Artificial Intelligence is Here: What You Need To Know How To … Intelligence... · 2018-07-10 ·...

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Artificial Intelligence is Here: What You Need To Know And How To Make AI Work For You

Joe BarkaiJune 19, 2018

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@JoeBarkai

Joe Barkai

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Artificial Intelligence and Machine Learning in Asset Maintenance

Promises, Perils and Best Practices

Joe BarkaiJune 19, 2018

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It Isn't Magic

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POC BuildTest &

ValidateAdoption & Scale

Maintain

Machine Self-learning

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Establishing a Baseline

Test and Validation

• Unmanageable number of scenarios, variants and configurations

• Validate against (absent) human knowledge

• The problem with black-box systems

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The Problem With Black-box Systems

• Explainability & transparency

• Proving coverage

• Hidden biases

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Bad Data Can Corrupt Machine Learning

(But you wouldn't know it…)

We Need Responsible AI

• Algorithmic transparency

• Explainability

• Use machine learning to uncover biases

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No novel science or technology of such magnitude arrives without disadvantages, even perils.

Pamela McCorduck“

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Scaling AI

Deciphering Trends:

• Configuration / task-specific or an anomaly?

• Normal wear and tear or a looming problem?

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Everything Is Changing

• Wear and tear

• Product improvement

• Spare parts policy

• Contractual obligations and SLAs

• Safety and compliance directives

• …

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But The Need Is Real

The Growing Knowledge Gap

Containing a Growing Knowledge Gap

• Increased product complexity

• From standard products to mass-customization to customization: many one-off configurations

• New manufacturing methods

• Reliability is a double-edge sword

• Departure of experienced workforce

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Formalizing Tribal Knowledge

Systemizing and applying internal and external knowhow and best practices

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We assumed engineers know what they were doing and use available tools and know-how.

GE Healthcare“

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Lets Get Practical

IoT Cliché Of The Day

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Think big, start small, scale fast

Do You Deliver Value?

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Source: McKinsey

Be Realistic

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Predictive maintenance enhanced by AI allows for better prediction and avoidance of machine failure by combining data from advanced Internet of Things (IoT) sensors and maintenance logs as well as external sources. Asset productivity increases of up to 20% are possible, and overall maintenance costs may be reduced by up to 10%.

Source: McKinsey

Technology: More Than Machine Learning…

AI Technologies for Asset Maintenance Applications

• Rule-based Systems

• Model-based Reasoning

• Case-based Reasoning

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Be Pragmatic

• Select the right problem and technology

• Focus on business outcomes

• Recognize challenges in validation and scaling

• Domain and data expertise are critical

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And Don’t Expect Magic!

To Stay in Touch

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@JoeBarkai

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Joe Barkai

(and request presentation slides)

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