artificial intelligence: 50 terms marketers should know

Marketers need to stay with the trend. And what’s the trend these days? AI. Here are Artificial Intelligence terms marketers need to know.

Artificial intelligence, or AI, is the practice of building computer-based systems that can perceive their environment and take actions based on the information they gather. AI systems can be trained to perform driving cars, interpreting medical images, or translating languages.

The AI revolution is happening all around us and is being driven by big data and unprecedented advances in computing power. As a result, we’re starting to see AI at work in almost every aspect of our lives.

Why Artificial Intelligence As a Marketer Matters?

One of the biggest reasons AI for marketers is necessary is because marketing has been evolving rapidly. There are many different forms and formats of content being used today, including blogs, social media, email, video content, and so on. Here’s how AI can prove to be a boon to marketers. 

  1. AI can learn from the mistakes and successes of its predecessors
  2. It alleviates the need for repetitive tasks
  3. You can use AI to predict consumer behaviors
  4. Automate your marketing strategy with AI
  5. It reduces cost while improving targeting

However, before you get into AI, here are some terms related to artificial intelligence that one should know.

While you are at it, you should also check out the most relevant blockchain terms for marketers. Along with AI, blockchain is another technology taking the world by the storm and needs to be in your campaign. 

Artificial Intelligence: 50 Terms marketers need to know

Let’s get started with Artificial Intelligence: Terms marketers need to know. 

1. Accuracy 

The accuracy of an artificial intelligence machine is the number of correct answers it gives compared to the total number of responses it provides.

For example, a 95% accuracy rating system gave 95 out of 100 correct answers. A system with an 80% accuracy rating gave 80 out of 100 correct answers. A 20% accuracy rating system gave only 20 out of 100 correct answers.

2. Adversarial Machine Learning 

Adversarial machine learning is a type of machine learning in which an adversary attempts to mislead a learning system by providing it with examples, known as negative examples, crafted to be misclassified by the target learning system.

3. Algorithm 

An algorithm is a set of instructions that programmers use for calculations and computer programming. These instructions are usually in the form of a list of steps that must be followed to reach the desired outcome.

4. Application Programming Interface (API)

An Application Programming Interface (API) is a set of routines, protocols, and tools for building software applications. An API defines how software components should interact.

In layman’s terms, an API is a tool that lets any company or app talk to other companies or apps. To use an API, you need to know a unique piece of code.

5. Artificial General Intelligence (AGI)

AGI is the ability of an AI system to perform all of the processes that humans can. In other words, AGI is when an AI system can perform any cognitive task that a human being can, including perception, speech recognition, planning, translation between languages, and reasoning about abstract concepts such as time, space, and cause.

6. Artificial Intelligence (Weak AI)

Weak AI refers to those systems that can only perform specific tasks based on what the instructions of the programmer into them. A human who tells the machine how to behave in certain situations usually is responsible to control them. 

Uses of Weak AI include working as an assistant for humans, allowing them to focus on more complex tasks rather than basic ones. For example, a weak AI system could search through a database of files looking for specific information such as a name or address, and it would then tell the user if it found what they were looking for or not. A weak AI system cannot learn new behaviors or improve at performing tasks over time.

7. Artificial Neural Network 

Artificial neural networks (ANNs) are machine learning algorithms with human brain as an inspiration. Their uses include computer vision, speech recognition, natural language processing, and audio classification.

The composition of an artificial neural network includes many simple processing elements connected with links, which can be analogous to axons in a biological neural network.

8. Big Data

Big data is a broad term that refers to the large sets of information people produce when they use computers, make purchases, communicate with each other, etc.

9. Bots

Chatbots are a new technology that combines artificial intelligence and natural language. Instead of users navigating a website or app, they get to interact with a bot that can help them locate information or buy a product. It’s similar to using an online customer service representative through email, but instead of typing back and forth, you’re chatting with the chatbot in real-time.

10. Brute Force Search 

Brute force search, or exhaustive or parallel search, is a problem-solving strategy in which an algorithm attempts to find a solution by considering every possible candidate solution.

11. Cluster

Cluster is the AI-powered technology that helps you find the correct email addresses for your mailing lists. It is a form of machine learning and artificial intelligence (AI) that involves grouping data into similar, related groups or “clusters.” It can be helpful in number crunching for business purposes. 

For example, a retailer could use clustering to group customers who use its website by their shopping preferences.

12. Cognitive Science 

Cognitive science studies cognitive processes, such as “thinking” and “reasoning,” using concepts from computer science, linguistics, philosophy, neuroscience, and psychology.

