Artificial Intelligence (AI) Glossary by Pixis

AI Infrastructure

Pixis’ codeless AI Infrastructure refers to the underlying system of pre-trained, customizable AI models and deep-learning technologies that enable training, testing, experimentation and deployment of Artificial Intelligence-powered marketing strategies.

Pixis’ codeless AI infrastructure consists of 120+ (and growing) AI models trained to optimize marketing and demand generation.

AI Bandits

AI bandits are algorithms that are used to make decisions in situations where there is uncertainty about the outcomes of different actions or where a choice needs to be made between a number of different options, but does not have enough information to make a fully informed decision. Based on information and experience gathered in previous input rounds, the algorithms are expected to make decisions in favor of maximizing a certain reward or objective. AI bandits allow AI systems to learn and adapt to changing situations in real time, making them more effective at achieving their objectives.

Artificial Neural Networks

Artificial Neural Networks (or Neural Networks) are modeled on the way neural networks of the biological brain function. They consist of a series of interconnected nodes, or “neurons,” which are essentially small processing units that can analyze and interpret data. These networks are trained to recognize patterns and make decisions based on that data, allowing them to perform tasks such as recognizing faces, translating languages, or even playing games.

Neural networks function by using input data to adjust the connections between the neurons to recognize patterns and make accurate predictions. For example, if you want a machine to recognize and identify cats in an image, its neural network must be fed, or trained on, a large number of cat images. Once a neural network has been trained, it can be used to make predictions or decisions based on new input data. Neural networks are used in a wide range of applications, including image recognition, Natural Language Processing (NLP), and even self-driving cars.

Autoregressive Language Model

An autoregressive language model is a type of Artificial Intelligence (AI) model that is used to predict the next word in a sentence, or sequence of words, by using patterns and trends in language. It does so by contextually analyzing the preceding words in a sentence to predict what the next word might be. This type of model is used in Natural Language Processing (NLP) tasks, such as language translation or text generation.

Autoregressive Models

Autoregressive Models are a class of statistical models that analyze and predict time series data – basically a machine’s way of measuring and analyzing the correlation between different observations at different time instances. These models are used to forecast and make accurate predictions based on past trends. They are also used to identify patterns and trends, and for predictive modeling based on the underlying dynamics of data.

Attention

Attention refers to the ability of a model to focus on a specific subset of its inputs, or “attend” to them, while processing a given task. It enables machines to focus on the most relevant information and ignore irrelevant or less important input. For example, in Natural Language Processing (NLP), an attention mechanism might allow a language model to focus on certain words or phrases in a sentence to better understand the meaning or intent of the text. Attention mechanisms have become an important tool in deep learning, as they help models handle large and complex input data and perform tasks that require selective focus, or “attention” to certain elements of the input.

Backpropagation

Backpropagation is crucial in training artificial neural networks to learn and improve their performance over time. The basic idea behind backpropagation is to propagate the error of the output back through the network, to let it adjust itself (learn) in a way that reduces the error in the future. It is a key component of many popular applications, including image and speech recognition, natural language processing, and financial modeling. Pixis AIs use backpropagation to decrease error rates and make better recommendations for improved efficiency.

BERT

BERT is a powerful AI model that is designed to understand and generate natural language. It is widely used in a variety of Natural Language Processing (NLP) tasks such as accurately predicting the next word in a sentence, identifying the main topic of a piece of text, or summarizing a long document. It is used to perform tasks related to NLP, such as language translation, text summarization, and question-answering, at scale with speed. Pixis uses BERT to facilitate the generation of contextual communication for our customers.

Black Box

Black Box artificial intelligence and machine learning refers to a system or algorithm whose internal workings are not transparent or easily understandable to the user or observer. In other words, the input-output behavior of the system is known, but the internal processes and decision-making mechanisms remain opaque.

Contextual AI Models

Contextual AI Models are Artificial Intelligence (AI) models that are able to take into account the context in which they are operating in order to provide more accurate and relevant responses to tasks or requests. For example, consider the word “bat.” Depending on the context, “bat” could refer to a flying mammal, a wooden club used in sports, or the act of hitting a ball with a bat. A contextual AI model would be able to understand the correct meaning of “bat” in each of these different contexts, based on the other words and phrases that appear alongside it.

Cosine Similarity

Cosine Similarity is a measure of similarity between two data sets. It is commonly used in information retrieval, recommendation systems, and other areas where it is necessary to compare the similarity of two documents or items. This is relatively robust to the effects of scaling, translation, and rotation. For example, in the case of textual data, cosine similarity can be used to find the similarity of texts in the document.

