Toes in the AI water

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Question: 

Give me a list of AI terminology that you think could teach someone who has never heard of AI before


CHATGPT

Basic AI Terms

  1. Artificial Intelligence (AI) – The simulation of human intelligence in machines, allowing them to perform tasks that typically require human cognition.
  2. Machine Learning (ML) – A subset of AI where computers learn patterns from data and improve performance over time without explicit programming.
  3. Algorithm – A step-by-step set of instructions a computer follows to solve a problem or make a decision.
  4. Neural Network – A computer system modeled after the human brain, consisting of layers of nodes (neurons) that process information.
  5. Deep Learning – A type of machine learning that uses multi-layered neural networks to analyze complex patterns in data.
  6. Data Training – The process of feeding an AI system large amounts of data to help it recognize patterns and make predictions.
  7. Big Data – Extremely large sets of data that can be analyzed by AI to identify trends and patterns.
  8. Automation – The use of AI to perform tasks with minimal human intervention.
  9. Natural Language Processing (NLP) – A branch of AI that enables computers to understand, interpret, and generate human language.
  10. Chatbot – An AI-powered software that can simulate a conversation with humans, often used in customer service or virtual assistants.

How AI Works

  1. Supervised Learning – A type of machine learning where AI is trained with labeled data, meaning it already knows the correct answers.
  2. Unsupervised Learning – AI learns patterns from data without predefined labels or correct answers.
  3. Reinforcement Learning – A machine learning method where an AI agent learns through trial and error, receiving rewards for good actions and penalties for bad ones.
  4. Bias in AI – When AI systems make unfair decisions due to imbalanced or flawed training data.
  5. AI Ethics – The study of moral implications and responsibilities when designing and using AI technologies.
  6. Turing Test – A test proposed by Alan Turing to determine whether a machine can exhibit human-like intelligence by engaging in a conversation.
  7. Generative AI – AI that can create new content, such as images, music, or text, based on learned patterns (e.g., ChatGPT, DALL·E).

AI in the Real World

  1. Computer Vision – AI’s ability to interpret and analyze visual information from images or videos.
  2. Autonomous Systems – AI-driven machines or vehicles that operate independently, such as self-driving cars.
  3. Recommendation System – AI-powered suggestions based on user behavior, used by platforms like Netflix and Spotify.
  4. Deepfake – AI-generated images, videos, or audio that can convincingly mimic real people.
  5. AI Singularity – A hypothetical point where AI surpasses human intelligence and becomes self-improving beyond human control.
  6. Explainable AI (XAI) – AI systems designed to be transparent so humans can understand their decisions.
  7. Robotics – The use of AI in machines to perform physical tasks, such as industrial automation or medical surgery.
  8. Edge AI – AI that runs directly on a device (like a smartphone) rather than in the cloud, improving speed and privacy.

GEMINI
  • Artificial Intelligence (AI):
    • This is the broad term for computer systems designed to perform tasks that typically require human intelligence, like learning, problem-solving, and decision-making.
  • Machine Learning (ML):
    • A subset of AI that allows computers to learn from data without explicit programming. They find patterns and improve their performance over time.
     
  • Deep Learning (DL):
    • A specialized form of machine learning that uses artificial neural networks with multiple layers (hence "deep"). It excels at complex tasks like image and speech recognition.
  • Algorithm:
    • A set of rules or instructions that a computer follows to solve a problem or perform a task.
  • Neural Network:
    • A computational model inspired by the human brain, consisting of interconnected nodes that process and transmit information.

Key AI Actions and Outputs:

  • Prompt:
    • An input or instruction given to an AI system to generate a response (e.g., asking a chatbot a question).
  • Generative AI:
    • AI that can create new content, such as text, images, or music, based on learned patterns.
  • Hallucination:
    • When an AI generates false or misleading information, presenting it as fact.

Important Related Terms:

  • Data:
    • Information used to train AI models. The quality and quantity of data significantly impact AI performance.
  • Bias:
    • Systematic errors in AI models that can lead to unfair or discriminatory outcomes, often caused by biased training data.
  • Natural Language Processing (NLP):
    • A field of AI that focuses on enabling computers to understand, interpret, and generate human language.  
  • Token:
    • A basic unit of text that AI models process, often a word or part of a word.

