Machine Learning Genetic Algorithm
Artificial Intelligence Neural Networks
Natural Language Processing Expert Systems
Computer Vision Deep Learning

 

An optimization algorithm that mimics the process of natural selection. A field of study that uses statistical algorithms to enable a machine to improve its performance on a specific task.
A type of machine learning algorithm modeled after the structure of the human brain, capable of learning complex patterns and relationships. The simulation of human intelligence processes by computer systems.
A type of artificial intelligence that uses a knowledge base and inference rules to solve complex problems in a specialized domain. The ability of a computer system to understand, interpret, and generate human language.
A type of machine learning algorithm that uses layers of neural networks to learn and make predictions from complex data sets. A field of artificial intelligence that enables computers to interpret and understand the visual world, including images and videos.

 

Chatbots Robotics
Data Mining Cognitive Computing
Autonomous Vehicles Artificial General Intelligence
Reinforcement Learning Algorithm

 

A field of artificial intelligence that focuses on the design, construction, and operation of robots. Computer programs that use natural language processing and machine learning to simulate conversation with human users.
A type of artificial intelligence that attempts to simulate human thought processes and decision-making. The process of discovering patterns in large data sets using machine learning and statistical techniques.
A hypothetical form of artificial intelligence that possesses human-like cognitive abilities across a broad range of domains. Vehicles that are capable of operating without human intervention, using sensors and machine learning algorithms to navigate and make decisions.
A step-by-step procedure for solving a problem or accomplishing a task, often used in artificial intelligence to guide machine learning. A type of machine learning that involves training a system through trial-and-error using feedback from its environment.

 

Big Data Genetic Algorithms
Intelligent Agents Artificial Neural Networks
Generative Models Unsupervised Learning
Generative AI Artificial Creativity

 

A type of machine learning algorithm that uses principles of evolution to generate solutions to complex problems. Extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations, often used in conjunction with machine learning.
Computational models inspired by the structure and function of biological neural networks that are used in artificial intelligence and machine learning to recognize patterns and make predictions. Machine learning software that acts autonomously on a user's behalf.
A type of machine learning where the model learns patterns and relationships in data without explicit supervision or labeled examples. Machine learning models that can generate new and original content, such as images, texts, or music.
The ability of machines or AI systems to produce original and creative works, such as art, music, or writing. A subfield of artificial intelligence that focuses on creating machines or models capable of generating new content or responses based on existing data or patterns.

 

Cost Savings Predictive Analytics
Data Analysis AI Bias
Lack Of Creativity Job Displacement
Ethical Dilemmas Privacy Concerns

 

Using historical data and machine learning algorithms to make predictions about future events or outcomes. The reduction in expenses or overhead through the implementation of AI.
The tendency for AI systems to favor certain groups or individuals based on factors such as race, gender, or socioeconomic status. The process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
The potential loss of jobs as AI systems become more capable and automated processes replace human labor. AI systems are limited to what they have been trained on and cannot generate truly original ideas or concepts.
AI systems collect and process vast amounts of personal data, raising concerns about privacy and data protection. AI systems often face ethical dilemmas where they have to make decisions that may have moral implications, raising concerns about accountability and responsibility.

 

Security Risks Dependency On AI
Limited Emotional Intelligence Transparency
Explainability AI Negative Impact On The Environment
Data Center Emissions Tokenization

 

As AI becomes more integrated into various domains, there is a risk of over-reliance and reduced human autonomy. AI systems can be vulnerable to hacking and manipulation, leading to potential security breaches and misuse of information.
Transparency refers to the ability to clearly understand and interpret the inner workings and decision-making processes of an AI system. AI lacks the ability to understand and empathize with human emotions, which can limit its effectiveness in certain applications.
The detrimental effects of artificial intelligence on the natural environment, including increased energy consumption and waste generation. Explainability is the ability to provide understandable explanations or justifications for the decisions and outcomes generated by an AI system.
The process of breaking text into smaller pieces, called tokens, which can be words or subwords. The carbon emissions produced by data centers that store and process massive amounts of data for AI applications.

 

Training Corpus Parameters
Inference Fine-Tuning
Transformer Overfitting
Semantic Analysis Bag Of Words

 

The internal variables of a model that are adjusted during training to minimize prediction error. A large set of texts used to train a model to understand and generate language.
A method of further training a pre-trained model on a specific dataset to improve performance on a particular task. The process of using a trained model to generate predictions or outputs based on new input data.
A modeling error that occurs when a model learns the training data too well, failing to generalize to new data. An architecture that uses self-attention mechanisms to process and generate sequences of data.
Where a text is represented as an unordered collection of words. The process of interpreting the meaning of words and phrases in context.