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Artificial Intelligence (AI)

Google Bard

Google's free experimental AI chatbot that extracts and analyzes information from the web to engage in conversations and respond to user queries. It serves as a complementary experience to Search, allowing users to delve deeper into responses by visiting Google's search engine directly within Bard's interface, leveraging natural language processing for more intuitive interactions.

Graph AI

Graph AI refers to the use of graph-based models and algorithms in artificial intelligence to analyze and draw insights from interconnected data represented as graphs. It is particularly effective in understanding relationships, patterns, and structures within complex networks, and has applications in a variety of fields, including social network analysis, recommendation systems, and fraud detection.


Guardrails in software development generally refer to predefined constraints or rules that help guide developers in creating secure, efficient, and reliable code. They act as a safety mechanism, preventing potential issues and ensuring adherence to best practices. In the context of AI, guardrails refer to ethical and safety guidelines implemented to ensure the responsible and secure use of artificial intelligence technologies. 


An open-source artificial intelligence and machine learning platform that offers scalable and distributed algorithms for data analysis and predictive modeling. H2O.ai is designed to help businesses and data scientists build and deploy machine learning models efficiently.


Hallucination in the context of artificial intelligence refers to a situation where a machine learning model generates outputs that are not grounded in reality or accurate representations of the input data. It is a term used to describe the generation of misleading or false information by AI models.


A combination of artificial intelligence, machine learning, robotic RPA, and other advanced technology used to automate and optimize business processes. It allows organizations to focus on high-value tasks, strategic initiatives, and creative problem-solving, while automated systems handle routine and repetitive activities. 


Hyperparameters in artificial intelligence and machine learning are external configuration settings that need to be set before the training process begins. These parameters, such as learning rates and regularization strengths, are not learned from the data but play a crucial role in determining the model's performance.

IBM Watson

A data analytics processor that uses natural language processing, a technology that analyzes human speech for meaning and syntax. IBM Watson performs analytics on vast repositories of data that it processes to answer human-posed questions, often in a fraction of a second.

Infosys Nia

A knowledge-based AI platform that combines machine learning with the deep knowledge of an organization to drive automation and innovation. It enables businesses to continuously reinvent their system landscapes.

Jupyter Notebooks

A web application for creating and sharing documents with live code, equations, visualizations, and narrative text. Jupyter Notebooks allows for interactive computing in a variety of programming languages, including Python and R.

It is widely used by data scientists, researchers, educators, and developers for a variety of purposes, including data analysis, machine learning prototyping, and scientific research.


A Python framework designed to make it easier to develop AI application, with a focus on real-time data processing and seamless integration with Large Language Models (LLMs). LangChain facilitates data communication, vector embedding generation, and interaction with LLMs, increasing efficiency for AI developers.

Multimodal AI

Multimodal AI represents a new approach to artificial intelligence. It combines various types of data such as images, text, spoken words, and numbers. The data is then processed using a variety of smart methods to yield better results.

Narrow AI

Narrow AI refers to AI systems designed to perform very specific tasks or follow specific commands. These technologies are good at one type of thinking and are incapable of learning new skills beyond their intended purpose.

Natural Language Understanding (NLU)

A branch of artificial intelligence and computational linguistics that focuses on the ability of computers to comprehend and interpret human language in a way that is meaningful and useful.

NLU enables computers to understand the nuances of human language, including syntax, semantics, context, and intent. It involves a variety of techniques and algorithms, including machine learning, deep learning, and NLP, to analyze and process text or speech data.

Neuro-Symbolic AI

An AI paradigm that combines neural and symbolic architectures to overcome the limitations of each, resulting in a resilient AI system proficient in reasoning, learning, and cognitive modeling.

No-Code AI

No-code AI refers to artificial intelligence development platforms that allow users to create and deploy machine learning models without the need for extensive coding or programming skills. These platforms typically provide a user-friendly interface, visual tools, and pre-built components to simplify the AI development process, making it accessible to individuals with varying technical backgrounds.


A conversational analytics platform designed to help businesses improve customer interactions and agent performance in call centers. It leverages AI and NLP technologies to analyze customer calls, providing insights and recommendations to enhance customer experiences and agent effectiveness.


Overfitting in AI happens when a machine learning model learns the training data too well, including its noise and outliers, making it less effective in making accurate predictions on new, unseen data. It's like memorizing answers instead of understanding the underlying patterns.


A logic programming language that is commonly used for artificial intelligence and symbolic reasoning applications. Popular open-source Prolog implementations include SWI-Prolog, GNU Prolog, and YAP. 

Quantum AI

Quantum AI represents a field of research and development that involves the integration of quantum computing principles with artificial intelligence techniques. 

Quantum Computing

Quantum computing relies on the principles of quantum mechanics and utilizes qubits, which can exist in multiple states, including both 0 and 1 simultaneously. The key distinction from classical computing lies in quantum computers' ability to conduct numerous calculations concurrently, enhancing their suitability for intricate tasks like artificial intelligence.

Predictive AI

Predictive AI refers to artificial intelligence systems and algorithms designed to forecast future outcomes or trends based on historical data and patterns, enabling proactive decision-making in various fields, including finance, healthcare, and marketing. These systems leverage machine learning techniques to analyze data and make predictions, contributing to more informed and anticipatory decision processes.


An open-source framework that simplifies the development of parallel and distributed applications for machine learning and artificial intelligence.

Reactive Machine AI

A type of artificial intelligence system that responds to immediate stimuli or inputs based on predefined rules or algorithms. Unlike more complex forms of AI, such as machine learning or deep learning, which can adapt and learn from data, reactive machine AI operates in a deterministic manner without the ability to learn from experience or adjust its behavior over time.

Retrieval-Augmented Generation

RAG is a natural language processing approach that combines information retrieval and text generation techniques. It entails using a pre-trained language model to generate responses based on retrieved data, allowing the model to access external knowledge sources during the generation process, thereby improving the quality and relevance of the generated content.

Development by Synergize.digital

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