Why these terms are important:

  • Understanding these terms provides a foundation for comprehending how AI systems function.
  • It helps in evaluating the capabilities and limitations of AI technologies.
  • It facilitates informed discussions about the ethical and societal implications of AI.

COPILOT

  1. Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems.
  2. Machine Learning (ML): A subset of AI that involves the development of algorithms that allow computers to learn from and make predictions based on data.
  3. Deep Learning: A type of ML that uses neural networks with many layers (deep networks) to analyze various factors of data.
  4. Neural Network: A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
  5. Natural Language Processing (NLP): A field of AI that focuses on the interaction between computers and humans through natural language.
  6. Computer Vision: An AI field that enables computers to interpret and make decisions based on visual data.
  7. Supervised Learning: A type of ML where the model is trained on labeled data, meaning the input data is paired with the correct output.
  8. Unsupervised Learning: ML where the model is trained on unlabeled data and must find patterns and relationships in the data.
  9. Reinforcement Learning: A type of ML where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.
  10. Algorithm: A set of rules or instructions given to an AI system to help it learn on its own.
  11. Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.
  12. Bias: In AI, bias refers to systematic errors that can lead to unfair outcomes, often due to prejudiced assumptions in the data or algorithms.
  13. Training Data: The dataset used to train an AI model.
  14. Model: In AI, a model is a mathematical representation of a real-world process that is trained using data.
  15. Inference: The process of making predictions or decisions based on a trained AI model.


GROK

Here’s a beginner-friendly list of AI terminology that can help someone new to the concept understand the basics. I’ve kept the explanations simple and clear:

  1. Artificial Intelligence (AI) - When computers or machines are designed to think and act like humans, such as solving problems, making decisions, or learning from experience.
  2. Machine Learning (ML) - A way for AI to learn from data without being explicitly told what to do. It’s like teaching a computer to recognize patterns by showing it examples.
  3. Algorithm - A set of instructions or rules a computer follows to complete a task, like a recipe for your favorite dish.
  4. Data - The information (numbers, words, images, etc.) that AI uses to learn or make decisions. More data often means better results.
  5. Training - The process of feeding data into an AI system so it can learn how to do something, like identifying cats in photos.
  6. Model - The result of training—an AI system that’s ready to make predictions or decisions based on what it learned.
  7. Prediction - When an AI guesses what might happen or what something is, based on the patterns it’s seen in the data.
  8. Neural Network - A type of AI inspired by the human brain, made up of layers of “nodes” that work together to process information.
  9. Deep Learning - A more advanced form of machine learning using big neural networks to tackle complex tasks, like recognizing faces or translating languages.
  10. Natural Language Processing (NLP) - The part of AI that helps machines understand and respond to human language, like how I’m chatting with you now.
  11. Computer Vision - AI’s ability to “see” and understand images or videos, such as spotting objects or reading text in a picture.
  12. Chatbot - An AI program designed to have conversations with people, often used for customer service or answering questions.
  13. Supervised Learning - A type of machine learning where the AI is given labeled data (like “this is a dog” or “this is a cat”) to learn from.
  14. Unsupervised Learning - When AI figures out patterns in data on its own, without any labels or hints.
  15. Reinforcement Learning - A method where AI learns by trial and error, getting rewards for good choices and penalties for bad ones, like training a pet.
  16. Overfitting - When an AI learns the training data too well, including its flaws, and struggles with new, real-world data—like memorizing answers instead of understanding the subject.
  17. Bias - When an AI makes unfair or skewed decisions because of problems in its data or design, like favoring one group over another.
  18. Automation - Using AI or machines to do tasks that humans used to do, like sorting emails or driving cars.
  19. Generative AI - AI that can create new things, like writing stories, drawing pictures, or composing music.
  20. Ethics in AI - Thinking about what’s right or wrong in how AI is built and used, like ensuring it’s fair and doesn’t harm people.


Chat gpt was able to give me important definitions while also applying it to the real world and how it works. Gemini was able to tell me the importance of key parts whereas Grok and Copilot didn't give anything other than the list of terms I asked for. 

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