Glossary

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Actuators: Actuators are devices responsible for moving or controlling a mechanism or system. They convert energy (often electrical, hydraulic, or pneumatic) into physical motion. Actuators are essential components in various automation systems, enabling precise control over machinery and processes.

AI Agents: AI Agents refer to autonomous entities that use artificial intelligence to perform tasks on behalf of users. These agents can learn from data, make decisions, and interact with their environment to achieve specific goals. AI agents are used in various applications, from customer service chatbots to complex decision-making systems in business and industry.

AI algorithms: Programming that tells the computer how to learn to operate on its own, thereby enabling it to attain artificial intelligence (AI).

AI Crypto: AI crypto refers to cryptocurrencies and blockchain projects that integrate artificial intelligence (AI) technologies to enhance various aspects of their functionality. This can include using AI for predictive analytics, automated trading, smart contract execution, or optimizing blockchain networks. Examples include projects like Fetch.ai and Numeraire.

AI-driven energy management systems: Using AI in production control to manage and optimize energy production, distribution, and consumption within energysystems.

AI-driven predictive maintenance systems: Using AI in production control, to monitor and detect missing materials or quality issues.

AI-driven robotics: Robots powered with AI that are augmented with a variety of sensors.

AI-powered quality control systems: Using AI in production control, to manage quality by enhancing efficiency, accuracy, and decision-making capabilities invarious fields.

AJAX: “Asynchronous JavaScript and XML” that encompasses more than asynchronous server calls through JavaScript and XML. It is not programming language or technology but rather a programming concept. Ajax represents a series of techniques that provide richer, interactive web applications through HTML, JavaScript, Cascading style sheets, and modifying the web page through the Document Object Model. The name is misleading though because nowadays, JSON is commonly used instead of XML.

Algorithm: Algorithm is a set of rules or instructions designed to perform a specific task or solve a particular problem. Algorithms are used in computing to process data, make calculations, and automate reasoning. They can range from simple procedures to complex sequences of operations, and are fundamental to the functioning of AI and machine learning systems.

Anonymous Functions: A type of function that has no name or we can say which is without any name. It is declared without any identifier and is often used as a parameter of another function. It is a common way to execute a function immediately after its declaration.

Array: A data structure that aids the programmer in the storage and retrieval of data by indexed keys. Arrays use a zero-based indexing scheme, meaning that the first element of an array has an index of zero. Arrays grow or shrink dynamically by adding or removing elements.

Artificial Intelligence: Artificial Intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem-solving, decision-making, creativity, and autonomy. AI systems can see and identify objects, understand and respond to human language, learn from new information and experiences, and act independently, replacing the need for human intelligence or intervention. This includes applications like self-driving cars and generative AI, which can create original text, images, and other content. AI is built on technologies such as machine learning and deep learning, allowing systems to improve their performance over time through experience.

Audio Models: Audio models are machine learning models designed to process and analyze audio data. These models can classify, recognize, and generate audio content. They often involve techniques such as feature extraction, embedding generation, and classification. Audio models are used in various applications, including speech recognition, music classification, and environmental sound detection.

Augmented Intelligence: Augmented Intelligence is a subset of artificial intelligence (AI) designed to enhance human intelligence rather than replace it. This approach focuses on using AI technologies to improve human decision-making and actions by providing insights and recommendations based on data analysis. Augmented Intelligence aims to work collaboratively with humans, leveraging the strengths of both human and machine intelligence to achieve better outcomes.

Authorization: The process of determining whether an authenticated user has the right to perform an operation.

Automation: Automation refers to the use of technology to perform tasks with reduced human intervention. This can include a wide range of technologies, from simple scripts to complex artificial intelligence systems, designed to streamline processes, improve efficiency, and reduce errors.

Autoregressive Models: Autoregressive Models are a machine learning technique commonly used for time series analysis and forecasting. These models use one or more values from previous time steps in a time series to create a regression. The key idea is to predict future values based on past values, leveraging the correlations across time steps. This method is particularly effective when the data exhibits autocorrelation, meaning that past values are predictive of future values.

