ARTIFICIAL INTELLIGENCE
History
● 1950s with Alan Turing's introduction of the Turing test and John McCarthy's coining of
the term "artificial intelligence."
○ Artificial intelligence simulates human intelligence through algorithms and data
to perform tasks that require human intelligence, like learning, problem-solving,
and decision-making. It can vary from simple automation to complex deep
learning.
● Progress was made with programs like ELIZA and SHRDLU in the '60s
● Expert systems in the '70s
● Machine learning in the '80s
● Neural networks in the '90s.
● From 2010-2020, AI applications, such as natural language processing (NLP) and computer
vision, spread across industries.
Types of AI
Broad
● Weak AI (Narrow AI): Designed to handle specific tasks (e.g., virtual assistants, language
translators).
● Strong AI (Generalized AI): AI capable of performing a wide range of tasks and learning
new skills autonomously.
● Super AI (Conscious AI): AI with human-level consciousness, capable of surpassing human
intelligence in various areas. This level of AI remains theoretical, as true consciousness in
machines is not yet achievable.
Specific
● Diagnostic/descriptive AI: Focuses on assessing the correctness of behavior by analyzing
historical data to understand what happened and why.
● Predictive AI: Concerned with forecasting future outcomes based on historical and
current data.
● Prescriptive AI: Focuses on determining the optimal course of action by providing
recommendations based on data analysis.
● Generative/cognitive AI: Involved in producing various types of content, such as code,
articles, images, and more.
● Reactive AI: Designed to respond to specific inputs with predetermined responses.
● Limited memory AI: Have the ability to use past experiences to inform current decisions.
● Theory of Mind AI: Advanced type of AI that aims to understand human emotions, beliefs,
and intentions.
● Self-aware AI: Represents the most advanced form of AI, which has its own
consciousness and self-awareness.
● Narrow AI (Weak AI): Designed to perform a specific task or a limited range of tasks.
● General AI (Strong AI): Can understand, learn, and apply knowledge across a wide range of
tasks like human intelligence.
, Human, Artificial vs Augmented Intelligence
1. Human Intelligence:
● Used for driving the car manually—turning the steering wheel, checking mirrors,
and making decisions.
● Humans excel at generalizing information, creativity, problem-solving, and
emotional intelligence (e.g., customer service, caregiving).
2. Artificial Intelligence (AI):
● The self-driving feature on the highway required no human input.
● AI performs tasks that normally require human intelligence, such as reasoning,
decision-making, and natural communication.
● AI is best for processing large amounts of data, performing repetitive tasks, and
ensuring high accuracy.
3. Augmented Intelligence:
● Used through driver-assist features like collision detection and blind-spot alerts,
which help the human driver rather than replacing them.
● Augmented intelligence combines human strengths with AI capabilities—for
example, screen readers for the blind or voice-driven navigation.
● It allows humans to make better decisions with AI assistance (best option).
Generative AI
What is it →
Type of AI technology that creates new content from scratch, including text, images, music, and
videos. It uses deep learning techniques and vast datasets to generate creative outputs.
Capabilities →
● Content Creation: Generates text, images, audio, and videos for diverse applications.
● Human-like Conversations: Enables interactive, natural conversations with AI.
● Data Augmentation: Can generate new training data to enhance machine learning
models.
● Boosting Productivity: According to Goldman Sachs, generative AI could boost the global
economy by 7% and increase productivity by 1.5% over ten years.
Specific uses →
● Marketing:
○ Personalizes advertisements, email campaigns, and social media posts based on
customer preferences.
● Creative Industries:
○ Generates unique digital art, music, video content, and soundtracks for films or
video games.
● Product Development:
○ Analyzes trends and feedback to optimize product designs and generate new
ideas.
● Healthcare:
○ Supports physicians with tailored treatments, simulates surgeries, and helps
create medical images for treatment planning.
● Gaming:
○ Generates interactive game worlds, new levels, characters, and objects based on
player behavior.
