When sifting through the plethora of relevant topics surrounding the logistics and supply chain sector, Artificial Intelligence (AI) and Machine Learning (ML) are top of mind. It's worth taking a moment to truly digest how critical these advances have become to the growth and expansion of many industries. No, these aren't shiny new buzz-worthy terms, this is tech that has been quietly increasing in scope over the past few decades. Up 24% since 2020, Gartner projects this market will reach $596 Billion in 2022. Globally, supply chain markets are facing more stress points than ever, as some recent news cycles have highlighted this year – think the Suez Canal and the Colonial Pipeline debacles.

As part of the Novetta Mission Analytics team, I work on a data pipeline that ingests traditional and social media from around the world, enriches it, and makes the enriched data available to customers. Enrichment can involve any number of steps, many of them powered by machine learning, and one of the earliest and most common steps is translation. When new content arrives, the source language is often unknown and must be detected; if the source language is different from the target language, the content is also translated. In order to translate this volume of content automatically, accurately, and cost effectively, we rely on multiple cloud translation services. To the surprise of no one, cloud translation services differ not only in pricing but also in the languages they support and in the quality of translation across them. It's often most cost effective to perform language detection with one service and, depending on the detected language, translation with another. In addition, these services occasionally use different identifiers to refer to the same language, which requires us to do some mapping on our end.

Retired Navy SEAL Commander Dave Sears suggests Russia, China and Pakistan could face national security issues once U.S. troops leave Afghanistan. This is a rush transcript of "Your World with Neil Cavuto" on July 8, 2021. This copy may not be in its final form and may be updated. QUESTION: Do you trust the Taliban, Mr. President? Do you trust the Taliban, sir? JOE BIDEN, PRESIDENT OF THE UNITED STATES: Are you -- is that a serious question? QUESTION: It is absolutely a serious question. Do you trust the Taliban? BIDEN: No, I do not. BIDEN: No, I do not trust the Taliban. QUESTION: Is the U.S. responsible for the deaths that happen the Afghans after you leave the country? QUESTION: Mr. President, will you amplify that question, please? Will you amplify your answer, please, why you don't trust the Taliban? BIDEN: It is a silly question. Do I trust the Taliban? And it almost seemed like a Donald Trump press conference, with angry reporters trying to get a simple answer from the president, and their agitation showing, as the questions and the nonanswers went on, all of this at a time U.S. forces are moving rapidly ahead of schedule. Better than 90 percent now have left Afghanistan. And we could see them all out well before the 9/11 deadline that the president has set. But he says he's not going to change his mind. And he says that, after 20 years, Afghans must look after themselves. Jennifer Griffin has more from the Pentagon.

This FREE online programme offers a deep-dive into the potential of artificial intelligence to journalists and media professionals from small newsrooms. It is designed by the JournalismAI team at the London School of Economics and Political Science (LSE) and powered by the Google News Initiative. The Academy is a 6-week online programme that starts in September 2021 and, in its first pilot edition, it is designed for 20 participants from small news organisations (fewer than 50 employees) in the EMEA region (Europe, Middle East and Africa). In line with JournalismAI's mission to inform media organisations about the potential offered by AI-powered technologies and to foster debate about the ethical, editorial, and social impact of AI on journalism, the Academy aims to support small newsrooms that want to learn how AI can be used to support their journalism. The programme combines a series of masterclasses given by experts working at the intersection of journalism and artificial intelligence with opportunities for discussion among participants.

In a bid to help small media publishers reach new audiences and drive more traffic to their content, the Google News Initiative (GNI) has launched a training academy for 20 media professionals to learn how Artificial Intelligence (AI) can be used to support their journalism. Google is partnering with Polis, the London School of Economics and Political Science's journalism think tank, to launch the training academy, it said in a statement on Thursday. The AI Academy for Small Newsrooms is a six-week long, free online programme taught by industry-leading journalists and researchers who work at the intersection of journalism and AI. It will start in September this year and will welcome journalists and developers from small news organisations in the Europe, Middle East, and Africa (EMEA) region.