Backward Pass: The backward pass is a crucial part of the backpropagation algorithm used in training neural networks. It involves calculating the gradient of the loss function with respect to each weight by moving backward from the output layer to the input layer. This process helps in updating the weights to minimize the error and improve the model’s accuracy.

Big data: A dynamic, large, and disparate volume of data being created by people, tools, and machines.

Big data stores: A larger, more complex data set, especially from new data sources.

Biometric: Application of statistical analysis to biological data of individuals by means of unique physical characteristics.

Blockchain: A blockchain is a decentralized, distributed ledger technology that records transactions across multiple computers in a secure and immutable manner, with examples including Bitcoin’s blockchain and Ethereum’s blockchain.

Classes: Classes act as a blueprint or template for building objects with similar characteristics and behaviours. A class encapsulates data (in the form of properties) and functions (in the form of methods) that work on that data.

Client-Side Script: A program that accompanies an HTML doc or embedded in HTML. Scripts run during load of a document or when an action is performed. They can be used to validate forms, process input, or dynamically create document elements.

Cloud Computing: Cloud Computing is the on-demand access to computing resources, such as physical or virtual servers, data storage, networking capabilities, application development tools, software, and AI-powered analytic platforms, over the internet with pay-per-use pricing. This model provides greater flexibility and scalability compared to traditional on-premises infrastructure.

Clustering Algorithm: A clustering algorithm is an unsupervised machine learning technique used to organize and classify objects, data points, or observations into groups or clusters based on similarities or patterns. This process helps identify natural groupings within a dataset, which can be useful for exploratory data analysis, anomaly detection, and reducing the complexity of large datasets. Clustering algorithms can be applied in various fields, such as market segmentation, image segmentation, and network traffic analysis. There are different types of clustering algorithms, including hard clustering, where each data point belongs to only one cluster, and soft clustering, where data points have a probability of belonging to multiple clusters.

Cognitive AI: Cognitive AI refers to systems that simulate human thought processes in a computerized model. These systems can understand, reason, learn, and interact naturally with humans. Cognitive AI uses technologies like machine learning, natural language processing, and signal processing to analyze large amounts of unstructured data, providing insights and augmenting human decision-making.

Cognitive Computing: Cognitive computing refers to a new class of technologies designed to deepen human engagement, scale expertise, enable new products and services, and enhance exploration and discovery. Cognitive systems can understand massive amounts of data, reason to form hypotheses, learn from experience, and interact naturally with humans. These systems are built on technologies such as machine learning, natural language processing, and signal processing, allowing them to process and analyze large, unstructured datasets. The goal is to create systems that can augment human intelligence and improve over time as they learn.

Collaborative Robots (Cobots): Collaborative Robots (Cobots) are robots designed to work alongside humans in a shared workspace. Unlike traditional industrial robots that operate in isolation, cobots are equipped with advanced sensors and safety features to interact safely and efficiently with human workers. They are used in various applications, from manufacturing and assembly to healthcare and service industries, enhancing productivity and reducing the physical strain on human workers.

Component Framework: Component frameworks provide pre-styled components and templates which are easy to add to any website.

Compound Systems: Compound Systems refer to advanced configurations that combine multiple AI models, techniques, or systems to solve complex problems more effectively than a single AI model could. These systems integrate different components, each specialized in a particular task, to work collaboratively or sequentially. This approach enhances efficiency, speed, and versatility, making compound systems highly effective for diverse applications.

Control Logic: Control Logic refers to the set of instructions and rules that govern the operation of a system or device. It encompasses the algorithms and decision-making processes that manage the behavior of hardware and software components. Control logic is essential for ensuring that systems operate correctly and efficiently, responding appropriately to inputs and changes in the environment.

Controllers: Controllers refer to devices or systems that manage, command, direct, or regulate the behavior of other devices or systems. Controllers are crucial in automation and robotics, where they ensure that processes operate within set parameters and respond appropriately to changes in the environment or system inputs.

