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Encyclopedia of Learning and Using AI

An AI generated image of aa tidal wave with a closed door in it.

Glossary

Concept

Artificial Intelligence (AI) is a field of computer science focused on creating systems that can perform tasks which typically require human intelligence.


AI systems are different from traditional computers because they don't just follow instructions; they learn from data. This gives them the ability to improve their performance autonomously, making them more adaptable and intelligent over time, a leap beyond the static operations of conventional computing.


In a day, or an hour, different AI systems can chat with you, analyze your emotions in social media, recognize pictures of your dog for your photo album, decide which route is the fastest for your morning commute, play chess with you on your phone, co-write your blog, and then when you get home, your smart grill can cooks you a perfect (for you) steak. This list is getting endless, but we can put them into categories, according to types of AI tech..


Common AI tasks, organized by their tech:


Understanding Natural Language (Chat)


Creative Generation (Text Generation): This could be seen as an extension of understanding natural language, where the AI not only understands but also generates new text in a meaningful way.


Emotion Recognition and Interaction: This involves understanding nuances in language and vocal tones, which can be viewed as a sophisticated form of natural language processing.


Recognizing Patterns in Data (Images)

Creative Generation (Image Generation): Generating images requires recognizing patterns in data and extrapolating from them to create something new, which falls under data pattern recognition.


Emotion Recognition and Interaction (Facial Expression Analysis): Recognizing emotions from facial expressions is a direct application of pattern recognition in visual data.


Making Decisions (Reasoning)


Autonomous Control Systems: These systems make real-time decisions based on sensor data and pre-defined logic, fitting into the decision-making category.


Personalization and Recommendation Systems: Deciding what content or products to recommend based on user data involves making decisions tailored to individual preferences.


Solving Problems Through Logic


Predictive Analytics: This is fundamentally about solving problems (e.g., forecasting future events or trends) through the application of statistical models and logic.


Augmented and Virtual Reality (AR/VR): Creating immersive experiences often involves solving complex spatial and interactive problems, which can be viewed as applications of logic to create simulated realities.


What is GPT?


GPT, short for Generative Pre-trained Transformer, is a type of artificial intelligence model designed to generate human-like text based on the input it receives. It's part of a broader category of models known as transformers, which are trained on a large corpus of text from the internet. GPT can write essays, answer questions, summarize documents, and even create content in various styles, making it versatile in applications ranging from writing assistance to conversational agents.


Let's Compare GPT to AI


GPT is a subset of AI, specifically within the realm of natural language processing and generation. While AI encompasses a wide range of technologies aimed at mimicking human intelligence, GPT focuses on understanding and producing human language based on pre-training on a vast dataset. Essentially, all GPT models are AI, but not all AI systems are GPTs; AI includes many other technologies like machine learning, robotics, and computer vision, which serve different purposes beyond text generation.

AI

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