To make computer chips, technologists around the world rely on atomic layer deposition (ALD), which can create films as fine as one atom thick. Businesses commonly use ALD to make semiconductor devices, but it also has applications in solar cells, lithium batteries and other energy-related fields. Today, manufacturers increasingly rely on ALD to make new types of films, but figuring out how to tweak the process for each new material takes time. Part of the problem is that researchers primarily use trial and error to identify optimal growth conditions. But a recently published study--one of the first in this scientific field--suggests that using artificial intelligence (AI) can be more efficient.

Computer scientist Lynne Parker made breakthroughs in getting robots to work together so they could perform difficult missions, like cleaning up after a nuclear disaster, waxing floors or pulling barnacles off a ship. Her job now is getting the U.S. government working together -- alongside American businesses, research universities and international allies -- as director of a new national initiative on artificial intelligence. She's America's first AI czar, at a time of rising promise and a heavy dose of both hype and fear about what computers can do as they think more like humans. "There's an increased need for education and training so that people know how to use AI tools, they know sort of what the capabilities are of AI so that they don't treat it as magic," Parker said in an interview with The Associated Press. A first task for Parker, who took on the role in the waning days of the Trump administration, is adapting to priorities set by the Biden administration.

The green-eyeshade public servants at the Treasury Department have dealt with large amounts of money for decades. But as the size of their mission has grown exponentially over the years, the never-ending repetitive tasks they tackle have increasingly relied on artificial intelligence and machine learning. The folks at the Bureau of the Fiscal Service are well aware of how technology leads to faster and more accurate results. "The Bureau of the Fiscal Service, I would say, is really kind of the operational arm of accounting in the federal government," said Adam Goldberg, the Treasury Department's Acting Assistant Commissioner of Financial Innovation and Transformation at the Bureau of Fiscal Service, on Federal Monthly Insights – Repurposing Manpower Through Automation. The Bureau issues checks to people and businesses, as well as collects funds for federal agencies.

"A period that had been broadly described as engagement has come to an end," Kurt Campbell, the Indo-Pacific Coordinator at the United States (US) National Security Council, told a virtual audience in May on the subject of US-China relations. "The dominant paradigm is going to be competition." On several occasions, Campbell has highlighted that one of the major arenas of this competition will concern technology. This is increasingly reflected in US national security structures. Today, there is both a senior director and coordinator for technology and national security at the White House; the National Economic Council has briefed the Cabinet on supply chain resilience; and the focus of Department of Defense policy reviews have been on emerging military technologies. The subject of intensifying technology competition is also making its way into new US avenues for cooperation with partners, including with India.

I have got a new doorbell. It should be; it cost £89. It's a Ring video doorbell; you'll have seen them around. There are others available, made by other companies, with other four-letter names such as Nest and Arlo. When someone rings my doorbell, I'm alerted on my smartphone. I can see who is there, and speak to them. C major first inversion chord, arpeggiated, repeated, for the musically trained – you'll recognise it if you've heard it. Amazon, as it happens; Amazon acquired Ring in 2018, reportedly for more than $1bn.

The city council of Presidio, Texas, voted on June 7, 2021 to approve locating a new camera system for Customs and Border Patrol on city property. The Sentry camera is a re-deployable 30-foot-tall tower bristling with sensors and powered by solar panels. It's made by Anduril, a security technology startup. As the city council agenda notes, Presidio approved locating one such Sentry "on city property near the City of Presidio Waste Water Treatment Plant." Presidio, population 4,000, sits on the US side of the confluence of the Rio Grande and Rio Conchos rivers, across from Ojinaga in Mexico, in the broader Big Bend region of the state.

While there has been a good deal of discussion about the use of AI in enterprises and the possibility of building ethical AI strategies, new research indicates that, even 10 years from today, it is unlikely that ethical AI design will be widely adopted. The research, based on a survey of 602 technology innovators, business and policy leaders, researchers and activists conducted by Pew Research Center and Elon University, showed that a majority worried that the evolution of AI by 2030 will continue to be primarily focused on optimizing profits and social control and that stakeholders will struggle to achieve a consensus about ethics. When asked whether AI systems being used by organizations will employ ethical principles focused primarily on the public good by 2030, 68% said they will not. The research added that "ethical" implies adopting AI in a manner that is transparent, responsible and accountable. For others, it means ensuring their use of AI remains consistent with laws, regulations, norms, customer expectations and organizational values.

North Dakota is not often one of the states in the United States that is considered to be foremost for technology adoption, but the state's thought leadership in a wide range of technology areas shouldn't be underestimated. After all, the current Governor of North Dakota, Doug Burgum, was not only president of Billion-dollar tech company Great Plains, which was acquired by Microsoft, but also he was the head of Microsoft Business Solutions, chairman of the board for Atlassian, and on the board of SuccessFactors, and co-founder of Arthur Ventures. It should come as little surprise, then, that North Dakota has been very forward thinking in their use of data, automation, and AI. In a recent AI in Government event North Dakota's Chief Data Officer Dorman Bazzell shared how states are approaching technologies such as automation and advanced data analytics, as well as how AI plays an increasing role in local government. He provides more detail in a recent interview on the AI Today podcast and he shares insights in this follow up interview here on Forbes.

We develop and study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence (AI) systems including deep learning neural networks. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself. Such a stealth attack could be conducted by a mischievous, corrupt or disgruntled member of a software development team. It could also be made by those wishing to exploit a "democratization of AI" agenda, where network architectures and trained parameter sets are shared publicly. Building on work by [Tyukin et al., International Joint Conference on Neural Networks, 2020], we develop a range of new implementable attack strategies with accompanying analysis, showing that with high probability a stealth attack can be made transparent, in the sense that system performance is unchanged on a fixed validation set which is unknown to the attacker, while evoking any desired output on a trigger input of interest. The attacker only needs to have estimates of the size of the validation set and the spread of the AI's relevant latent space. In the case of deep learning neural networks, we show that a one neuron attack is possible - a modification to the weights and bias associated with a single neuron - revealing a vulnerability arising from over-parameterization. We illustrate these concepts in a realistic setting. Guided by the theory and computational results, we also propose strategies to guard against stealth attacks.

We present a novel multi-view training framework and CNN architecture for combining information from multiple overlapping satellite images and noisy training labels derived from OpenStreetMap (OSM) to semantically label buildings and roads across large geographic regions (100 km$^2$). Our approach to multi-view semantic segmentation yields a 4-7% improvement in the per-class IoU scores compared to the traditional approaches that use the views independently of one another. A unique (and, perhaps, surprising) property of our system is that modifications that are added to the tail-end of the CNN for learning from the multi-view data can be discarded at the time of inference with a relatively small penalty in the overall performance. This implies that the benefits of training using multiple views are absorbed by all the layers of the network. Additionally, our approach only adds a small overhead in terms of the GPU-memory consumption even when training with as many as 32 views per scene. The system we present is end-to-end automated, which facilitates comparing the classifiers trained directly on true orthophotos vis-a-vis first training them on the off-nadir images and subsequently translating the predicted labels to geographical coordinates. With no human supervision, our IoU scores for the buildings and roads classes are 0.8 and 0.64 respectively which are better than state-of-the-art approaches that use OSM labels and that are not completely automated.