Convolutional Neural Networks (CNNs): Convolutional Neural Networks (CNNs) are a type of deep learning algorithm primarily used for image classification and object recognition tasks. CNNs utilize three-dimensional data and consist of three main types of layers: (1) Convolutional Layer: The core building block where most computations occur. It involves applying filters to the input data to create feature maps. (2) Pooling Layer: Reduces the dimensionality of the feature maps while retaining important information. (3) Fully-Connected Layer: The final layer that connects all neurons and produces the output. CNNs are distinguished by their ability to automatically and adaptively learn spatial hierarchies of features from input images, making them highly effective for computer vision tasks.

CSS: “Cascading Style Sheet”s is a style sheet language that describes how HTML elements are displayed. It is the design that is layered over the top of an HTML web page.

Data Analytics: Data Analytics refers to the process of examining data sets to draw conclusions about the information they contain, typically with the aid of specialized systems and software. This process involves various techniques and tools to analyze raw data and extract meaningful insights.

Data augmentation: A technique commonly used in machine learning and deep learning to increase the diversity and amount of training data.

Decision Tree: A decision tree is a non-parametric supervised learning algorithm used for both classification and regression tasks. It has a hierarchical structure consisting of a root node, branches, internal nodes, and leaf nodes. The root node represents the initial condition, while branches represent possible values of the condition. Internal nodes, also known as decision nodes, evaluate features to form homogenous subsets, which are represented by leaf nodes or terminal nodes. Leaf nodes denote all possible outcomes within the dataset.

Deep feed-forward Neural Networks: Deep feed-forward neural networks are an extension of feed-forward neural networks that consist of multiple hidden layers between the input and output layers. These deep networks allow for more complex representations and can capture intricate patterns in data. Each layer processes the data and passes it forward to the next layer, enabling the network to learn hierarchical features and improve performance on tasks such as image and speech recognition.

Deep Learning: A subset of machine learning that focuses on training computers to perform tasks by learning from data. It uses artificial neural networks.

Descriptive AI: Descriptive AI focuses on understanding what happened by providing a clear and comprehensive view of past performance. It uses machine learning algorithms and natural language processing to analyze large volumes of structured and unstructured data, identifying patterns, trends, and correlations. This helps organizations make data-driven decisions by offering insights into historical data.

Diagnostic AI: Diagnostic AI involves analyzing data to determine why certain events or outcomes occurred. It uses machine learning algorithms and natural language processing to sift through large volumes of data, identifying patterns and correlations to provide insights into the causes of past performance.

Diffusion model: A type of generative model that is popularly used for generating high-quality samples and performing various tasks, including image synthesis. They are trained by gradually adding noise to an image and then learning to remove the noise. This process is called diffusion.

Discriminative AI: A type of artificial intelligence that distinguishes between different classes of data.

Discriminative AI models: Models that identify and classify based on patterns they observe in training data. In general, they are used in prediction and classification tasks.

Discriminator Network: A Discriminator Network is a component of Generative Adversarial Networks (GANs), which are a class of machine learning frameworks. In GANs, the discriminator network’s role is to distinguish between real and generated (fake) data. It acts as a classifier that evaluates the authenticity of the data produced by the generator network. The discriminator network is trained to improve its ability to identify fake data, while the generator network simultaneously learns to produce more realistic data to fool the discriminator. This adversarial process drives both networks to enhance their performance over time.

Document Objects: Document representing the main web page that gives access to all HTML elements on the page. When page is loaded HTML doc becomes a document. It is referred to with “document”.

DOM Tree: “Document Object Model” is the data representation of the objects that comprise the structure and content of a document on the web. It allows for dynamically accessing and updating content, structure, and style. JavaScript uses the DOM to access and modify web page elements in the browser.

Edge AI: Edge AI refers to the deployment of artificial intelligence (AI) algorithms and models directly on local edge devices, such as sensors or Internet of Things (IoT) devices. This approach enables real-time data processing and analysis without constant reliance on cloud infrastructure. By combining edge computing and AI, Edge AI allows machine learning tasks to be executed directly on interconnected edge devices, providing faster insights and reducing latency.

