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Introduction
Many algorithms are used in AI, and they all have strengths and weaknesses. One of the most common is the support vector machine (SVM), which is used to classify data into one of many categories.
Discussion
SVMs strength is that it can work with large datasets, but it has a high learning rate and requires large amounts of training data. Another standard algorithm is neural networks, similar to SVM, in that they learn from data like humans (Chang et al.,2018). Neural networks can be trained to recognize patterns in data, but they are also sensitive to noise and require more training time than SVM. Another type is a random forest classifier, which uses multiple decision trees to make predictions about new examples that have yet to be seen or have not been seen often enough for classification algorithms like SVM to work well on them.
One of the strengths of using one algorithm over another is that it can be more easily adapted to fit the needs of different problems. For example, if one is trying to find a solution to an optimization problem and has an algorithm that performs well in that environment, it is best to stick with it. Nevertheless, if one is working on a problem where many factors affect results, something else is recommended.
The strength of choosing one algorithm over another is that they tend to produce similar results for similar problems. This means that if ones computer has access to one sort of algorithm and has already run it many times before without finding an optimal solution, it should be able to find one quickly using whatever variation given. However, The most significant weakness of this approach is that it can take quite a bit longer than other methods, especially when one is trying to solve problems that require human input or creativity.
Conclusion
There are a lot of different methods to choose from when it comes to AI Algorithms; some include reinforcement learning and support vector machine methods. I use the reinforcement learning algorithm since this method can teach me how to solve problems and make decisions without being told how. A reinforcement learning algorithm can be used in many different ways, such as when it comes to machine learning or robotics. The main reason I would use this method is that it allows for flexibility, which means it can be used in any situation or for any purpose.
Reference
Chang, C. W., Lee, H. W., & Liu, C. H. (2018). A review of artificial intelligence algorithms used for intelligent machine tools. Inventions, 3(3), 41. Volume 3. Web.
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