Agent Spotlight: Olasubomi Awere, a Crucial Contributor
January 13, 2023
February 7, 2023
6
min to Read
GPT-3 Chatbot
AI has revolutionized the way we interact with machines. As the technology continues to advance, it's important to stay up to date on the latest developments and understand the terminology used in the field.
Note: This is an evolving list. Have suggestions on terms to add? Email andrew@invisible.email.
A set of steps or procedures used to solve a problem or perform a task. In AI, algorithms are used to train models, make predictions, and perform various other tasks.
The process of identifying instances in data that do not conform to expected behavior, often used in a variety of applications including fraud detection, network security, and healthcare.
A theoretical form of AI that would possess human-level intelligence across a wide range of cognitive tasks, as opposed to being specialized in a specific task.
A field of computer science that focuses on creating machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
A computer program designed to mimic conversation with human users, typically through messaging applications, websites, or mobile apps.
A field of AI that focuses on enabling computers to interpret and understand visual information from the world, such as images and videos.
A type of chatbot specifically designed to provide customer support and assistance, such as answering frequently asked questions, resolving customer issues, and providing product information.
A subset of machine learning that involves training artificial neural networks on large amounts of data to make predictions.
A type of machine learning algorithm used for classification and prediction tasks, in which a model is trained to make decisions based on a series of questions about the data.
The process of controlling the flow of a conversation between a chatbot and a user. This includes identifying the user's intent, selecting the appropriate response, and managing follow-up questions.
A type of deep learning algorithm that involves two neural networks, a generator and a discriminator, competing against each other to generate new data that is similar to the original data.
The third generation of OpenAI's Generative Pretrained Transformer language model, a highly advanced AI system capable of generating human-like text and performing various natural language processing tasks.
A process in which a human intervenes in the decision-making process of an AI system, typically to correct errors or improve performance. This is often used in the development and training of AI models.
The process of determining the user's intent based on the language they use in a conversation with a chatbot. This allows the chatbot to understand the user's request and provide an appropriate response.
A database of information used by a chatbot to provide answers to customer queries. The knowledge base can be updated with new information to improve the chatbot's accuracy over time.
A subfield of AI that involves training algorithms to learn patterns in data and make predictions based on that information.
A type of machine learning algorithm used for classification tasks, based on Bayes' theorem, which states that the probability of an event is equal to the prior probability multiplied by the likelihood of the event given the evidence.
A subfield of NLP that focuses on the automatic generation of text that is similar to human language.
A subfield of AI that deals with the interaction between computers and humans through language. NLP is crucial for chatbots as it enables them to understand and respond to human language.
A type of machine learning algorithm that is modeled after the structure of the human brain. Neural networks can be used for a variety of tasks, including image and speech recognition, language translation, and decision-making.
An ensemble learning technique that involves training multiple decision trees and combining their predictions to make a final prediction. Random forests are often used for complex, non-linear prediction tasks.
A type of machine learning that involves training an algorithm on a combination of labeled and unlabeled data, taking advantage of the benefits of both supervised and unsupervised learning.
A type of machine learning in which the algorithm is trained on labeled data, with the goal of learning a mapping from inputs to outputs. The algorithm is then able to make predictions on new, unseen data based on the patterns it learned during training.
A type of machine learning algorithm used for classification and regression tasks, in which a model is trained to find the optimal boundary between different classes in the data.
A technique in which a pre-trained machine learning model is fine-tuned for a new task, leveraging the knowledge it has learned from the original task to improve its performance on the new task.
A method of training machine learning models using weak labels, which are noisy or imprecise labels, instead of accurate annotations. This allows for a larger quantity of data to be used for training, increasing the model's ability to learn from a diverse set of examples.
01|
02|
03|
.svg)
AI has revolutionized the way we interact with machines. As the technology continues to advance, it's important to stay up to date on the latest developments and understand the terminology used in the field.
Note: This is an evolving list. Have suggestions on terms to add? Email andrew@invisible.email.
A set of steps or procedures used to solve a problem or perform a task. In AI, algorithms are used to train models, make predictions, and perform various other tasks.
The process of identifying instances in data that do not conform to expected behavior, often used in a variety of applications including fraud detection, network security, and healthcare.
A theoretical form of AI that would possess human-level intelligence across a wide range of cognitive tasks, as opposed to being specialized in a specific task.
A field of computer science that focuses on creating machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
A computer program designed to mimic conversation with human users, typically through messaging applications, websites, or mobile apps.
A field of AI that focuses on enabling computers to interpret and understand visual information from the world, such as images and videos.
A type of chatbot specifically designed to provide customer support and assistance, such as answering frequently asked questions, resolving customer issues, and providing product information.
A subset of machine learning that involves training artificial neural networks on large amounts of data to make predictions.
A type of machine learning algorithm used for classification and prediction tasks, in which a model is trained to make decisions based on a series of questions about the data.
The process of controlling the flow of a conversation between a chatbot and a user. This includes identifying the user's intent, selecting the appropriate response, and managing follow-up questions.
A type of deep learning algorithm that involves two neural networks, a generator and a discriminator, competing against each other to generate new data that is similar to the original data.
The third generation of OpenAI's Generative Pretrained Transformer language model, a highly advanced AI system capable of generating human-like text and performing various natural language processing tasks.
A process in which a human intervenes in the decision-making process of an AI system, typically to correct errors or improve performance. This is often used in the development and training of AI models.
The process of determining the user's intent based on the language they use in a conversation with a chatbot. This allows the chatbot to understand the user's request and provide an appropriate response.
A database of information used by a chatbot to provide answers to customer queries. The knowledge base can be updated with new information to improve the chatbot's accuracy over time.
A subfield of AI that involves training algorithms to learn patterns in data and make predictions based on that information.
A type of machine learning algorithm used for classification tasks, based on Bayes' theorem, which states that the probability of an event is equal to the prior probability multiplied by the likelihood of the event given the evidence.
A subfield of NLP that focuses on the automatic generation of text that is similar to human language.
A subfield of AI that deals with the interaction between computers and humans through language. NLP is crucial for chatbots as it enables them to understand and respond to human language.
A type of machine learning algorithm that is modeled after the structure of the human brain. Neural networks can be used for a variety of tasks, including image and speech recognition, language translation, and decision-making.
An ensemble learning technique that involves training multiple decision trees and combining their predictions to make a final prediction. Random forests are often used for complex, non-linear prediction tasks.
A type of machine learning that involves training an algorithm on a combination of labeled and unlabeled data, taking advantage of the benefits of both supervised and unsupervised learning.
A type of machine learning in which the algorithm is trained on labeled data, with the goal of learning a mapping from inputs to outputs. The algorithm is then able to make predictions on new, unseen data based on the patterns it learned during training.
A type of machine learning algorithm used for classification and regression tasks, in which a model is trained to find the optimal boundary between different classes in the data.
A technique in which a pre-trained machine learning model is fine-tuned for a new task, leveraging the knowledge it has learned from the original task to improve its performance on the new task.
A method of training machine learning models using weak labels, which are noisy or imprecise labels, instead of accurate annotations. This allows for a larger quantity of data to be used for training, increasing the model's ability to learn from a diverse set of examples.
.png)
