Artificial intelligence (AI) refers to the development of computer systems to perform tasks such as visual perception, speech recognition, or decision-making. Traditional goals of AI include learning, natural language processing, and computer vision. Each of these may refer to a different focus area for AI. For example, natural language processing refers to a machine's ability to read and understand human language. This may allow for information retrieval, text mining, or machine translation. By way of another example, computer vision allows machines to recognize speech, text, faces, or objects in images or video.
Machine learning came about through advancements in the study of pattern recognition and computational learning theory. Machine learning is a field of computer science that sometimes uses statistical techniques to teach computer systems how to improve on a particular task without being explicitly programmed to do so. Using machine learning, computer systems may be able to provide descriptive, diagnostic, predictive, or prescriptive information or feedback.
Machine learning usually uses input data that serves as a training data set to develop a model to achieve its tasks. The model uses training data to learn patterns within the data set so that the model algorithms may make correct and proper predictions. Training data may be a set of examples, such as inputs with corresponding labels or values. Training data may include a target, which a model would try to achieve and be compared to. In some cases, the model may be unsupervised and must learn from a training data set with only inputs and no corresponding targets. Learning models may be based on support vector machines, linear regression, logistic regression, naïve Bayes, linear discriminant analysis, decision trees, and neural networks.
Deep learning is a subclass of machine learning. Deep learning may use neural networks to help train a computer system. A neural network consists of processing elements that are interconnected to reproduce and model nonlinear processes, originally inspired by the human brain. The neural networks can contain hierarchical levels of representations corresponding to different levels of abstraction. The machine itself determines what characteristics they find relevant to determine an answer. For example, in image recognition, machines may learn what images are labeled “car” and “not car.” Using these results, the machine may review other images to identify whether other images have cars or not.
Usually, training data examples are labeled in advance. For example, an object recognition system may be given thousands of labeled images of a variety of types of objects, to be tasked with finding visual patterns in the images to consistently correlate with particular labels. Training data may be labeled by a person, or the object recognition system may try to generate its own labels once it receives a set of training data.
Since machine learning may need historical data to learn patterns and optimize algorithms, larger training sets generally produce better results. Deep learning models in particular may benefit from a greater volume of training data compared to linear or less complex models. One of the issues with training for machine learning is that there are often very few historical examples available and generating additional examples, particularly if human labeling is needed, is costly and time-consuming. To produce better models, and to enhance or expedite machine learning, there is a need for a greater volume of reliable and varied training data.
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