Edge Computing: Edge Computing is a distributed computing framework that brings enterprise applications closer to data sources, such as IoT devices or local edge servers. This proximity to data at its source can deliver significant business benefits, including faster insights, improved response times, and better bandwidth availability.

Element Objects: The most general base class that all element objects in a Document inherit. It only has methods and properties common to all elements. Everything in a HTML page is an element. And one element can have other elements nested within itself.

Event: An event is something either a browser or a user does that the JavaScript can react to such as a button click or when a user submits input on a form.

Event Handlers: A function that declares what to do when an action is performed such as the click of a button.

Feed-forward Neural Networks: Feed-forward neural networks are a type of artificial neural network where data moves in one direction—from the input layer through one or more hidden layers to the output layer. This structure allows the network to process data without any cycles or loops. Each layer’s neurons apply their weights and activation functions to the input data, transforming it and passing it to the next layer. Feed-forward neural networks are commonly used for tasks such as classification and regression.

Forward Pass: The forward pass in a neural network is the process where input data is passed through the network layer by layer, from the input layer to the output layer. During this process, each layer’s neurons apply their weights and activation functions to the input data, transforming it and passing it to the next layer. The forward pass is essential for making predictions and calculating the loss, which is then used in the backward pass to update the network’s weights.

Foundation Models: Foundation Models are large-scale AI models trained on vast datasets to perform a wide range of general tasks. These models serve as the base for creating more specialized applications, leveraging their extensive training to adapt to various domains such as computer vision, natural language processing, and speech recognition. Foundation models use transfer learning to apply knowledge from one task to another, making them highly flexible and reusable. This approach allows for the development of robust AI systems that can handle diverse and complex challenges.

Functions: Functions are modules of code that execute a particular task. They may take-in data, called arguments or parameters, and sometimes return data as well, called the return value.

Generative Adversarial Networks (GANs): Generative Adversarial Networks (GANs) are a type of machine learning model consisting of two neural networks: a generator and a discriminator. The generator creates fake data, while the discriminator evaluates the data to distinguish between real and fake. This adversarial process continues until the generator produces data that is indistinguishable from real data. GANs are used for generating realistic images, videos, and audio, as well as for data augmentation, style transfer, and anomaly detection.

Generative AI (GenAI): Generative AI (GenAI) is a type of artificial intelligence that can create original content such as text, images, video, audio, or software code in response to a user’s prompt or request. This technology relies on sophisticated machine learning models, particularly deep learning models, which simulate the learning and decision-making processes of the human brain. Generative AI works by identifying and encoding patterns and relationships in vast amounts of data, enabling it to understand natural language requests and generate relevant new content.

Generative AI Models: Generative AI models are advanced machine learning models designed to create new data that is similar to the data they were trained on. These models learn the patterns and distributions of their training data and use this understanding to generate novel content in response to new inputs. Generative AI models are capable of producing a wide range of content, including text, images, audio, and even software code. They work by identifying and encoding the relationships within large datasets, allowing them to generate relevant and high-quality outputs based on user prompts.

Generative pre-trained transformer (GPT): A series of large language models developed by OpenAI. They are designed to understand language by leveraging a combination of two concepts: training and transformers.

Hallucinations: AI hallucinations occur when a large language model (LLM) or generative AI tool perceives patterns or objects that don’t exist, leading to outputs that are nonsensical or inaccurate. This phenomenon can result from various factors, such as overfitting, biased training data, or high model complexity. Hallucinations can have significant consequences, such as spreading misinformation or making incorrect decisions in critical applications like healthcare. For example, a healthcare AI model might mistakenly identify a benign lesion as malignant, leading to unnecessary medical interventions.

Hidden Layer: A hidden layer in a neural network is a layer of nodes situated between the input layer and the output layer. These hidden layers are crucial because they enable the network to extract and process features from the input data. The number of hidden layers and the number of neurons in each layer depend on the complexity of the problem being solved.

Input Layer: The input layer in a neural network is the first layer that receives the initial data. This layer passes the data directly to the first hidden layer, where it is multiplied by the hidden layer’s weights. The input layer also processes the data through an activation function before passing it on.

