Machine Learning vs Machine Reasoning: What is The Difference?

Individuals frequently misinterpret the crucial distinction between machine learning and machine reasoning.

Artificial intelligence has altered the way businesses use data. The problem is that many people still don't comprehend the differences between AI technology types and the distinct advantages they offer. Despite their distinctions and specialized use cases, the phrases machine learning, machine reasoning, and AI are frequently used interchangeably. Most significantly, individuals frequently misinterpret the crucial distinction between machine learning and machine reasoning, that is, identifying patterns vs comprehending relationships. Each has a specific function in the analytical process and, while they are distinct, are equally crucial in extracting the greatest value from the other.

Machine Learning vs Machine Reasoning: Basics

Even though machine learning and machine reasoning are both strong AI technologies, they take quite distinct approaches to problem-solving. Human-like common sense is discussed in machine reasoning, where concepts and ideas are encoded as symbols in a computer network. Then logic or rules are employed to integrate those symbols to arrive at a conclusion. Machine learning is unique. It is best characterized as the application of advanced statistical methods to uncover subtle patterns in very big data sets.

The first advances to AI were in the realms of logic. Logical reasoning addressed difficulties linked to many types of games, such as chess, that is, problems where you have all of the knowledge and possibilities at your disposal, and where you reason through to a given solution from some beginning conditions. For the first three or four decades of the discipline, this strategy was widely used. However, it was quickly realized that certain issues were not receptive to this method.

Machine Learning vs Machine Reasoning: Applications

Machine learning models have been used in a variety of settings for quite some time. However, the complexity of the networks in which they have been incorporated has grown over time. This is true in telecommunications networks, where operations are getting more sophisticated and automated.

With the industrialization of machine learning across telecommunication services, we are beginning to discover several limitations of this technology:

• Machine learning relies on vast volumes of learned data to create suggestions, therefore when there are fewer past data to rely on, another solution is required.
• Machine learning algorithms do not provide a straightforward method for tracing the causes behind suggestions.
• Consolidating and prioritizing contradictory suggestions from different machine learning agents may be tough.

This is where machine reasoning can be useful in addition to machine learning.

Machine reasoning solves difficulties by applying learned facts to human-like common sense. It extends the capabilities of machine learning by evaluating massive knowledge and data sources to provide clear, comprehensible insight into the increasingly complicated world of network operations, with the ultimate goal of achieving intent-based networks. Machine reasoning can capture the corporate purpose and translate it into attainable network objectives and KPIs. It may then balance and prioritize these depending on the established business goal to make suggestions and choices, allowing for additional network automation.

Machine Learning vs Machine Reasoning: Challenges

The immediate difficulty with the emergence of AI in networks is that goods, systems, and applications must represent human values. Consideration must also be given to how the design of these systems, and their openness, or lack thereof, may be codifying values or supporting or hiding specific human-based points of view. Machine reasoning is important here as a supplement to machine learning since it delivers explainable recommendations. This allows people to track any choices taken back through the system, increasing the system's auditability and explainability. To assure the installation of entirely non-biased AI systems, however, regulations and technical measures must be introduced industry-wide to allow us to recognize when this occurs and eradicate or criminalize it as we go forward with this essential technology.