Alice LaPlante & Maliha Balala

Solving Quality and Maintenance Problems With AICombining Machine Learning, Deep Learning, and Associative Memory Reasoning to Improve Operations

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Alice LaPlante and Maliha Balala

Solving Quality and MaintenanceProblems with AI

Combining Machine Learning, Deep Learning,and Associative Memory Reasoning

to Improve Operations

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Solving Quality and Maintenance Problems with AIby Alice LaPlante and Maliha Balala

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Table of Contents

Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

1. Introduction and Primer on Predictive Quality and Maintenance. . . . . . . . . . . . . . . 1Overview 1Artificial Intelligence: Clarifying the Terminology 5More Companies Looking Toward Cognitive Computing 10

2. Complementary Learning and Intel Saffron AI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Complementary Learning as the Future of Predictive Quality and

Maintenance Solutions 13Intel Saffron AI: Associative-Memory Learning and Reasoning and

Complementary Learning in Action 15

3. Using AI-Based PQM Solutions to Solve Issues in Manufacturing,Aerospace, and Software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19PQM Issues in the Manufacturing, Aerospace, and Software Industries 19AI-Based PQM Solving Real-World Issues: Two Use Cases 21Getting Started with AI-Based PQM Solutions 25

iii

Executive Summary

As artificial intelligence (AI) enters the business mainstream, one of its mostpromising applications is anticipating quality and maintenance problems beforethey cause real damage. Called predictive quality and maintenance (PQM), thesesolutions are being deployed at an accelerating rate, especially in the manufactur‐ing, aerospace, and software industries.

But not all PQM solutions are created equal. Those based on a combination ofmachine learning, deep learning, and—in particular—cognitive computing createa truly unique out-of-the-box AI-based PQM solution.

This report is organized into three chapters. In Chapter 1, we introduce AI-basedPQM and show how today’s market for quality and maintenance applications isevolving. In Chapter 2, we show that because none of the various types of AI cansolve all PQM problems alone, applying them simultaneously is the key to suc‐cess. This has led to cognitive computing as a basis for what is called complemen‐tary learning. We also introduce Intel Saffron AI as the only solution applyingcomplementary learning principles to today’s PQM challenges. Finally, in Chap‐ter 3, we discuss using AI-based PQM solutions to solve issues in the manufac‐turing, software, and aerospace industries.

v

CHAPTER 1

Introduction and Primer on PredictiveQuality and Maintenance

OverviewFollowing years of being dismissed as largely “hype,” we’re seeing a growing num‐ber of positive headlines about artificial intelligence (AI): “The artificial intelli‐gence race heats up” (The Japan Times); “Healthcare’s Artificial IntelligenceMarket May Hit $6 Billion” (Forbes); and “Most Americans Already Using Artifi‐cial Intelligence Products” (Gallup). Even the Wall Street Journal is reporting onrecent market advances. “After decades of promise and hype, artificial intelli‐gence has finally reached a tipping point of market acceptance,” wrote IrvingWladawsky-Berger in early 2018.

Indeed, the artificial intelligence market is expected to grow to $190.61 billion by2025 from $21.46 billion in 2018, at a compound annual growth rate (CAGR) of36.62%, according to IDC. To put that in perspective, in 2018 the average tech‐nology budget for US businesses is expected to grow just under 6%, according toForrester.

AI is transforming virtually all industries—from retail, to healthcare providers, tomanufacturing, aerospace, and banking. Why? Because AI can deliver results inthe form of insights. A report by Forrester forecasts that companies that useinsight to drive their businesses will grow at a 27% annual rate at a time when theglobal gross domestic product (GDP) will rise only 3.5% annually (seeFigure 1-1).

1

Figure 1-1. Revenue forecasts for insight-driven businesses (source: Predictions 2017:Artificial Intelligence Will Drive The Insights Revolution, November 2016, Forres‐ter)

One segment—and a growing one—of the overall AI applications market is AI-based predictive quality and maintenance (PQM). PQM is a relatively new tech‐nology area designed to help companies predict when issues or defects mightoccur in a product, advise on how to identify and fix them, and—the ultimategoal—prevent problems before they cause serious damage. AI is significantlyadding value to PQM solutions on the market today.

PQM: A PrimerPQM solutions focus on detecting quality issues and improving operational pro‐cesses to address them by accessing and analyzing data, sometimes in real time.PQM is a relatively new merger of predictive quality and predictive maintenancesolutions. These separate technology areas previously addressed the two issues—ensuring product quality and anticipating maintenance needs—as discrete, dis‐tinct technologies. With PQM solutions, both quality and maintenance activitiesare addressed together rather than as separate issues.

The idea behind a PQM solution is that if companies want to gain a competitiveedge, they must prioritize how to allocate their resources, cost, and time when itcomes to both improving product quality and maintaining equipment in a moretimely and efficient manner.

Here are some examples of questions that PQM solutions are helping to answer:

• How can we capture experts’ knowledge and skills and streamline themwithin workflows and processes so that they can be shared and accessed byeveryone?

• How can we detect anomalies and failure patterns to determine which equip‐ment and operational processes are likely to fail?

2 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

• How can we efficiently triage issues and conduct comprehensive root–causeanalyses?

• How can we optimize spare-parts inventory to reduce inventory costs whileremaining proactively responsive?

• How can we catch and address product quality issues more quickly and cost-effectively and anticipate where they will occur next?

• How can we identify areas to create efficient preventative maintenance pro‐grams to ensure maximum uptime and safety while still maintaining effi‐ciency?

