In view of More than half of Americans use AI regularlyit quickly became a normal part of our daily life. chatgpt,,,,, Google Gemini and Microsoft Copilot AI is being pushed to all technologies, changing the way we interact with everything. Suddenly, people are able to have meaningful conversations with machines, which means you can ask a question AI chatbot In natural language, it can respond with novel answers, just like humans.
But this aspect of AI chatbots is only part of the AI landscape. Of course, there is chatgpt helps with homework Or let Midjourney create Charming images of mechas based on country of origin It’s cool, but the potential to generate AI can completely reshape the economy. Probably worth it Give the global economy $4.4 trillion every yearAccording to McKinsey Global Institute, that’s why you should expect to hear more and more about artificial intelligence.
It appears in a series of dazzling products – a short short list includes Google’s GeminiMicrosoft’s Co-pilothuman Claudethis Puzzled AI search tools and gadgets humane and rabbit. You can read our reviews and hands-on evaluations of these and other products, as well as our news, interpreters and how-to posts AI Map Atlas Hub.
As people become more accustomed to the world intertwined with AI, new terms appear everywhere. So whether you want to sound smart when drinking or impress people during an interview, these are some important AI terms you should know.
This glossary is updated regularly.
Artificial General Intelligence or AGI: This concept proposes a more advanced version of AI than we know today, which can be much better than the tasks humans perform, while also teaching and advancing their own abilities.
acting: Demonstrate a system or model that takes autonomous action to achieve goals. In the context of AI, proxy models can act without continuous supervision, such as advanced autonomous vehicles. Unlike the “proxy” framework in the background, the proxy framework is not in front of it, the focus is on user experience.
AI Ethics: Principles designed to prevent AI from harming humans are achieved by determining how AI systems should collect data or process bias.
AI Security: An interdisciplinary field that involves the long-term impact of AI and how it suddenly develops into a superintelligence that can be hostile to humanity.
algorithm: A series of instructions allows a computer program to learn and analyze data in a specific way, such as identifying patterns, and then learn from it and complete tasks on its own.
Alliance: Adjust AI to better produce the expected results. This can refer to any content that regulates content to maintain positive interactions with humans.
Anthropomorphization: Human characteristics when humans tend to give non-human objects. In AI, this can include believing that a chatbot is more human and conscious than actually realizing it, just as believing that it is happy, sad, and even completely conscious.
Artificial Intelligence or Artificial Intelligence: Use technology to simulate human intelligence in computer programs or robotics. Computer science is designed to build systems that can perform human tasks.
Independent agent: An AI model with functions, programming and other tools to complete specific tasks. For example, self-driving cars are an autonomous agent because they have sensory input, GPS and driving algorithms that can bike lanes. Stanford University researchers It has been shown that autonomous agents can develop their own culture, traditions and shared languages.
bias: Regarding large language models, errors caused by training data. This can lead to falsely attribute certain traits to certain races or groups based on stereotypes.
Chatbot: A program that simulates human language communication with humans.
chatgpt: Developed by AI chatbots Openai Use large language modeling techniques.
Cognitive computing: Another term for artificial intelligence.
Data Enhancement: Remix existing data or add more diverse datasets to train AI.
Deep Learning: AI’s method and subfields of machine learning, which uses multiple parameters to identify complex patterns in pictures, 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 takes existing data like a photo and adds random noise. The diffusion model trains their network to redesign or restore the photo.
Emergency behavior: When AI models show unexpected abilities.
End-to-end learning or E2E: A deep learning process in which the model is instructed to perform tasks from beginning to end. It is not trained to complete tasks sequentially, but instead learns from input and solves all tasks immediately.
Moral considerations: Aware of the ethical implications of AI and issues related to privacy, data use, fairness, abuse and other security issues.
Forum: Also known as a fast takeoff or a tough takeoff. If someone builds an AGI, it might be too late, and it might be too late to save humanity.
Generate adversarial networks or gans: Generative AI model consisting of two neural networks to generate new data: generator and discriminator. Generator creates new content and discriminator checks whether it is real.
Generated AI: A technique to create content generation of text, video, computer code or images using AI. AI is fed a large amount of training data and found patterns to produce their own novel responses, sometimes similar to the original material.
