With approximately 450,000 background investigations in inventory and an executive order shifting its operations under the Defense Department, the National Background Investigations Bureau is optimistic about its future. The agency this year has made significant progress in churning through its backlog, which reached 725,000 at its peak in April 2018, said NBIB Director and Acting Defense Counterintelligence and Security Agency Director Charles Phalen in a roundtable interview. As Phalen assured progress would continue, he was quick to note that the agency operates in a steady state with thousands of cases in its working inventory. The backlog, in a sense, will never be eliminated. "When you think about what our goal here is, it is never to get to zero because that just will never work out. Our working inventory that allows us to stay timely is roughly 200,000," he said.

The Defense Advanced Research Projects Agency (DARPA) has awarded a contract of up to $4.7 million to BAE Systems to integrate machine learning (ML) into platforms deciphering radio frequency signals. Officials said the Controllable Hardware Integration for Machine-learning Enabled Real-time Adaptivity (CHIMERA) program provides a reconfigurable hardware platform for ML algorithm developers to make sense of radio frequency (RF) signals in increasingly crowded electromagnetic spectrum environments. "CHIMERA brings the flexibility of a software solution to hardware," said Dave Logan, vice president and general manager of Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) Systems at BAE Systems. "Machine-learning is on the verge of revolutionizing signals intelligence technology, just as it has in other industries." CHIMERA will enable ML software development to adapt the hardware's Radio Frequency (RF) configuration in real-time to optimize mission performance, officials said, adding the capability has never before been available in a hardware solution, with the system providing multiple control surfaces for the user, enabling on-the-fly performance trade-offs maximizing sensitivity, selectivity and scalability depending on mission need.

The'Vegebot', developed by a team at the University of Cambridge, was initially trained to recognise and harvest iceberg lettuce in a lab setting. It has now been successfully tested in a variety of field conditions in cooperation with G's Growers, a local fruit and vegetable co-operative. Although the prototype is nowhere near as fast or efficient as a human worker, it demonstrates how the use of robotics in agriculture might be expanded, even for crops like iceberg lettuce which are particularly challenging to harvest mechanically. The results are published in The Journal of Field Robotics. Crops such as potatoes and wheat have been harvested mechanically at scale for decades, but many other crops have to date resisted automation.

With 340,000 trained volunteers in 189 countries and 140 languages, 7 Cups has an unparalleled reach in the behavioral health space.

The big data market is in a state of upheaval as companies begin shifting their data strategies from "nothing" or "everything" in the cloud to a strategic mix, squeezing out middle-market players and changing what gets shared, how that data is used, and how best to secure it. This has broad implications for the whole semiconductor supply chain, because in many cases it paves the way for more data to move freely between different vendors, no matter where they sit in that chain. That can go a long way toward improving the quality of chips and systems, reducing the cost of design and manufacturing, and shed light on supply chain constraints. It also opens up many more opportunities for data analysis to help offset rising concerns about liability in markets such as automotive, medical and mil/aero. "For years, the Fortune 500 to the Global 5,000 were reticent about moving to the cloud, but all of a sudden in the last 12 to 18 months there has been a massive shift to the cloud," said Michael Schuldenfrei, corporate technology fellow at Optimal Plus.

As artificial intelligence re-writes business models, how will its application and adoption revolutionize business and commerce further? From the production and marketing era to the relationship and intelligence era, business models have been evolving over the centuries. Over the years, the rise of artificial intelligence (AI) has fundamentally transformed the very meaning of ideas, innovation, and inventions. As a result, business models are evolving further. As we witness businesses across industries undergo a profound and dramatic shift in the relative balance of intelligence power, AI applications and adoption are offering each business entity as many new opportunities as it does challenges.

Black Knight, Inc. (BKI), a leading provider of integrated software, data and analytics to the mortgage and real estate industries, announced the completion of an independent analysis of the real-world impact of the company's artificial intelligence (AI) solution, AIVA. Using actual findings from the operations of a top 50 lender in Q4 2018, MarketWise Advisors, LLC – a management consulting firm focused on technology in the financial services industry – found that using AIVA can significantly drive down the cost of mortgage originations. The study identified a savings of up to $437 per originated loan, with the cost impact continuing to grow significantly as AIVA's machine learning and unique pattern recognition capabilities mature to include more skills. "Through the independent analysis of an unbiased third party, we have been able to show that lenders can expect to see significant savings by incorporating AIVA into their origination process," said Anthony Jabbour, Chief Executive Officer of Black Knight. "Leveraging AIVA results in significant cost savings, provides the ability to redirect tens of thousands of man hours to items more focused on satisfying customers and produces a return on investment nearing 500%."

Agriculture has been facing major challenges like lack of irrigation, change in temperature, groundwater density, food wastage, cold storage, and much more. The technology will be useful in helping farmers in high yielding and having a better seasonal crop at regular interval. In this digital transformation age, technology companies across the world have been developing the best solutions based on agriculture technology (AgTech) to enhance production. Digital transformation and technology adoption have brought many radical changes in many sectors including agriculture. It is speculated that the implementation of Artificial Intelligence (AI) in agriculture will transform the sector.

For years, hopeful parents pursuing in vitro fertilization (IVF) treatment have had the option of screening embryos for severe heritable diseases like cystic fibrosis, hemophilia, and Tay-Sachs disease. These rare and often deadly conditions, known as monogenic disorders, can be easily identified through genetic screening because they arise due to a mutation on a single gene. For doctors, diagnosis is a simple positive or negative. But the diseases that are most likely to shadow the average person's life -- cancer, heart disease, diabetes -- are polygenic, meaning that they result from interactions between thousands of genetic signals. In the past, this has made these diseases -- which kill millions of Americans each year -- all but impossible to screen for with genetic tests. But Genomic Prediction, a New Jersey-based company that analyzes genetic data using machine learning, is hoping to change that.

Just like a ray of light, a wireless signal bounces off of certain objects and surfaces. Different materials affect the wireless signal in different ways. Human bodies, therefore, cause particular changes when reflecting wireless signals. Using this property, MIT researchers from the Computer Science and Artificial Intelligence Laboratory (CSAIL) designed a wireless system that captures reflections off of humans and collects health and behavioral data. The system, dubbed Marko, broadcasts radio-frequency (RF) signals that bounce off people in motion and return with specific changes. Specially-designed algorithms then analyze the reflected signals and associate them with specific people.