The document provides an overview of artificial intelligence (AI), defining it as systems capable of tasks requiring human-like intelligence, and detailing its characteristics, subfields, and key components such as machine learning and natural language processing. It traces the history of AI from its inception in the 1950s through periods of decline and resurgence, highlighting significant developments and applications across various industries including healthcare, finance, and transportation. Additionally, it explains the Turing Test, designed to determine if a machine can mimic human behavior indistinguishably.

2. Artificial Intelligence (AI)
Title: Artificial Intelligence: Introduction, History, Turing Test,
Applications, and Future Trends
Presented by: Miss Asma Abubakar

3. What is Artificial Intelligence (AI)
•Definition:
AI refers to the development of computer systems that can
perform tasks typically requiring human intelligence, such as
understanding language, recognizing patterns, solving problems
and making decisions.

4. Key Characteristics:
Adaptability: AI systems learn from experience and adapt to new situations.
Automation: Ability to automate complex processes.
Autonomy: AI can operate independently with minimal human intervention.
Subfields of AI:
Artificial Narrow Intelligence (ANI): Specialized in one area.
Artificial General Intelligence (AGI): General human-like intelligence.
Artificial Superintelligence (ASI): Surpasses human intelligence.
Subfields of AI:
Artificial Narrow Intelligence (ANI): Specialized in one area.
Artificial General Intelligence (AGI): General human-like intelligence.
Artificial Superintelligence (ASI): Surpasses human intelligence.

5. Key Components of AI
Machine Learning (ML):
Supervised Learning: Learning with labeled data (e.g., classification, regression).
Unsupervised Learning: Learning without labeled data (e.g., clustering, dimensionality reduction)
Reinforcement Learning: Learning by trial and error (e.g., game AI).
Deep Learning:
Neural Networks: Layers of interconnected nodes (neurons) inspired by the human brain.
Convolutional Neural Networks (CNNs): Used for image processing.
Recurrent Neural Networks (RNNs): Used for sequential data (e.g., language models).
Natural Language Processing (NLP):
Text Analysis: Sentiment analysis, language translation.
Speech Recognition: Converting spoken language to text.
Language Generation: Creating human-like text (e.g., GPT models).
Computer Vision:
Image Recognition: Identifying objects in images (e.g., face recognition).
Object Detection: Locating objects in images.
Image Generation: Creating realistic images (e.g., GANs).
Robotics:
Autonomous Navigation: Self-driving cars, drones.
Manipulation: Robot arms in manufacturing.
Human-Robot Interaction: Social robots, AI assistants.

6. History of AI
Early Foundations (1940s - 1950s):
AI began with pioneers like Alan Turing, who proposed the idea of machines simulating human
intelligence. The 1956 Dartmouth Conference marked AI’s formal inception as a field of study.
Symbolic AI (1950s - 1970s):
Focused on rule-based systems (e.g., Logic Theorist). However, AI faced limitations in handling
real-world complexity, leading to the first "AI Winter."
AI Winter (1970s - 1980s):
Due to unmet expectations and technical challenges, interest and funding in AI research declined,
slowing progress.
Machine Learning Era (1980s - 1990s):
AI shifted towards data-driven approaches, with neural networks and machine learning gaining
prominence. IBM’s Deep Blue defeated Garry Kasparov in chess (1997).
AI Renaissance (2000s - Present):
With advancements in computing power and data, AI has seen a resurgence, driven by deep learning
breakthroughs (e.g., AlphaGo’s victory in Go, 2016).

7. Applications of AI in Different Domains
1. Healthcare
Medical Diagnostics: AI analyzes medical images to detect diseases like cancer (e.g.,
DeepMind’s retinal scan analysis).
Drug Discovery: Accelerates identification of new drugs (e.g., AI for COVID-19 treatments).
Personalized Medicine: Tailors treatments based on genetic data (e.g., IBM Watson for
Oncology).
2. Finance
Fraud Detection: AI monitors transactions to prevent fraud (e.g., PayPal’s fraud detection
system).
Algorithmic Trading: Executes trades at high speeds using AI (e.g., Renaissance
Technologies).
Credit Scoring: Assesses creditworthiness with AI (e.g., ZestFinance).
3. Manufacturing
Predictive Maintenance: AI predicts equipment failures (e.g., Siemens’ predictive analytics).
Automation: Robots automate manufacturing tasks (e.g., Tesla’s production line).
Quality Control: AI inspects products for defects (e.g., AI visual inspection systems).

9. 4. Transportation
Autonomous Vehicles: Self-driving cars navigate with AI (e.g., Waymo).
Traffic Management: AI optimizes traffic flow (e.g., AI-driven traffic lights in Pittsburgh).
Supply Chain Optimization: AI improves logistics and inventory management (e.g.,
Amazon’s warehouse operations)
5. Education
Personalized Learning: AI adapts educational content to students’ needs (e.g., Khan
Academy).
Automated Grading: AI grades assignments and provides feedback (e.g., EdTech
platforms).
Virtual Tutors: AI tutors assist with learning (e.g., Duolingo).

10. Turing Test
The Turing Test is a concept in artificial intelligence (AI) proposed by the British
mathematician and computer scientist Alan Turing in 1950. It is designed to assess a
machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from,
that of a human.
Concept:
The Turing Test evaluates whether a machine can mimic human responses so
convincingly that a human evaluator cannot reliably distinguish between the machine and
a human. If the evaluator cannot tell which is the machine, the AI is said to have passed
the test.
How it Works:
The test involves a human judge interacting with both a human and a machine through a
computer interface (usually via text communication, to avoid giving away non-verbal
clues). The judge's task is to determine which respondent is the human and which is the
machine.