AI-based PQM solutions differ from traditional predictive quality and mainte‐nance ones because they analyze the actual condition of a product rather thanjust using average or expected statistics to predict when quality corrections ormaintenance will be required.

The latest PQM solutions harness the data gathered by both the Internet ofThings (IoT) and data from traditional legacy systems. Recent research suggeststhat the market for PQM applications will grow from $2.2 billion in 2017 to $10.9billion by 2022, a 39% annual growth rate (see Figure 1-2). Of the top 10 usespredicted for AI in 2021, PQM comes in fifth place, according to IDC.

Figure 1-2. PQM market growth to 2022 (source: IOT Analytics)

PQM solutions can be said to have two separate but equal concerns: quality andmaintenance.

The longer that companies put off fixing quality issues in products—whether indesign or manufacturing phases—the more costs accrue. Indeed, the most expen‐

Overview | 3

sive time to fix a problem is after it’s shipped—when there’s a brand reputationcost exposure added to the costs of recalling the product and fixing the issue.

First, consider quality—the “Q” in PQM. AI-based PQM solutions allow compa‐nies to work through defects and other quality issues much faster. For example,when Intel ships a new chip, there are inevitably bugs reported by OEMs andcustomers. Such products are, after all, very complex, with many integrated parts,and Intel must act quickly to resolve any issues that arise.

We can use AI-based PQM solutions to solve quality problems faster, which low‐ers the time-to-market or time-to-resolution while increasing customer satisfac‐tion. Not incidentally, PQM solutions don’t just identify the root cause of a single,isolated quality problem, but provide insights into more general issues withindesign or manufacturing, which allows businesses to build better products and,ultimately, increase customer satisfaction and revenues.

Next, consider maintenance—the “M” in PQM. Intel believes that three of the topdrivers of predictive maintenance include the need to increase uptime, reducerisk, and cut maintenance.

Increase uptimeUnplanned downtime is a major cost driver in any industry that must main‐tain large inventories of capital assets. For an airline, for example, delayingflights due to unplanned maintenance can cost thousands of dollars eachminute. Unplanned shutdowns of oil platforms can run into the millions ofdollars. And in manufacturing plants, the costs of disruptions go directly tothe bottom line. It is the goal of every organization to eliminate unplanneddowntime in favor of planned maintenance.

PQM solutions can help with planned maintenance also, by shorteningmaintenance operations windows.

Reduce riskBusinesses strive to comply with safety regulations. They also perform pre‐ventive maintenance and take common sense precautions. Because of this,the potential for catastrophic accidents to happen is minimized. But the riskis always there. The Deepwater Horizon disaster was caused in large part toequipment failure. Recently, the engine of a United Airlines flight fell apart inmid-flight. Although the aircraft was able to make a safe emergency landing,this incident occurred despite United’s compliance with Federal AviationAssociation (FAA) regulations that are defined to mitigate such risks. Whensomething of this magnitude happens, the repercussions go well beyondfinancial.

Stop over-maintaining assetsBoth the fear of unplanned downtime and the risk of catastrophe occurringlead many businesses to actually over-maintain most of their capital assets.

4 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

Indeed, many businesses feel that regulatory bodies, such as the FAA for air‐lines and the Federal Drug Administration (FDA) for medical devices,actually require companies within their jurisdiction to maintain assets signif‐icantly more frequently than they need to.

Some predictive maintenance studies report that PQM solutions can reducedowntime by as much as 50%, while reducing maintenance costs between 10%and 40%. Manufacturers, for example, can move from a reactive maintenancemodel to a proactive one, giving them insight into when and where machinebreakdowns might occur so that they can keep the manufacturing line going.According to McKinsey, in the manufacturing industry alone, these savings willhave a potential economic impact of nearly $630 billion per year by 2025.

Harnessing Dark Data with PQM SolutionsData is continuously increasing, and businesses are challenged to make sense of itall. The vast majority of data is “dark data”—referring to the vast amounts ofuntapped data in the form of human interactions, intelligence, printed content,photos, video, voices, and social media interactions that come in unstructuredforms. Notably, IDC estimates that only slightly more than 20% of data is beingutilized today, meaning that 80% is “dark.”

To use this dark data, businesses need to convert this information into a formthat they can understand and use.

The AI-based PQM solutions championed by Intel, IBM, and GE harness darkdata from multiple sources to predict potential quality and maintenance issuesbefore they affect customers—and the bottom line. In particular, Intel Saffron AIuses several key AI technologies—machine learning, deep learning, and, espe‐cially, cognitive computing—together in what is called complementary learning tooffer a truly unique out-of-the-box PQM solution.

In this report, we interviewed companies from manufacturing, aerospace, andsoftware industries to talk about the key business challenges they face, how AI-based PQM solutions are helping them address these challenges, and how theysee Intel Saffron AI helping them make better decisions, solve problems, and gainlucrative returns.

Artificial Intelligence: Clarifying the TerminologyIt can be difficult to decrypt all the talk about AI because so many different termsare used—some of them interchangeably—and AI’s capabilities seem to span somany possible scenarios.

The best way to think about AI is as a large umbrella of technologies, methodolo‐gies, and algorithms that help humans perform tasks easier, faster, and more effi‐

Artificial Intelligence: Clarifying the Terminology | 5

ciently. Under this umbrella resides a large—and growing—collection oftechniques such as machine learning, image recognition, neural networks, speechrecognition, deep learning, natural-language processing, handwriting recogni‐tion, and cognitive computing, among others, many of which overlap or comple‐ment one another to help enterprises resolve challenges.

