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Info Tech Quiz Ch3
Infor Tech Quiz #2 Chapter 3 Terms
| Question | Answer |
|---|---|
| AI (Artificial Intelligence) | The simulation of human intelligence processes by machines, particularly computer systems |
| Symbolic AI | the rise of the AI discipline and expert systems before the 1990s |
| Connectionist AI | the rise of machine learning and deep learning starting in the 2000s |
| Expert Systems | a computer system designed to hold accumulated knowledge of domain experts |
| Artificial Narrow Intelligence (ANI) | machine learning that specializes in one area and solves one problem (ex. An AI that looks at what products customers like or don't like) |
| Artificial General Intelligence (AGI) | machine intelligence that refers to a computer that is as smart as a human across the board |
| Artificial Super Intelligence (ASI) | Machine consciousness that is much smarter than the bets human brains in every field |
| Algorithms | The “brains” or “how to” of the AI system and set the rules for what the AI system can do |
| Regression Algorithms | A line of best fit |
| Classification Algorithms | using data of known facts like looking at patterns to predict the future |
| Clustering Algorithms | unsupervised learning that puts data into groups of maximum commonalities |
| Time Series Algorithms | look at data series overtime and make predictions based on the time periods |
| Optimization Algorithms | max or min an amount given constraints and the value to get the best outcome |
| NLP Algorithms | natural language processing of human languages such as grammar checkers and notes transcription |
| Anomaly Detection Algorithms | outlier detection, where it looks at unusual patterns that do not follow the expected behavior like a machine about to fail or true vs false alarms |
| Agentic (Agent) AI | mimics how humans act, give and receive information (ex. Asking for travel advice, grocery shopping basket, etc.) |
| Forward Chaining | Data driven, beginning with facts and applies rules to infer new facts |
| Backward Chaining | Goal driven, starts with a goal and works backwards from it |
| Generative AI | Estimates the probability of a token or sequence of tokens (common sequences of characters found in a set of text) occurring within a longer sequence of tokens |
| Models | A mathematical (sometimes) representation of a real-world process, created or learned by applying an algorithm to data |
| Supervised learning | you always know what you are looking for, you know the different characteristics between things and can describe them based on what you know |
| Unsupervised learning | you don’t know what has happened in the past; you look at other people’s features and experiences to define them into groups based on their characteristics |
| Input Step | AI is given text, images and other data to learn from |
| Analysis Step | analyzes the data to detect patterns and relationships |
| Learning Step | paying attention to understand the context within the data |
| Creation Step | based on learned patterns, Ai makes new text, images, etc |
| Refining Step | the AI checks if the output makes sense and refines it as needed |
| New Content | final output is delivered like a story, artwork or answer |
| Autoencoders | Used in image denoising, dimensionality reduction and anomaly detection in healthcare |
| Variational Models | applied in image synthesis, data augmentation and supervised learning |
| Transformers | used for machine translation, chatbots and text summaries |
| Recurrent Neural Networks | applied in text generation, language translation, and speech recognition systems |
| Generative Adversarial Networks | used for image-to-image translation and creating deep fake videos |
Created by:
evehec
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