Creative Adversarial Network

A Creative Adversarial Network (CAN) is a type of Artificial Intelligence (AI) system that generates original content in a specific domain, such as text, images, or music. It is called a “creative” adversarial network because it uses a type of machine learning called adversarial training, in which two AI models are trained to work together and compete against each other in order to generate high-quality content. In a CAN, one model called the “generator,” is responsible for generating new content, while the other model, called the “discriminator,” is responsible for evaluating the quality of the content and determining whether it is original or not. Simply put, it uses a combination of machine learning techniques and adversarial training to generate new and creative content that is similar to the training data, but not identical and may contain novel elements or variations.

Clarity Scoring

Clarity Scoring, also known as readability scoring or readability assessment, is a process of evaluating the readability of written text using AI algorithms, with the goal of identifying and improving the clarity and simplicity of the text. There are several different measures of readability that can be used, each of which takes into account different aspects of the text such as the length of sentences, the complexity of the vocabulary, and the use of technical terms. Clarity Scoring algorithms use these measures to assign a score to a piece of text, indicating how easy or difficult it is to understand.

Content Intelligence

Content intelligence is a field of Artificial Intelligence (AI) that focuses on extracting insights, knowledge, and meaning from large volumes of content, such as text, audio, or video. It involves the use of AI techniques, such as Natural Language Processing (NLP), machine learning, and text analytics, to analyze and understand the content, and extract valuable information from it.

For example, Pixis uses Content Intelligence to analyze and identify trends and patterns in large datasets, with the goal of enabling organizations to gain a better understanding of their content and to use this understanding to make informed decisions, improve processes, and drive business value.

Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a type of artificial neural network which are tasked with analyzing and understanding complex data for a wide range of applications, including image and video analysis, Natural Language Processing (NLP), object detection, and face recognition.

Data Agnostic

Data Agnostic refers to the ability of an Artificial Intelligence (AI) system to operate without being specifically tailored or trained on a particular type or set of data. This is useful in situations where an AI system needs to be applied to a diverse range of data sources or types, or where the data available for training is limited or unreliable. For example, the Pixis AI systems are data agnostic and can learn from any and all types of data that are provided to them.

Domain Agnostic AI

Domain Agnostic AI refers to an AI model or system that is designed to be flexible and adaptable to all business domains. It can be trained and used on a wide range of tasks and data types. It is useful in situations where there is a limited prior understanding of the type of data or tasks to which the model will be applied. They offer a more flexible approach to AI, as opposed to domain-specific models, which are limited to specific tasks or types of data.

Diagnostic Analytics

In the context of Artificial Intelligence (AI), Diagnostic Analytics involves the use of machine learning algorithms and other AI techniques to analyze data and identify patterns that can help diagnose and solve problems. This can involve analyzing large amounts of data from multiple sources to identify patterns or trends that might not be immediately visible to humans.

Descriptive Analytics

In the context of Artificial Intelligence (AI), Descriptive Analytics involves using AI algorithms and models for understanding patterns and trends in data to inform decision-making and guide business strategy. Please note that descriptive analytics does not necessarily provide insights into what will happen in the future or how to take action based on the data. For that, other types of data analysis, such as predictive analytics or prescriptive analytics, may be more appropriate.

Deep Learning

Deep Learning is a subfield of machine learning that involves the use of artificial neural networks, which are complex mathematical models inspired by the structure and function of the human brain. Deep Learning algorithms are designed to learn from large amounts of data by identifying patterns and features in the data and using these patterns to make predictions or decisions. They are particularly effective at tasks such as image and speech recognition, natural language processing, and decision-making, and are used in a wide variety of applications, including self-driving cars, language translation, and personal assistants.

Deterministic Dependency Parsing

Deterministic Dependency Parsing is a process used by Artificial Intelligence (AI) systems to analyze and understand the relationships between words in a sentence. Essentially, it helps the AI to understand the meaning of a sentence and accurately interpret the intended message. For example, “the cat sat on the mat.” In this sentence, Deterministic Dependency Parsing would help the AI understand these relationships and recognize that “cat” is the noun that is performing the action of “sitting,” and “mat” is the noun that is being acted upon.

Data Augmentation

In the context of Artificial Intelligence (AI), Data Augmentation refers to the process of generating additional data samples from existing ones. It is a common technique used in machine learning to increase the size and diversity of the training dataset, in order to improve the performance of the models, especially when the original dataset is small or lacks diversity. Pixis uses data augmentation to train its codeless AI infrastructure and to refine the performance of its AI models to produce better results for the users.