History
● 1950s with Alan Turing's introduction of the Turing test and John McCarthy's coining of
the term "artificial intelligence."
○ Artificial intelligence simulates human intelligence through algorithms and data
to perform tasks that require human intelligence, like learning, problem-solving,
and decision-making. It can vary from simple automation to complex deep
learning.
● Progress was made with programs like ELIZA and SHRDLU in the '60s
● Expert systems in the '70s
● Machine learning in the '80s
● Neural networks in the '90s.
● From 2010-2020, AI applications, such as natural language processing (NLP) and computer
vision, spread across industries.
Types of AI
Broad
● Weak AI (Narrow AI): Designed to handle specific tasks (e.g., virtual assistants, language
translators).
● Strong AI (Generalized AI): AI capable of performing a wide range of tasks and learning
new skills autonomously.
● Super AI (Conscious AI): AI with human-level consciousness, capable of surpassing human
intelligence in various areas. This level of AI remains theoretical, as true consciousness in
machines is not yet achievable.
Specific
● Diagnostic/descriptive AI: Focuses on assessing the correctness of behavior by analyzing
historical data to understand what happened and why.
● Predictive AI: Concerned with forecasting future outcomes based on historical and
current data.
● Prescriptive AI: Focuses on determining the optimal course of action by providing
recommendations based on data analysis.
● Generative/cognitive AI: Involved in producing various types of content, such as code,
articles, images, and more.
● Reactive AI: Designed to respond to specific inputs with predetermined responses.
● Limited memory AI: Have the ability to use past experiences to inform current decisions.
● Theory of Mind AI: Advanced type of AI that aims to understand human emotions, beliefs,
and intentions.
● Self-aware AI: Represents the most advanced form of AI, which has its own
consciousness and self-awareness.
● Narrow AI (Weak AI): Designed to perform a specific task or a limited range of tasks.
● General AI (Strong AI): Can understand, learn, and apply knowledge across a wide range of
tasks like human intelligence.
, Human, Artificial vs Augmented Intelligence
1. Human Intelligence:
● Used for driving the car manually—turning the steering wheel, checking mirrors,
and making decisions.
● Humans excel at generalizing information, creativity, problem-solving, and
emotional intelligence (e.g., customer service, caregiving).
2. Artificial Intelligence (AI):
● The self-driving feature on the highway required no human input.
● AI performs tasks that normally require human intelligence, such as reasoning,
decision-making, and natural communication.
● AI is best for processing large amounts of data, performing repetitive tasks, and
ensuring high accuracy.
3. Augmented Intelligence:
● Used through driver-assist features like collision detection and blind-spot alerts,
which help the human driver rather than replacing them.
● Augmented intelligence combines human strengths with AI capabilities—for
example, screen readers for the blind or voice-driven navigation.
● It allows humans to make better decisions with AI assistance (best option).
Generative AI
What is it →
Type of AI technology that creates new content from scratch, including text, images, music, and
videos. It uses deep learning techniques and vast datasets to generate creative outputs.
Capabilities →
● Content Creation: Generates text, images, audio, and videos for diverse applications.
● Human-like Conversations: Enables interactive, natural conversations with AI.
● Data Augmentation: Can generate new training data to enhance machine learning
models.
● Boosting Productivity: According to Goldman Sachs, generative AI could boost the global
economy by 7% and increase productivity by 1.5% over ten years.
Specific uses →
● Marketing:
○ Personalizes advertisements, email campaigns, and social media posts based on
customer preferences.
● Creative Industries:
○ Generates unique digital art, music, video content, and soundtracks for films or
video games.
● Product Development:
○ Analyzes trends and feedback to optimize product designs and generate new
ideas.
● Healthcare:
○ Supports physicians with tailored treatments, simulates surgeries, and helps
create medical images for treatment planning.
● Gaming:
○ Generates interactive game worlds, new levels, characters, and objects based on
player behavior.