For example, machine learning focuses on real-world problems by processing—and learning from—large amounts of data. Deep learning, which many considera subset of machine learning, uses neural networks to be able to sort throughnearly unimaginable volumes of structured and unstructured data to come toconclusions. Cognitive computing is a subset of AI that attempts to mimic theway humans think in a way that addresses more complex scenarios for decisionmaking.

John Launchbury from the US Defense Advanced Research Projects Agency(DARPA) gives an interesting overview of the evolution of AI in his talk “ADARPA Perspective on Artificial Intelligence.”

At its heart, Launchbury says, AI takes different kinds of mathematically basedformulas (algorithms) to make sense of data and come to a decision on what todo with it, and in this way creates “intelligent” systems and “smart” things.

We’re in what Launchbury calls the “third wave” of AI. Today, AI systems havemoved beyond the data-crunching algorithms to human-like cognitive ones withthe ability to explain its reasoning on decisions by making associations based onthe context. The ability to form associations autonomously by connecting con‐cepts, observations, knowledge, and senses together. Discovering associated pat‐terns for reasoning and inferences is fundamental to both human intelligenceand cognitive computing.

In the PQM solutions space, the relevant AI technologies are machine learning,deep learning, and cognitive computing.

Machine LearningUnder the larger umbrella of AI, machine learning refers to a broad range ofalgorithms and methodologies that can process large amounts of data so as toidentify issues or trends. For example, a machine learning system can learn todistinguish malfunction scenarios of a network router by learning from the train‐ing examples of previous episodes of malfunctions and normal operations of therouter.

In other words, it learns from example. There’s no need to manually code in“rules” that it must follow. The more data it consumes, the more accurate it willbe.

6 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

Commonly used machine learning techniques include support vector machines,decision trees, Bayesian belief networks, case-based reasoning, instance-basedlearning, and regression.

Machine learning is experiencing a renaissance within the growth of the AI mar‐ket. The “Machine Learning Market - Global Forecast to 2022” report fromResearch and Markets shows that the global machine learning market is expectedto grow from $1.41 billion in 2017 to $8.81 billion by 2022 with a CAGR of44.1%. McKinsey estimates that 60% of all current AI spending is on machinelearning.

According to one survey, 65% of organizations are already using or planning touse machine learning to help them make better business decisions, whereas 74%of all respondents called the technology “a game changer” that had the potentialto transform their jobs and industries. A full 61% said it was their company’smost significant data initiative for the next 12 months. (See Figure 1-3.)

Figure 1-3. Machine learning initiatives are number one for today’s enterprises(source: MemSQL)

Deloitte anticipates that the number of enterprise machine learning deploymentswill double between 2017 and 2018, and double again by 2020. However, onedrawback of machine learning systems is that they are “data hungry” and need toprocess large volumes of data—sometimes over a long period of time—beforethey can detect patterns. More on this later.

Deep LearningDeep learning is a subset of machine learning that relies on building neural net‐works, which are loosely modeled on how neurons work in the human brain. Inthis type of AI, the system extracts digital value from every piece of data it ingestsby asking a series of binary (true/false) questions. For example, if trying to pro‐cess an image and determine whether it is the correct face of the owner of a

Artificial Intelligence: Clarifying the Terminology | 7

smartphone, it will ask such things as: “is the hair brown?” “are the eyes blue?” Itthen classifies and weights each piece of data. Nodes are arranged in several lay‐ers, including an input layer where the data is fed into the system, an output layerwhere the answer is given, and one or more hidden layers, which is where thelearning occurs by adjusting interconnection weights between the layers to mini‐mize discrepancies between the predictions and the answers.

Deep learning works for large complex datasets on the scale of Google’s imagelibrary or Twitter’s tweets. It is not new, but it is rapidly gaining popularitybecause the volume of data that is available is increasing so rapidly, and faster andmore powerful processors can return results in a timely manner.

You can apply deep learning to any kind of data, even unstructured data such asaudio, video, speech, and the written word. It is being used for a number of real-world issues. For example, by using data collected by sensors, self-driving cars arelearning to identify when they come to an obstacle, and how to react appropri‐ately using deep learning. British and American researchers recently demon‐strated a deep learning system capable of being able to correctly predict a court’sdecision when given the facts of the case.

Cognitive ComputingCognitive computing systems process unstructured as well as structured data andcan learn from experiences much like humans do.

Because they use computational neuroscience, cognitive computing systems imi‐tate the way humans learn and reason—and the “learning” here refers to the factthat humans can learn with significantly fewer numbers of examples than typicaldeep learning solutions require. They work especially well in dynamic and com‐plex environments such as manufacturing, engineering, and energy industries.This form of AI combines elements from cognitive psychology, neuroscience,and computer science.

A new update to the Worldwide Semiannual Cognitive Artificial Intelligence Sys‐tems Spending Guide from International Data Corporation (IDC) forecastsworldwide revenues for cognitive computing systems reached $12.5 billion in2017, an increase of 59.3% over 2016. Global spending on cognitive computingsolutions will continue to see significant corporate investment over the next sev‐eral years, achieving a CAGR of 54.4% through 2020 when revenues will be morethan $46 billion.

Following are the cognitive computing use cases that will see the greatest levels ofinvestment in the near future:

• Quality management investigation and recommendation systems• Diagnosis and treatment systems

8 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

• Automated customer service agents• Automated threat intelligence and prevention systems• Fraud analysis and investigation

Combined, these five use cases delivered nearly half of all cognitive computingsystems spending in 2017, according to IDC.

