Chapter 2. Complementary Learning and Intel Saffron AI
Complementary Learning as the Future of Predictive Quality and Maintenance Solutions
Because none of the types of artificial intelligence (AI) can solve all problems, applying them simultaneously is the key to success. This need for a combined approach is giving rise to cognitive computing as a basis for complementary learning. This is what DARPA’s John Launchbury refers to as the “contextual adaptation systems” in the third wave of AI.
Strengths and weaknesses of different AI approaches are giving rise to complementary learning because solving a challenging problem often requires solving underlying subproblems effectively, which calls for different models or approaches.
To understand how machine learning, deep learning, and cognitive computing-based AI can work together in a predictive quality and maintenance (PQM) solution, it’s important to understand that a comprehensive AI-based PQM solution needs to solve two types of problems: surveillance and prescriptive.
Surveillance use cases involve scenarios in which businesses need to recognize problems by observation. By detecting patterns and alerting businesses, the surveillance approach to AI allows companies to act quickly when something out of the ordinary is detected in their equipment or other assets. For example, manufacturers want to understand what the sensor data coming in from the factory floor via the Internet of Things (IoT) is telling them. In the past, they would have needed to build rules into the sensor network to send alerts when certain thresholds were passed, or anomalies sensed.
But the problem was identifying all those rules. Although it’s possible to define the 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 to impossible.
That’s when machine learning and deep learning come in. These two types of AI can process the data, access the knowledge, and specify what those parameters are 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 thousands of examples of an issue before a system is fully trained. And if the system did not perform as expected in some circumstances, humans will need to provide additional 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 the system with exception cases. The goal here is to help the machine learn quickly from 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 computing capabilities are required. After all, for a system to do those things, it would require the ability to reason. It would need to extract and consolidate relevant information 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 are good for knowledge extraction. Cognitive computing is good with knowledge representation—finding connections and insights from data.
Let’s walk through a basic example. The first step toward solving a problem with a piece of equipment or product is that data—which can be structured or unstructured—needs to be processed and identified. If it’s text, natural-language processing (NLP) will be used to parse the meaning. If it’s an object, computer vision 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 the patterns detected by machine learning and deep learning. In effect, the system has answered the question, “What is it?”
When the “what” question has been understood, cognitive computing can then come in to ask questions such as: Have I ever seen this before? What type of a problem is it? Who knows how to fix this? What do I do next? What caused this problem? And, will it happen again?” Cognitive computing systems then answer those questions.
When we talk about complementary learning with respect to PQM applications, we’re talking about combining surveillance, or knowledge extraction, with the second, more prescriptive, knowledge representation application that uses memory-based reasoning.
Intel Saffron AI: Associative-Memory Learning and Reasoning and Complementary Learning in Action
Intel Saffron AI is based on cognitive computing that utilizes associative memory learning and reasoning, along with patterns detected from machine learning and deep learning, in the complementary way previously discussed. By using human-like reasoning to find hidden patterns in data, Intel Saffron AI enables decisions that can deliver rapid return on investment (ROI).
The core of Intel Saffron AI is the Intel Saffron Memory Base, a long-term persistent knowledge store built on an associative-memory matrix. It stores unified data about entities in an associative-memory store. That memory store correlates similar information together and makes it faster to query and easier to retrieve for analysis. This means that Intel Saffron AI mimics how a human naturally observes, perceives, and remembers by creating memory-based associations.
Intel Saffron AI uses data from a mix of machine learning and deep learning AI subsystems, like NLP for entity extraction, sentiment analysis to establish links, and topic mapping for content mapping. The platform is both semantic and statistical in nature.
Intel Saffron AI ingests all types of data, including structured, unstructured text, nonschematic, and on-schema. This data then resides in a hyperdimensional matrix that connects one node (data or entities like people, places, things, or events) to another node using edges (which are statistical connections).
Although most graph stores work as a key–value pair, Intel Saffron AI acts like a multidimensional graph store that allows for N connections between nodes, and functions like a hyper matrix. The connections make associations based on context, frequency, and time.
When a new node (data) comes in, the platform applies memory-based cognitive techniques and creates weighted associations between people, places, things, and events. In this way, Intel Saffron AI acts like a massive correlation engine that calculates the statistical probabilities using the Kolmogorov Complexity (K Complexity). It then derives a universal distance measure that shows how closely two objects are related and to find regular patterns in the data. This way of cognition by similarity enables anticipatory decision making, which involves making decisions by estimating the current situation, using diagnoses, prescribing possible actions, and predicting likely outcomes.
Customers can implement Intel Saffron AI across industries. Its bedrock technology is the patented Intel SaffronMemoryBase, which provides a layer of REST APIs that customers can develop and customize for their own needs. Intel Saffron AI now offers industry-specific applications that will harness the power of the platform to solve specific quality and maintenance problems for manufacturing, software, and aerospace.
What Makes Intel Saffron AI Different?
A complementary learning solution like Intel Saffron AI enables powerful machine and human interactions. It aims to help humans make decisions better and faster. It does this by relieving human workers of having to perform repetitive, 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. In addition, Intel Saffron AI keeps advancing, learning from human feedback and interactions.
It does this by excelling in three ways: its transparency—which makes it easy to understand its results and recommendations; for the fact that no statistical models are required; and that it brings together both structured and unstructured data from multiple sources.
Transparency
Intel Saffron AI works by identifying similarities. But unlike a traditional machine learning or deep learning application, which makes its decisions by algorithms and “black box” methodologies—that is, businesses have no insight into why they got a particular result—Intel Saffron AI is completely transparent. Because it works by knowledge representation, it stores all the attributes that led it to a particular decision or conclusion and makes them readily available to users. 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 it thinks they’re similar. Businesses have full access to all of this information, giving them a chance to tell Intel Saffron AI when it’s wrong, so it can learn for next time.
One-shot learning: No statistical models required
The key benefit of not needing to model data is flexibility, especially when data is sparse, dynamic, or incomplete. This is what Intel calls “one-shot learning”: see something once, and Intel Saffron AI learns.
Here’s an example: if a child is burned by a hot stove, hopefully she learns from that experience and avoids the stove in the future. If the child was acting based on a statistical-based learning, however, she would have to experience pain multiple times before she had enough data to build a statistically relevant model—and not 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 possible moves. 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 system needs to be able to adjust to evolving scenarios.
Intel Saffron AI, different from machine learning and deep learning, learns through association rather than by modeling possible outcomes. It builds signatures of entities that it gradually learns more about. Then it compares those signatures to identify hidden connections, patters and trends—surfacing insights that are otherwise invisible.
Unifies both structured and unstructured data across multiple sources
A lot of insights in the real world come from unstructured data—maintenance logs, manuals, handwritten notes, audio and video recordings, and emails. The ability to analyze both structured and unstructured data is one of the strengths of Intel Saffron AI. When you couple this with the insights from machine and deep learning, you can reveal much more insights.
In other words, deep and machine learning analyze structured data to identify symptoms, whereas associative-memory learning and reasoning analyzes unstructured data to provide a diagnosis.