The concept of data streaming is not new. But one of the most critical emerging uses for streaming data is in the public sector, where government agencies are eyeing its game-changing capability to advance everything from battlefield decision-making to constituent experience. IDC predicts that the collective sum of the world's data will grow 33%, to 175 zettabytes, by 2025. For context, at today's average internet connection speeds, 175 zettabytes would take 1.8 billion years for one person to download. Streaming has only further accelerated the velocity of data growth.
MILAN – Perhaps no single aspect of the digital revolution has received more attention than the effect of automaton on jobs, work, employment, and incomes. There is at least one very good reason for that – but it is probably not the one most people would cite. Former US President Donald Trump is not Hitler, and America is not the Weimar Republic. But, as four excellent recent books about the interwar years show, false narratives and craven political choices can have dreadful consequences that may not emerge immediately. Using machines to augment productivity is nothing new.
Colby College is carving out space in the liberal arts canon for artificial intelligence. Thanks to a $30 million gift from an alumnus, the small, selective college in Maine is establishing the Davis Institute for Artificial Intelligence, which aims to integrate machine learning, natural language processing and big data into instruction and research across the college. "We want to be sure we're preparing students well for their futures: lives and careers of meaning and purpose," says Margaret McFadden, provost and dean of faculty at Colby. "Well-educated people have to understand AI, what these tools are and how to use them." Artificial intelligence has homes at other U.S. higher ed institutions, including Massachusetts Institute of Technology, the University of Georgia, Stevens Institute of Technology in New Jersey, and Stanford University.
The mass affordability of sequencing enables a paradigm shift from sequencing only those with risk factors (such as someone's family history or medical symptoms) to sequencing proactively to identify risk factors. It will allow every individual to build up genomic data capital, opening the door for new applications and business models across health insurance, care delivery, and everyday life. New approvals & patents - Ava, a Swiss digital healthcare company focused on women's reproductive health, announced that the United States Food and Drug Administration (FDA) clearance for its fertility tracking wearable. BrainQ, an Israeli start-up, announced that the FDA has designated its AI-powered electromagnetic field therapy that aims to enhance recovery and reduce disability after neurological damage caused by stroke as a Breakthrough Device, giving access to the new Medicare Coverage of Innovative Technology (MCIT) pathway. Voluntis (French DTx) announced the issuance of a new patent by the European Patent Office (EPO) for intelligent patient support in drug dosing applied in the field of diabetes for insulin titration support.
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It's easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for "machine learning" since 2012 – but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems. With new AI buzzwords being created weekly, it can seem difficult to get ahold of what applications are viable, and which are hype, hyperbole or hoax. At Emerj, our market research focuses on cutting through the AI hype, and helping innovation and strategy leaders make a better business case for AI. This includes both our AI Opportunity Landscape research with enterprise clients, and our Emerj Plus best-practices guides for consultants and vendors. In this article, we'll break down categories of business problems that are commonly handled by ML, and we'll also provide actionable advice to begin a ML initiative with the right approach and perspective (even it's the first such project you've undertaken at your company).
The Internet of Things (IoT) is creating a new dynamic in the industrial space, specifically manufacturing. Large manufacturing companies are moving to leverage IoT technologies and predictive analytics as a part of increasing profitability and staying competitive. In the U.S. alone, existing capital stock worth over $6.8 trillion dollars is being fitted with sensors to drive key insights and create connected environments. This next wave of industrial evolution, coined as Industry 4.0, will generate $14.2 trillion of global output by 2030, according to the World Economic Forum. Predictive analytics using automation and machine learning technology is key in monetizing these connected plant environments.
For Bollywood, beautiful women have fair skin, according to an Artificial Intelligence (AI)-based computer analysis which reveals that conception of beauty has remained consistent through the years in the film industry centred in Mumbai. The automated computer analysis was led by Indian-origin researchers at Carnegie Mellon University (CMU) in the US. The research revealed that babies whose births were depicted in Bollywood films from the 1950s and 60s were more often than not boys; in today's films, boy and girl newborns are about evenly split. In the 50s and 60s, dowries were socially acceptable; today, not so much. The researchers, led by Kunal Khadilkar and Ashiqur KhudaBukhsh of CMU's Language Technologies Institute (LTI), gathered 100 Bollywood movies from each of the past seven decades along with 100 of the top-grossing Hollywood moves from the same periods.