Internet of Things (IoT): The Internet of Things (IoT) refers to a network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and network connectivity, enabling them to collect and share data. These “smart objects” can range from simple devices like smart thermostats to complex industrial machinery and transportation systems.

Large Language Models (LLMs): Large Language Models (LLMs) are a category of foundation models trained on immense amounts of data, enabling them to understand and generate natural language and other types of content. These models are designed to perform a wide range of tasks, such as text generation, translation, summarization, and question answering. LLMs leverage deep learning techniques and neural network architectures, like transformers, to capture intricate patterns in language and provide coherent, contextually relevant responses. They have revolutionized applications in various fields, from chatbots and virtual assistants to content creation and research assistance.

Lemmatization: Lemmatization is a text preprocessing technique in natural language processing (NLP) that reduces words to their base or dictionary form, known as a “lemma.” Unlike stemming, which simply removes affixes, lemmatization considers the context and part of speech of a word to ensure accurate transformation21. For example, the words “running,” “ran,” and “runner” would all be reduced to the lemma “run.”

Limited Memory AI: Limited Memory AI refers to systems that can recall past events and outcomes to inform current decisions. Unlike reactive AI, which only responds to immediate stimuli, limited memory AI can use both past and present data to decide on a course of action most likely to achieve a desired outcome. This type of AI is commonly used in applications such as self-driving cars and virtual assistants.

LLM Agents: LLM Agents refer to AI agents built on large language models (LLMs). These agents leverage the advanced natural language processing capabilities of LLMs to autonomously perform tasks, make decisions, and interact with their environment. LLM agents can call on external tools and APIs to enhance their performance, compensate for knowledge gaps, and refine their actions based on feedback. This makes them highly versatile and capable of handling complex tasks in various applications.

Logistic Regression: Logistic regression is a statistical model used to estimate the probability of an event occurring based on a given set of independent variables. This model is often used for classification and predictive analytics. The dependent variable in logistic regression is bounded between 0 and 1, representing probabilities. Logistic regression applies a logit transformation to the odds, which is the probability of success divided by the probability of failure. This transformation helps in modeling the relationship between the dependent and independent variables.

Machine Learning (ML): Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on enabling computers and machines to imitate human learning processes. ML systems can perform tasks autonomously and improve their performance and accuracy over time through experience and exposure to more data. This involves using algorithms to make predictions or classifications based on input data, evaluating the accuracy of these predictions, and optimizing the model to reduce errors. Essentially, machine learning allows systems to learn from data and make informed decisions without explicit programming.

Metaverse: The metaverse is a collective virtual space created by the convergence of virtually enhanced physical reality and physically persistent virtual worlds, where users can interact, socialize, and engage in various activities through digital avatars. It integrates elements of augmented reality (AR), virtual reality (VR), and the internet to create immersive and interconnected digital environments.

Modular Neural Networks: Modular Neural Networks consist of multiple independent neural networks, each working on a specific subtask. These networks operate separately and are coordinated by an intermediary to achieve the overall task. This modular approach allows for more efficient processing and can handle complex problems by breaking them down into smaller, manageable parts.

Multimodal Models: Multimodal Models are machine learning models capable of processing and integrating information from multiple modalities or types of data. These modalities can include text, images, audio, video, and other forms of sensory input. Multimodal models combine and analyze different forms of data inputs to achieve a more comprehensive understanding and generate more robust outputs. This ability to work across multiple modalities gives these models powerful capabilities, such as enhancing human-computer interaction and improving accuracy in tasks like image recognition, language translation, and speech recognition.

Named Entity Recognition (NER): Named Entity Recognition (NER) is a subtask of natural language processing (NLP) that involves identifying and classifying predefined categories of entities within a body of text. These entities can include names of individuals, organizations, locations, dates, quantities, and more. NER helps in extracting meaningful information from unstructured text data, making it easier to analyze and utilize.

Natural Language Generation (NLG): Natural Language Generation (NLG) is a subset of artificial intelligence (AI) that focuses on creating natural language outputs from structured and unstructured data. NLG enables computers and generative AI applications to interact with users in comprehensible human language. It is a part of natural language processing (NLP), alongside natural language understanding (NLU).

