Problem Identification with Artificial Intelligence Course

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What is Problem Identification?

Clearly identifying a problem's core cause. Depending on the discipline, the term "problem solving" has a slightly varied connotation. It is a mental process in psychology, for example, and a computerized process in computer science. There are two categories of problems: ill-defined problems and well-defined problems; distinct approaches are employed for each. Well-defined problems have obvious end goals and solutions, but ill-defined ones do not. In comparison to ill-defined difficulties, well-defined problems allow for more initial planning. Problem-solving sometimes necessitates dealing with pragmatics, or the way context adds to meaning, semantics, or the interpretation of the problem. The capacity to comprehend what the problem's end aim is and what rules could be implemented is the key to addressing the problem.

Problem Identification with AI?

Problem-solving in computer science relates to artificial intelligence techniques such as designing efficient algorithms, and heuristics and performing root cause analysis to identify desirable answers. The fundamental goal of artificial intelligence is to solve problems in the same way that people do.

Problems in artificial intelligence can be solved utilizing searching algorithms, evolutionary computations, knowledge representations, and so on. In general, searching refers to the process of locating the information that is required.

The following are the steps in the problem-solving process using searching.

Define the issue

Examine the issue

Identifying potential solutions

Choosing the best solution


Properties of search algorithms:


When a search algorithm gives a solution or returns any solution for a given random input, it is considered to be complete.


If a solution is found that is the best (has the lowest route cost) among all the solutions found, that solution is said to be optimum.

The complexity of time

Time complexity refers to how long it takes an algorithm to perform its task. When an algorithm completes a task in less time, it is said to be efficient.

The complexity of space

It is the greatest amount of storage or memory used by the algorithm at any point in time while searching.

These characteristics are often used to compare the efficiency of various types of searching algorithms.

Use of Problem Identification in different fields:


When products or processes fail, problem resolution is utilized so that corrective action can be taken to prevent further failures. It can also be used for a product or process prior to a failure event—when a possible problem can be identified and examined, and mitigation implemented so that the problem does not occur. Failure mode and impacts analysis, for example, can be used to proactively lower the risk of problems developing.

Forensic engineering is a critical failure analysis technique that involves tracing product flaws and problems. Following that, corrective action can be made to prevent further failures. By disassembling a product, reverse engineering aims to discover the original problem-solving reasoning utilized in its development.


The process of finding answers to difficulties encountered in life is referred to as problem-solving in psychology. Typically, solutions to these difficulties are situational or context-specific. The method begins with problem identification and problem shaping, during which the problem is discovered and simplified. The following phase is to generate and assess potential solutions. Finally, a solution is chosen to be implemented and tested. Problems have an ultimate objective that must be met, and how you get there is determined by problem orientation (problem-solving coping style and skills) and methodical analysis. Introspection, behaviorism, simulation, computer modeling, and experimentation are some of the tools used by mental health experts to explore human problem-solving processes. Social psychologists investigate the problem's person-environment link as well as independent and interdependent problem-solving strategies.

Computer science

Problem-solving techniques in computer science and artificial intelligence that deal with algorithms include algorithms, heuristics, and root cause analysis. Computational complexity theory describes the number of resources (e.g., time, memory, and energy) necessary to solve tasks. In a broader sense, problem resolution is part of a bigger process that includes problem identification, de-duplication, analysis, diagnosis, and repair, among other phases.

Linear and nonlinear programming, queuing systems, and simulation are further problem-solving approaches. Much of computer science is concerned with creating entirely autonomous systems that will subsequently solve a specific problem—systems that will accept input data and, in a reasonable period of time, generate the proper response or a correct-enough approximation.

Military science

Problem-solving is tied to the concept of "end-states" in military science, which is the desired condition or circumstance that strategists aim to create. The capacity to solve problems is crucial in any military position, but it is most important at the command and control level, where it is tightly linked to a thorough understanding of qualitative and quantitative scenarios. The effectiveness of issue solving is used to assess the outcome of problem-solving as it relates to achieving the goal. The process of identifying how to achieve a goal is known as problem-solving planning.


One of the most important advantages of AI Problem Identification is its capacity to sift through massive amounts of data in a short period of time. These aid researchers in determining areas of focus for their research. A collaboration between Barrow Neurological Institute and IBM Watson Health, an artificial intelligence corporation, resulted in a recent ground-breaking discovery in the treatment of Amyotrophic Lateral Sclerosis (ALS). IBM Watson, an artificial intelligence machine, analyzed tens of thousands of research papers and discovered novel genes associated with ALS.

The discovery offers ALS researchers new information to develop new medication targets and treatments to combat one of the world's most lethal diseases.

Another interesting application of AI problem-solving in healthcare is its ability to anticipate pharmacological treatment outcomes. Cancer patients, for example, are frequently given the same drug and evaluated to see if it is effective. AI predicts which patients will benefit from a given treatment based on data, resulting in a highly tailored strategy that saves time and money.

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Course Syllabus

Getting Started 11 lectures 17 mins
Introduction Preview 01:42
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