Business Capabilities Using AI

Classification and Prediction models analyze patterns in your data, and learns to associate historical patterns with outcomes. Based on past results, the prediction model learns patterns, and detects them in new data to predict future outcomes.

Use the prediction AI model to explore business questions that can answered as one of two available options (binary), multiple possible outcomes, or where the answer is a number.

Similar to classification, regression tasks are also a common supervised learning task. However, where predicted output values are categorical, regression models predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable impacts the others. For example, automobile price based on features like, gas mileage, safety rating

In general, clustering uses iterative techniques to group cases in a dataset into clusters that possess similar characteristics. These groupings are useful for exploring data, identifying anomalies in the data, and eventually for making predictions. Clustering models can also help you identify relationships in a dataset that you might not logically derive by browsing or simple observation. For these reasons, clustering is often used in the early phases of machine learning tasks, to explore the data and discover unexpected correlations

Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand. You can use automated ML to combine techniques and approaches and get a recommended, high-quality time-series forecast. An automated time-series experiment is treated as a multivariate regression problem. Past time-series values are “pivoted” to become additional dimensions for the regressor together with other predictors. This approach, unlike classical time series methods, has an advantage of naturally incorporating multiple contextual variables and their relationship to one another during training. Automated ML learns a single, but often internally branched model for all items in the dataset and prediction horizons. More data is thus available to estimate model parameters and generalization to unseen series becomes possible

NLP can be used to classify documents, such as labeling documents as sensitive or spam. The output of NLP can be used for subsequent processing or search. Another use for NLP is to summarize text by identifying the entities present in the document. These entities can also be used to tag documents with keywords, which enables search and retrieval based on content. Entities might be combined into topics, with summaries that describe the important topics present in each document. The detected topics may be used to categorize the documents for navigation, or to enumerate related documents given a selected topic. Another use for NLP is to score text for sentiment, to assess the positive or negative tone of a document

Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do.

Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects. One of the driving factors behind the growth of computer vision is the amount of data we generate today that is then used to train and make computer vision better. Along with a tremendous amount of visual data (more than 3 billion images are shared online every day), the computing power required to analyze the data is now accessible. As the field of computer vision has grown with new hardware and algorithms so has the accuracy rates for object identification.