In a business setting, these analytic techniques can be applied to solve real-life problems. The most prevalent problem types are classification, continuous estimation, and clustering. I will try and give some clarification about the types of problems we face with AI and some specific examples for applications.
Classification: Based on a set of training data, categorize new inputs as belonging to one of a set of categories. An example of classification is identifying whether an image contains a specific type of objects, such as a cat or a dog, or a product of acceptable quality coming from a manufacturing line.
Continuous Estimation: Based on a set of training data, estimate the next numeric value in a sequence. This type of problem is often described as “prediction,” particularly when it is applied to time series data. One example of continuous estimation is forecasting the sales demand for a product, based on a set of input data such as previous sales figures, consumer sentiment, and weather.
Clustering: These problems require a system to create a set of categories, for which individual data instances have a set of standard or similar characteristics. An example of clustering is creating a set of consumer segments, based on a set of data about individual consumers, including demographics, preferences, and buyer behavior.
Optimization: These problems require a system to generate a set of outputs that optimize outcomes for a specific objective function (some of the different problem types are considered types of optimization depending on the business use-case). Outputting the parameters for optimizing Oil production from a deepwater oil rig is an example of optimization.
Anomaly Detection: Given a training set of data determine whether specific inputs are out of the ordinary. For instance, a system could be trained on a set of historical vibration data associated with the performance of an operating piece of machinery, and then determine whether a new vibration reading suggests that the machine is not operating normally. Anomaly detection can be considered a subcategory of classification.
Ranking: Ranking algorithms are used most often in information retrieval problems where the results of a query or request need to be ordered by some criterion. Recommendation systems are suggesting the next product to buy using these types of algorithms as a final step, sorting suggestions by relevance, before presenting the results to the user.
Recommendations: These systems provide recommendations based on a set of training data. A typical example of recommendations are systems that suggest “next product to buy” for an individual buyer, based on the buying patterns of similar individuals, and the observed behavior of the specific person.
Data Generation: These problems require a system to generate appropriately “original” data based on training data. For instance, a music composition system might be used to generate new pieces of music in a particular style, after having been trained on pieces of music in that style.
Written By: Eliya Elon, Director, Head of International Business Development at Razor Labs.
Originally posted on Eliya’s LinkedIn.