A subset of cognitive computing called associative-memory learning and reason‐ing is also very much based on how humans think. First of all, people creatememories. Those memories involve entities, where an entity is a person, a place,a thing, or an event. People learn about these entities and create memories. Then,they associate these memories to one another. When do they see them together?In what context? How often?

This is how associated-learning and reasoning systems work, too. As entitieschange and new data is added, an associative-memory learning and reasoningsystem incrementally adds the new data into memory and builds out more nodesand connections. This process of enabling a system to learn on the fly and pas‐sively develop assumptions about what’s important is called lazy learning or latentlearning.

Cognitive computing systems that use associative-memory learning and reason‐ing unify data at the entity level. They create correlations of related data (similarbugs, similar parts, and more) and associate a weight to the similarity. Theadvantages of this approach include the following:

• Less data needed• Less data science involved (model-free)• Faster and more agile• More transparent (auditable data)• Great for individual use cases because data is unified around similar entities

(360 views of customers, precision medicine, etc.)

All of these things add up to deliver significant benefits for companies applyingcognitive computing to PQM.

According to Keystone Strategy, a Boston-based strategic consulting firm, if 5%of heavy maintenance costs were prevented via changes to maintenance plans,that would result in $20 million to $40 million of savings for a medium-sized UScommercial passenger airline annually. If just 2% of carrier-caused delays wereprevented via changes to maintenance plans, it would yield $5 million in savingsfor that carrier. If just 5% of cancellations were prevented due to changes tomaintenance plans, this would yield $23 million in savings for that carrier.

Artificial Intelligence: Clarifying the Terminology | 9

More Companies Looking Toward Cognitive ComputingA number of firms have entered the cognitive computing market, but accordingto Keystone, the offerings vary widely depending on the particular approach thevendors take.

One approach has been to offer DIY commercial tools focused on data scienceusing a traditional fee-based licensing approach and then selling enabling tools tointernal analytics teams to build solutions. This sector is largely dominated byopen source.

Another approach has been to offer broad, multipurpose AI platforms that areprimarily monetized through consulting services. In many cases, there’s not a lotin the box beyond wrappers around open source components. Keystone assem‐bled an expert panel made up of Fortune 100 companies, which expressed consis‐tent skepticism around the solution value of these platforms, “describing thedeployment costs as largely services driven build scenarios,” says Dan Donahue, apartner at Keystone Strategy.

Then there are actual packaged solutions for specific industries and use cases.Such vendors tie their solutions to a measurable ROI—whether that’s increasedrevenue or increased labor efficiency—and price it accordingly. Customers candeploy these solutions rapidly, without complex and costly services efforts. “Wesee these packaged vertical solutions, if they’re done right, as one of the most suc‐cessful ways to commercialize the technology,” says Donahue.

This is the category that Intel Saffron AI fits into (more on this later).

And finally, there’s the whole AI-as-a-Service category, which to date has focusedlargely on providing standard algorithms around text, speech, and image recog‐nition offered via consumption-based business models (see Figure 1-4).

10 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

Figure 1-4. Different types of players in the cognitive computing space (source: Key‐stone Strategy)

Keystone’s research indicated that the market could evolve in one of two direc‐tions: either commercial platforms coupled with professional services offeringswould dominate, or the industry-specific/vertical applications would win out.

The company asked its expert panel and found that most of them leaned towardthe industry-specific/vertical solutions, indicating that we would soon see a richecosystem of packaged vertical solutions in the marketplace.

“We’ve definitely seen some scars from long-running, ambiguously successful AIplatform deployments—and negative reactions to how much the services cost toactually get something running,” says Donahue. “A packaged solution to solve aspecific problem, priced in a way that’s tied to a measurable value creation metric,would be much more attractive given these are still emerging, often unproventechnologies.”

More Companies Looking Toward Cognitive Computing | 11

CHAPTER 2

Complementary Learningand Intel Saffron AI

Complementary Learning as the Future of PredictiveQuality and Maintenance SolutionsBecause none of the types of artificial intelligence (AI) can solve all problems,applying them simultaneously is the key to success. This need for a combinedapproach is giving rise to cognitive computing as a basis for complementarylearning. This is what DARPA’s John Launchbury refers to as the “contextualadaptation systems” in the third wave of AI.

Strengths and weaknesses of different AI approaches are giving rise to comple‐mentary learning because solving a challenging problem often requires solvingunderlying subproblems effectively, which calls for different models orapproaches.

To understand how machine learning, deep learning, and cognitive computing-based AI can work together in a predictive quality and maintenance (PQM) solu‐tion, it’s important to understand that a comprehensive AI-based PQM solutionneeds to solve two types of problems: surveillance and prescriptive.

Surveillance use cases involve scenarios in which businesses need to recognizeproblems by observation. By detecting patterns and alerting businesses, the sur‐veillance approach to AI allows companies to act quickly when something out ofthe ordinary is detected in their equipment or other assets. For example, manu‐facturers want to understand what the sensor data coming in from the factoryfloor via the Internet of Things (IoT) is telling them. In the past, they would haveneeded to build rules into the sensor network to send alerts when certain thresh‐olds were passed, or anomalies sensed.

13

But the problem was identifying all those rules. Although it’s possible to definethe parameters in which, for example, a network router should be operating,when a large number of assets exists—such as a fleet of airplanes—it’s next toimpossible.

