«

»

Oct 11 2017

Print this Post

MACHINES THAT LEARN: Artificial intelligence may transform manufacturing, but adoption is slow

Manufacturers recognize that artificial intelligence offers an exciting future, enabling greater automation, improved predictive maintenance and a move to mass customization. While adoption so far remains slow, experts agree that the combination of human expertise and industry-wide collaboration will pave the way for success.

FANUC is running a Zero Down Time application on its new FIELD system, which collects data from more than 6,000 robots in 26 factories and analyzes it with a form of AI known as machine learning. (Image © FANUC)

Advances in artificial intelligence (AI) – defined by San Francisco-based computing company NVIDIA as “human intelligence exhibited by machines,” are being made at breakneck speed. Al comes into play every time we ask Siri or Alexa a question, view a recommendation by Netflix or add a friend suggested by Facebook.

While AI helps drive many everyday consumer interactions, its power has only recently been felt among businesses.

“AI has reached a tipping point in what it can do for enterprises,” said Mark Purdy, managing director and chief economist at Accenture Research in London. “This is thanks to developments in processing power, data storage, data retrieval, sensors, and algorithms. As a result, businesses are now able to optimize processes with intelligent automation systems, augment human labor and physical capital and propel new innovations.”

Business AI breakthroughs are everywhere. Computer scientists at Stanford University’s AI Laboratory in California have trained an algorithm to visually diagnose potential skin cancers. Microsoft has demonstrated a speech- recognition system that makes the same or fewer errors than professional transcriptionists. Scientists at MIT’s Computer Science and AI Laboratory in Massachusetts have mined data from more than 3 million taxi rides to develop a smarter way to move people around Manhattan. And major automakers have used deep learning, a machine learning implementation technique, to create autonomous vehicles that scan, analyze and then respond to their surroundings, aiding drivers in optimizing their decisions and actions.

ADOPTION IN MANUFACTURING

The manufacturing sector, however, is lagging. In an article for media intelligence company Meltwater, Brent Dykes, director of data strategy at Utah-based software company Domo, said that “analytics maturity is a key milestone on the path to being successful with AI.” According to global consulting firm McKinsey, however, manufacturing industries to date have only captured about 20%-30% of the potential value of data and analytics – and most of that has occurred at a handful of industry-leading companies.

Forrester, a global business and technology research and advisory firm, said that much of this existing value is in preventive maintenance, a specialty of global factory automation equipment producer FANUC. The company is running a Zero Down Time (ZDT) application on its new FIELD system, which collects data from more than 6,000 robots in 26 factories and analyzes it with a machine-learning application. Any issues that could lead to a failure are highlighted, and FANUC sends parts and support to address the issue before downtime occurs.

“FANUC’s FIELD system enables companies to utilize the vast amount of data available to them,” said Steve Capon, technical manager at FANUC UK. “Manufacturing is set to become more intelligent than ever before. By using AI, the scheduling of predictive maintenance requirements to reduce downtime is a reality.”

Continue reading the rest of this story here, on COMPASS, the 3DEXPERIENCE Magazine

Permanent link to this article: http://www.apriso.com/blog/2017/10/machines-that-learn-artificial-intelligence-may-transform-manufacturing-but-adoption-is-slow/

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>

Time limit is exhausted. Please reload CAPTCHA.