13. Content Moderation 

Content moderation involves using software to monitor user-generated content to prevent inappropriate or illegal material from being published online. Social networks and internet companies employ a variety of content moderation strategies, including:

  • Human moderators who are responsible for flagging inappropriate content for removal
  • Automated tools that assess whether the content is safe or not by referencing rulesets that the company has set out. Some platforms combine these two strategies with an automated assessment followed by a human check.

14. Corpus

The term corpus is used in AI for the body of text (the collection of documents) that trains a model.

A corpus contains texts; it is not necessarily restricted to data. A corpus is an extensive collection of documents that serves as a repository of information, examples, or instances. It would be an appropriate term to use in most cases where data would be used.

15. Data 

Data is a set of facts that can be learned, compared, or made conclusions. In artificial intelligence, data, such as pictures and sounds, are used to teach computers how to recognize objects and speech patterns.

16. Data Mining

Data mining is an inclusive term and has various uses. In its most general sense, data mining refers to discovering previously unknown patterns from large, seemingly unrelated datasets. It is used in everything from marketing to medicine to security. A recent example of data mining being used for medical purposes is the ability to predict heart attacks based on patients’ blood pressure, cholesterol, and other vitals.

The most common use of data mining is in marketing. Companies like Amazon and Netflix have employed it to help them provide more targeted advertising and better recommendations based on customers’ past purchases and ratings.

17. Deep Learning 

Deep learning is a class of machine learning algorithms inspired by the brain’s structure and function. These algorithms are capable of learning from experience and have been applied to fields such as computer vision, natural language processing, and speech recognition, among others.

The idea is to create artificial neural networks that mimic how real neurons communicate with each other. They’re called “deep” because they can consist of many layers — hence the term “deep learning.”

18. Deep Neural Network 

Deep Neural Networks (DNNs) are among the best models for solving complex problems. DNNs are not only limited to computer vision applications but also can be used for NLP (Natural Language Processing), time series prediction, audio classification, and prediction.

These networks are different from other types of machine learning models in two ways: they can learn features and representations that generalize well and be trained using large amounts of data.

19. Domain Adaptation 

Domain adaptation is the ability of a machine-learning algorithm to generalize from samples of one domain to another seemingly unrelated field. The basic idea is quite simple: train your model once, then keep retraining it using different data sets and find ways to combine these models into one unified model.

20. F Score 

F Score (F1 score) measures the accuracy of the automatically generated predictive model. It combines Precision, recall, and F measure for binary classification problems. The highest possible F1 score is 1, which means the model performs perfectly.

However, it is almost impossible to achieve such a high F1 score in practice. Therefore the second-best option is to choose a value close to 1 but not more than 0.9.

21. Facial Recognition 

Facial recognition is a biometric identification technique that uses the unique configuration of a person’s facial features to identify them accurately, instead of (or in addition to) their names.

It can detect or verify a person’s identity by comparing selected facial features from one image to another image or a database of such images.

22. Human Workforce 

In the broadest sense, the human workforce is the composition of the crew (the people employed) in an organization, including all employees. It is simply that group of individuals working for a company and getting paid for their labors. It could be people working in an office environment. Still, it could also include those who work from home running the business in various ways such as telemarketing, faxing, or emailing.

23. Image Recognition 

Image recognition is a technology that allows computers to look at photos, objects, or scenes and recognize what they see. The simplest example of image recognition is a Google search. If you search for a picture of a dog, Google will bring up other photos of dogs. If you’re looking for a photo of the Empire State Building, you’ll get images of the famous New York City landmark.

24. Image Segmentation 

Segmentation is the process of extracting meaningful and valuable information from images. It is a part of computer vision. Many photographers use this technique to capture the desired objects from a photo. We can also use this technique while editing pictures to focus on the thing we want to edit and cut out the unnecessary parts of the picture. So this process is also called image segmentation.

25. ImageNet

ImageNet is the image classifier, which is a supervised machine learning system. It is one of the most common networks used for many different applications. It has been used to predict things, such as whether an object is a car or not, but it can also be used in medical imaging and facial recognition.

26. Machine Learning 

Machine learning is a method for teaching computers to recognize patterns in large datasets using computational methods. It is related to statistical classification and data mining, which also attempt to learn from data automatically. 

The difference between machine learning and other learning is that the computer uses algorithms to “learn” from data in machine learning. In contrast, in statistical classification and data mining, humans specify the rules and concepts they want to learn.

