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Artificial Intelligence and Machine Learning

Five Hammers (Wayne Thiebaud, 1972 )

The Problem With This Big Hammer Called Artificial Intelligence

By | Artificial Intelligence and Machine Learning | No Comments

Everything is Artificial Intelligence

If your only tool is a hammer, then every problem looks like a nail.  This overly-user cliché seems very apropos when thinking about the rush to suggest artificial intelligence for practically every system and flaunt machine learning as a silver bullet to solve whatever value the new product or service is purported to provide.

Indeed, AI technology can be very powerful.  Recent advances in machine learning, aided by ubiquitous connectivity and cloud computing, demonstrate how potent this technology is and promise much more to come.  But AI-based systems can also be difficult to build and even more so to perfect and scale and deploy.

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Wine is a Mocker (Jan Steen, 1663–64)

The Industrial Internet of Things: When the Party is Over

By | AR/VR, Artificial Intelligence and Machine Learning, Internet of Things, Manufacturing, Mergers & Acquisitions | No Comments

IoT Industry Snapshot and Predictions

The industrial Internet of Things community is finally beginning to sober up from the bacchanalia of counting connected IoT devices and terabytes of cloud data storage that has dominated the IoT narrative for too long.

IoT platform vendors and consultants are shifting their focus from the lower rung of the IoT technology stack that focuses on device connectivity to the other end of the stack, to technologies that provide meaningful business value: multidisciplinary data aggregation, complex data analytics and higher capacity for optimal decision-making.

Robust articulation of the business value of industrial IoT has been absent from much of the narrative, in the vein of “if you build it, they will come.” Many IoT platform vendors provide tools to draw snazzy dashboards, plot complex data graphs and display virtual gauges. But their data analytics tools are not as robust and trending and predictive capabilities are over optimistic. And the recent rush to add statistical analysis tools (often linear regression tools masqueraded as artificial intelligence and machine learning) will face real-world challenges of data biases, inconsistency and scale. Read More

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 | No Comments

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

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

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), Service Technology | 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