Understanding Common AI Terms: A Comprehensive Guide
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Here are some key terms to help you grasp the fundamentals of artificial intelligence (AI): 1. AI: Systems that mimic human thinking. 2. Machine learning: Making predictions by analyzing data. 3. Artificial general intelligence: Achieving or surpassing human-level intelligence. 4. Generative AI: Using training data to create text, images, or code. 5. Hallucinations: Errors made by generative AI due to limited or biased data. 6. Bias: Prejudice in AI tools caused by the data they are trained on. 7. AI models: Trained systems capable of independently performing tasks or making decisions. 8. Large language models: Specialized in processing and generating natural language text. 9. Diffusion models: Creating images, audio, or video based on text prompts. 10. Foundation models: AI models extensively trained on diverse data for various purposes. 11. Frontier models: New AI models with enhanced capabilities. 12. Natural language processing: AI's ability to comprehend human language using machine learning. 13. Inference: The response produced by generative AI. 14. Tokens: Units of text analyzed and generated by AI models. 15. Neural networks: Enable machines to process data like the human brain. 16. Transformers: Neural networks that utilize attention mechanisms to process information. 17. RAG models: Employ external context for more accurate generation. 18. Hardware: High-performance hardware, such as Nvidia's H100 chips and neural processing units (NPUs), is essential for efficient AI inference. By familiarizing yourself with these terms, you will develop a better understanding of AI concepts and applications.Artificial intelligence (AI) is a popular topic in the tech industry, but the terminology can be confusing. Here is a summary of some common AI terms: 1. AI: The discipline of computer science dedicated to creating computer systems that can think like humans. 2. Machine learning: AI systems trained on data to make predictions and learn from new information. 3. Artificial general intelligence (AGI): AI that is as smart or smarter than humans. 4. Generative AI: Technology capable of generating new text, images, code, etc. 5. Hallucinations: When generative AI tools confidently make up answers due to their training data, resulting in errors or gibberish. 6. Bias: AI systems may demonstrate biases based on their training data. 7. AI model: Trained on data to perform tasks or make decisions. 8. Large language models (LLMs): AI models that process and generate natural language text. 9. Diffusion models: AI models used to generate images from text prompts. 10.
Foundation models: Generative AI models trained on vast amounts of data and used as a basis for various applications. 11. Frontier models: Unreleased future models that could be more powerful but come with potential risks. 12. Natural language processing (NLP): The ability of machines to understand human language. 13. Inference: When a generative AI application generates a response. 14. Tokens: Chunks of text used for analysis and generation by AI models. 15. Neural network: Computer architecture that helps process data using nodes. 16. Transformer: A type of neural network architecture that uses attention mechanisms to understand relationships in a sequence. 17. RAG (retrieval-augmented generation): AI models that can find and incorporate external context to improve accuracy. 18. Nvidia's H100 chip: A popular GPU used for AI training. 19. Neural processing units (NPUs): Dedicated processors in devices that perform AI inference. 20. TOPS (trillion operations per second): A measure used to showcase the AI capabilities of chips. These terms will help you better understand AI and its applications.
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Understanding Common AI Terms: A Comprehensive Guide
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