ChatGpt Glossary: 50 AI Terminology All Should Know
given that Over half of Americans use AI regularlyit is quickly becoming a normal part of our daily lives. chatgpt, Google Gemini and Microsoft Copilot It’s pushing AI into all technology and changing the way we interact with everything. Suddenly, people can have meaningful conversations with the machines. In other words, you Broadcast In natural language, like humans, you can respond with novel answers.
But that aspect of AI chatbots is just part of the AI landscape. Certainly, I have chatgpt helps you do homework Or create it in Midjourney Attractive images of mecha based on country of origin It’s cool, but the possibilities of generative AI can completely change the economy. It may be worth it $4.4 trillion to the global economy every yearaccording to the McKinsey Global Institute, that’s why we should expect to hear more and more about artificial intelligence.
It appears in a series of dizzy products – a short short list includes Google’s GeminiMicrosoft’s Co-pilothumanity Claude, Confused AI Search Tools and Gadgets from Humanitarian and Rabbit. You can read reviews and practical ratings, news, explanators and how-to posts for these and other products. You have an Atlas hub.
New terms are appearing everywhere as people become accustomed to the world of AI intertwining. So whether you’re trying to sound smarter than a drink or trying to impress in a job interview, here are some important AI terms you need to know.
This glossary is updated periodically.
Artificial general information or AGI: The concepts suggest a more advanced version of AI can teach and advance unique abilities while performing tasks that are far better than humans than we know today.
agent: A system or model that illustrates an agency with the ability to autonomously pursue actions to achieve its goals. In the AI context, agent models can act without constant supervision, such as high-level self-driving cars. Unlike the “agent” frameworks in the background, the agent framework is at the forefront and focuses on the user experience.
AI Ethics: Principles aimed at preventing AI from harming humans are achieved through measures such as determining how AI systems collect data and address bias.
AI Safety: An interdisciplinary field that relates to the long-term impact of AI and how it suddenly moves into superintelligence that could be hostile to humans.
algorithm: A set of instructions that allow computer programs to learn and analyze data in a specific way, such as patterns, will then learn and accomplish their own tasks.
Alignment: Finely tune your AI to better produce the desired results. This can be referenced anywhere from moderated content to maintaining positive human interactions.
Personification: When humans tend to impart human properties to non-human objects. In AI, this involves believing that chatbots are more human and perceived than they are.
Artificial Intelligence or AI: The use of technology to simulate human intelligence, either in computer programs or in robotics. The field of computer science, which aims to build systems that can perform human tasks.
Autonomous Agent: An AI model with features, programming and other tools to accomplish a specific task. For example, self-driving cars are autonomous agents, as they have sensory input, GPS, and driving algorithms that navigate the roads on their own. Stanford University Researcher We have shown that autonomous agents can develop their own cultures, traditions and shared languages.
bias: For large-scale language models, errors arise from training data. This can cause certain traits to be misaligned to a particular race or group based on stereotypes.
Chatbot: A program that communicates with humans via text that simulates human language.
chatgpt: AI chatbot developed by Openai It uses large-scale language model technology.
Cognitive Computing: Another term for artificial intelligence.
Data Enhancement: Train your AI by remixing existing data or adding more diverse data sets.
Deep Learning: AI methods and machine learning subfields use multiple parameters to recognize complex patterns of photographs, sounds and text. This process is inspired by the human brain and uses artificial neural networks to create patterns.
diffusion: A machine learning method that retrieves existing data like photos and adds random noise. The diffusion model trains the network to redesign or recover its photos.
Emergency action: When the AI model shows unintended capabilities.
End-to-end learning, or E2E: A deep learning process in which the model is instructed to perform tasks from start to finish. They are not trained to accomplish tasks in sequence, but instead learn from input and resolve them at once.
Ethical considerations: The ethical implications of AI and recognition of issues related to privacy, data use, fairness, misuse and other safety issues.
: It is also known as fast takeoff or hard takeoff. The concept that if someone constructed an AGI, it might already be too late to save humanity.
Generic adversary networks, or GANs: A generated AI model consisting of two neural networks for generating new data. Generators and discriminators. The generator creates new content and the discriminators check if it is authentic.
Generated AI: Content generation technology that uses AI to create text, video, computer code, or images. AI is fed with a large amount of training data and find patterns that generate new unique responses. This may be similar to the source material.
