Glossary
Application Programming Interface (API):
An API, or application programming interface, is a collection of guidelines and protocols that enables information sharing and communication across various software applications. It serves as a sort of middleman, allowing several programs to communicate and cooperate even if they were not created using the same technologies or programming languages. Through the usage of APIs, many software applications can communicate with one another and share information, improving the interconnectedness and coherence of the user experience.
Artificial Intelligence (AI):
The ability of machines to accomplish tasks that traditionally require human intelligence, such as language comprehension, problem-solving, and learning. The creation of algorithms and systems that can process, analyse, and analyze huge volumes of data and make decisions based on that data is how artificial intelligence (AI) is realized.
Compute Unified Device Architecture (CUDA):
Through the use of CUDA, large and difficult issues can be divided into smaller ones and solved simultaneously by computers. Using specialized components called GPUs, the computer is made to operate faster and more effectively. Like when you have several friends assist you in solving a puzzle, it moves along much more quickly than if you try to do it all by yourself.
The term “CUDA” is a trademark of NVIDIA Corporation, which developed and popularized the technology.
Data Processing:
The procedure of cleaning, converting, and normalizing raw data in order to make it suitable for inclusion in a machine learning model.
Deep Learning (DL):
A branch of computer learning that extracts complicated patterns from data using deep neural networks with many layers.
Embedding:
Because computers can only comprehend numbers, we must portray words as numbers if we want a computer to understand language. To do it, use an embedding. As an example, we take the word “cat” and translate it into a numerical representation that accurately conveys its meaning. In order to accomplish this, we employ a unique algorithm that considers the word in relation to the surrounding words. The resulting number is a representation of the word’s meaning that the computer can use to comprehend the word’s meaning and its relationship to other words. For instance, due to their same meaning, the words “kitten” and “cat” may have an embedding that is comparable. The term “dog” might be embedded differently from “cat” because of its varied connotations. This enables the computer to comprehend word associations and comprehend language.
Feature Engineering:
The method of choosing and developing additional features from unprocessed data that can be utilized to enhance a machine learning model’s performance.
Freemium:
On Ai-Hunter.io, the phrase “Freemium” may appear frequently. Simply said, it indicates that the tool you’re looking at has both free and premium versions. The utilization of the tool is typically fairly limited at the free tier but infinite, with further access and features being added at the subscription tiers.
Generative Adversarial Network (GAN):
A kind of computer program that builds new things by pitting two neural networks against one another, such visuals or music. As the other network, known as the discriminator, verifies the data’s authenticity, the first network, known as the generator, generates new data. With feedback from the discriminator, which gets better at spotting bogus data, the generator develops its data generation. Unless the generator is able to provide data that is nearly difficult for the discriminator to distinguish from genuine data, this back and forth process must continue. GANs have a number of uses, such as producing realistic photos, movies, and music, eliminating noise from photographs and videos, and inventing new art forms.
Generative Art:
A computer program or algorithm is used to generate visual or auditory output in the creation of generative art. It frequently entails applying randomization or mathematical formulas to produce singular, unpredictable, and perhaps chaotic outcomes.
Generative Pre-trained Transformer (GPT):
Generative Pretrained Transformer is referred to as GPT. It is a specific kind of expansive language model created by OpenAI.
Giant Language model Test Room (GLTR):
GLTR is a tool that enables users to determine whether a text was authored by a human or a computer. By examining how each term is used in the text and how likely it is that a machine would have selected that word, it achieves this. GLTR functions as a kind of assistant that provides hints by giving distinct parts of the sentence different colors. Green indicates that the term was most likely written by a person, yellow that it was possibly computer-written, red that it was more likely computer-authored, and violet that it was most likely computer-written.
GitHub:
A platform for hosting and working together on software projects is called GitHub.
Google Colab:
An online tool called Google Colab enables users to share and execute Python programs on the cloud.
Graphics Processing Unit (GPU):
The complex calculations required to show images and video on a computer or other device are handled by a special kind of computer processor known as a GPU, or graphics processing unit. It functions as the graphics system’s brain and excels at performing a ton of math quickly. Computers, smartphones, and gaming consoles are just a few of the devices that utilise graphics processing units (GPUs). They are particularly helpful for activities like playing video games, producing 3D graphics, or executing machine learning algorithms that demand a lot of computing power.
Langchain:
A framework called LangChain enables users to link artificial intelligence models to outside data sources. The technology enables the development of agents or chatbots that may act on behalf of users by allowing users to chain together commands or questions from various sources. It seeks to streamline the procedure for integrating AI models with outside data sources, opening the door to more sophisticated and potent AI applications.
Large Language Model (LLM):
A kind of machine learning model that can produce language that sounds natural since it has been trained on a lot of text data.
Machine Learning (ML):
A strategy for instructing computers to learn from data without explicit programming.
Natural Language Processing (NLP):
A branch of artificial intelligence that aims to educate computers how to interpret, analyze, and produce human language.
Neural Networks:
An algorithm for machine learning that is based on how the brain works and is structured.
Neural Radiance Fields (NeRF):
Among other things, Neural Radiance Fields are a class of deep learning model that can be applied to image synthesis, object detection, and segmentation. NeRFs are modeled after the idea of utilizing a neural network to simulate an image’s radiance, which is a gauge of how much light an item emits or reflects.
OpenAI:
A research organization called OpenAI is dedicated to creating and advancing ethical, open-source, and societally useful artificial intelligence technology.
Overfitting:
One issue that frequently arises in machine learning is when a model performs well on training data but poorly on fresh, untainted data. That happens when the model is overly complicated and has learned too many specifics from the training data, which makes it difficult for it to generalize.
Prompt:
A prompt is a passage of text used to fuel and direct the construction of a substantial language model.
Python:
Popular high-level programming language Python is renowned for its ease of use, readability, and versatility (many AI tools use it)
Reinforcement Learning:
A kind of machine learning where the model picks up new skills through error, getting rewarded or penalized for its actions, and changing its behavior as necessary.
Spatial Computing:
Spatial computing is the application of technology to augment the real world with digital data and experiences. This can include technologies like virtual reality, which allows you to fully immerse yourself in a digital environment, or augmented reality, which adds digital information to what you see in the actual world. It can alter how we connect with the outside world and one another and has a wide range of applications, including those in design, education, and entertainment.
Stable Diffusion:
On the basis of word prompts, Stable Diffusion creates intricate creative pictures. It is an open source, publicly available image synthesis AI model. There are various online user interfaces that employ Stable Diffusion models as well as scripts that can be used to install Stable Diffusion locally.
Supervised Learning:
A kind of machine learning where the input data is labeled for training and the model is taught to produce predictions based on the associations between the input data and the related labels.
Unsupervised Learning:
A type of machine learning where the model is trained to look for patterns and relationships in the data on its own and where the training data is unlabeled.
Webhook:
Using a webhook, one computer program can instantly communicate data or messages to another program over the internet. It operates by directing the message or data to the other program’s specified URL. Process automation and making it simpler for various programs to cooperate and communicate are two common uses for webhooks. They are a helpful tool for developers who want to make connectors across various software systems or build bespoke applications.
