big data


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

#artificialintelligence

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.


The Impact Of Artificial Intelligence In Web Design

#artificialintelligence

When Alan Turing invented the first intelligent machine, few could have predicted that the advanced technology would become as widespread and ubiquitous as it is today. Since then, companies have adopted AI for pretty much everything, from self-driving cars to medical technology to banking. We live in the age of big data, an age in which we use machines to collect and analyze massive amounts of data in a way that humans couldn't do on their own. In many respects, the cognition of machines is already surpassing that of humans. With the explosion of the internet, AI has also become a critical element of web design.


Why IoT Needs Machine Learning to Thrive IoT For All

#artificialintelligence

Meanwhile, companies are installing more and more sensors hoping to improve efficiency and cut costs. However, machine learning consultants from InData Labs say that without proper data management and analysis strategy, these technologies are just creating more noise and filling up more servers without actually being used to their potential. Is there a way to convert simple sensor recordings into actionable industrial insights? The simple answer is yes, and it lies in machine learning (ML). The scope of ML is to mimic the way the human brain processes inputs to generate logical responses.


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

#artificialintelligence

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


IoT's Role in Revolutionizing Agriculture

#artificialintelligence

It's rare to see tech headlines about agriculture, but the field (pardon the pun) is often at the forefront of implementing new technology Perhaps no recent tech development has had a greater impact on the industry than smart technology, and this IoT data is being used to improve operations across nearly all modern farming operations around the globe. Here are a few examples. Farmers were among the first to adopt GPS technology; John Deere was the first tractor manufacturer to implement GPS technologies in the early 1990s, and farmers quickly began using GPS assistance and even automated steering to reduce user errors. GPS technology can be combined with sensor data to create ultra-precise maps of varying factors. Knowing how soil quality varies across large plots of land, for example, can help farmers know which areas need which type of fertilizers.


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

#artificialintelligence

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

#artificialintelligence

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.


Text Analytics: the convergence of Big Data and Artificial Intelligence

#artificialintelligence

The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers-- comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain--s representation, and many more.


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

#artificialintelligence

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

#artificialintelligence

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.