Google Gemini: Google’s AI chatbot works similarly to ChatGPT, but pulls information from the current web, but ChatGPT is limited to data until 2021 and is not connected to the internet.
guardrail: Policies and restrictions placed in AI models so that data is processed responsibly and the model does not create disturbing content.
Hallucinations: Incorrect response from AI. You can include answers that generate AI that are incorrect but state with confidence as if they were correct. The reason for this is not entirely understood. For example, the AI chatbot says, “When did Leonardo da Vinci draw the Mona Lisa?” We may respond with incorrect statements “Leonardo da Vinci painted Mona Lisa in 1815,” and it is 300 years after it was actually painted.
inference: The AI model process is used to generate text, images, and other content about new data. Speculation From their training data.
Large language models or LLM: An AI model that trains large amounts of text data to understand languages and generate new content in human-like languages.
Waiting time: A time delay from when an AI system receives inputs or prompts and generates output.
Machine Learning, or ML: The AI components allow computers to learn and produce better predictions without explicit programming. It can be combined with a training set to generate new content.
Microsoft Bing: Microsoft’s search engine can now use ChatGpt on power supplies to provide AI-powered search results. It’s similar to Google Gemini that has an internet connection.
Multimodal AI: A type of AI that can handle multiple types of input, including text, images, video, audio, etc.
Natural Language Processing: A branch of AI that provides computers with the ability to understand human language using machine learning and deep learning.
Neural Network: A computational model that resembles the structure of the human brain and aims to recognize patterns of data. It consists of interconnected nodes or neurons that can recognize patterns and learn over time.
Overfitting: It works closely with machine learning error training data and can identify only specific examples of that data, but it may not be new.
paper clip: Paper clip maximizer theory created by philosophers Nick Bostorm It is a hypothetical scenario in which an AI system creates as many literal documents as possible within Oxford University. With the goal of generating the largest amount of paper clips, the AI system hypothetically consumes or transforms all materials to achieve the goal. This includes dismantling other machines to produce more strings, machines that are beneficial to humans. The unintended consequence of this AI system is that it can destroy humanity with its goal of document creation.
parameter: Numerical values that give the structure and behavior of LLMS allow predictions to be made.
Confusing: Names of AI-powered chatbots and search engines owned by Perplexity AI. Answer your questions with new answers using larger language models like those found in other AI chatbots. An open internet connection also allows you to abandon the latest information and extract results from around the web. Perplexity Pro, a paid tier for services, is also available, with other models such as the GPT-4O, Claude 3 Opus, Mistral Large, Open-Source Llama 3 and its own sonar 32K. Pro users can upload documents, generate images and interpret the code for analysis.
prompt: Suggestions or questions to enter into the AI chatbot to get a response.
Prompt Chain: The ability of AI to use information from previous interactions to color future responses.
Probabilistic Parrot: Software is an analogy from LLMS that shows that you don’t really understand the meaning behind the language and the surrounding world, regardless of how the output is convinced. This phrase refers to the way in which parrots mimic human language without understanding the meaning behind them.
Style Transfer: The ability to adapt the style of one image to the content of another image allows AI to interpret the visual attributes of one image and use it in another image. For example, take a Rembrandt self-portrait and recreate it in Picasso style.
temperature: Parameters set to control random output of the language model. High temperature means that the model takes more risk.
Generate images from text: Create images based on text descriptions.
token: A small bit of written text that AI language models the process to formulate a response to a prompt. A token can be about four letters of English, or about a quarter of a word.
Training data: Datasets are used to aid in learning that AI models contain text, images, code, or data.
Transformer model: A neural network architecture and deep learning model that learns context by tracking data relationships, such as sentences and images. So instead of analyzing the sentences one word at a time, you can look at the entire sentence and understand the context.
Turing Test: Named after Alan Turing, a well-known mathematician and computer scientist, it tests the capabilities of machines to act like humans. If a person cannot distinguish a machine’s response from another person, the machine passes.
Unsupervised learning: A form of machine learning in which no labeled training data is provided to the model and instead the model must identify the pattern of the data on its own.
Weak AI, also known as narrow AI: AI focuses on specific tasks and cannot learn beyond skill sets. Most AI today is weak AI.
Zero Shot Learning: A test in which the model must complete a task without being given the necessary training data. An example is to recognize a lion while being trained with the Tigers.