Researchers from MIT, Stanford University, and the University of Pennsylvania have devised a method for predicting failure rates of safety-critical machine learning systems and efficiently determining their rate of occurrence.

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