AI Concepts

Artificial Intelligence (AI)

• Core Concept: The development of computer systems capable of tasks that typically require human intelligence, such as perception, reasoning, learning, and decision-making.

Key Branches of AI

• Machine Learning (ML)

  • Core Concept: Algorithms that enable systems to improve their performance through experience (i.e., data), without being explicitly programmed.

  • Types:

    • Supervised Learning: Learning from labeled examples to make predictions (e.g., image classification).

    • Unsupervised Learning: Finding hidden structures in unlabeled data (e.g., customer segmentation).

    • Reinforcement Learning: Learning through trial-and-error interactions with an environment (e.g., game-playing AI or robotic control).

• Deep Learning

  • Core Concept: A subset of ML using multi-layered artificial neural networks inspired by the structure of the brain.

  • Strengths:

    • Excellent performance on complex, unstructured data (images, text, audio).

    • Automatic feature extraction reduces the need for human-designed features.

    • Drives breakthroughs in computer vision, natural language processing, and other domains.

• Computer Vision (CV)

  • Core Concept: Giving computers the ability to interpret and understand visual information from the world.

  • Techniques:

    • Image Classification: Assigning labels or categories to images.

    • Object Detection: Identifying and locating specific objects within images.

    • Image Segmentation: Dividing images into meaningful regions (e.g., separating foreground from background).

    • Facial Recognition: Identifying or verifying individuals from images or videos.

• Natural Language Processing (NLP)

  • Core Concept: Enabling computers to understand, interpret, and generate human language (text and speech).

  • Techniques:

    • Text Classification: Categorizing text (e.g., sentiment analysis, topic modeling).

    • Machine Translation: Automated translation between languages.

    • Question Answering: Systems that can answer questions posed in natural language.

    • Text Summarization: Condensing long pieces of text into shorter summaries.

    • Chatbots/Conversational AI: Systems that can engage in dialogue with humans.

• Robotics

  • Core Concept: Design, control, and application of robots. AI transforms robotics in several ways:

    • Perception: Computer vision and sensor fusion help robots understand their environment in real-time.

    • Decision-Making: AI enables autonomous decisions, adapting to changing circumstances.

    • Control: Advanced control algorithms powered by AI allow for smoother, more efficient movement and adaptation to unanticipated forces.

• Generative AI

  • Core Concept: AI models that can create new content, including text, images, code, music, 3D models, and more.

  • Key Difference: Generative AI goes beyond analyzing existing data; it learns patterns to produce novel and creative outputs.

• Explainable AI (XAI)

  • Core Concept: Developing techniques to understand the reasoning and decision-making processes of AI models, especially complex ones like deep neural networks.

  • Importance: XAI is crucial for building trust in AI systems, especially in high-stakes domains like healthcare, finance, and law.

When Gemini Advanced was asked about itself, it said it falls into several categories of AI:

• Natural Language Processing (NLP): “At my core, I'm designed to understand and generate human language. I use NLP techniques to process your questions, break them down into meaningful parts, and formulate a response that is both relevant and easy to understand.”

• Generative AI: “I can create new content in the form of text. I can continue conversations, write different kinds of creative text formats, and even try my hand at writing different kinds of code. This generative ability draws from my training on a massive dataset of text and code.”

• Machine Learning (broadly speaking): “I'm built using machine learning techniques. My ability to process language and generate responses improves and adapts based on the feedback I receive and the additional data I'm exposed to. I specifically utilize deep learning models, with neural networks that allow me to learn complex patterns from massive amounts of data.”