That’s when machine learning and deep learning come in. These two types of AIcan process the data, access the knowledge, and specify what those parametersare in a much more adaptable and scalable way. The systems learn—or rather,construct—the rules themselves by learning from the data.

But to do this, an enormous amount of data is needed—perhaps tens of thou‐sands of examples of an issue before a system is fully trained. And if the systemdid not perform as expected in some circumstances, humans will need to provideadditional feedback—although that feedback might not be in the form of rules,but in the form of new data illustrating the desired outcomes or instructing thesystem with exception cases. The goal here is to help the machine learn quicklyfrom as few examples as possible.

After the issues have been identified using machine learning and deep learning,the natural next step for businesses is to solve those issues.

This is where prescriptive use cases come in—and where cognitive computingcapabilities are required. After all, for a system to do those things, it wouldrequire the ability to reason. It would need to extract and consolidate relevantinformation from heterogeneous unstructured data sources such as audio, video,and emails to indicate or assist businesses to find the root causes of issues.

Another way to think about it is that machine learning and deep learning aregood for knowledge extraction. Cognitive computing is good with knowledgerepresentation—finding connections and insights from data.

Let’s walk through a basic example. The first step toward solving a problem witha piece of equipment or product is that data—which can be structured orunstructured—needs to be processed and identified. If it’s text, natural-languageprocessing (NLP) will be used to parse the meaning. If it’s an object, computervision will identify whether it is an airplane, an engine, or a network router.

Computer vision and NLP are part of the knowledge extraction. Those are thepatterns detected by machine learning and deep learning. In effect, the systemhas answered the question, “What is it?”

When the “what” question has been understood, cognitive computing can thencome in to ask questions such as: Have I ever seen this before? What type of aproblem is it? Who knows how to fix this? What do I do next? What caused thisproblem? And, will it happen again?” Cognitive computing systems then answerthose questions.

14 | Chapter 2: Complementary Learning and Intel Saffron AI

When we talk about complementary learning with respect to PQM applications,we’re talking about combining surveillance, or knowledge extraction, with thesecond, more prescriptive, knowledge representation application that usesmemory-based reasoning.

Intel Saffron AI: Associative-Memory Learning andReasoning and Complementary Learning in ActionIntel Saffron AI is based on cognitive computing that utilizes associative memorylearning and reasoning, along with patterns detected from machine learning anddeep learning, in the complementary way previously discussed. By using human-like reasoning to find hidden patterns in data, Intel Saffron AI enables decisionsthat can deliver rapid return on investment (ROI).

The core of Intel Saffron AI is the Intel Saffron Memory Base, a long-term persis‐tent knowledge store built on an associative-memory matrix. It stores unifieddata about entities in an associative-memory store. That memory store correlatessimilar information together and makes it faster to query and easier to retrievefor analysis. This means that Intel Saffron AI mimics how a human naturallyobserves, perceives, and remembers by creating memory-based associations.

Intel Saffron AI uses data from a mix of machine learning and deep learning AIsubsystems, like NLP for entity extraction, sentiment analysis to establish links,and topic mapping for content mapping. The platform is both semantic and stat‐istical in nature.

Intel Saffron AI ingests all types of data, including structured, unstructured text,nonschematic, and on-schema. This data then resides in a hyperdimensionalmatrix that connects one node (data or entities like people, places, things, orevents) to another node using edges (which are statistical connections).

Although most graph stores work as a key–value pair, Intel Saffron AI acts like amultidimensional graph store that allows for N connections between nodes, andfunctions like a hyper matrix. The connections make associations based on con‐text, frequency, and time.

When a new node (data) comes in, the platform applies memory-based cognitivetechniques and creates weighted associations between people, places, things, andevents. In this way, Intel Saffron AI acts like a massive correlation engine that cal‐culates the statistical probabilities using the Kolmogorov Complexity (K Complex‐ity). It then derives a universal distance measure that shows how closely twoobjects are related and to find regular patterns in the data. This way of cognitionby similarity enables anticipatory decision making, which involves making deci‐sions by estimating the current situation, using diagnoses, prescribing possibleactions, and predicting likely outcomes.

Intel Saffron AI: Associative-Memory Learning and Reasoning and Complementary Learning in Action | 15

Customers can implement Intel Saffron AI across industries. Its bedrock technol‐ogy is the patented Intel SaffronMemoryBase, which provides a layer of RESTAPIs that customers can develop and customize for their own needs. Intel Saf‐fron AI now offers industry-specific applications that will harness the power ofthe platform to solve specific quality and maintenance problems for manufactur‐ing, software, and aerospace.

What Makes Intel Saffron AI Different?A complementary learning solution like Intel Saffron AI enables powerfulmachine and human interactions. It aims to help humans make decisions betterand faster. It does this by relieving human workers of having to perform repeti‐tive, time-consuming tasks so that they can focus on what humans can do best:build relationships and apply judgment and creativity to more complex issues. Inaddition, Intel Saffron AI keeps advancing, learning from human feedback andinteractions.

It does this by excelling in three ways: its transparency—which makes it easy tounderstand its results and recommendations; for the fact that no statistical mod‐els are required; and that it brings together both structured and unstructureddata from multiple sources.

TransparencyIntel Saffron AI works by identifying similarities. But unlike a traditionalmachine learning or deep learning application, which makes its decisions byalgorithms and “black box” methodologies—that is, businesses have no insightinto why they got a particular result—Intel Saffron AI is completely transparent.Because it works by knowledge representation, it stores all the attributes that ledit to a particular decision or conclusion and makes them readily available tousers. It’s easy to get explanations.

