Q20.Marks: +2.0UGC NET Paper 2: Computer Science 11 March 2023
Which Artificial intelligence technique enables the computers to understand the associations and relationships between objects & Events?
1.Heuristic Processing.
2.Cognitive Science.
3.Relative symbolism.
4.Pattern Matching.✓ Correct
Solution
The correct answer is Pattern Matching.
Key Points
The AI technique that primarily enables computers to understand the associations and relationships between objects & events is generally related to pattern matching.
Pattern matching supports algorithms to understand and recognize patterns in data which may represent relationships and associations. Machine learning techniques, including both supervised and unsupervised learning, make use of pattern recognition to infer these associations.
That being said, aspects of all the options play a role in some form. Cognitive science influences how AI is designed, heuristic processes can be used in decision-making processes, and relative symbolism could be related to how relationships are represented.
Additional Information
Supervised Learning: In this type of machine learning, we provide the algorithm with labeled training data. Each set of data includes the input values and the corresponding expected output. The algorithm learns from this data, creating a model that can make accurate predictions when given new, similar data. It's called "supervised learning" because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. Examples of supervised learning are Linear Regression, Decision Tree, Random Forests, and SVM (Support Vector Machines).
Unsupervised Learning: In contrast, unsupervised learning algorithms are used when the information used to train is neither classified nor labeled. The system isn't provided with the right answers (output). The algorithm must figure out for itself how to perform the task. Essentially, the machine is shown input and is asked to find patterns, features, or relationships within that data. Clustering (grouping similar items) and Association (discovering rules that govern large portions of the data) are two main techniques in unsupervised learning. K-means and Hierarchical Clustering, as well as Apriori and FP-Growth, are examples of each respectively.