Natural Language Processing (NLP): Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that uses machine learning to enable computers to understand, interpret, and generate human language. NLP combines computational linguistics, which involves rule-based modeling of human language, with statistical, machine learning, and deep learning models. This technology allows computers to process and analyze large amounts of natural language data, facilitating applications such as chatbots, voice-operated systems, and language translation.

Natural Language Understanding (NLU): Natural Language Understanding (NLU) is a subset of artificial intelligence (AI) that uses semantic and syntactic analysis to enable computers to understand human-language inputs. NLU aims to holistically comprehend intent, meaning, and context, rather than focusing on the meaning of individual words.

Neural Networks: Neural Networks are machine learning models that make decisions in a manner similar to the human brain by mimicking the way biological neurons work together. These networks consist of layers of nodes, or artificial neurons, including an input layer, one or more hidden layers, and an output layer. Each node connects to others and has its own associated weight and threshold. Neural networks rely on training data to learn and improve their accuracy over time, making them powerful tools for tasks such as speech recognition and image recognition.

Optimistic Rollups: Optimistic Rollups are a Layer 2 scaling solution for blockchains that process transactions off-chain while assuming they are valid unless proven otherwise. They batch multiple transactions and submit them to the main chain, where they are verified periodically. If a dispute arises, a challenge mechanism is in place to resolve incorrect transactions. This method improves scalability and reduces fees while maintaining security through periodic checks.

Output Layer: The output layer in a neural network is the final layer that produces the results of the network’s computations. This layer takes the processed data from the hidden layers and generates the final output, which can be a classification, prediction, or other types of results depending on the specific task the neural network is designed to perform.

Part of Speech (POS) Tagging: Part of Speech (POS) Tagging is a fundamental task in natural language processing (NLP) that involves assigning parts of speech to each word in a sentence. This process helps in understanding the grammatical structure and meaning of the text. POS tagging categorizes words into various parts of speech, such as nouns, verbs, adjectives, adverbs, etc., based on their context and usage.

Perceptron Neural Network: A Perceptron is a type of artificial neuron used in machine learning. It is the simplest form of a neural network and consists of a single layer of nodes. Each node in the perceptron uses the Heaviside step function as its activation function. The perceptron algorithm is designed for binary classification tasks, where it decides whether an input belongs to a specific class based on a linear predictor function that combines a set of weights with the feature vector.

Predictive AI: Predictive AI involves using statistical analysis and machine learning algorithms to identify patterns, anticipate behaviors, and forecast future events. It extracts insights from historical data to make accurate predictions about the most likely upcoming event, result, or trend. Predictive AI is widely used for business forecasting, customer behavior analysis, and optimizing decision-making across various industries.

Prescriptive AI: Prescriptive AI involves analyzing data to identify patterns and make predictions, then recommending optimal actions or decisions to achieve desired outcomes. This type of AI uses machine learning and optimization algorithms to provide actionable insights, helping organizations make informed decisions and improve their operations.

Prompt Engineering: Prompt Engineering is the process of writing, refining, and optimizing inputs to guide generative AI models in producing specific, high-quality outputs. This involves crafting precise and effective prompts that help AI systems understand the intent and context behind a query, ultimately improving the relevance and accuracy of the generated content.

Random Forests: Random Forests are a popular machine learning algorithm that combines the output of multiple decision trees to reach a single result. This ensemble method enhances the accuracy and robustness of predictions by averaging the results of individual trees, which helps to reduce overfitting and improve generalization. Random Forests can handle both classification and regression tasks, making them versatile and widely used in various applications.

React Agents: React Agents refer to AI agents that utilize the ReAct (Reasoning and Acting) framework. These agents are designed to interpret and respond to various scenarios by reasoning through the context and then taking appropriate actions. The ReAct framework involves a few-shot prompting format that guides large language models (LLMs) in decision-making processes, enabling them to perform tasks such as searching the web, executing SQL queries, or sending emails.

