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.
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 on massive data-sets to predict specific outcomes.
Artificial intelligence for IT Operations is the use of AI to help automate traditional IT activities and tasks. AIOps helps IT or DevOps teams work more efficiently to detect and address IT problems more quickly.
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.
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 around the clock.
A field of AI that gathers insight from images or videos. It can be used for image classification, object detection and object tracking, among other applications.
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.
Professionals who are trained to collect, prepare and manage the big data used by AI programs.
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.
A data-set is a collection of information from multiple sources that is cleaned, classified and used to train AI algorithms.
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.
Generative artificial intelligence (AI), such as ChatGPT, is used to create content, such as a blog posts, program code, poetry, and artwork, using audio, code, images, text, and video. The software uses complex machine learning models to predict the next word or image based on previous word sequences.
The process of detecting and identifying a variety of objects in images and videos using computer vision.
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.
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This is a form of AI that is capable of learning by recognizing patterns in identified variables in the training data.
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.
A subset of artificial intelligence that works to give computers the ability to understand text and spoken words in much the same way as humans.
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.
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.
Based on a large amount of historical data, these AI programs are designed to predict future outcomes and behaviors based on past data.
A private group of data specialists, usually under NDA 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 crowdsourcing).
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.
Pre-packaged data-sets that can be purchased or leveraged to supplement training data to accelerate the development of AI solutions.
A test developed by Alan Turing, to determine a machine’s ability to exhibit intelligent behavior equivalent or indistinguishable from a human.