Machine Learning & Cognitive Computing

From 2011 We Trust in Value

Machine Learning & Cognitive Computing

Over the last several decades, organizations have relied on analytics to provide them with competitive advantage and enable them to be more effective. Analytics have become an expected part of the bottom line and no longer provide the advantages that they once did. Organizations are now forced to look deeper into their data to find new and innovative ways to increase efficiency and competitiveness. With recent advances in science and technology, particularly in machine learning, organizations are adopting larger, more comprehensive analytics strategies.

Machine learning draws from numerous fields of study—artificial intelligence, data mining, statistics, and optimization. It can go by other aliases and consists of overlapping concepts from the analytic disciplines. Data mining, a process typically used to study a particular commercial problem with a particular business goal in mind, uses data storage and data manipulation technologies to prepare the data for analysis. Then, as part of the data mining task, statistical or machine learning algorithms can detect patterns in the data and make predictions about new data.

There are a number of learning scenarios, or types of learning algorithms, that can be used depending on whether a target variable is available and how much labeled data can be used. These approaches include supervised, unsupervised, and semi-supervised learning; reinforcement learning is an approach often used in robotics but also used in several recent machine learning breakthroughs. Machine learning gives organizations the potential to make more accurate data-driven decisions and to solve problems that have stumped traditional analytical approaches, such as those involving new sources of unstructured data, including graphics, sound, videos, and other high-dimensional, machine-generated data.

Machine learning is already in use by a variety of industries, including:

  • Automotive: for driverless cars, automatic emergency response systems can make maneuvers without driver input.
  • Banking: big data sources provide the opportunity to market new products, balance risk, and detect fraud. Furthermore can be used for automatic trading systems
  • Government: pattern recognition in images and videos enhance security and threat detection while the examination of transactions can spot healthcare fraud.
  • Healthcare: big data analysis system can create support diagnostic systems for prevention and cure.
  • Manufacturing: pattern detection in sensor data or images can diagnose otherwise undetectable manufacturing defects.
  • Retail: micro-segmentation and continuous monitoring of consumer behavior can lead to nearly instantaneous customized offers.

T4V can offer its customers an ecosystem of technologies, models and approaches to test and deploy Machine Learning solutions to support their business.

Technologies and products like SAS Visual Analytics, SAS Visual Statistics, SAS Enterprise Miner, SAS Text Miner, SAS Model Manager, Cloudera already provide an enabling platform to implement machine learning solutions for customers, looking forward to a near future where using a solution like SAS Viya it will be possible to implement them in Private / Public Cloud or Hybrid environments.

(part of this introduction is derived by the book “The Evolution of Analytics”, Patrick Hall, Wen Phan, and Katie Whitson, O’Reilly Media Inc)