He was given extensive access to the secret files of the intelligence agency, although some limits were placed on what he could see and write about, including more recent interceptions of other country's diplomatic messages and some of the technical secrets of code-breaking.

With its unique ability to identify and'learn' from data patterns and to develop predictive mappings between variables – machine and deep learning – artificial intelligence (AI) has proved to be an indispensable tool in the fight against the coronavirus pandemic. AI has enabled the deployment of predictive models of potential disease contagion and containment, and has been used for screening and tracking patients. AI has been deployed across the globe to improve understanding of the potential consequences of the viral infection for different economy sectors. Companies have increasingly relied on machine-learning-enabled systems to reengineer production delivery in the face of a massive disruption in supply chains. Policy-makers have also turned to AI technologies due to their great promise in strengthening the quality of remote education delivery, at times where schools and education systems struggle to remain accessible to learners.

Last month, an artificial intelligence agent defeated human F-16 pilots in a Defense Advanced Research Projects Agency challenge, reigniting discussions about lethal AI and whether it can be trusted. Allies, non-government organizations, and even the U.S. Defense Department have weighed in on whether AI systems can be trusted. But why is the U.S. military worried about trusting algorithms when it does not even trust its AI developers? Any organization's adoption of AI and machine learning requires three technical tools: usable digital data that machine learning algorithms learn from, computational capabilities to power the learning process, and the development environment that engineers use to code. However, the military's precious few uniformed data scientists, machine learning engineers, and data engineers who create AI-enabled applications are currently hamstrung by a lack of access to these tools.

Google detailed a host of new improvements at its "Search On" event that it will make to its foundational Google search service in the coming weeks and months. The changes are largely focused on using new AI and machine learning techniques to provide better search results for users. Chief among them: a new spell checking tool that Google promises will help identify even the most poorly spelled queries. According to Prabhakar Raghavan, Google's head of search, 15 percent of Google search queries each day are ones that Google has never seen before, meaning the company has to constantly work to improve its results. Part of that is because of poorly spelled queries.

Companies are leveraging data and artificial intelligence to create scalable solutions -- but they're also scaling their reputational, regulatory, and legal risks. For instance, Los Angeles is suing IBM for allegedly misappropriating data it collected with its ubiquitous weather app. Optum is being investigated by regulators for creating an algorithm that allegedly recommended that doctors and nurses pay more attention to white patients than to sicker black patients. Goldman Sachs is being investigated by regulators for using an AI algorithm that allegedly discriminated against women by granting larger credit limits to men than women on their Apple cards. Facebook infamously granted Cambridge Analytica, a political firm, access to the personal data of more than 50 million users.

In a proof-of-concept study, education and artificial intelligence researchers have demonstrated the use of a machine-learning model to predict how long individual museum visitors will engage with a given exhibit. The finding opens the door to a host of new work on improving user engagement with informal learning tools. "Education is an important part of the mission statement for most museums," says Jonathan Rowe, co-author of the study and a research scientist in North Carolina State University's Center for Educational Informatics (CEI). "The amount of time people spend engaging with an exhibit is used as a proxy for engagement and helps us assess the quality of learning experiences in a museum setting. It's not like school--you can't make visitors take a test."

Machine learning typically requires tons of examples. To get an AI model to recognize a horse, you need to show it thousands of images of horses. This is what makes the technology computationally expensive--and very different from human learning. A child often needs to see just a few examples of an object, or even only one, before being able to recognize it for life. In fact, children sometimes don't need any examples to identify something.

The Boston Fire Department started to use emerging technology to fight fires in the last couple of years. In collaboration with Karen Panetta, an IEEE fellow and dean of Graduate Education at Tufts University's School of Engineering, the department is using AI for object recognition. The goal is to be able to use a drone or robot that can locate objects in a burning building. Panetta worked with the department to develop prototype technology that leverages IoT sensors and AI in tandem with robotics to help first responders "see" through blazes to detect and locate objects – and people. The AI technology she developed analyzes data coming from sensors that firefighters wear, and it recognizes objects that can be navigated in a fire.

HitchBot, a friendly-looking talking robot with a bucket for a body and pool-noodle limbs, first arrived on American soil back in 2015. This "hitchhiking" robot was an experiment by a pair of Canadian researchers who wanted to investigate people's trust in, and attitude towards, technology. The researchers wanted to see "whether a robot could hitchhike across the country, relying only on the goodwill and help of strangers." With rudimentary computer vision and a limited vocabulary but no independent means of locomotion, HitchBot was fully dependent on the participation of willing passers-by to get from place to place. Fresh off its successful journey across Canada, where it also picked up a fervent social media following, HitchBot was dropped off in Massachusetts and struck out towards California. But HitchBot never made it to the Golden State.

Modern machine learning methods are often overparametrized, allowing adaptation to the data at a fine level. This can seem puzzling; in the worst case, such models do not need to generalize. This puzzle inspired a great amount of work, arguing when overparametrization reduces test error, in a phenomenon called "double descent". Recent work aimed to understand in greater depth why overparametrization is helpful for generalization. This leads to discovering the unimodality of variance as a function of the level of parametrization, and to decomposing the variance into that arising from label noise, initialization, and randomness in the training data to understand the sources of the error.