Decision Tree

A Decision Tree is a type of machine learning algorithm that is used to make predictions or decisions based on a set of rules. It is called a “decision tree” because it is structured like a tree, with a series of branches representing different decisions or outcomes. At each branching point in the tree, the algorithm considers a different feature or attribute of the data and uses this information to decide which branch to follow.

Decision Intelligence

Decision Intelligence is a field of Artificial Intelligence (AI) that focuses on using data and algorithms to make informed decisions. This can involve analyzing large amounts of data, using machine learning techniques to identify patterns and trends, and using these insights to make predictions or recommendations.

Pixis enables Decision Intelligence in marketing and demand generation to help organizations make more informed and data-driven decisions, which can lead to improved efficiency and effectiveness.

Enterprise Internet of Things (IoT)

Enterprise Internet of Things (IoT) refers to the use of connected devices, sensors, and systems within a business or organization to collect, transmit, and analyze data. AI can be used to analyze this data to identify patterns and trends and make recommendations or decisions based on this analysis, to improve operational efficiency, and optimize business processes.

Generative Adversarial Networks

A class of machine learning models that consist of two neural networks: a generator and a discriminator. GANs are used for generating new data that resembles a given training dataset. The generator attempts to create synthetic data samples, such as images, audio, or text, that resemble the real data from the training set. The discriminator network, on the other hand, aims to distinguish between real data samples from the training set and the synthetic data samples created by the generator.

GPT-3

GPT-3 (Generative Pre-training Transformer 3) is a state-of-the-art Artificial Intelligence (AI) language model designed to process and generate human-like language. GPT-3 is trained on a massive amount of data, which allows it to have a deep understanding of language and the ability to generate responses that are relevant and appropriate in a given context. It is also able to learn and adapt over time, allowing it to improve its performance on various language-based tasks.

One of the key features of GPT-3 is its ability to generate text that is coherent and flows naturally, making it well-suited for tasks such as translation, text generation, and text summarization. Pixis AI uses GPT-3 to help marketers generate contextual communication for their marketing campaigns across platforms with speed, and at scale.

Hyperparameters

Hyperparameters are settings or parameters that are chosen before training a machine learning model to adjust or control its learning process and improve its performance and accuracy. Some examples of Hyperparameters include the number of layers in a neural network, the number of neurons in each layer, the learning rate, etc. The Hyperparameters are usually determined through a process called hyperparameter tuning, where different combinations of hyperparameters are tested to find the optimal combination that results in the best model performance.

For example, we may have a Hyperparameter called the “learning rate” which determines how fast the algorithm learns from the data. If the learning rate is too high, the algorithm may overshoot the optimal solution and not perform well on the test data. On the other hand, if the learning rate is too low, the algorithm may take too long to learn and also not perform well.

Inference

Inference in the context of AI refers to the process of using previously learned knowledge to make predictions or conclusions about new situations or data. This can be useful in a variety of applications, such as helping robots navigate unfamiliar environments or allowing AI assistants to understand and respond to natural language input from users.

For example, if an AI system has learned about different types of animals, it can infer that a creature with a long neck and four legs is likely a giraffe, even if it has never seen a giraffe before. Essentially, it is using what it has learned to make an educated guess about something it has not encountered before. In this way, AI systems can make intelligent decisions and predictions based on what they have learned from previous experiences.

Latent Space

Latent space refers to a mathematical representation or space where complex data or information is encoded into a more condensed and meaningful form. It is often used in machine learning and artificial intelligence to capture the essence or underlying structure of data, allowing for exploration, manipulation, and generation of new data points with similar characteristics.

LSTM

LSTM stands for Long Short-Term Memory. It is a type of artificial neural network used in the field of Artificial Intelligence (AI) for analyzing and making predictions based on large amounts of data, and they are often used in a variety of AI applications, including natural language processing, speech recognition, and machine translation. For example, an LSTM might be used to analyze a large dataset of customer reviews and make predictions about which products will be most popular in the future.

One of the key features of LSTMs is their ability to remember important information over a long period of time. This is important because it allows the AI to make better predictions based on patterns that it has observed over a longer period of time.

Metaheuristic

Metaheuristics are a type of Artificial Intelligence (AI) algorithm that can be used to solve complex optimization problems by finding good solutions that are not necessarily optimal. They work by using a set of heuristics, or rules of thumb, to guide the search for solutions in a flexible and adaptive way. Metaheuristics are often used when traditional optimization techniques are too slow or too expensive, or when the problem is too large to solve using exact methods. They can be applied to a wide range of problems, including optimization, scheduling, routing, and resource allocation.

Pixis AI uses metaheuristic models to find the best-optimized solutions for problem statements and challenges in marketing.

