Algorithm

A computer follows a set of instructions or a step-by-step process called an algorithm. Within the realm of artificial intelligence, machine learning systems can process and interpret data using an algorithm.

Artificial Intelligence

The term artificial intelligence (AI) refers to the computer systems’ or machines’ ability to simulate human intelligence processes. AI has the ability to simulate human traits including learning, decision-making, and communication.

Bias

Systematic errors or biases in data, algorithms, or decision-making processes that lead to unjust or discriminating results are referred to as bias.

Big Data

The term “big data” describes the enormous amounts of structured and unstructured data that are produced every day from a variety of sources, such as social media, sensors, transactions, etc.

Chatbot

An artificial intelligence (AI) program or application that replicates human-user communication, usually via the Internet. Chatbots can be as basic as rule-based systems or as complex as AI-powered models that can comprehend natural language, decipher user intent, and offer pertinent answers or help across a number of industries.

Computer Vision

Machines can read and comprehend visual data from their surroundings due to computer vision.

Data Mining

The process of looking through datasets to find fresh patterns that could enhance the model.

Deep Learning

An aspect of artificial intelligence that replicates the way the brain works by gaining knowledge from the structure of data instead of from an algorithm designed to perform a single task.

Edge Computing

Edge computing minimizes response times and energy consumption by moving processing and data storage closer to data sources. Edge artificial intelligence systems do not require cloud connectivity to execute AI applications locally on devices.

Embodied Agents

Embodied agents, sometimes known as embodied artificial intelligence, are AI agents that have a physical body and carry out particular activities in the real world.

Fine Tuning

The process of modifying a language model that has already been trained to perform effectively on a given task by further training it on a different dataset or task. Customizing the model’s parameters to enhance performance on task- or domain-specific goals is possible through fine-tuning.

Generative AI

A subfield of artificial intelligence that focuses on developing algorithms that can produce fresh text, images, videos or music that can’t be told apart from content made by people. To generate unique and realistic results, generative AI techniques frequently rely on deep learning models, such as autoregressive models or generative adversarial networks (GANs).

Generative Adversarial network

An artificial intelligence model where two neural networks compete with one another to produce new data that has the same statistics as the training data set.

Hallucinations

When a model produces results that are not compatible with the input data or reality, this is referred to as a hallucination. Overfitting, biases in the model, or restrictions in the training set can all lead to hallucinations. To guarantee the dependability and credibility of AI systems, hallucination evaluation and mitigation are essential.

Hyperparameter

An AI model’s learning process can be influenced by parameters, or values, called hyperparameters. Most of the time, it is manually adjusted outside the model.

Internet of Things (IoT)

The physical network of appliances, vehicles, and other items that have sensors, software, and connection built into them. AI methods can be used to analyze, automate, and make decisions using data from the Internet of Things (IoT).

Image recognition

AI systems’ capacity to recognize and categorize objects, patterns, or other properties seen in digital images. In computer vision, it is a subfield.

Knowledge Graph

Knowledge graphs are data structures that enable AI systems to traverse and comprehend big datasets by connecting information in a web of relationships.

Large Language Model

An AI model that has been trained on a lot of text to understand language and produce writing that looks like it belongs on a human is called a large language model (LLM).

Machine Learning

A branch of artificial intelligence that allows computers to learn from their experiences naturally and without explicit programming. It focuses on creating algorithms that can use data to learn from and forecast or make judgments.

Model

A broad term for the output of artificial intelligence (AI) training, which is produced by applying a machine learning algorithm to training data.

Natural Language Processing

Computers that use natural language processing (NLP) can comprehend both written and spoken human language. NLP allows devices to include features like speech and text recognition.

Neural Network

A neural network is a deep learning method whose architecture is inspired by the architecture of the human brain. Large data sets are needed for neural networks to process inputs, produce outputs, and enable features like voice and visual recognition.

Overfitting

The creation of a model that too closely resembles a specific set of data and hence struggles to generalize to new observations; the model’s inadvertent identification of patterns in the noise and the assumption that those patterns represented the underlying structure.

Predictive Analytics

This kind of analytics is designed to predict what will occur inside a specific timeframe based on past data and patterns by combining data mining and machine learning.

Quantum Computing

The process of performing calculations using quantum-mechanical phenomena like superposition and entanglement is known as quantum computing. Because quantum computers operate far more quickly than traditional computers, quantum machine learning leverages these methods to speed up work.

Reinforcement Learning

A technique for training AI that encourages the model to try various scenarios rather than coming up with a single solution by defining a goal without specifying metrics. The model can then alter the following scenario to get better outcomes based on user feedback.

Sentiment Analysis

The process of locating and classifying viewpoints within a text, frequently in order to ascertain the author’s perspective on a certain subject.

Supervised Learning

In supervised learning, machine learning, the machine is trained and generated using the appropriate algorithms using classified output data. Compared to unsupervised learning, it is far more prevalent.

Training Data

An artificial intelligence (AI) system needs training data to identify patterns, learn new things, and produce new material.

Unsupervised Learning

A technique for training models that identifies patterns in training data without the need for label examples.

Virtual Reality

A digitally produced environment known as virtual reality (VR) can be used to create an alternate world, imitate an actual area, or blend the two.