AI for everyone

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

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.

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

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.

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

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.

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.

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.