Reactive AI: Reactive AI refers to the most basic type of AI systems that are designed to perform specific tasks without the ability to form memories or use past experiences to inform current decisions. These systems operate solely based on present data and do not retain any information from previous interactions. Examples include simple chess-playing programs and basic recommendation systems.

Recurrent Neural Networks (RNNs): Recurrent Neural Networks (RNNs) are a class of neural networks designed to handle sequential data. Unlike traditional feed-forward neural networks, RNNs have connections that form cycles, allowing them to maintain a memory of previous inputs. This makes RNNs particularly effective for tasks involving time series data, such as language translation, natural language processing, sentiment analysis, speech recognition, and image captioning.

Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, which helps it learn the optimal behavior over time. This technique is used in various applications, such as robotics, game playing, and autonomous systems.

Retrieval Augmented Generation (RAG): Retrieval Augmented Generation (RAG) is an AI framework designed to enhance the performance of large language models (LLMs) by integrating external knowledge bases. This approach allows generative AI models to access and incorporate up-to-date, domain-specific information into their responses, improving accuracy and relevance. RAG helps mitigate the limitations of finite training datasets by connecting models with real-time data sources, making them more adaptable and cost-efficient.

Robotic Process Automation (RPA): Robotic Process Automation (RPA) is the technology that allows software robots to emulate and integrate the actions of a human interacting within digital systems to execute a business process. RPA robots utilize the user interface to capture data and manipulate applications just like humans do. They interpret, trigger responses, and communicate with other systems to perform a variety of repetitive tasks.

Robotics: Robotics refers to the interdisciplinary branch of engineering and science that includes the design, construction, operation, and use of robots. Robotics involves the integration of mechanical engineering, electrical engineering, computer science, and other fields to create machines that can assist or replicate human actions. These robots can be used in various applications, from manufacturing and healthcare to exploration and service industries.

Scientific Models: Scientific models are representations of systems or phenomena that help scientists understand, predict, and simulate complex processes. These models can be mathematical, computational, or physical, and are used across various scientific disciplines to analyze data, test hypotheses, and make predictions. Scientific models are essential tools in fields such as climate science, physics, biology, and engineering.

Script: Offers developers means to modify and extend HTML documents in highly interactive ways. Scripts can be used to validate forms or to process input as it is typed. Scripts can be triggered by events that occur on a web page, such as the clicking of a button. Scripts can be used to dynamically create document elements on an HTML page.

Self-aware AI: Self-aware AI is a theoretical form of artificial intelligence that would possess self-awareness and consciousness. This type of AI would have the ability to understand its own internal states, emotions, and thoughts, as well as those of others. It would be capable of introspection and autonomous decision-making based on its own experiences and understanding. However, self-aware AI remains a purely hypothetical concept and has not yet been realized.

Sensors: Sensors are devices that detect and respond to some type of input from the physical environment. The specific input could be light, heat, motion, moisture, pressure, or any one of a great number of other environmental phenomena. Sensors are crucial in automation and control systems, providing the data needed for controllers to make informed decisions.

Smart Contracts: Smart contracts are self-executing contracts with the terms of the agreement directly written into code, automatically enforcing and executing contractual agreements on a blockchain without the need for intermediaries.

Stemming: Stemming is a text preprocessing technique in natural language processing (NLP) that reduces words to their base or root form. This process involves stripping affixes (prefixes and suffixes) from words to leave only the stem. For example, the words “running,” “runner,” and “ran” would all be reduced to the stem “run”. Stemming helps improve the efficiency and accuracy of various NLP tasks, such as text classification, clustering, and information retrieval, by reducing the dimensionality of the data and grouping morphologically related words.

Strong AI (Generalized AI): Strong AI, also known as Artificial General Intelligence (AGI), is a hypothetical form of AI that would possess intelligence and self-awareness equal to that of humans. This type of AI would be capable of understanding, learning, and applying knowledge across a wide range of tasks, solving problems autonomously and adapting to new situations just like a human.

Super AI: Super AI, also known as Artificial Superintelligence (ASI), is a hypothetical form of AI that surpasses human intelligence in all aspects. This type of AI would possess advanced cognitive abilities, including reasoning, problem-solving, and creativity, far beyond human capabilities. ASI would be able to understand and process complex information, learn from experience, and make decisions autonomously. However, it remains a theoretical concept and has not yet been realized.

