Category

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

Jul 22
Alice Meets the Caterpillar (Sir John Tenniel, 1865)

Will The Rise In Computing Power Make Ubiquitous Artificial Intelligence A Reality?

By Artificial Intelligence and Machine Learning One Comment

Like the Internet of Things that shed its drab M2M image to become the centerpiece of the digital transformation of industrial enterprises, artificial intelligence is sprouting a new life from its 50-plus years old roots. (Yes, we’ve been doing AI, admittedly with limited success, since the late 1950s.)

Conversations about AI seem to follow a course similar to that of the IoT narrative.  Initially, IoT pundits were obsessed with the ability to connect billions of “things” to the Internet. Not only did most of these predictions proved overly optimistic, but the connection between sheer connectivity and meaningful business outcomes was loose, at best.

Today’s IoT narrative shifted to focus on business outcomes enabled by the data generated by connected devices. Industry matured from counting conduits to measuring the value of their content. Read More

May 22
Suprematist Composition (Kazimir Malevich, 1916)

Design for Manufacturing as a Knowledge Management Tool

By Artificial Intelligence and Machine Learning, Manufacturing No Comments

In the first part of this two-part blog article, I discussed how two global high-tech manufacturing companies use validation tools to formalize and automate the design review process for downstream manufacturing, thereby reducing costly and time-consuming rework and engineering change orders, manufacturing defects, and costs.

In this article, I discuss the benefits of using formal manufacturability validation tools as a mechanism for best-practice knowledge capture and continuous improvement.

A Growing Manufacturing Knowledge Gap

Many design engineers lack theoretical and practical manufacturing process knowledge in well-established manufacturing disciplines such as injection molding, casting, and sheet metal fabrication. This gap is more pronounced in newer manufacturing processes that involve composite materials and additive manufacturing. Read More

Feb 19
The Conjurer (School of Hieronymus Bosch, after 1500)

Predictive Maintenance: Myths, Promises, and Reality

By Artificial Intelligence and Machine Learning, Field Service, Service Lifecycle Management (SLM) One Comment

The industrial Internet of Things and its Digital Twin surrogate are fueling exciting conversations about business process innovation on the factory floor and in industrial equipment manufacturing. One hot area in particular is the broad and often loosely-defined practice of Predictive Maintenance (PdM) of complex machinery. As is frequently the case when technology innovation is trying to penetrate (should I have said “disrupt”?) an established business practice, there’s a good dose of hype and optimism on the side of technology pundits, countered by skepticism and resistance to change from maintenance organizations and experienced field service technicians.

A business associate sent me an article written by a technology vendor seeking to debunk what the authors deem to be myths about PdM (try searching for “predictive maintenance myths” and you will find a few on the topic). That article and recent vendor presentations I attended suggest that further examination of misunderstandings and perhaps exaggerated expectations from PdM technology is in order. Read More

Dec 15

Automation and Orchestration for the 21st Century IT Organization

By Artificial Intelligence and Machine Learning No Comments

20th Century IT Practices Are No Longer Sufficient

The information technology that fuels the digital economy is energizing the modern enterprise and redefining how companies manage their businesses. Forced to move at hyper speed, many businesses are scrambling to keep pace with constant relentless change and struggling to confront an unfamiliar technology landscape and new complex decisions for which there are no precedents.

Everything is Hyper Connected

Hyper-connectivity between people, processes, data, and things (as in “the Internet of Things”) is challenging traditional data management practices and processes. Once siloed by lines of business, data repositories are being connected and aggregated, and integrated business platforms share data across different enterprise business functions and multiple user groups.

Rich Decision-Making Context

Forward-looking organizations are deploying data analytic tools and experimenting with machine learning technologies in a quest to turn vast amounts of data collected from across the enterprise and the extended supply chain into insights that drive better business outcomes. But aggregating and synthesizing information from multitude of data repositories that employ inconsistent semantic models is no small feat. is challenging existing IT systems and tools. Furthermore, the growth in connected communities of information producers and consumers raise ongoing concerns about data security, privacy and ownership. Read More

Dec 12
Pandora's Box (Bezt (Etam Cru), 2011)

Artificial Intelligence and Real Bias

By Artificial Intelligence and Machine Learning 3 Comments

Is There a Need to Regulate Machine-Learning Algorithms?

We live in a world that is increasingly shaped by machine intelligence. Conversational bots, artificial-intelligent-based automatons, self-learning software and other seemingly intelligent systems appear to be in cars, businesses and in our homes.

Thanks to advances in artificial intelligence and virtually limitless availability of cloud-based storage and computing bandwidth, it is inevitable that computer algorithms will be used extensively throughout our economy and society and will have an increasingly far-reaching impact on all aspects of our everyday lives, from basic assistive tasks to complete decision authority in education, healthcare, finance, and employment.

But as Pamela McCorduck observed, no novel science or technology of such magnitude arrives without disadvantages, even perils. The more sophisticated and powerful artificial intelligence algorithms become, the more reason to pay attention to the risks associated with the design and operation of these algorithms, and to curtail errors and biases from the outset. Read More