There is a stretch of highway through the Ozark Mountains where being data-driven is a hazard. Jason Tashea (@justicecodes), a writer and technologist based in Baltimore, is the founder of Justice Codes, a criminal justice and technology consultancy. Heading from Springfield, Missouri, to Clarksville, Arkansas, navigation apps recommend the Arkansas 43. While this can be the fastest route, the GPS's algorithm does not concern itself with factors important to truckers carrying a heavy load, such as the 43's 1,300-foot elevation drop over four miles with two sharp turns. The road once hosted few 18-wheelers, but the last two and half years have seen a noticeable increase in truck traffic--and wrecks.

The artificial intelligence market is often cited as the next frontier for many tech companies, since AI algorithms can quickly crunch large amounts of data to automate decisions. However, AI is frequently tossed around as a buzzword, which makes it tough for investors to identify the top investments in the market. Today, a trio of our Motley Fool contributors will highlight three companies that have established firm footholds in the nascent AI market: Baidu (NASDAQ:BIDU), NVIDIA (NASDAQ:NVDA), and AMD (NASDAQ:AMD). Leo Sun (Baidu): Chinese tech giant Baidu, which owns the country's largest search engine, is also one of the world's biggest players in artificial intelligence. Like its overseas counterpart Alphabet's Google, Baidu accumulated large amounts of data through its search engine, mapping platform, mobile app, and cloud services.

The oil price crash of 2014 and the global'digitalization and disruption' drive coincided in a rather bizarre way to push the oil industry to seek cost cuts through innovation and new technologies. Big Tech was only too pleased to help Big Oil, seeing a new revenue stream in an industry long thought to be of the'dinosaur' type that was too slow to embrace new ways of doing things. Many oil and gas firms, especially the world's biggest, are already using data analytics, cloud computing, digital oil fields, digital twins, robotics, automation, predictive maintenance, machine learning, and even AI. The technology giants have seized the opportunity to sell such services to Big Oil, and top managers at Amazon Web Services, Microsoft Azure, and ABB Group, to name a few, flocked to this week's top energy industry event CERAWeek by IHS Markit in Houston to pitch their solutions to a wider audience. "A great wave of innovation and technology is transforming the industry and reshaping the energy future," said Daniel Yergin, conference chair and vice chairman of IHS Markit.

Companies in Japan have a little more than six weeks to revamp their computer software to respond to the country's first era change in the digital age when a new Emperor is enthroned on May 1. The Ministry of Economy, Trade and Industry, or METI, is calling on companies to check where they use the Japanese calendar in their computer systems, modify necessary programs and carry out tests to detect potential problems. Eras are how Japan defines its history, so drivers' licenses, newspapers and a host of official documents use it to mark the years, with 2019 currently referred to as the "31st year of Heisei." The government will announce on April 1 the name of the new era, which will begin on May 1 in line with Crown Prince Naruhito's accession to the throne. According to the Information-Technology Promotion Agency, an independent administrative agency, systems using the current Heisei and other era names require program modifications.

We recently announced the strategic alliance between Jidoka and BigML, where we explained the integration of RPA with other technologies such as Machine Learning. With this integration, Jidoka can provide Machine Learning capabilities in their RPA process automation platform. To explain the advantages and possibilities offered by this integration, today we present a practical example of the application of both technologies, Jidoka's RPA and BigML's Machine Learning: the automation of an e-mail classification process, a use case that will be presented by Jidoka's CEO, Víctor Ayllón, at the #MLSEV, our first Machine Learning School in Seville, which will be held on March 7-8 in Seville (Spain). Imagine for a moment that you are responsible for the customer service department of a large company. You and your team receive on a daily basis a very large number of customer emails that are addressed to different departments of the company.

Access to skilled workers is already a key factor that sets successful companies apart from failing ones. In an increasingly data-driven future - the European Commission believes there could be as many as 756,000 unfilled jobs in the European ICT sector by 2020 - this difference will become even more acute. Skills gaps across all industries are poised to grow in the Fourth Industrial Revolution. Rapid advances in artificial intelligence (AI), robotics and other emerging technologies are happening in ever shorter cycles, changing the very nature of the jobs that need to be done - and the skills needed to do them - faster than ever before. At least 133 million new roles generated as a result of the new division of labour between humans, machines and algorithms may emerge globally by 2022, according to the World Economic Forum.

Geeta Manjunath turned entrepreneur in the backdrop of a tragedy. In 2017, a cousin she was really close to succumbed to breast cancer at a relatively young age. Breast cancer is the most commonly occurring cancer in women and the second most common worldwide. Gopinath, who has a PhD in computer science from the Indian Institute of Science, applied her scientific mind to the issue. Ubiquitous screening and early detection vastly reduces fatality from cancer. Gopinath had been part of the team that built India's first supercomputer, and most recently headed data analytics at Bengaluru's Xerox Research Centre and worked in various roles at Hewlett Packard Labs for 16 years.

Machine Learning (ML) and Artificial Intelligence (AI) are behind almost everything we do at Amazon. Some of this work is highly visible, such as autonomous Prime Air delivery drones, eliminating checkout lines at Amazon Go, and making everyday life more convenient for customers with Alexa. But much of what we do with AI and ML happens beneath the surface – from the speed in which we deliver packages, to the broad selection and low prices we're able to offer customers, to automatic extraction of characters and places from books and videos with X-Ray. This is in addition to the unrivaled breadth and depth of AI and ML services that AWS offers businesses of all sizes. Today we are excited to announce we are bringing together some of the brightest leaders across science, academia, and business to explore innovation, scientific advancements, and practical applications of AI and ML.

In the marketplace for artificial intelligence technology, giant companies like Google, Amazon, and Microsoft offer a powerful, centralized approach: They sell access to platforms for machine learning that hoover up vast amounts of users' personal and proprietary information and use that data to train AI models. A new development called federated learning offers an alternative to the centralized model. It promises to distribute the power of machine learning to mobile phones, IoT devices, and other equipment on the network edge. The payoff: Better performance and enhanced data security. By distributing AI training to the edge, "you speed up the training process significantly, and you get better accuracy," says Marcin Rojek, co‑founder at byteLAKE, a Poland‑based company working on federated learning solutions using Internet of Things (IoT) devices.

Researchers from the National Institutes of Health (NIH) and Global Good have created a computer algorithm capable of identifying precancerous changes in women which place them at risk of developing cervical cancer. Known as automated visual evaluation, this new form of artificial intelligence (AI), "has the potential to revolutionize cervical cancer screening" for women in low income communities worldwide by giving their healthcare providers the ability to use digitized images collected during routine, annual screenings for cervical cancer to identify potential precancerous changes. According to America's National Cancer Institute (which is part of the NIH), this technology holds the promise of enabling physicians to more quickly catch and treat such potential changes before they develop into cancer, and could eventually replace visual inspection with acetic acid (VIA) -- the current method of screening used by healthcare professionals who work with limited resources in challenging medical care environments -- a testing system which is "known to be inaccurate." The researchers involved in this project "trained" the machine learning algorithm (automated visual evaluation) to recognize patterns in medical images and other "complex visual inputs" by digitizing and entering more than 60,000 images from an NCI archive of photographs which had been collected from more than 9,400 women in Costa Rica during a 1990s cervical cancer screening study which included follow-up studies for roughly 18 years. These images subsequently enabled the algorithm to "learn" which "cervical changes became precancers and which did not," according to NIH representatives, who added that the AI approach to cervical cancer screening was developed by NCI researchers in collaboration with the Intellectual Ventures Fund, Global Good, with findings confirmed independently by personnel from the National Library of Medicine (NLM), another component of the NIH.