Palmer Luckey's Anduril Industries has won a contract on the Pentagon's controversial AI program, Project Maven, reports The Intercept. Since founding the company in 2017, Luckey has focused on the defense industry, building advanced systems that could be used for border surveillance. Project Maven is a controversial artificial intelligence program that uses machine learning to sort through millions of hours of drone footage to help systems distinguish people from their surroundings. The project would help reduce the burden on human analysts and improve the intelligence that's captured in cameras. The project's goal is to get better information to military officers, with the idea that with better decisions, there's less of a change of mistakes that result in civilian casualties.

Two developments have ushered in this golden age of data analytics. One is the gargantuan, always-expanding volume of data available. The other is advanced new machine-learning and artificial-intelligence technology. One might think these two dogs could race to the finish by themselves. There is, however, another element needed in data science today. Without it, skewed inputs and outputs and general bias could mar the best analytics efforts.

A computer science professor has built a tool that could allow people to identify Civil War veterans in their families with the help of artificial intelligence. Kurt Luther, an assistant professor at Virginia Tech, developed a platform called Photo Sleuth after he discovered a picture of a distant relative who had fought in the Civil War. "Seeing my distant relative staring back at me was like traveling through time," said Luther in a statement. "Historical photos can tell us a lot about not only our own familial history, but also inform the historical record of the time more broadly than just reading about the event in a history book." The professor, who considers himself a history buff, was inspired to create the software when he visited the Heinz History Center's exhibit called "Pennsylvania's Civil War" in Pittsburgh in 2013.

The FDA today approved what it's calling "the first medical device to use artificial intelligence to detect greater than a mild level of the eye disease diabetic retinopathy." The AI-powered, cloud-based system will be available for use by primary care providers. Over 30 million Americans have diabetes, and diabetic retinopathy--which occurs when blood sugar levels result in damage to retinal blood vessels--is considered mostly preventable. Still, it causes vision loss in tens of thousands of people each year and is the leading cause of blindness among working-age Americans. "Many patients with diabetes are not adequately screened for diabetic retinopathy since about 50 percent of them do not see their eye doctor on a yearly basis," Malvina Eydelman, MD, said in the FDA's official announcement.

Artificial intelligence systems are powerful tools for businesses and governments to process data and respond to changing situations, whether on the stock market or on a battlefield. But there are still some things AI isn't ready for. We are scholars of computer science working to understand and improve the ways in which algorithms interact with society. AI systems perform best when the goal is clear and there is high-quality data, like when they are asked to distinguish between different faces after learning from many pictures of correctly identified people. Sometimes AI systems do so well that users and observers are surprised at how perceptive the technology is.

We know tech giants like Amazon, Baidu, Facebook and Google have AI advantages like collecting enormous amounts of data, access to top talent, huge investments in research and development, over smaller companies. However, the possibilities offered by AI are not reserved only for the largest companies and biggest economies. Estonia is looking for ways how to attract international talent and investments; and on the other hand, its small size with limited resources requires the public administration and government to work efficiently. No wonder that in Estonia, both the government and companies have noticed the potential of AI technologies to solve these current demographic and economic challenges, as the impact of AI on GDP in the Nordics alone is expected to be considerable: 9.9% of GDP (1.8 trillion). There is a large spread of AI readiness in Europe, but even the most advanced countries are lagging the US in AI frontier.

Artificial intelligence (AI) is proving to be one of the most influential and game-changing technology advancements in the business world. As more and more enterprises go digital, companies all over the globe are constantly engineering new ways to implement AI-based functions into practically every platform and software tool at their disposal. As a natural consequence, however, cybercriminals too are on the rise, and view the increasing digitization of business as wide open window of opportunity. It should come as no surprise, then, that AI is affecting cybersecurity – but it's affecting it in both positive and negative ways. Cybercrime is a massively lucrative business, and one the greatest threats to every company in the world.

Chinese companies have surged ahead of their U.S. counterparts on a Nikkei ranking of the top 50 patent filers for artificial intelligence over the past three years, expanding their presence in the world's most prominent high-tech battleground. In the three years between 2016 and 2018, China more than doubled the amount of companies in the top 50 to 19 - up from eight in the previous three-year span. Meanwhile, the U.S. kept a tight grip on the top three spots, but only had 12 companies in the top 50 - down from 19 in the previous ranking. The rankings come after U.S. President Donald Trump announced the American AI Initiative last month, a plan to increase research and development in the sector but lacked any specific funding to towards that aim. IBM led the way for the U.S. with 3,000 applications.

Recently, new defense techniques have been developed to tolerate Byzantine failures for distributed machine learning. The Byzantine model captures workers that behave arbitrarily, including malicious and compromised workers. In this paper, we break two prevailing Byzantine-tolerant techniques. Specifically we show robust aggregation methods for synchronous SGD -- coordinate-wise median and Krum -- can be broken using new attack strategies based on inner product manipulation. We prove our results theoretically, as well as show empirical validation.

Most recently, Chinese telecom equipment and consumer electronics giant Huawei filed suit against the U.S. government on Thursday, alleging that a law passed last August banning the company's hardware is unconstitutional because it unfairly targets Huawei. The arms race between the two superpowers over the tech talent, physical infrastructure and industrial muscle--not to mention the IP that comes with the technologies--that will unlock these advancements has been increasingly evident in diplomatic machinations, public investments of varying size and outward rhetoric from state leaders, as each country's government has moved the technologies to the top of their respective economic agendas. These struggles are about more than economic opportunity alone. The shift to 5G networks, which promise to eventually reach speeds up to 100 times faster than what's currently available, could expand the role of wireless communications systems into everything from power grids to traffic, while AI is already facilitating the mass collection and processing of data that will power critical tools like autonomous driving and facial recognition. In that future state, control over telecom equipment and AI-powered data centers could be like holding the keys to a society.