Intel Saffron AI in effect takes an entity and creates a “neighborhood” around it,showing the most similar issues it has ever seen to this particular one, and why itthinks they’re similar. Businesses have full access to all of this information, givingthem a chance to tell Intel Saffron AI when it’s wrong, so it can learn for nexttime.

One-shot learning: No statistical models requiredThe key benefit of not needing to model data is flexibility, especially when data issparse, dynamic, or incomplete. This is what Intel calls “one-shot learning”: seesomething once, and Intel Saffron AI learns.

Here’s an example: if a child is burned by a hot stove, hopefully she learns fromthat experience and avoids the stove in the future. If the child was acting based

16 | Chapter 2: Complementary Learning and Intel Saffron AI

on a statistical-based learning, however, she would have to experience pain multi‐ple times before she had enough data to build a statistically relevant model—andnot get burned.

After all, the real world isn’t a closed system. Unlike the game of checkers, chess,or even the more complex game of Go, there aren’t a fixed number of possiblemoves. But in an open and ever-changing place like real life—and markets—there is no way to monitor for every possible contingency. A good PQM systemneeds to be able to adjust to evolving scenarios.

Intel Saffron AI, different from machine learning and deep learning, learnsthrough association rather than by modeling possible outcomes. It builds signa‐tures of entities that it gradually learns more about. Then it compares those sig‐natures to identify hidden connections, patters and trends—surfacing insightsthat are otherwise invisible.

Unifies both structured and unstructured data across multiple sourcesA lot of insights in the real world come from unstructured data—maintenancelogs, manuals, handwritten notes, audio and video recordings, and emails. Theability to analyze both structured and unstructured data is one of the strengths ofIntel Saffron AI. When you couple this with the insights from machine and deeplearning, you can reveal much more insights.

In other words, deep and machine learning analyze structured data to identifysymptoms, whereas associative-memory learning and reasoning analyzesunstructured data to provide a diagnosis.

Intel Saffron AI: Associative-Memory Learning and Reasoning and Complementary Learning in Action | 17

CHAPTER 3

Using AI-Based PQM Solutions toSolve Issues in Manufacturing,

Aerospace, and Software

PQM Issues in the Manufacturing, Aerospace, andSoftware IndustriesResolving quality and maintenance issues is well suited by a complementarylearning approach because the data necessary for decision-making is large, var‐ied, both structured and unstructured, and processed in both batch and real time.For instance, in addition to data stored in a traditional database, engineers couldbe capturing their notes on handwritten pieces of papers, or invaluable informa‐tion could be mined from online chats or email exchanges. In the sections thatfollow, we discuss some of the challenges that are being faced by businesses in themanufacturing, aerospace, and software industries that complementary predic‐tive quality and maintenance (PQM) could address.

PQM in ManufacturingCurrently, many manufacturers still predict machine failures in assembly lines bydetermining root causes from maintenance notes or by depending on humansubject matter experts. But they are tasked to reducing unplanned downtime andavoiding the revenue losses associated with failed lines. They must acceleratetime to market while improving quality. They also need to identify similarities inparts to predict defects or failures that could occur during manufacturing andassembly, and balance cost with quality by gaining visibility into the life cycle ofparts from different vendors. And corrective actions need to be taken before

19

assets break down, allowing service to be performed preemptively, and the dataof all corrective actions captured for future use.

PQM in the Software IndustryPQM is becoming an essential part of software quality assurance, management,debugging, performance, and cost-estimation exercises. As we noted earlier, thegoals of deploying complementary learning-based PQM solutions when develop‐ing software include minimizing bugs, fixing bugs faster, reducing costs, reduc‐ing the strain on senior engineers, and increasing return on investment (ROI).

In general, the sooner a company detects software problems, the easier and lessexpensive the troubleshooting and fixing process. This is where complementarylearning-based PQM can be invaluable. Additionally, one especially irksomechallenge for software developers is de-duplicating bugs. Having multiple teamsworking on the same defect is a waste of valuable resources. When trying to testfor a reported bug, the correct environment needs to be set up to reproduce it,which can take heavy resources in both equipment, system, and engineeringtime.

Complementary learning-based PQM can help identify when two bugs—whichcould have occurred under different circumstances and been described using dif‐ferent language—are really the same, avoiding this waste.

PQM in the Aerospace IndustryMaintenance, repair, and overhaul (MRO) are the daily tasks involved in manag‐ing the upkeep and safety of large aircraft. Key to successful MRO is gatheringand analyzing data that helps airlines check that all systems are operational andthat they interconnect successfully with others.

This is a huge job. According to Boeing, 70% of a $2.6 trillion aerospace servicesmarket is spent on quality and maintenance.

Many carriers have traditionally taken a reactive approach to maintenance: prob‐lems are addressed only as they occur. Unfortunately, this tactic leads to down‐time, delayed flights, and aircraft-on-ground issues. In 2016, in the United Statesalone, the cost of maintenance related delays for airlines was well more than halfa billion dollars. And almost a third of total delay time is due to unplanned main‐tenance.

Making repairs at the right time to avoid problems before they occur is key topreventing these problems—and these costs.

An enormous amount of unstructured data exists in the airline industry that hashistorically been very difficult to access. Utilizing it effectively could have a trans‐

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formative impact on aerospace maintenance strategies—something that aero‐space companies are beginning to understand.

