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Glossary for Artificial Intelligence Terms

Artificial Intelligence (AI) is increasingly being used in a variety of industries to solve critical business challenges, but despite its growth, it’s still a nascent technology that continues to evolve. As such, there can be some confusion about AI terminology. To provide greater clarity, the following glossary, while not scientific or authoritative, provides a basic understanding of widely used terms and their definitions.

One term absent in this glossary is Robotic Process Automation (RPA) because it is not a type of AI, even though some people mistakenly categorize it as AI. RPA is a software robot that automates rote, repeatable tasks, such as insurance claims processing. The software is not learning, but rather repeating the same process over and over.

The AI Litmus Test

In order to be classified as AI, the software needs to incorporate some type of “intelligence” that is not explicitly programmed and have the capability to continually learn by recognizing patterns in the data.

The Glossary

AI Algorithm – A set of mathematical instructions that are able to learn from data. Data is run through the AI algorithm to provide the AI solution with the intelligence to look for and identify patterns to answer business questions. Data scientists train AI algorithms to predict specific outcomes.

Chatbot or Bot – A type of AI program that can mimic human communication in speech or text and is based on an area of AI called Natural Language Processing (NLP). Chatbots are often used in call centers or for customer service support on websites to answer basic questions or engage with customers.

Computer Vision – A field of AI that strives to gather insight from images or videos. It can be used for image classification, object detection and object tracking, among other applications.

Crowdsourcing Services – These services distribute, among many people, the work involved in collecting and preparing data, including image recognition, data normalization, and algorithm training for machine learning, among other tasks.

Data Engineers – Professionals who are trained to collect, prepare and manage the big data that will be used by AI programs.

Data Scientists – These highly skilled professionals design and continually train mathematical algorithms to be able to answer business questions or help predict outcomes. They follow the scientific method, formulating hypotheses and carrying out experiments to prove their assumptions, leading to new discoveries and insights.

Deep Learning – This highly cognitive field of AI encompasses the next generation of machine learning, where machines can teach themselves by making connections or comparisons in the data that are often overlooked or unknown. Deep learning software requires a huge amount of data and advanced processors to get accurate outcomes.

Image Recognition – The process of detecting and identifying a variety of objects in images and videos using computer vision.

Insights as a Service – A cloud-delivered, pre-built approach to AI, which allows a company to purchase previously sourced data — without owning the data or the AI solution – to help support a business case it is trying to solve. This approach is helpful if a company is looking for insights into a universal question that does not require personalized data.

Machine Learning – This is a form of AI that is capable of learning by recognizing patterns in identified variables in the training data.

Model as a Service – In this model companies collect their own data, but turn to a cloud-based provider to develop, re-train, maintain and operate the AI algorithm. This approach particularly makes sense if a company has very customized or proprietary information, but not the in-house expertise or resources to build or manage an AI solution. A churn model predicting which customers might leave a company is an example of this.

Natural Language Processing – An AI discipline that enables computers to process and understand human language.

Artificial Neural Networks – This is a component of an AI solution that is similar to biological neural networks. These networks use stages of learning to give AI the ability to solve complex problems by breaking them down into smaller problems.

Pragmatic AI – This is the type of AI that is most prevalent today. It applies AI principles to help answer business questions and predict outcomes for tasks, such as predicting the stock market or customer churn.

Pure AI – This type of AI is futuristic, with the ambitious goal of building human-like AI programs that have the same – or even superior – intelligence and capabilities as humans. For instance, this could refer to human-like robots that can make better decisions than humans and pass the Turing test.

Turing Test – A test developed by Alan Turing, to determine a machine’s ability to exhibit intelligent behavior equivalent or indistinguishable from a human.

Predictive Analytics – Based on a large amount of historical data, these AI programs are designed to predict future outcomes and behaviors based on past data.

Pre-packaged Data – Pre-packaged data sets that can be purchased. They offer a quick way to gather training data for AI algorithms.

Private Crowd – A private group of data specialists, usually under NDAs and personally known to the employer, who help with data collection, identification, labeling and preparation of training data or images. Private crowds provide greater accuracy and business-specific knowledge than more generic public crowds (or crowd sourcing).

Public Crowd – A group of non-professionals who can help collect, identify and label large data sets for AI training, but don’t have any knowledge of the company’s specific business. These people are usually not under NDA and not personally known to the employer.

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