Natural Language Generation

Natural Language Generation (NLG) is a field of Artificial Intelligence (AI) that involves creating a human-like language from data or computer-generated information. It allows computers to generate texts or spoken language that can be understood by humans, enabling a more natural and intuitive way of communication. Pixis enables companies to use NLG to create highly relevant and creative content that can effectively engage and convert their target audience.

Natural Language Processing

Natural Language Processing (NLP) is a field within Artificial Intelligence (AI) that focuses on the ability of computers to understand, interpret, and generate human language. This can include tasks such as language translation, speech recognition, and text analysis.

In practical terms, this means that NLP allows computers to process and understand written or spoken language in the same way that a human would. For example, a computer program using NLP could be trained to understand that “I am hungry” means the same thing as “I need food,” and it could respond appropriately.

Optimization Events

Optimization Events refer to the process of improving the performance or efficiency of a machine learning model or algorithm. This can be done through various techniques, such as adjusting the model’s parameters, selecting different data sets to train the model on, or using different optimization algorithms to find the optimal solution. In the context of Artificial Intelligence (AI), Optimization Events play a critical role in ensuring that machine learning models are able to accurately predict outcomes and make decisions based on the data they are given.

Predictive Analytics

Predictive Analytics is a type of Artificial Intelligence (AI) that helps to predict future outcomes or events based on past data and patterns. It uses machine learning algorithms to analyze large amounts of data and make predictions about what is likely to happen in the future. This can be used to make better-informed decisions and plan for potential outcomes.

Pico Segmentation

Pico segmentation is a way of dividing a large group of people or things into smaller, more specific subgroups. In the context of AI, pico segmentation might be used to identify different characteristics or behaviors within a larger dataset, in order to better understand and predict the actions or preferences of different subgroups within the larger group.

For example, Pixis AI systems use pico segmentation to identify different trends or patterns within a large group of customers, in order to more effectively target marketing efforts or make recommendations based on their interests.

Q-Learning

Q-Learning is a type of Artificial Intelligence (AI) training that is often used in situations where the computer needs to make decisions based on complex or changing environments. The machine is given a set of possible actions it can take in a given situation, and it is also given a reward or penalty for each action, based on which it then tries different actions and sees which ones result in the best rewards. Over time, the machine learns which actions are most likely to result in positive outcomes and begins to make decisions based on that learning.

Q-Learning is a powerful technique used by the Pixis AI to optimize marketing campaigns by identifying the most effective strategies for reaching target audiences and maximizing ROI.

Recursive Neural Network

A Recursive Neural Network is a type of artificial neural network that takes a piece of data, analyzes it, and then uses that analysis to inform how it processes the next piece of data. This process is repeated until all of the data has been analyzed, and the network is able to draw conclusions and make predictions based on the patterns it has identified. Recursive Neural Networks are often used in tasks that require the analysis of complex, hierarchical data structures, such as Natural Language Processing (NLP), image recognition, and computer vision. They are particularly useful for tasks that involve analyzing the relationships between different pieces of data, as they are able to follow the logical structure of the data and identify patterns that may not be apparent to a traditional machine learning algorithm.

Recurrent Neural Network

A Recurrent Neural Network (RNN) is a type of artificial neural network that has a loop in its architecture, allowing it to process sequences of data with the output being dependent on the previous computations. For example, in the sentence “The cat sat on the mat,” an RNN could use the hidden state to remember that “the” refers to a specific object, rather than just being a generic article. This allows the RNN to correctly translate the sentence into another language, even if the words are not in the same order as the original sentence.

ResNet

ResNet is a type of artificial neural network that is particularly useful for tasks that require a lot of processing power, such as image recognition or language translation. It works by breaking down a task into smaller pieces and then processing each piece separately, which makes it more efficient and accurate than other types of neural networks.

The Res in ResNet stands for Residual, which refers to a unique feature of this model. In most AI models, the data is processed through a series of interconnected layers, called neurons, that are designed to recognize specific patterns. However, in a ResNet model, the neurons are connected in such a way that some of the data is residual, meaning it is not processed through all of the layers. This allows the model to recognize patterns that are more complex or subtle, making it more accurate and efficient at tasks such as image or video recognition.

Reinforcement Learning

Reinforcement Learning is a type of Artificial Intelligence (AI) training that involves training a machine to take actions in a specific environment in order to achieve a certain goal. It involves giving the computer rewards or punishments for certain actions in order to teach it how to make decisions that lead to the desired outcome. This is often done through trial and error, with the computer learning from its mistakes and adjusting its behavior accordingly. In this way, reinforcement learning allows AI systems to learn and adapt to new situations and environments, making them more versatile and efficient.

