Albert Hsiao, M.D., and his colleagues at the University of California, San Diego (USCD) health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the U.S., they decided to see what it could do. The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs developed in a calmer time into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

Albert Hsiao, M.D., Ph.D., and his colleagues at the University of California San Diego (UCSD) health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the U.S., they decided to see what it could do. The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs developed in a calmer time into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.

Dr. Albert Hsiao and his colleagues at the UC San Diego health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed their program, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

Dr. Albert Hsiao and his colleagues at the UC San Diego health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed their program, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

Brandon Moak felt as if a freight train had hit him. It was mid-March, and the cofounder and CTO of the autonomous- trucking startup Embark Trucks had been keeping tabs on the emergence of covid-19. As a shelter-in-place order went into effect throughout the San Francisco Bay Area, where Embark is based, Moak and his team were forced to ground almost all their 13 self-driving semi-trucks (a few stayed on the road moving essential freight but weren't in autonomous mode) and send home the majority of their workforce, with no idea how long it'd be before they could return. For safety reasons, autonomous vehicles typically have two operators apiece. That's a no-go in the age of social distancing, and leaders of autonomous-vehicle companies knew they'd have to mothball their fleets.

Dr. Albert Hsiao and his colleagues at the University of California, San Diego health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed their program, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

As tensions and tech rivalry between the U.S. and China intensify, artificial intelligence is taking center stage. During the recent Tortoise Global AI Summit, panelists discussed the increasingly fraught relationship between these global superpowers, whose rivalry had shown signs of bitterness even before President Trump launched a trade war. While this competition extends across a wide range of technologies, the panelists agreed AI has increasingly become a focal point, thanks to the essential role many predict it will play in the coming decades. And not only is the race for AI supremacy pitting China against the U.S., it is forcing every other country to reassess their place in this technological duel. "We're seeing a technology competition in the context of a worsening relationship between the world's two great powers," said John Sawers, former head of the U.K.'s MI6 spy agency.

It's very rare to see any travel video or brochure these days that doesn't have an image shot from overhead, on a drone. Life seems more dramatic up from above, right? The units themselves have gotten way easier to use, more affordable and the camera quality is pretty amazing. Imagine being able to throw a high-quality camera in the air that can get stunning overhead shots, smooth video even in wind and always somehow return home to sender. It's one of the major tech advancements of our time, but it can be a little confusing.

The developers of a speech recognition bot assigned to analyze the public statements of politicians hit a major stumbling block when it tried to make sense of Donald Trump. Built by a tech startup called FactSquared, the bot AI was assigned to go through more than 11 million words Trump has spoken or tweeted since 1976--in interviews, campaign speeches, media appearances, and social media posts. According to FactSquared's CEO Bill Frischling, the bot failed to understand Trump's speeches until he brought in a specialist to strip out all of the bot's grammar and syntax coding. The tech startup FactSquared created an AI bot to try and catalog and analyze Donald Trump's public appearances and interviews, but they were so incoherent and rambling the bot actually crashed. 'It was still trying to punctuate it like it was English, versus trying to punctuate it like it was Trump,' Frischling told The LA Times.