Big Data


Harnessing big data and machine learning to forecast wildfires in the western U.S.

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The area burned by wildfires each year across the Western United States has increased by more than 300 percent over the past three decades, and much of this increase is due to human-caused warming. Warmer air holds more moisture, and the thirsty air sucks this from plants, trees, and soil, leaving forest vegetation and ground debris drier and easier to ignite. Future climate change, accompanied by warming temperatures and increased aridity, is expected to continue this trend, and will likely exacerbate and intensify wildfires in areas where fuel is abundant. Park Williams, a Lamont-Doherty Earth Observatory associate research professor and a 2016 Center for Climate and Life Fellow, studies climatology, drought, and wildfires. He has received a $641,000 grant from the Zegar Family Foundation that he'll use to advance understanding of the past and future behavior of wildfires.


Current status of use of big data and artificial intelligence in RMDs

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Objective To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).


Machine Learning, AI & Big Data Analytics in the Travel & Hospitality Industry: Applications, Scopes, and Impact on the Job Market

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Travel & tourism is on its rise nowadays. This may be explained by the fact that it has become more affordable to a broader audience. But, in today's fast-paced world, finding time to travel to a ticket office and get your tickets is a luxury few can afford. Like any other industry, machine learning, AI and big data analytics have changed the travel & hospitality industry as well. In this post, we will discuss the major applications and future scopes of machine learning, AI & big data analytics in the travel & hospitality industry – across the globe and India. Additionally, we will also have a look at how machine learning, AI and big data analytics are reshaping the hospitality job market. Due to rapid digital transformation, over 500 billion dollars ($564.87 billion) was made in the travel & hospitality sector in the year 2016 alone. The number is expected to reach $817.54 billion by 2020.


Global Big Data Conference

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Businesses that work with artificial intelligence (AI) and machine learning (ML) are set to grow their usage of the technology in the next few years, according to a new Gartner report. The analyst firm found that most top businesses currently have four AI or ML projects running today on average, and expect to add six more projects within the next 12 months. Within three years, businesses expect to add another 15 AI / ML projects, meaning that by the time we hit 2022, many companies will have an average of 35 AI / ML projects in place. Businesses use AI and ML mostly to improve their customer experience, but they find the technology super useful internally, to support decision-making and give employees valuable recommendations. The second most important project type seems to be task automation, as 20 per cent of respondents claimed it was their number one motivator.


How AI Can Help Marketers Harness Big Data Opportunities in 2019 - insideBIGDATA

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In this special guest feature, Solomon Thimothy, CEO of DMA Digital Marketing Agency, believes that digital marketing advancements in 2018 have set a high bar for customer expectations. Customers now expect, deserve and demand that personalized, seamless transactions will only increase in 2019. By focusing on data-based AI solutions, organizations can ensure the customer journey will be more personalized and more profitable in the year to come. Solomon focuses his expertise and passion in helping businesses invest in long-term digital marketing for financial growth. His education from Northeastern Illinois University and North Park University provide him with the tools needed to lives up to its digital marketing commitments.


Global Big Data Conference

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At the 2019 Semicon Conference Applied Materials (AMAT) had a day-long seminar focused on technology, particularly memory, for artificial intelligence (AI) applications. In addition to talks by AI experts, the company also talked about their tools for manufacturing magnetic random access memory (MRAM) as well as resistive random access memory (RRAM) and Phase Change Memory (PCM). We will talk about a workshop at Stanford in August will explore emerging memories enabling artificial intelligence, especially for embedded products, such as IoT devices. Gary Dickerson from Applied Materials gave a kick-off talk at the seminar. He talked about the growth of data and the importance of memory to support data centers as well as the edge.


The Dangers of Demonizing AI - InformationWeek

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How do you benchmark the "evil" quotient in your AI app? That may sound like a facetious question, but let's ask ourselves what it means to apply such a word as "evil" to this or any other application. And, if "evil AI" is an outcome we should avoid, let's examine how to measure it so that we can certify its absence from our delivered work product. Obviously, this is purely a thought experiment on my part, but it came to mind in a serious context while I was perusing recent artificial intelligence industry news. Specifically, I noticed that MLPerf has recently announced the latest versions of its benchmarking suites for both AI inferencing and training.


Human Capital Management Technology May Be 'Demo Candy' - InformationWeek

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AI is finding its way to more places in organizations, including human resources. Human capital management providers are building AI into their solutions, but depending on the details, it may be wiser to build your own application than buy something off-the-shelf. Earlier this year, Gartner issued a research note exploring AI use cases in human capital management (HCM). Its author, VP Analyst Helen Poitevin, concluded that many of these applications were still in the "demo candy" stage, mainly to demonstrate product roadmaps. In other words, AI-related expectations are outpacing reality.


AI in manufacturing: adopt early to gain market share - The Manufacturer

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With research suggesting artificial intelligence in manufacturing could become mainstream within 24 months, what can manufacturers gain from taking an early adopter approach? With AI and advanced analytics to identify patterns and trends in the wealth of data generated by the IoT, the barriers between operational technology and information technology are breaking down. Manufacturers can become data-driven in all aspects of business, enabling the companies to transform operations, restructure supply chains, improve efficiency, address skills shortages and create entirely new revenue streams and business models. Despite the many benefits, the Manufacturing Leadership Council's'Factories of the Future' survey revealed that less than one in 10 (8%) of manufacturers are currently using AI – though a further 50% expect to deploy it within two years. AI is still nascent in manufacturing today, yet these results suggest it could become mainstream in under 24 months.


Harnessing Big Data and Machine Learning to Forecast Wildfires in the Western U.S. 7wData

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The area burned by wildfires each year across the Western United States has increased by more than 300 percent over the past three decades, and much of this increase is due to human-caused warming. Warmer air holds more moisture, and the thirsty air sucks this from plants, trees, and soil, leaving forest vegetation and ground debris drier and easier to ignite. Future climate change, accompanied by warming temperatures and increased aridity, is expected to continue this trend, and will likely exacerbate and intensify wildfires in areas where fuel is abundant. Park Williams, a Lamont-Doherty Earth Observatory associate research professor and a 2016 Center for Climate and Life Fellow, studies climatology, drought, and wildfires. He has received a $641,000 grant from the Zegar Family Foundation that he'll use to advance understanding of the past and future behavior of wildfires.