Supervised Learning: Supervised learning is a machine learning technique that uses labeled datasets to train algorithms to identify patterns and relationships between input features and outputs. The goal is to create a model that can predict correct outputs on new, real-world data. This process involves feeding input data into the algorithm, which adjusts its weights until the model is appropriately fitted.

Support Vector Machine (SVM): A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding an optimal hyperplane that maximizes the margin between different classes in an N-dimensional space. This hyperplane acts as a decision boundary, separating the data points of different classes. SVMs are particularly effective in high-dimensional spaces and are used in various applications, including image recognition and bioinformatics.

Theory of Mind AI: Theory of Mind AI is a theoretical form of artificial intelligence that would understand the thoughts, emotions, and beliefs of other entities. This type of AI aims to simulate human social intelligence, allowing it to interact more naturally and effectively with humans by recognizing and responding to their mental states.

Tokenization: In the context of natural language processing (NLP), tokenization refers to breaking down text into smaller units, such as words or phrases, that a machine can understand and process.

Tokenomics: Tokenomics refers to the study of the economic structure and value dynamics of a cryptocurrency or token, including its creation, distribution, supply, demand, and overall impact on the ecosystem.

Training data: Data (generally, large datasets that also have examples) used to teach a machine learning model.

Transformers: Transformers are a type of deep learning model that has become fundamental in natural language processing (NLP) and other machine learning tasks. Introduced in 2017, transformers process input sequences in parallel, making them highly efficient for training and inference. This architecture allows the model to focus on the most relevant parts of the input data, which is particularly useful for tasks like translation, text generation, and summarization.

Turing Test: The Turing Test, developed by Alan Turing in 1950, evaluates a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. In this test, a human evaluator interacts with both a machine and a human through a text-based interface and tries to determine which is which. If the evaluator cannot reliably tell the machine apart from the human, the machine is considered to have passed the test.

Unimodal Models: Unimodal models are designed to handle a single type of data. For example, a unimodal model might receive text inputs and generate text outputs. These models focus on processing and analyzing one specific modality, making them simpler and more specialized compared to multimodal models, which can handle multiple types of data such as text, images, and audio.

Unsupervised Learning: Unsupervised learning is a machine learning technique that uses algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Unsupervised learning is ideal for exploratory data analysis, customer segmentation, and image recognition.

Variational Autoencoders (VAEs): Variational Autoencoders (VAEs) are generative models used in machine learning to generate new data by creating variations of the input data they are trained on. VAEs consist of an encoder that learns to isolate important latent variables from the training data and a decoder that uses these latent variables to reconstruct the input data. Unlike traditional autoencoders, VAEs encode a continuous, probabilistic representation of the latent space, allowing them to generate new data samples that resemble the original input data.

Vision Models: Vision models are a subset of artificial intelligence (AI) that focus on enabling computers to interpret and understand visual information from the world, such as images and videos. These models use machine learning and neural networks to process visual data, extract meaningful features, and make decisions based on the visual input. Vision models are widely used in applications like image classification, object detection, and facial recognition.

Weak AI (Narrow AI): Weak AI, also known as Narrow AI, refers to AI systems designed to perform a specific task or a set of tasks. These systems are highly specialized and can often perform their designated tasks more efficiently than humans. Examples of Narrow AI include voice assistants like Siri and Alexa, chatbots, and recommendation systems.

Web Storage APIs: APIs that allow data storage in a browser.

Web3: Web3 is the vision of a decentralized internet built on blockchain technology, aiming to give users more control over their data, identity, and transactions. It emphasizes decentralization, transparency, and the use of cryptocurrencies and smart contracts to create a more open and user-driven online ecosystem.

XHTML: An “eXtensible Hypertext Markup Language” similar to HTML but with stricter formatting rules.

XML: An “eXtensible Markup Language” Designed to store and transport data allowing users to define their own markup languages, primarily to display documents on the web.