Knowledge Systems Consultancy

Knowledge engineering is considered the most important field of artificial intelligence, specializing in creating rules applied to data to simulate the expert human thinking process. Therefore, it is concerned with the crucial structure of determining how to reach a conclusion by tracking the skills of experts and the solution methods they adopt with human intelligence and experience. A library of problem-solving methods (algorithms) and the knowledge associated with each of them can then be created, and provided as ready-made templates to solve problems analysis by the system from new inputs. The resulting program can assist in identifying, troubleshooting, and problem-solving, either independently or as a support role for the human expert (an automated consultant).

Knowledge Engineering

Details: Knowledge engineering is a branch of artificial intelligence that develops rules that are applied to data to simulate the thinking process of an expert on a specific topic. In its early days, knowledge engineering focused on transferring an expert's problem-solving expertise to a program capable of producing smart conclusions (expert systems). Over time, this transfer process proved to be limited; it did not accurately reflect how humans make decisions, nor did it possess the intuition and feeling properties known as analogical reasoning and nonlinear reasoning, which can often be illogical without sufficient data.

The goal of KE is that to transfer the problem-solving expertise of human experts to an application capable of consuming the same data and arriving at the same outputs. This approach dominated early knowledge engineering attempts. In later developments, scientists and programmers realized that the knowledge humans use in decision-making is not always straightforward. While many decisions can be traced back to successful past experiences, it has become increasingly clear that humans rely on parallel sources of practical knowledge (accumulated experience) that do not always seem logically connected to the problem at hand. Some of what CEOs and high-profile consultants call "intuition" is better described as analogical and nonlinear reasoning. Unfortunately, these modes of reasoning are not suited to the abundant, straightforward decision trees and can require reliance on data sources that may be more expensive to acquire and process than their actual value.

Today, knowledge engineering uses modeling processes that rely on creating a system that produces the same results as the expert without using the same information sources and without having to follow the same procedures. Therefore, the transfer process has been abandoned in favor of modeling. Instead of attempting to follow the decision-making process step by step, knowledge engineering focuses on creating an intelligent system that achieves the same results as the expert without following the same path or relying on the same information sources. This avoids some of the problems associated with knowledge tracking used in nonlinear reasoning, where those who implement it often do not know the information they rely on when constructing the required knowledge models.

For knowledge engineering to outperform human experts, it must be integrated into decision support software. Knowledge engineers specializing in diverse fields can develop human-like functions with greater flexibility. The most consistent rule in this field is that as long as the conclusions are comparable, the model is performing efficiently. Once the model approaches human expertise, it can be improved, erroneous conclusions can be tracked and corrected, and processes that produce equivalent or improved conclusions can be encouraged. This includes the ability of machines to recognize faces or analyze what a person says for meaning.

In the Generative AI phase, as the model becomes increasingly complex, knowledge engineers may not fully understand how the conclusions are reached, but they are confident in the correctness or quality of the output. By combining these knowledge engineering models with other capabilities such as natural language processing, facial and object recognition, and high-precision sorting, AI could become the best server, financial advisor, or travel agent the world has ever seen. Ultimately, however, the field of knowledge engineering will transition from creating systems that solve problems efficiently with humans to systems that perform them quantitatively better than humans.

Examples of knowledge engineering include the development of expert systems for medical diagnosis, financial risk assessment, and smart manufacturing. Additionally, knowledge engineering principles are applied to customer service chatbots, natural language processing systems, and even architectural design such as computer-aided design (CAD) software. Consulting services in this field assist clients to utilize these knowledge systems, acquired from human experts—employed or stakeholder members of the company's—often gathered through knowledge acquisition and absrevation, to solve complex problems or automate tasks during daily operations.

For data mining and analysis, knowledge engineering principles are used to extract valuable insights from massive data sets, helping businesses and organizations make better decisions. Fraud detection systems are a direct target of this scientific approach; intelligent systems can be built to identify fraudulent activities by learning patterns and anomalies from historical data.