Jan 20, 2021

6 Posts

Table with information related to data and datasets
Jan 20, 2021

The Batch: Detecting Guns, Fighting Lead Poisoning, Adversarial Training for Language-and-Vision, Financial Reports for Robots

Experience gained in building a model to solve one problem doesn’t always transfer to building models for other problems. How can you tell whether or not intuitions honed in one project are likely to generalize to another?
Series of images related to water lines replacement and poisoned water
Jan 20, 2021

AI Versus Lead Poisoning

An algorithm is helping cities locate pipes that could release highly toxic lead into drinking water. BlueConduit, a startup that focuses on water safety, is working with dozens of North American municipal governments to locate lead water lines so they can be replaced.
Data related to adversarial learning
Jan 20, 2021

Adversarial Helper

Models that learn relationships between images and words are gaining a higher profile. New research shows that adversarial learning, usually a way to make models robust to deliberately misleading inputs, can boost vision-and-language performance.
Gun detecting system working and alerting the police
Jan 20, 2021

Draw a Gun, Trigger an Algorithm

Computer vision is alerting authorities the moment someone draws a gun. Several companies offer deep learning systems that enable surveillance cameras to spot firearms and quickly notify security guards or police.
Some findings from a study by the U.S. nonprofit National Bureau of Economic Research.
Jan 20, 2021

Annual Report, Robot Edition

Corporations are tailoring their financial reports to be read by machines. Automated systems download far more company financial reports than humans, according to a study by the U.S. nonprofit National Bureau of Economic Research.
Table with information related to data and datasets
Jan 20, 2021

Structured and Unstructured Data: Implications for AI Development

Experience gained in building a model to solve one problem doesn’t always transfer to building models for other problems. How can you tell whether or not intuitions honed in one project are likely to generalize to another?

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