27. Model

In machine learning, a model is a function that describes some data, as in predictive modeling. For example, if you’re trying to identify the species of iris your plant is, you could fit a model to the data on species and flower measurements and then use it to predict the species of any new plant you measure.

28. Natural Language Processing 

NLP is one of the significant parts of AI because it enables a computer to learn by example. Without NLP, a computer would have to be fed thousands or millions of models before it could begin to form generalizations about the world around it. 

The first time you taught a child how to play baseball, you had to show them specific cases: how to hold a bat, how to throw the ball, and where to stand on the field. After that, though, you could generalize (“If you want a good aim, bring your arm back like this”) and explain rules (“The pitcher tries to throw the ball so that it lands between home plate and the batter”). 

Without having seen any of those things before in real life, your child could still learn about them from what you showed them.

29. Neural Networks

A neural network is a branch of machine learning algorithms inspired by how neurons in the brain are connected to form a network. Their uses include computer vision, speech recognition, natural language processing, etc.

Its uses include image recognition, natural language processing, speech recognition, and machine translation. They have achieved remarkable results by learning from large amounts of data. 

30. Noise 

Noise is simply what interferes with the data you are trying to learn.

The most significant source of noise in machine learning is the human input. The data we provide can be noisy because of poor labeling or because the information itself isn’t representative of the real world. For example, let’s say we want to teach a computer how to recognize cats in images. One way would be to show it thousands and thousands of pictures of cats and tell it when it sees one.

But what happens if some of those pictures are dogs? Or if some are simply severely lit? In other words, our set of training data isn’t complete or accurate, and it would cause our computer vision system to make mistakes when it sees new pictures that it hasn’t seen before.

31. Object Detection 

It is a task of identifying objects in images. For example, it can be used for analyzing photos and videos to classify them into predefined categories.

Object detection aims to automatically identify objects of interest (such as faces) in a single image or a scene containing multiple images and then locate those objects within the image. For example, the application may try to find all faces in a given image or detect all cars on the road.

32. Object Recognition

In simple terms, it is detecting and classifying objects in images, videos, or other media. For example, if you were to point your phone’s camera at a flower and click “take a picture,” the device would recognize that it is a flower and classify it as such.

33. Overfitting 

Overfitting is a common cause of model failure, and it occurs when a model is excessively complex, often due to excessive tuning of the model parameters.

It can be broken down into two main categories:

  • Underfitting: When your model does not have enough training examples to learn from appropriately.
  • Overfitting: When your model has too many parameters and learns the noise in your training data instead of the underlying pattern.

34. Precision 

Precision in AI is the proportion of the correct predictions, and this proportion is calculated based on the dataset used to train your model.

35. Predictive Analytics

Predictive analysis is a technique used in machine learning and data mining to anticipate outcomes. Businesses use it to predict customer behavior, the likelihood of specific products, and more. In layman’s terms, predictive analysis is a way of looking forward to the future.

It can be used for a wide range of uses, including business and economic forecasting, marketing and sales analytics, logistics planning, and various forms of research.

36. Positive Predictive Value

Positive predictive analysis in AI is a mathematical method to predict a future event based on past events. It is a machine learning method of estimating the probability of events, such as determining whether a patient has cancer or whether an employee will stay with a company.

37. Regression 

Regression is the task of using past data to make predictions about future events. For example, if you have historical data on the price of a stock, a regression could be used to predict what the stock price will be in the future. Regression analysis is a standard method in statistics and machine learning.

Moreover, analysis of past data is helpful for prediction because trends tend to continue over time. But sometimes, these trends change direction, and it becomes essential to use this information to adjust your forecasts.

38. Reinforcement Learning 

Reinforcement learning is an area of machine learning that deals with how software agents ought to take actions in an environment to maximize some notion of cumulative reward. In other words, it’s a way for computers to learn to do things that humans or other computers find valuable; by doing so, they win some kind of “points” or “experience.”

39. ROC Curve 

ROC stands for Recall and Precision. Recall measures how good your model is at identifying the positive results (e.g., true positives). In contrast, Precision measures how good your model is at identifying the adverse effects (e.g., true negatives).

The curve itself shows you the tradeoff between Recall and Precision by plotting these two metrics against one another.

40. Search Query 

A search query is a string with which one can identify records in a database or other information retrieval system. It results in a list (the result set) of documents that are relevant to the search query.

