The outcome of the 2020 US Presidential election is becoming less and less predictable by the day. Will a vaccine be available by November? How many people will (be able to) vote? There's not even agreement on how many swing states there are -- 6, 10, 11, perhaps 12 or more? There are many opinionated arguments to be made, but there's hardly a rigorous way to analyze how unprecedented current events will impact voting habits.

It's not surprising that we've experienced an explosion in artificial intelligence (AI) patent activity over the past several years. As recently as 2016, the United States Patent & Trademark Office (USPTO) issued less than 1,000 AI-related patents. As this explosion has occurred, so have interesting questions concerning patentability, inventorship, ownership, and disclosure issues. To address these (and other) concerns, the USPTO launched its Artificial Intelligence Initiative in 2019, engaging the innovation community and experts to determine whether AI required any changes to the U.S. Patent system. In response to requests for public comments on these topics, the USPTO received comments from 43 organizations, ranging from domestic and international patent/IP bar associations to companies such as Ford Motor Co. and Merck, and also from 55 individuals.

With part of the world dealing with the adverse effects of hurricanes and intense tropical cyclones, it has become imperative for researchers and scientists to develop a way to predict and analyse these hurricane patterns. Thus in an attempt to forecast future hurricane intensity, scientists at NASA's Jet Propulsion Laboratory in Southern California have proposed a machine learning model that claims to predict rapid-intensification events of the future accurately. The critical factor in understanding the intensity of a hurricane is the wind speed. Traditionally it has been a challenge to predict the severity of storms or hurricanes while it's brewing. However, NASA's new ML model can improve the accuracy of the prediction and provide better results.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In an amazing first, an artillery cannon took out a cruise missile. An M109 Paladin 155 mm howitzer made history recently by shooting down a fast-moving maneuvering cruise missile with a "hypervelocity projectile" able to travel at speeds up to Mach 5, according to an Air Force announcement. Historically, armored vehicles such as tanks, howitzers or infantry carriers have not operated with an ability to destroy fast-moving, long-range cruise missiles, yet the successful demonstration breaks new ground.

New Delhi: In a bid to increase use of artificial intelligence in the military, the US Air force conducted a major exercise with robot dogs trained to scout for threats before their human counterparts enter the field. The four-legged, headless, mechanical creatures were made to exit an aircraft and look for signs of danger at the Nellis Air Force Base in the US state of Nevada last week. They are part of an Advanced Battle Management System (ABMS) that the US Air force is building, which will use artificial intelligence and data analytics to detect counter threats to the US military. "Valuing data as an essential war fighting resource, one no less vital than jet fuel or satellites, is the key to next-gen warfare," Will Roper, assistant secretary of the Air Force for acquisition, technology and logistics, told CNN. The dogs have been manufactured by Ghost Robotics, a company based in Philadelphia, and are called Vision 60 UGVs, or "autonomous unmanned ground vehicles".

In this tutorial, you will learn how to OCR a document, form, or invoice using Tesseract, OpenCV, and Python. On the left, we have our template image (i.e., a form from the United States Internal Revenue Service). The middle figure is our input image that we wish to align to the template (thereby allowing us to match fields from the two images together). And finally, the right shows the output of aligning the two images together. At this point, we can associate text fields in the form with each corresponding field in the template, meaning that we know which locations of the input image map to the name, address, EIN, etc. fields of the template: Knowing where and what the fields are allows us to then OCR each individual field and keep track of them for further processing, such as automated database entry.

The U.S. Bureau of Labor statistics estimates that 11.5 million new data science jobs will be added by 2026. This projection suggests that the field of data science will continue to rapidly grow with the high demand for these positions in industry. For those just starting out in the space or considering a transition from another industry, podcasts are useful for developing an understanding of data science and machine learning in industry and research. In this post, I will discuss three of my favorite podcasts that discuss the data science field in research and industry. These podcasts also suggest learning resources for those how are just starting out.

With telcos and enterprises alike fast tracking their digitalisation initiatives across the region, the Middle East finds itself on the cutting edge when it comes to digital innovation. In the Gulf Cooperation Council (GCC), the launch of 5G will bring a plethora of opportunities for enterprises to redefine the way they do business and deliver a diverse portfolio of digital services and products to an increasingly demanding client base. Similarly, across the greater Middle East and Africa region, the proliferation of 4G and LTE networks is kick starting a digital revolution that will fundamentally change the way people live their lives – opening up a veritable smorgasbord of digital opportunities, from remote working and e-learning initiatives, to mobile financial applications and e-health solutions. As telcos continue to evolve their product offering way beyond the mere provision of "dumb pipe" connectivity, they will increasingly look to leverage partnerships with service providers who can facilitate their own digitalisation on an end-to-end basis. Most of the world's biggest and most ambitious digital transformation champions are looking to sharpen their focus on the Middle East and Africa region.

It's a big election year, and one party's candidate is the successor to a popular two-term president. A little-known company offers the other party, which is in disarray, technology that uses vast amounts of data to profile voters. The election is incredibly close -- and the longshot candidate wins. This was 1960, not 2016, and the winning ticket was John F. Kennedy, not Donald Trump. The little-known -- and now nearly entirely forgotten -- company was called Simulmatics, the subject of Harvard historian and New Yorker writer Jill Lepore's timely new book, If Then: How the Simulmatics Corporation Invented the Future.

A project launched by the Pentagon's chief artificial intelligence office in response to the coronavirus pandemic has provided several lessons to help in a pivot toward developing AI tools for war. The Joint Artificial Intelligence Center's COVID-19 project, called Project Salus, delivered AI tools to leaders at U.S. Northern Command, with which they predicted shortfalls in food and medical supplies. With Project Salus, the JAIC focused on quick development and delivery of the tools -- an effort that has taught several lessons as the center moves forward with its joint war-fighting effort and as its acting director pushes the organization to become "the world's best software company." "With Project Salus, one of the key things that we pivoted on was the ability for us very quickly -- in a couple weeks -- to get a base product that really didn't have a great looking [user interface] or other pieces there, but had the core functionality in the hands of NORTHCOM leaders," JAIC acting Director Nand Mulchandani said at a Sept. 10 news conference. The leaders provides "feedback on what was working and not working," Mulchandani added.