Introduction to Emerging Technologies: Artificial Intelligence Maturity and Applications
Levels of Artificial Intelligence
Artificial Intelligence development is categorized into several distinct stages, reflecting its progression from simple rule-based automation to hypothetical transcendence.
Stage 1 – Rule-Based Systems: This is the most prevalent form of AI today. It encompasses everything from business software such as Robotic Process Automation (RPA) and domestic appliances to aircraft autopilots.
Stage 2 – Context Awareness and Retention: These algorithms develop information specific to the domain in which they are applied. They are trained using the knowledge and experience of the best human experts, and their knowledge base is updated as new situations and queries arise.
Stage 3 – Domain-Specific Expertise: These systems exceed human capability in specific contexts. They build expertise by processing massive volumes of information, which they utilize for complex decision-making.
Stage 4 – Reasoning Machines: These algorithms possess the ability to attribute mental states to themselves and others. They have a sense of beliefs, intentions, and knowledge, and they understand how their own logic works. Consequently, they can reason or negotiate with humans and other machines.
Stage 5 – Self-Aware Systems / Artificial General Intelligence (AGI): These systems possess human-like intelligence. While commonly portrayed in media, there is currently no evidence of such technology in use today.
Stage 6 – Artificial Super intelligence (ASI): It is logically difficult for humans to articulate the exact capabilities of ASI. However, it is hoped that such systems would solve problems humans have failed to address, specifically world hunger and dangerous environmental changes.
Stage 7 – Singularity and Transcendence: This stage involves the idea that development provided by ASI leads to a massive expansion in human capability. Human augmentation could connect brains to one another and to a future successor of the current internet.
The Seven Layers of AI Maturity
According to Figure 3.4, the maturity of AI can be viewed through seven layers, framed by specific questions and goals:
Layer 1: Perception: Focuses on "What's happening now?"
Layer 2: Notification: Addresses "What do I need to know?"
Layer 3: Suggestion: Asks "What do you recommend?"
Layer 4: Automation: Determines "What should I always do?"
Layer 5: Prediction: Asks "What can I expect to happen?"
Layer 6: Prevention: Asks "What can I avoid?"
Layer 7: Situational Awareness: Asks "What do I need to do right now?"
Continuous Learning: This process underlies the progression through these layers to achieve higher maturity.
Categorization of AI Based on Capabilities
AI is primarily categorized based on its capabilities into three major types:
Weak AI or Narrow AI: * Designed to perform a dedicated task with intelligence. * The most common and currently available form of AI. * It cannot perform beyond its specific field or limitations. * It may fail unexpectedly if pushed beyond its predefined limits. * Examples: * Apple Siri: Operates within a limited, pre-defined range of functions. * IBM's Watson: Uses an Expert system approach combined with Machine Learning and Natural Language Processing (NLP). * Others: Google Translate, playing chess, e-commerce purchasing suggestions, self-driving cars, speech recognition, and image recognition.
General AI: * Intelligence capable of performing any intellectual task with the same efficiency as a human. * The goal is to create systems that are smarter and can think like humans independently.
Super AI: * A level of system intelligence that surpasses human intelligence. * Capable of performing any task better than a human, possessing cognitive properties.
Categorization of AI Based on Functionality
AI is also classified by how it functions and interacts with information:
Reactive Machines: * These systems do not store memories or past experiences for future actions. * They focus exclusively on current scenarios and react with the best possible action for that specific moment. * Examples: IBM's Deep Blue and Google's AlphaGo.
Limited Memory Machines: * Capable of storing past experiences or data for a short period. * This stored data is only used for a limited timeframe. * Example: Self-driving cars. These vehicles store the recent speed of nearby cars, distance to other cars, speed limits, and other navigational data.
Theory of Mind: * A type of AI intended to understand human emotions, people, and beliefs. * It should be able to interact socially in a human-like manner. This technology has not yet been developed.
Self-Awareness: * Hypothetical machines that possess their own consciousness, sentiments, and self-awareness. * These would be smarter than the human mind. This currently does not exist.
Human Thinking Process vs. AI Components
Human intelligence and cognitive processes consist of three main stages, which can be mapped directly to AI layers:
Observation and Input: Humans gather information through senses (sight, hearing, smell, taste, touch). In AI, this is the Sensing Layer.
Interpretation and Evaluation: Humans evaluate input from the environment. In AI, this is the Interpretation Layer, which involves reasoning and thinking about gathered input.
Decision and Reaction: Humans make decisions based on evaluation. In AI, this is the Interacting Layer, which performs necessary tasks.
Main Influencers of Artificial Intelligence
The advancement of AI is driven by four primary factors:
Big Data: The availability and processing of both structured and unstructured data.
Hardware Advancements: Improvements in computer processing speed and the development of new chip architectures.
Cloud Computing and APIs: Infrastructure that allows for distributed processing and integration.
Data Science: The emergence of data science as a discipline to extract insights from data.
Applications of AI Across Sectors
AI is utilized in a wide variety of domains to enhance efficiency and capability:
Agriculture: Includes agricultural robotics, soil and crop monitoring, and predictive analysis.
Healthcare: Used to make faster and more accurate diagnoses than humans.
Education: Automates grading to free up tutor time and uses chatbots as teaching assistants.
Finance and E-commerce: Implements automation, chatbots, adaptive intelligence, algorithmic trading, and machine learning.
Gaming: Strategic thinking in games like chess where a machine evaluates numerous possible plays.
Data Security: Enhances safety via tools like the AEG bot and AI2 Platform to detect software bugs and cyber-attacks.
Social Media: Efficiently manages and stores billions of user profiles (e.g., Facebook, Twitter, Snapchat).
Travel and Transport: Manages travel arrangements and suggests hotels, flights, and optimal routes.
Automotive Industry: Provides virtual assistants for better driver performance.
Robotics: Creation of intelligent robots that learn from experience rather than just pre-programmed instructions.
Entertainment: Powering recommendation services for platforms such as Netflix or Amazon.
AI Tools, Platforms, and Simple Applications
AI platforms consist of hardware architectures or software frameworks that simulate human cognitive functions such as problem-solving and social intelligence.
Natural Language Processing (NLP) Applications: * Information retrieval and text mining. * Question answering and machine translation. * Voice recognition and predictive recommendation systems.
Computer Science Tools Created by AI: * Search and optimization. * Logic and probabilistic methods for uncertain reasoning. * Classifiers, statistical learning methods, and Neural networks. * Control theory and specialized languages.
Daily Consumer Applications: * Pinterest: Uses computer vision to identify objects in images and recommend visually similar "pins." * Instagram: Uses machine learning to identify the contextual meaning of emojis. * Snapchat: Employs filters that track facial movements to add animated effects or masks. * Voice-to-Text: A standard smartphone feature now accurate enough for basic conversation. * Smart Personal Assistants: * Siri and Google Now: Precursors to modern assistants, performing internet searches and calendar integration. * Google Assistant: A more sophisticated successor to Google Now. * Alexa: Accepts voice commands for to-do lists, online ordering, and reminders.