NASA says its presence on the moon won't just be for show. With new technology, the agency hopes to mine natural resources on the lunar surface as well as reachable asteroids. Through NASA's Innovative Advanced Concepts (NIAC) program, the agency said it will begin to explore the feasibility of robotic rovers and mining technology that could make space mining a reality. To do so, it has green-lit two mission concepts this month. NASA wants to get a jump-start on mining in space with a tandem of proposals that would develop future technology.

Vencore is a proven provider of information solutions, engineering and analytics for the U.S. Government. With more than 40 years of experience working in the defense, civilian and intelligence communities, Vencore designs, develops and delivers high impact, mission-critical services and solutions to overcome its customers most complex problems. Headquartered in Chantilly, Virginia, Vencore employs 3,800 engineers, analysts, IT specialists and other professionals who strive to be the best at everything they do. Responsibilities: The Data Engineer will work with a team supporting a wide range of activities, including information systems development, integration of scalable solutions using various platforms, and architecting automated and scalable data process monitoring processes.

Compared to other global powers, the European Union (EU) is rarely considered a leading player in the development of artificial intelligence (AI). Why is this, and does this in fact accurately reflect the EU's activities related to AI? What would it take for the EU to take a more leading role in AI, and to be internationally recognised as such?

Home » Security Boulevard (Original) » News » Vectra Raises $100M More for Cybersecurity AI Vectra has garnered another $100 million in funding to accelerate development of a threat detection and response system running in the cloud that makes extensive use of artificial intelligence (AI). This latest round of funding brings the total investment in Vectra to $200 million. Company CEO Hitesh Sheth said Vectra's Cognito platform applies machine learning algorithms to network metadata captured across the extended enterprise.

Over the past few years, the rise of AI fakes has got a lot of people very worried, with experts warning that this technology could be used to spread lies and misinformation online. But actual evidence of this happening has so far been thin on the ground, which is why a new report from the Associated Press makes for such interesting reading. The AP says it found evidence of a what seems to be a would-be spy using an AI-generated profile picture to fool contacts on LinkedIn. The publication says that the fake profile, given the name Katie Jones, connected with a number of policy experts in Washington. These included a scattering of government figures such as a senator's aide, a deputy assistant secretary of state, and Paul Winfree, an economist currently being considered for a seat on the Federal Reserve.

In developing countries like India agriculture plays an extremely important role in the lives of the population. In India, around 80\% of the population depend on agriculture or its by-products as the primary means for employment. Given large population dependency on agriculture, it becomes extremely important for the government to estimate market factors in advance and prepare for any deviation from those estimates. Commodity arrivals to market is an extremely important factor which is captured at district level throughout the country. Historical data and short-term prediction of important variables such as arrivals, prices, crop quality etc. for commodities are used by the government to take proactive steps and decide various policy measures. In this paper, we present a framework to work with short timeseries in conjunction with remote sensing data to predict future commodity arrivals. We deal with extremely high dimensional data which exceed the observation sizes by multiple orders of magnitude. We use cascaded layers of dimensionality reduction techniques combined with regularized regression models for prediction. We present results to predict arrivals to major markets and state wide prices for `Tur' (red gram) crop in Karnataka, India. Our model consistently beats popular ML techniques on many instances. Our model is scalable, time efficient and can be generalized to many other crops and regions. We draw multiple insights from the regression parameters, some of which are important aspects to consider when predicting more complex quantities such as prices in the future. We also combine the insights to generate important recommendations for different government organizations.

Deep neural networks have achieved impressive performance in many applications but their large number of parameters lead to significant computational and storage overheads. Several recent works attempt to mitigate these overheads by designing compact networks using pruning of connections. However, we observe that most of the existing strategies to design compact networks fail to preserve network robustness against adversarial examples. In this work, we rigorously study the extension of network pruning strategies to preserve both benign accuracy and robustness of a network. Starting with a formal definition of the pruning procedure, including pre-training, weights pruning, and fine-tuning, we propose a new pruning method that can create compact networks while preserving both benign accuracy and robustness. Our method is based on two main insights: (1) we ensure that the training objectives of the pre-training and fine-tuning steps match the training objective of the desired robust model (e.g., adversarial robustness/verifiable robustness), and (2) we keep the pruning strategy agnostic to pre-training and fine-tuning objectives. We evaluate our method on four different networks on the CIFAR-10 dataset and measure benign accuracy, empirical robust accuracy, and verifiable robust accuracy. We demonstrate that our pruning method can preserve on average 93\% benign accuracy, 92.5\% empirical robust accuracy, and 85.0\% verifiable robust accuracy while compressing the tested network by 10$\times$.

According to foreign-policy experts and the defense establishment, the United States is caught in an artificial intelligence arms race with China--one with serious implications for national security. The conventional version of this story suggests that the United States is at a disadvantage because of self-imposed restraints on the collection of data and the privacy of its citizens, while China, an unrestrained surveillance state, is at an advantage. In this vision, the data that China collects will be fed into its systems, leading to more powerful AI with capabilities we can only imagine today. Since Western countries can't or won't reap such a comprehensive harvest of data from their citizens, China will win the AI arms race and dominate the next century. This idea makes for a compelling narrative, especially for those trying to justify surveillance--whether government- or corporate-run.

A LinkedIn user named Katie Jones, who connected with prominent members of the Washington D.C. political sphere, may actually have been a spy's made up persona -- Jones' picture was almost certainly generated a deepfake by an artificial intelligence algorithm. There are distinct flaws in "Jones'" photo, like how her earring is blurred by an algorithm that can generate facial features but not jewelry, or how there's an eerie light surrounding her hair, the Associated Press reports. But the ease with which whoever operates the account connected with a veritable who's who of D.C. politics shows just how dangerous these AI-generated images could be in future intelligence operations. Many of Jones' connections told the AP that they were initially suspicious of the profile but accepted anyway. Others, like Paul Winfree, who used to be deputy director of Trump's domestic policy council, told the AP that he accepts every single request he gets -- a move that lent further credibility to the fake account by proxy.

I am often asked about artificial intelligence and the future of work. My answer is that A.I. will change 100% of current jobs. It will change the job of a factory worker. It will change the job of a software developer, of a customer service agent, of a professional driver. And it will change my job as the CEO of one of the biggest technology companies in the world.