Reasoning Engine

A Reasoning Engine is a component of Artificial Intelligence (AI) that is responsible for making logical deductions and reaching conclusions based on a set of given facts or data. It works by analyzing data, identifying patterns and relationships, and using that information to make decisions or predictions. A Reasoning Engine can be used to analyze a company’s data to help predict future profits or to identify areas where costs can be reduced. This can help organizations make better decisions and improve efficiency by automating tasks and reducing the need for human intervention.

Sentiment Analysis

Sentiment Analysis is a way for Artificial Intelligence (AI) to analyze and understand the underlying emotions and opinions of words and language. For example, if someone writes a review of a product and they say they love it, the AI can recognize that the person has a positive sentiment towards the product. On the other hand, if someone writes a review saying they hate the product, the AI can recognize that the person has a negative sentiment. This can be helpful for businesses to understand how people feel about their products or services and make improvements or changes based on customer feedback.

Semantic Mapping

Semantic Mapping in the context of Artificial Intelligence (AI) refers to the process of assigning meaning to different elements or concepts within a system. This can involve mapping words or phrases to specific definitions or concepts, or it can involve linking related ideas or concepts together in a way that allows an AI system to understand and interpret them more effectively. Semantic Mapping is an important aspect of Natural Language Processing (NLP) and machine learning, as it helps to make sense of large amounts of data and allows AI systems to communicate more effectively with humans.

Self-Supervised Learning Framework

Self-Supervised Learning is a type of machine learning in which the model is given a task to perform, but is not explicitly given the correct answers. Instead, the model must figure out how to solve the task on its own, using the input data as a guide. The Self-Supervised Learning Framework allows the model to learn more complex patterns and relationships in the data. Overall, it allows machines to learn and adapt in a more autonomous and flexible way.

Style Transfer

Style Transfer is a process in which the style of one image is applied to another image, creating a new and unique image. It is used by AI to enhance the creative potential of digital images and help people create beautiful and unique works of art. AI algorithms are able to understand the unique characteristics and features of an image’s style, such as brushstrokes, color palette, and texture, and apply these characteristics to the other image. Style Transfer is used in marketing to create unique and creative images without having to spend a lot of time and effort on manual editing, or to match the style of a brand’s existing aesthetic.

Supervised Learning

Supervised Learning is a type of machine learning that involves training a machine model on a dataset that has already been labeled or classified with the correct output or response. The computer is then trained to recognize patterns and relationships within the data and use that knowledge to make predictions about new data. Once the model has learned these patterns, we can then give it new, unlabeled images and it should be able to correctly identify the type based on patterns it learned during training. Supervised Learning is a powerful tool for automating tasks that require making decisions based on data, such as predicting outcomes, classifying objects, or detecting patterns.

Transfer Learning

Transfer Learning is a machine learning technique that involves taking a pre-trained model developed for a task and adapting it for use on a new and different task. This can be especially useful when the new task is similar to the original task, or when there is a shortage of data or resources to train a model from scratch. It also allows us to leverage the knowledge learned by a model on one task and apply it to a new task, potentially improving the performance of the new model.

Training Dataset

A Training Dataset is a collection of data that is used to teach a machine learning model how to perform a particular task. This dataset is used to train the model to recognize patterns and make predictions or decisions based on those patterns. For example, if you want to teach a model to recognize pictures of dogs, you would provide it with a training dataset that consists of hundreds or thousands of pictures of dogs.

Testing Dataset

A Testing Dataset is a set of data that is used to evaluate the performance of an AI model. It is used to determine how well the model is able to make predictions or decisions based on the data it has been trained on. It is usually a smaller, representative data sample in comparison to the training dataset used to simulate real-world situations to test the model’s accuracy and reliability. The purpose of the Testing Dataset is to provide a realistic evaluation of the AI model’s performance and to help identify any weaknesses or areas for improvement. It helps ensure that the AI model is reliable and effective before it is deployed in real-world applications.

Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is not given any labeled training data or feedback on its performance. Instead, it’s given a dataset and is asked to learn patterns and relationships within the data on its own. This is in contrast to supervised learning, where the model is given labeled training examples and is trained to make predictions based on those examples. Unsupervised learning can be used for a variety of tasks, such as clustering data points into groups, detecting anomalies or outliers in the data, and finding hidden patterns or relationships within the data.

Variational Auto Encoders (VAEs)

Variational Autoencoders (VAEs) are generative models that are used to learn and generate new data samples, typically in the form of images, but they can be applied to other types of data as well. It consists of an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation, called the latent space or code. The decoder then reconstructs the original input data from the compressed representation.