Type: Predictive AI vs Generative AI - Deepstash

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Type: Predictive AI vs Generative AI

Type: Predictive AI vs Generative AI

Predictive AI analyzes existing data to make predictions or decisions, while Generative AI creates new data instances, such as images or text. Predictive AI predicts future outcomes based on historical data, while Generative AI generates new content, like images or music, based on learned patterns.

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Machine Learning

Machine Learning

Machine learning is a subset of AI that enables computers to learn from data without being explicitly programmed. It involves algorithms that improve automatically through experience, allowing computers to recognize patterns and make decisions without human intervention.

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What is Artificial Intelligence

What is Artificial Intelligence

AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.

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Predictive AI examples

Predictive AI examples

Predictive AI examples include recommendation systems (like those used by Netflix or Amazon), fraud detection algorithms in banking, and predictive maintenance in manufacturing.

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Generative AI examples

Generative AI examples

Generative AI examples in use include ChatGPT, Gemini like tools that help in creating text and DALL-E and Sora by OpenAI, which creates images and videos from textual descriptions respectively.

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Fields of AI

Machine Learning, Deep learning, Natural Language Processing, Computer Vision

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CHATGPT GENERATED

"AI won't replace you rather it will empower you. Embrace its potential to augment your capabilities, streamline tasks, and unlock new opportunities for creativity and innovation."

CHATGPT GENERATED

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Computer Vision

Computer Vision

Computer vision enables computers to interpret and understand visual information from images or videos. It's used in applications like facial recognition, object detection, and autonomous vehicles to process and analyze visual data.

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Challenges in AI

Challenges in AI

Challenges in AI today include bias in algorithms, data privacy concerns, and the ethical implications of AI technologies. Additionally, the complexity and resource-intensive nature of AI algorithms pose challenges for implementation and scalability.

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Deep Learning

Deep Learning

Deep learning is a subfield of machine learning that uses artificial neural networks with multiple layers to learn representations of data. It's particularly effective for tasks like image and speech recognition, where large amounts of data are available

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Natural language processing

Natural language processing

Natural language processing (NLP) enables computers to understand, interpret, and generate human language. It's used in applications like language translation, sentiment analysis, and chatbots to analyze and generate text-based data.

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CURATED FROM

CURATED BY

rajaathota72

Serial Entrepreneur | AI ML and Blockchain

Must know AI terminology and concepts

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