The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. The new SAS Visual Data Mining and Machine Learning software will feed this need for smarter analytics. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks.
Berkeley Lab researchers Vahe Tshitoyan, Anubhav Jain, Leigh Weston, and John Dagdelen used machine learning to analyze 3.3 million abstracts from materials science papers. Sure, computers can be used to play grandmaster-level chess, but can they make scientific discoveries? Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials.
Britain's manufacturing sector has just posted its strongest growth in over two years. Latest figures show that export orders have increased at their fastest rate since January 2014 and factories have also taken on more workers, with employment rising for the second consecutive month. However, the export benefits of a weakened pound will not last forever, and so, as the manufacturing sector continues to evolve, this year's FT Future of Manufacturing Summit looked at how big data analytics, advanced robotics, the Internet of Things (IoT) and additive manufacturing are shaping the economics of production and distribution within the sector. With the opportunities big data brings referred to as the Fourth Industrial Revolution in manufacturing, the estimated £57bn boost to the industry over the next five years will be driven by gains in efficiency through the use of big data analytics. The winners will be those who can adapt, embrace technologies and respond to new demands.
"Hiding within those mounds of data is knowledge that could change… the world." We are currently living in a post-modern world, an age where technology, data, and information rule the world. Consequently, it is easy to believe that the concept of Big Data is a unique phenomenon that started in 2012 when Barack Obama (and his government) announced the Big Data Research and Development Initiative. However, large amounts of data have been around since the development of the Internet in 1991. The fundamental difference between then and now is that our ability to interpret these masses of data has evolved to the point that we can now utilise this data as part of our business decision-making process.