(AI) Artificial Intelligence Terms Every Beginner Should Know
Published on: March 01, 2024
Last updated: May 02, 2024 Read in fullscreen view
Last updated: May 02, 2024 Read in fullscreen view
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Artificial intelligence (AI)
Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.
Intelligence Amplification (IA)
Intelligence Amplification is the use of technology to support and amplify human intelligence. Virtual assistants, chatbots, and communications platforms all already use IA in some form.
Weak AI
Also called narrow AI, this is a model that has a set range of skills and focuses on one particular set of tasks. Most AI currently in use is weak AI, unable to learn or perform tasks outside of its specialist skill set.
AI agent
AI agents are entities designed to perceive their environment and take actions in order to achieve specific goals. These agents can be software-based or physical entities and are often built using artificial intelligence techniques. They perceive their environment through sensors, process the information using algorithms or models, and then take actions using actuators or other means.
AI Detector
A detection tool will flag AI-generated content in your text.
AI Humanize
AI rewriting tool to rewrite that sound genuine.
Cognitive computing
Cognitive computing is a subset of AI, focused on mimicking human-like cognition and natural learning versus programmed intelligence.
They may utilise some of the same technologies, but the difference lies in their respective applications and aims. In short, the purpose of AI is to think on its own and make decisions independently, whereas the purpose of Cognitive Computing is to simulate and assist human thinking and decision-making.
Corpus
A "corpus" is a data set used to train an AI system, varying based on the system's intended use, aiding in its domain-specific intelligence development.
Data corpus
A data corpus is a collection of genuine audio or text that is spoken or written by a native speaker. A corpus can be generated from numerous sources including social networks, like Facebook and WhatsApp, Google and Apple app stores, Amazon marketplaces, tweets, movies, television shows, newspapers, etc. This data is extremely useful in the natural language processing domain to model artificial intelligence and machine learning models. To support a specific problem in an identified domain, it is a must to have a bunch of data that represents the given domain, and the problem to be achieved with ML and NLP.
Machine learning
This subset of AI is particularly focused on developing algorithms that will help machines to learn and change in response to new data, without the help of a human being.
TensorFlow
TensorFlow is a free, end-to-end and open-source platform (a type of software library) for machine learning and artificial intelligence. It was created by Google and continues to own and maintain the framework.
Generative AI
Generative AI is a category of artificial intelligence that can create new text, images, video, audio, or code.
Generative AI can create customized learning materials, cater to individual learning styles, and provide real-time feedback. OpenAI's focus on fairness would ensure that such AI-powered learning tools are free from bias and promote equal educational opportunities.
Search Generative Experience (SGE)
Browse web pages with generative AI-powered tools. With Search Generative Experience (SGE) while browsing, user can:
- Quickly find the web page’s key points.
- Find questions answered on the web page and jump to the relevant section to learn more.
Explainable AI (XAI)
Explainable artificial intelligence (XAI) aims to explain what has been done, what is being done, and what will be done next, and to unveil which information these actions are based on. This makes it possible to confirm existing knowledge, challenge existing knowledge, and generate new assumptions. XAI often overlapping with Interpretable AI, or Explainable Machine Learning, either refers to an AI system over which it is possible for humans to retain intellectual oversight, or to the methods to achieve this.
Google-fu
Skill in using search engines (especially Google) to quickly find useful information on the Internet.
GPT
GPT (Generative Pre-trained Transformer) is a multimodal large language model created by OpenAI, and the fourth in its series of GPT foundation models. GPT-4 is more creative and collaborative than the previous versions. It can generate, edit, and iterate with users on creative and technical writing.
Large Language Models (LLM)
Large language models (LLM) are very large deep learning models that are pre-trained on vast amounts of data that can perform a variety of natural language processing (NLP) tasks.
GPT models are known for their proficiency in text generation, text completion, and a multitude of language-related tasks. In contrast, LLMs cover a range of architectures, including Transformers, RNNs, and CNNs, designed for scalability and adaptability.
Prompt
A prompt is an input that a user feeds to an AI system in order to get a desired result or output.
Prompt Engineering
Prompt engineering is the practice of designing, writing, refining and optimizing inputs for AI tools that will produce optimal and specific, high-quality outputs.
AI infusion
Infusing AI is about being able to apply AI across your enterprise, drawing on predictions, automation, and optimization to improve your business decisions/
Data Annotation
The process of labeling data to show the outcome you want your machine learning model to predict. You are marking - labeling, tagging, transcribing, or processing - a dataset with the features you want your machine learning system to learn to recognize.
Semantic annotation
Tagging different search queries or products with the goal of improving the relevance of a search engine.
Sentiment analysis
The process of identifying and categorizing opinions in a piece of text, often with the goal of determining the writer’s attitude towards something.
Predictive analytics
By combining data mining and machine learning, this type of analytics is built to forecast what will happen within a given timeframe based on historical data and trends.
Reinforcement learning (RL)
RL is a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals.
Supervised machine learning
Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It uses labeled datasets to learn the relationship between input and output, then predict outcomes and recognize patterns.
Unsupervised machine learning
Unsupervised learning in artificial intelligence is a type of machine learning that learns from data without human supervision.
The biggest difference between supervised and unsupervised machine learning is the type of data used. Supervised learning uses labeled training data, and unsupervised learning does not. More simply, supervised learning models have a baseline understanding of what the correct output values should be.
AI ethics
AI ethics are the set of guiding principles that stakeholders (from engineers to government officials) use to ensure artificial intelligence technology is developed and used responsibly.
Guardrails
Guardrails refers to restrictions and rules placed on AI systems to make sure that they handle data appropriately and don't generate unethical content.
Predictive analytics
Predictive analytics is a type of analytics that uses technology to predict what will happen in a specific time frame based on historical data and patterns.
Prescriptive analytics
Prescriptive analytics is a type of analytics that uses technology to analyze data for factors such as possible situations and scenarios, past and present performance, and other resources to help organizations make better strategic decisions.
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