Artificial Intelligence and Machine Learning for Business

Machine learning is a powerful branch of AI that enables a computer program to automatically learn from historical data and improve its performance through experience without the need for explicit programming by a human. Machine learning algorithms are designed to automatically find patterns in historical data and make inferences from new data to perform a number of tasks across a wide range of domains.

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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

Identify a behavior as unusual to determine if an asset or process is in need of attention or maintenance before an event like a critical failure.

Unsupervised: Normal Behavior Modeling

Learn to recognize normal machine behavior and deviations from the norm based on time-series data of a machine’s operation.

Unstructured Analytics

Unlock unstructured data from a broad range of sources and streamline business decisions with deep learning.

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.

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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.

Key concepts of deep learning in AI:

Multilayer neural networks

Deep learning is defined by the use of multiple layers of interconnected neural networks. These layers allow for complex patterns and relationships to be learned and extracted from data. Each layer processes the previous layer’s output enabling the network to learn and refine features from raw input.

Representation learning

Deep learning is characterized by its ability to automatically learn and extract meaningful representations from raw data. Its ability to select the most relevant features from the data without explicit feature engineering has enabled breakthrough applications (image denoising, for example).

Large-scale model training

Deep learning models require vast amounts of data to train effectively. Its models contain millions or even billions of parameters that need to be tuned to minimize errors in the training data. As a result, deep learning often requires large-scale distributed computing and specialized hardware such as GPUs or TPUs.

Resources on machine learning in AI

Machine learning in AI—enabling computers to learn without explicit programming—is revolutionizing many aspects of modern life across personal, professional, and industrial settings. Learn more about this powerful branch of AI in these resources.

Pop Quiz: What is Machine Learning?

We’re always talking here about artificial intelligence (AI) and machine learning (ML). Every now and then, it’s good to circle back to underlying concepts at the core of our powerful industrial applications. Today we’re giving you a pop quiz. What is machine learning, and how does it work? Don’t worry if you don’t have a perfect answer. We’re going to try to break it down for you. Read our blog

Transfer Learning & Machine Learning: How It Works, What It’s Used For, and Where it’s Taking Us

Human knowledge is cumulative. We learn the alphabet so that we can learn to read and write, and we then use those (theoretical) literacy skills to write blogs on the internet. By retaining the knowledge we’ve acquired before, we are capable of greater intellectual feats. By contrast, machine learning models have historically been trained for a single, specific task and no more. But a technique called transfer learning is changing this status quo. Read our blog

Basics of Machine Learning

What is machine learning, and how does it work? And what are AI, deep learning, and neural networks? There’s no doubt that the age of AI has arrived, and it’s here to stay. A cursory glance at the headlines on any given day are enough to support this idea. Amidst all of this fervor, however, very few are stopping to discuss what artificial intelligence actually is. So here, we’re going to break down the basics of AI and machine learning and how they work. Read our blog

Oil and Gas Needs Automated Machine Learning

In an industry like oil and gas, where asset failures and unscheduled downtime can be catastrophic to the bottom line, few things are as critical as having an efficient and effective system in place to monitor and repair assets. It’s for this reason that predictive maintenance is becoming an increasingly popular approach in oil and gas operations. But to make predictive maintenance work at scale, oil and gas companies must look to automated machine learning. Read our blog