AI is thus beginning to be deployed in the aerospace industry, to find patterns inthat data, and to be able to look at historical maintenance activities and identify aparticular problem. What is the best solution? Who actually worked on this itemin the past? How do we find an expert quickly who can help address this?

According to Keystone Strategy, a Boston-based strategic consulting firm, if 5%of heavy maintenance costs were prevented via changes to maintenance plans,that would result in $20 million to $40 million of savings annually. If just 2% ofcarrier-caused delays were prevented via changes to maintenance plans, thatwould yield $5 million in savings annually. If just 5% of cancellations were pre‐vented due to changes to maintenance plans, that would yield $23 million in sav‐ings.

AI-Based PQM Solving Real-World Issues:Two Use CasesIn the sections that follow, we look at some real-world scenarios in which theways complementary learning-based PQM solutions are helping companies inchip design and software development.

Chip Design: IntelIntel is the world’s largest chipmaker. It is the inventor of the x86 series of micro‐processors, the processors found in most personal computers. Considered a lead‐ing global innovator, Intel is responsible for much of the growth of thetechnology sector over the past decades. Based in the heart of Silicon Valley, itachieved record annual revenues of $62.8 billion in 2017, a 9% increase over2016.

With each release of a new chip platform—which can occur several times a year—Intel, like all chip manufacturers, must manage a sizable population of bugs. Itdiligently captures all the data about these bugs and stores them in a defect data‐base.

But Intel faces significant challenges because information about the bugs is in theform of both structured and unstructured data. The structured data came fromresponses from the person who reported a bug to standard questions such as:What is the platform release number where the bug was found? What operatingsystem was being used? What software was running? What is the error codenumber? This data was relatively easy to store.

AI-Based PQM Solving Real-World Issues: Two Use Cases | 21

But the unstructured data includes such things as notes from engineers, logs,emails, and other types of text descriptions. Although all the data was storedtogether, there was no way to consolidate and make sense of it.

When a bug was reported, senior engineers would manually perform textsearches on the database, using keywords and hoping to find any informationthat had been previously entered. But that was inefficient and often failed. Per‐haps someone had already reported the bug but used different language todescribe it. Some notes were written in other languages. There was no way to doan effective search and make use of the existing data.

One of the biggest challenges was determining whether a bug had already beenreported. Different engineers could be working on the same version of a bug sub‐mitted by different people. Additionally, there was no way to search to seewhether a similar problem had previously been solved.

Important questions, included the following, could not be easily answered:

• Is this bug a duplicate?• Have we seen it before?• How did we fix it before?• What other bugs are similar to this?

“We have to be very efficient in how we perform triage and debugs,” says RandyHall, senior principle engineer with Intel’s Client Computing Group. “We enable2,000 designs each year. We don’t want to spend our resources fixing the samebug multiple times.”

All this would come to a head during important project milestones, when Intelwould bring its major customers—such as Dell, Lenovo, and HP—to its Taiwanconference center to identify any issues in a chip design and hammer out solu‐tions. For competitive reasons, customers would meet with Intel senior engineersin separate rooms, but frequently they were flagging the same bugs. Intel had noway of correlating them.

Intel initially estimated that 15% of its bug “sightings” were duplicates and repre‐sented a lot of wasted resources, says Hall. But when it did an actual audit, Intelfound that almost 30% of bug sightings were duplicates in the earlier stages of theprogram. This meant that a significant chunk of the efforts of highly skilled,highly paid senior engineers was going to waste.

After evaluating various PQM solutions on the market, Hall chose Saffron.

This occurred prior to Intel’s acquisition of Saffron, and indeed the success ofIntel’s own internal experience with the solution was one of the key reasons Intelpurchased it.

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Hall began seeing immediate results with Saffron. Senior engineers saved 25% to30% of their time by eliminating duplicate bugs right at the original sighting. Thisalone saved Intel millions of dollars. Engineers could also pull reports on similarbugs, look at the previously applied fixes, and complete root–cause analysesmuch faster. This not only saved time, it improved the overall quality of Intelchip designs—an improvement that is difficult to put a price tag on.

“We worked directly with the Saffron team,” says Hall. “We described the prob‐lem we were trying to solve, and they said, ‘Oh yeah, we know how to do thisstuff.’” So today, Intel sends all sightings to Intel Saffron AI and lets it find theduplicates.

This allows Intel’s senior engineers to concentrate on the things that really matter—such as triaging particularly complex bugs—or finding issues before customersdo. “You don’t want to be debugging simple configuration issues when you’re ask‐ing people to travel across the planet to meet with you,” says Hall. “You reallywant to be focused on the issues that require closer collaboration and expertise.”

One benefit that Saffron had over other AI-based PQM systems was that itworked right away. “Out of the box, Saffron did not require a lot of tuning andthat was a good thing for us,” says Hall. “And the support we got out of the Saf‐fron team was top-notch.”

Software: Accenture® Touchless Testing PlatformAccenture is a leading global professional services company, providing a broadrange of services and solutions in strategy, consulting, digital, technology, andoperations. Accenture works at the intersection of business and technology tohelp clients improve their performance and create sustainable value for theirstakeholders. As such, they are committed to investing a significant portion ofthe global R&D capabilities to help clients across industries integrate AI-drivensoftware testing automation as an agent for speed, change, and customer experi‐ence.

This is especially important considering that a lot of the organizations have notchanged their software testing techniques much from 10 to 20 years ago. Theirprocesses are either heuristic-based or too rule-centric. The question of testingwhat matters is usually answered through imprecise human judgment based onperceived risk-based approaches.