41. Selective Filtering 

Selective filtering is a technique of artificial intelligence systems to determine with greater accuracy the desired output. The most simple of such systems would produce the same result for any input, which would not be beneficial. 

It allows the system to be trained to predict results more accurately. In applications, we can use this technique to recognize specific objects or speech patterns by allowing the computer to learn and remember them.

42. Semantic Analysis

A semantic analysis program can understand natural language, but it cannot produce natural language, and it will just give you the data and information you need without communication.

It can help an AI system figure out what a person intends to convey through their choice of words. It can also help an AI system determine whether two or more documents are about the same subject or topic.

43. Strong AI

Strong AI, also known as Artificial General Intelligence, is a field of artificial intelligence research intended to create an agent that possesses human-like general intelligence.

Furthermore, there is no consensus among artificial intelligence researchers as to whether the creation of strong AI, nor what properties a strong AI would need to have to be considered indeed “intelligent.”

44. Supervised Learning 

Supervised learning, or learning from examples, is a machine-learning approach in which the computer is “taught” what to do by being shown examples of correct answers.

Furthermore, in supervised learning, the programmers gives labeled data to the computer. This is in contrast to unsupervised learning, where the computer is given data that has no tag.

45. Training Data

Training data is the best way to define what is a particular entity or class. And it has to be appropriately labeled so that the training algorithm can understand and identify it.

In machine learning, to teach a machine how to do something, one can use training data. It’s simply a data set where each data element is an input and output pair. You feed in an example, and you want the machine to learn from it.

46. Turing Test

The Turing test is a benchmark for artificial intelligence (AI) research. Alan Turing proposed it in 1950 to determine whether machines can think: if a human conversing with an AI program cannot tell it apart from another human, the AI is said to pass the test. 

Moreover, the test has come to serve as a shorthand for evaluating new AI technologies and determining what sort of intelligence they might display.

He proposed that whether machines are capable of thought could be replaced with “Are there imaginable digital computers which would do well in the imitation game?”

47. Unsupervised Learning

Unsupervised learning is a branch of machine learning where the goal is to discover patterns in unlabeled data. It is related to, but different from statistical inference in that inference provides probabilistic and statistical properties of the data (e.g., finding the mean or median). At the same time, unsupervised learning aims to summarize the inherent structures of the data. 

For example, clustering algorithms find groups of objects that are “similar” according to some predefined similarity measure. In many cases, these groups reflect meaningful (and perhaps discoverable) structures in the underlying data.

48. Visual Recognition 

Visual recognition is a computer function that enables machines to identify and distinguish objects from each other. It is a type of artificial intelligence (AI) that has been made possible through the advancement of computer vision technology. 

Additionally, the ability to recognize images is one of the essential skills in machine learning since it allows computers to determine the content of an image or even map its pixels to corresponding physical dimensions.

49. Web Crawler

A web crawler is a software or a bot that crawls the links present in search engine result pages. The crawler uses the links present on the page to crawl other pages. The main aim of this process is to gather information from the web and create an index of it.

For example, a business can have a website with the latest company news and product information. To keep its website up-to-date and relevant, the company uses a unique automated program to visit its website and gather the latest information. 

The program gathers this information from the company’s website and any external sites that provide relevant information about the company or its products.

50. Web Scraper 

Scraping involves a program that reads the content of a web page and copies it into another program. The program will save the data for later use. Web scraping can extract data from all websites, including e-commerce sites, job search sites, and social media pages.

It is an integral part of AI because it allows computers to read, understand and efficiently interpret large amounts of data. This software is helpful for businesses with large databases that the computers need to analyze. However, companies also use web scraping in other ways, such as financial institutions or government agencies.

Wrapping Up

We hope that you are now understand the basics of Artificial Intelligence: Terms marketers need to know. 

In a nutshell, Artificial Intelligence (AI) for marketing uses machine learning to automate various processes and actions to increase marketing efficiency.

Furthermore, AI for marketing can create better and more personalized content, increase conversion rates, lower churn, and reduce cost per lead/customer.

Want to take advantage of the recent explosion of AI and make your marketing strategy state-of-the-art? Get in touch with our experts and explore your options and how to turn your marketing strategy more targeting and precise. 

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1. What are the five components of AI?

Components of AI are: learning, reasoning, problem-solving, perception, and understanding language.

2. How many types of AI are there?

There are four types of AI: reactive, limited memory, theory of mind, and self-awareness. 

3. What is NLP AI?

NLP or natural language processing is a branch of computer science that gives computers the ability to understand the text and spoken language as humans can. 

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