Artificial Intelligence

Core Concepts

Core Concepts

1. What is Intelligence in AI? Intelligence in the context of AI can be broken down into several capabilities:

• Reasoning: The ability to solve problems through logical deduction.
• Knowledge Representation: Storing what it knows or hears.
• Planning: The ability to set and achieve goals.
• Learning: Adapting to new circumstances and detecting patterns (this is the domain of Machine Learning).
• Natural Language Processing (NLP): Understanding and generating human language.
• Perception: The ability to take in and interpret sensory data (e.g., from cameras and microphones, known as Computer Vision and Speech Recognition).

2. The Spectrum of AI

• Artificial Narrow Intelligence (ANI) or Weak AI: This is the only type of AI we have achieved so far. It is designed and trained for a particular task, such as playing chess, filtering spam, or voice recognition. ANI can be extremely powerful at its specific task but cannot operate outside of it.
• Artificial General Intelligence (AGI) or Strong AI: A type of AI that possesses the ability to understand, learn, and apply its intelligence to solve any problem that a human being can. AGI is the goal of many AI researchers and remains hypothetical.
• Artificial Superintelligence (ASI): A hypothetical form of AI that would surpass the intelligence and ability of the brightest human minds.

3. Major Subfields of AI

• Machine Learning (ML): The study of algorithms that allow computers to learn from data. (See the Machine Learning topic for more detail).
  • Deep Learning: A subfield of ML based on artificial neural networks with many layers (deep neural networks). It has led to breakthroughs in computer vision and NLP.
• Natural Language Processing (NLP): Enables computers to understand, interpret, and generate human language. Powers chatbots, translation services, and sentiment analysis.
• Computer Vision: Gives computers the ability to "see" and interpret visual information from images and videos. Used in self-driving cars, facial recognition, and medical imaging analysis.
• Robotics: Integrates AI with physical robots, enabling them to perceive their environment and perform tasks autonomously.
• Search and Planning: The study of algorithms for finding the optimal sequence of actions to reach a goal. This is fundamental to logistics, gaming AI, and robotics.

4. The AI Development Process While varying by subfield, a typical AI project (especially ML-based) involves:

1. Problem Definition: Clearly defining the problem to be solved and the success criteria.
2. Data Acquisition and Preparation: Gathering and cleaning the vast amounts of data needed to train the system.
3. Model Training and Experimentation: Choosing algorithms and training models.
4. Evaluation: Rigorously testing the model's performance.
5. Deployment and Monitoring: Integrating the AI into a real system and continuously monitoring its performance.

Examples & Use Cases

Examples & Use Cases

Natural Language Processing (NLP)

• Service: Google Translate
• How it works: Uses a massive deep learning model (a Transformer model) trained on billions of sentences from the web to learn the patterns of language translation.

Computer Vision

• Service: A self-driving car's perception system.
• How it works: Uses deep learning models (like Convolutional Neural Networks - CNNs) to analyze real-time video from cameras to identify pedestrians, other vehicles, traffic lights, and lane lines.

Reinforcement Learning

• Service: AlphaGo by DeepMind.
• How it works: The AI learned to play the game of Go by playing millions of games against itself, receiving a "reward" for winning and a "penalty" for losing, allowing it to discover strategies far beyond human capability.

Practice Questions

Practice Questions

1. MCQ: Which subfield of AI is most concerned with enabling computers to understand and interpret images and videos? A) Natural Language Processing B) Robotics C) Computer Vision D) Machine Learning

2. Short Answer: What is the difference between Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI)? Which type do we have today?

3. Scenario: You want to build an AI system that can read customer reviews for a product and automatically classify them as "positive", "negative", or "neutral". Which major subfield of AI would be most relevant for this task?

Quick Summary

Quick Summary

• Artificial Intelligence (AI) is a broad field focused on creating systems that can perform tasks requiring human-like intelligence.
• AI can be categorized as Narrow (ANI), which exists today, and General (AGI), which is still hypothetical.
• Major subfields include Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, and Robotics.
• Deep Learning, a subset of ML using deep neural networks, has driven many of the recent breakthroughs in AI.
• Developing AI systems is an iterative process of defining a problem, gathering data, training models, and evaluating performance.