What Is An Inference Engine in Machine Learning?
An inference engine is a key component of an expert system, one of the earliest types of artificial intelligence. An expert system applies logical rules to data to deduce new information. The primary function of an inference engine is to infer information based on a set of rules and data. It is the core of an expert system, which applies the rules to a knowledge base to make decisions.
An inference engine has the ability to reason—it can interpret the data, draw conclusions, and make predictions. It is a critical component in many automated decision-making processes, as it helps computers understand complex patterns and relationships within data.
Expert systems are still commonly used in fields like cyber security, project management, and clinical decision support. However, in many fields they have been replaced by newer machine learning architectures, such as decision trees or neural networks. Inference engines are also sometimes used as part of diagnostic systems, recommendation systems, and natural language processing (NLP) processes.
This is part of a series of articles about Machine Learning Inference
In this article:
What are the Components of an Inference Engine?
The knowledge base is typically a database that stores all the information that the inference engine uses to make decisions. This information can include facts, rules, and data about the problem domain. The knowledge base is used by the inference engine to infer new information, make predictions, and make decisions.
The knowledge base is a dynamic entity and it continuously evolves as new data is added or existing data is modified. The inference engine uses this information to make intelligent decisions. The more comprehensive and accurate the knowledge base, the better the inference engine can make informed decisions.
Set of Reasoning Algorithms
Reasoning algorithms are the logic that the inference engine uses to analyze the data and make decisions. The algorithms take the data from the knowledge base and apply logical rules to it to infer new information.
The type of reasoning algorithms used by an inference engine can vary based on the problem domain and the specific requirements of the system. Some of the common types of reasoning algorithms used by inference engines include deductive reasoning, inductive reasoning, and abductive reasoning.
Set of Heuristics
Heuristics are rules of thumb or guidelines that the inference engine uses to make decisions. These heuristics are used to guide the reasoning process and help the inference engine make more efficient and effective decisions.
Heuristics can be based on past experiences, expert knowledge, or other types of information. They are used to simplify the decision-making process and to help the inference engine make better decisions.
Related content: Read our guide to inference model (coming soon)
Techniques Used in Inference Engines Reasoning
Here are two major techniques inference engines use to analyze data and make decisions:
Backward chaining is a type of deductive reasoning where the inference engine starts with the conclusion and works backward to find the evidence that supports that conclusion.
Backward chaining is particularly useful when the goal is known, but the path to that goal is not clear. The inference engine will start with the goal and work backward, looking for evidence that supports that goal. This method is often used in problem-solving and decision-making processes.
Forward chaining is a type of inductive reasoning where the inference engine starts with the available evidence and uses that to deduce the conclusions.
Forward chaining reasoning is particularly useful when the evidence is known, but the conclusion is not. The inference engine will start with the evidence and use that to deduce possible conclusions. This method is often used in prediction and forecasting processes.
Applications of Inference Engines
Expert systems are computer systems that mimic the decision-making ability of a human expert. They are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.
The inference engine is at the core of these systems, making logical deductions based on the provided rules. It's akin to a human brain, processing information, making connections, and reaching conclusions. A key advantage of inference engines in expert systems lies in their ability to handle uncertainty and make informed decisions, even when complete information is not available.
Inference engines are also extensively used in diagnostic systems, particularly in the medical field. These systems use the inference engine to analyze symptoms, compare them with known diseases, and then infer possible diagnoses.
The benefit of using an inference engine in diagnostic systems is its ability to process vast amounts of data rapidly and accurately. It outperforms human capability in terms of speed and precision, making it a valuable tool in medical diagnostics.
An inference engine can sift through thousands of medical records, identify patterns, and suggest potential diagnoses. However, it is limited to straightforward logical reasoning and cannot exhibit creativity or identify patterns outside its predefined rules.
Recommendation systems are widely used in online platforms like Amazon, Netflix, and Spotify, to provide personalized recommendations to users. Some recommendation systems use inference engines to analyze user behavior, identify patterns, and make recommendations based on these patterns.
The role of an inference engine in recommendation systems is to process the collected data, infer user preferences, and predict future behavior. Modern recommendation systems are augmenting, or replacing, inference engines with modern machine learning algorithms like neural networks.
Natural Language Processing
Inference engines also find application in the field of natural language processing (NLP), where they are used to understand and generate human language. In the past, inference engines played a critical role in machine translation, sentiment analysis, and language generation. However, they are quickly being replaced by more advanced techniques based on recurrent neural networks (RNNs) and their successor, Transformer architectures.
Best Practices for Using Inference engines in AI
Optimization for Speed and Memory Usage
When using an inference engine, it's crucial to optimize it for speed and memory usage. This involves streamlining the data processing pipeline, reducing the complexity of the model, and optimizing the code for efficient execution. Optimization is particularly important in real-time applications.
Hardware acceleration techniques can be particularly beneficial in applications that involve processing large amounts of data or complex computations.
Leveraging Pre-Existing Models When Appropriate
Pre-existing models are inference models that have been created for an existing use case and include a large number of rules and heuristics. By leveraging pre-existing models, you can save time and resources, as you won't have to prepare your model from scratch.
For example, a cybersecurity company analyzing suspicious web traffic can use an existing inference engine with thousands of rules designed to identify known attacks.
Auditing for Bias in Inference Outputs
Bias in machine learning is a serious issue that can lead to inaccurate predictions and unfair outcomes. Therefore, when using an inference engine in machine learning, it's crucial to audit for bias in inference outputs.
Bias can creep into your inference engine in various ways—through biased data, biased rules or heuristics, or biased decision-making processes. By auditing your system regularly, you can identify and mitigate these biases, ensuring that your system delivers fair and accurate results.
Inference Engine Optimization with Run:ai
Run:ai automates resource management and orchestration for machine learning infrastructure, including expert systems and inference engines. With Run:ai, you can automatically run as many compute intensive experiments as needed.
Here are some of the capabilities you gain when using Run:ai:
- Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
- No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
- A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.
Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run:ai GPU virtualization platform.