Google Gemini: Google’s AI chatbots are similar to ChatGpt but draw information from the current network, which is limited to data until 2021 and is not connected to the Internet.
Guardrail: Strategies and limitations on AI models to ensure data is processed responsibly and that the model does not create disturbing content.
Hallucination: Incorrect response from AI. It can include the generated AI to generate incorrect but confidently stated answers. The reasons for this are not entirely known. For example, when asked an AI chatbot, “When did Leonardo da Vinci draw Mona Lisa?” May respond with incorrect statements Said: “Leonardo da Vinci drew Mona Lisa in 1815,” which was actually 300 years later.
reasoning: The process of AI models being used to generate text, images, and other content about new data infer From their training data.
Large language model or LLM: AI models train large amounts of text data to understand language and generate novel content in human-like languages.
Incubation period: The AI system receives input or prompts and generates a time delay in output.
Machine Learning or ML: A component in AI that allows computers to learn and make better predictions without explicit programming. New content can be generated in conjunction with training sets.
Microsoft Bing: Microsoft’s search engines can now use technology to power Chatgpt to provide AI-powered search results. Similar to Google Gemini, when connected to the Internet.
Multimodal AI: An AI that can handle multiple inputs, including text, images, video, and voice.
Natural Language Processing: Using AI branches of machine learning and deep learning enables computers to frequently use learning algorithms, statistical models and language rules, allowing computers to understand human language.
Neural Network: A computational model similar to the structure of the human brain and aims to identify patterns of data. Consisting of interconnected nodes or neurons that can recognize patterns and learn over time.
Overfitting: Errors in machine learning are too close to the functionality of training data and may only identify specific examples in the above data, rather than new data.
Roll of paper: Paper strip maximization theory created by philosophers Nick Bostrom Oxford is a hypothetical situation where the AI system will create as many text rolls as possible. To produce the goal of the largest roll, the AI system will assume that all materials are consumed or converted to achieve their goals. This may include dismantling other machinery to produce more rolls of paper, which may be beneficial to humans. The unexpected consequence of this AI system is that making paper rolls could undermine human goals.
parameter: Assign numerical values to the LLMS structure and behavior so that it can make predictions.
Puzzled: Chleplexity The name of AI-powered chatbots and search engines owned by AI. It uses large language models, just like those in other AI chatbots, answering questions with novel answers. Its connection to the open internet also enables it to provide the latest information and attract results from around the network. Cllexity Pro is the paid tier of the service, and other models are available, including GPT-4O, Claude 3 Opus, Missstral Gim, open source llama 3 and its own Sonar 32K. Pro users can additionally upload documents for analysis, generate images and interpret code.
Quickly: You go into an AI chatbot for a response to suggestions or questions.
Timely links: Artificial intelligence’s ability to use information from previous interactions to dye future responses.
Random parrot: LLM’s analogy shows that the software does not have a greater understanding of the meaning behind the language or the world around it, regardless of how the output sound is convinced. The phrase refers to how parrots imitate human words without knowing what they mean.
Style transfer: The ability to adapt the style of one image to the contents of another image allows the AI to interpret the visual properties of one image and use it on another image. For example, recreate it with the help of Rembrandt’s self-portrait and recreate it in Picasso’s style.
temperature: Parameters are set to control the randomness of the output of the language model. Higher temperatures mean that the model requires more risks.
Text to image generation: Create an image based on text description.
Token: A small portion of the written text of the AI language model can formulate its response to your prompts. A token is equivalent to four characters, or about three-quarters of a word.
Training data: Datasets used to help AI models learn, including text, images, code, or data.
Transformer model: Neural network architecture and deep learning models that learn context by tracking relationships in data, such as sentences or parts of images. So it can look at the entire sentence and understand the context instead of analyzing one sentence at a time.
Turing test: It is named after the famous mathematician and computer scientist Alan Turing, which tests the ability of machines to behave like humans. If a human cannot distinguish a machine’s response from another person, the machine passes.
Unsupervised learning: A form of machine learning that does not provide the model with marked training data, and the model must individually identify patterns in the data.
Weak AI, also known as narrow AI: AI that focuses on specific tasks and cannot surpass its skills. Most AI today is weak.
Zero strike learning: The model must complete the test of the task without obtaining the necessary training data. An example is identifying a lion while only being trained by tigers.