Supervised machine learning, in which machine learning models learn from labeled training data, is only as good as the quality of that data. In a study published in the journal Quantitative Science Studies, researchers at consultancy Webster Pacific and the University of California, San Diego and Berkeley investigate to what extent best practices around data labeling are followed in AI research papers, focusing on human-labeled data. They found that the types of labeled data range widely from paper to paper and that a "plurality" of the studies they surveyed gave no information about who performed labeling -- or where the data came from. While labeled data is usually equated with ground truth, datasets can -- and do -- contain errors. The processes used to build them are inherently error-prone, which becomes problematic when these errors reach test sets, the subsets of datasets researchers use to compare progress. A recent MIT paper identified thousands to millions of mislabeled samples in datasets used to train commercial systems.

In recent decades, AI has become a pervasive technology, affecting companies across industries and throughout the world. These innovations arise from research, and the research objectives in the AI field are influenced by many factors. Together, these factors shape patterns in what the research accomplishes, as well as who benefits from it -- and who doesn't. In an effort to document the factors influencing AI research, researchers at Stanford, the University of California, Berkeley, the University of Washington, and University College Dublin & Lero surveyed 100 highly cited studies submitted to two prominent AI conferences, NeurIPS and ICML. They claim that in the papers they analyzed, which were published in 2008, 2009, 2018, and 2019, the dominant values were operationalized in ways that centralize power, disproportionally benefiting corporations while neglecting society's least advantaged.

For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments. In this paper, we empirically show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically, we demonstrate strong correlations between in-distribution and out-of-distribution performance on variants of CIFAR-10 & ImageNet, a synthetic pose estimation task derived from YCB objects, satellite imagery classification in FMoW-WILDS, and wildlife classification in iWildCam-WILDS. The strong correlations hold across model architectures, hyperparameters, training set size, and training duration, and are more precise than what is expected from existing domain adaptation theory. To complete the picture, we also investigate cases where the correlation is weaker, for instance some synthetic distribution shifts from CIFAR-10-C and the tissue classification dataset Camelyon17-WILDS. Finally, we provide a candidate theory based on a Gaussian data model that shows how changes in the data covariance arising from distribution shift can affect the observed correlations.

MONTREAL and VANCOUVER, British Columbia, July 08, 2021 (GLOBE NEWSWIRE) -- Rithmik Solutions, whose mission is building the world's most advanced and reliable analytics for mobile mining equipment, today announced the closing of a US$1.2M investment led by Chrysalix Venture Capital and joined by Fonds Ecofuel. The funding will accelerate the commercialization of the company's flagship product, Rithmik Asset Health Analyzer (AHA), which has been in development for the past three years and is currently undergoing real-time onsite trials in Alberta, Quebec and Zambia. Rithmik AHA applies a multi-tiered machine learning approach to increase mobile equipment uptime while reducing maintenance costs and lowering greenhouse gas emissions. Mining companies typically spend anywhere from 20%-50% of their annual operating budgets on equipment maintenance, and lost production from unplanned downtime has an even bigger financial impact. "We were impressed by the Rithmik team's deep technical experience in the space of mobile mining equipment data, across equipment types and OEM brands, and that experience has strongly resonated with their early customers," said Alicia Lenis, Vice President at Chrysalix Venture Capital, an industrial innovation fund.

In the popular Terminator movies, a relentless super-robot played by Arnold Schwarzenegger tracks and attempts to kill human targets. It was pure science fiction in the 1980s. Today, killer robots hunting down targets have not only become reality, but are sold and deployed on the field of battle. The new Turkish-made Kargu-2 quadcopter drone can allegedly autonomously track and kill human targets on the basis of facial recognition and artificial intelligence--a big technological leap from the drone fleets requiring remote control by human operators. A United Nations Security Council report claims the Kargu-2 was used in Libya to mount autonomous attacks on human targets.