For many organizations, software testing still involves a lot of manual labor, espe‐cially in the area of test-case management that requires step-by-step test docu‐mentation. In some mission-critical systems, test engineers report spending up to90% of their time managing test cases and documenting rather than actually test‐ing.

AI-Based PQM Solving Real-World Issues: Two Use Cases | 23

The other issue in software testing is viewing quality assurance as one isolatedcomponent in the software development life cycle instead of permeating qualitychecks across the entire life cycle, which requires a shift in mindset and dedicatedresources spread from the inception of product design all the way to deployment.

Testing cannot be performed in silos outside of the development process any‐more.

With the increasing complexity of software on one hand and the exponentialgrowth of connected products and devices on the other hand, test engineersrequire access to domain, analytics, and data management tools that are moresophisticated than traditional testing platforms.

Accenture has embraced an open innovation strategy and augments its test engi‐neers with AI technologies to operate more smartly and efficiently by automatinghigh-end decision making and eliminating repetitive, manual tasks. Accenture isnow using Intel Saffron AI to answer questions with which test engineers oftenstruggle:

• How do we test what matters?• Is my test suite bloated, resulting in unnecessary effort?• Is my test coverage correct?• Is there a way to measure the effectiveness of my test cases using a data-

centered approach?• How can I clean and merge duplicate defects? Am I uncovering root defects

or symptoms?• Can I prevent similar defects being initiated during test execution?• Can I predict the best person/team to fix or retest my defect?

Accenture is applying AI and cognitive computing capabilities in software testingto reduce the cycle time, rationalize test cases, and optimize coverage. Its Touch‐less Testing Platform aims to bring together leading open source, commercial,and Accenture-proprietary tools and algorithms to automate a testing process forsoftware.

“Testing is transforming into quality engineering where applied intelligence is atthe core of driving productivity and agility,” said Kishore Durg, senior managingdirector, Growth and Strategy and Global Testing Services Lead for Accenture.“The Accenture Touchless Testing Platform is augmented with artificial intelli‐gence technology from Intel Saffron AI that bring in analytics and visualizationcapabilities. These support rapid decision-making and help reduce over-engineering efforts that can save anywhere from 30 to 50 percent of time andeffort.”

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Accenture’s platform chose Intel Saffron AI to drive intelligence in analytics toaccelerate automation, spot trends, manage risks, and continuously respond tocustomer feedback. Some of the features of their platform include the following:

Defect similarity analyticsEnable defect coalescence and prevention of duplicate defects

Test case similarity clusteringIdentify redundancy and optimize the test suite

Failure-based testingDetermine the probability of failure of test cases through algorithms

Expert finderMake quick resource assignment decisions

Usage-based test optimizationAnalyze how business users are using the software and develop test cases onfunctions that matter to them

Regression optimizationEnable the right coverage of test scenarios that undermines over testing orunder testing

Automatic defect or failure root–cause predictionGain meaningful insights based on past data on bugs and their resolution

According to Accenture, this approach has reduced time-to-market and cost oftesting by more than 20% and delivers more than 90% accuracy, as proven in trialruns.

An insurance company that piloted the Accenture Touchless Testing Platformdiscovered that it could accelerate its pace of delivery by improving its test suite.Using Intel Saffron AI, Accenture identified up to 22% test cases as duplicates orsimilar that could be eliminated.

By applying data insights into defect detection and analysis, test execution, andretesting, the company significantly improved the speed and quality of the soft‐ware development and accelerated overall cycle time. AI and analytics are chang‐ing the testing landscape. Even though Accenture’s platform is meant to helpautomate certain tasks, it also aims to free up test engineers to work on areas thatrequire greater judgment.

Getting Started with AI-Based PQM SolutionsDecades ago, airport operators typically managed relatively small fleets of air‐planes, and could stick their heads out of windows if they need to know what waspreventing an on-time takeoff. Today, in industries from software development,

Getting Started with AI-Based PQM Solutions | 25

to manufacturing, to oil and gas, there are too many products, too many movingparts. There is too much data. There are too many complex systems spread acrossthe entire world. No single human being could possibly know what’s going on allthe time.

AI, specifically complementary learning, is the way forward. We are on the cuspof exciting innovations that will make up for the fact that human intelligencesimply cannot scale at the same rate as data. AI promises to meet us at the edge ofour limitations and extend our capabilities for greater good and productivity.

It’s time to get started. You might want to ponder the following questions, eitherinternally or with a vendor:

• What does it take to get started?• What kind of data is needed?• What problems can be solved?• What resources are required (hardware, software, human resource)?• What process do I have to go through?• What kind of training is needed?• What kind of ROI can I expect?

With out-of-the-box vertical solutions like Intel Saffron AI becoming available, itmakes sense to invest in a complementary AI PQM solution.

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About the AuthorsAlice LaPlante is an award-winning writer who has been writing about technol‐ogy and the business of technology for more than 20 years. Author of sevenbooks, including Playing For Profit: How Digital Entertainment Is Making BigBusiness Out of Child’s Play (Wiley) and Method and Madness: The Making of aStory (W. W. Norton & Company), LaPlante has contributed to InfoWorld, Com‐puterWorld, InformationWeek, Discover, BusinessWeek, and other national busi‐ness and technology publications.

Maliha Balala currently leads the technical communications team at WhirlWindTechnologies. She is a polymath who enjoys researching and talking about tech‐nology, education, psychology, literature, and philosophy.