Artificial Intelligence and Machine Learning for Business
Learn about machine learning-based predictive maintenance.
What is machine learning?
Machine learning enables machines to learn from data without being explicitly programmed. Leveraging algorithms and statistical models that analyze large datasets and identify patterns and relationships between data points, machine learning models can be trained on labeled data, where the correct answers are supplied (supervised learning), or on unlabeled data, where the algorithms must find patterns on their own (unsupervised learning).
Machine learning aims to develop algorithms that can learn from data and improve their performance over time without human intervention. The training process involves providing the machine learning algorithm with large amounts of data and iteratively adjusting its parameters to improve its predictions.
Machine learning features:
Unsupervised: Anomaly Detection
Unsupervised: Normal Behavior Modeling
Supervised machine learning
Supervised learning is a form of learning by example, or function approximation, and consists of two stages. In the first stage, also known as the training phase, a given supervised learning algorithm is applied to an input training data set that has been previously tagged or labeled. In this way, the training set consists of a set of sample inputs each mapped to predefined output (the label).
During the training process, the supervised learning algorithm learns the inherent relationship between inputs and outputs, resulting in a model that will be used in the next stage to perform tasks like classification, regression, or forecasting.
In the second stage, the trained model is put to use, taking new data as input to generate some form of prediction in a process known as inference. These predictions can then be used to drive some form of actionable result.
Supervised learning has many applications but comes at a cost–it requires labeled training data to build a model. Such data may not exist or requires expensive, manual methods to procure.
Unsupervised machine learning
Unsupervised learning techniques are applied in scenarios involving unlabeled input data. In these cases, an unsupervised learning algorithm identifies patterns in the data without human oversight, inferring the inherent structure in the data on its own. In essence, unsupervised learning is about learning how to automatically organize the data in order to best describe it.
An essential task in unsupervised learning is clustering–dividing a data set into groups of similar objects. Unsupervised learning techniques are also well suited for anomaly detection and driving predictions based on detecting subtle changes encountered in a data source over time.
Reinforcement machine learning
Reinforcement learning involves learning by trial and error. In this form of machine learning, software “agents” learn what actions to take in response to a reward-based mechanism applied during the training phase. The actions constitute the behavior the agent will take in response to its environment. The reward serves as a form of feedback, allowing the agent to learn over time the optimal policy to employ in the future when it’s put into use in a live environment.
A commonly cited metaphor can be found in Ivan Pavlov’s famous experiments in which he trained his dogs to salivate in response to hearing a ringing bell after previously conditioning them to associate its sound with receiving a reward (meat). From automated stock market trading to self-driving cars to robotic vacuum cleaners, reinforcement learning has been successfully applied in many daily areas of life, automating actions that were once solely taken by humans.
What is deep learning in AI?
Deep learning is a type of machine learning that involves using neural networks to process and analyze complex datasets. Inspired by the structure and function of the human brain (the neural networks in which axons and dendrites process inputs and outputs), deep learning models utilize algorithms designed to automatically learn and improve from experience without being explicitly programmed.
Deep learning models consist of layers of interconnected nodes, called artificial neurons, which process and transmit information. Neurons in each layer receive input from the previous layer and pass output to the next layer until the final layer produces the model’s output. The hidden layers between the input and output layers give deep learning models unique power, allowing them to learn and extract features from raw data.
Deep learning has shown remarkable success in wide-ranging applications, including image and speech recognition, natural language processing, autonomous driving, etc. Its ability to learn from large amounts of data has enabled breakthroughs in various fields, including healthcare, finance, and transportation.
Deep neural networks require vast amounts of labeled data to train effectively, but they are a critical area of research and development in AI, with proven applications for use cases like face recognition, extracting maintenance log information from unstructured documents, detecting abnormal machine behavior, and cybersecurity.