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What is AI?
AI refers to systems that can perform tasks that require human intelligence.
AI vs. HI (Human Intelligence)
AI aims to replicate behaviors associated with human intelligence, such as planning,
learning, problem-solving, knowledge representation, perception, motion, reasoning,
social intelligence, and creativity.
3. Thinking humanly: Cognitive Science
● Cognitive Science explores how humans think and aims to understand the internal
activities of the brain through scientific theories.
● Approaches include introspection, psychological experiments, observation, and brain
imaging.
4. Acting humanly: The Turing test
● The Turing test, proposed by Alan Turing, evaluates whether a machine can exhibit
intelligent behavior indistinguishable from that of a human.
● The test involves conducting an imitation game to determine if a machine can fool a
person into thinking they are interacting with a human.
5. The Turing test and system capabilities
● System capabilities for passing the Turing test include natural language processing,
knowledge representation, automated reasoning, and machine learning.
6. Achieving ethics through rational behavior
● Ethics can be achieved by utilizing logic and reasoning in one's actions.
7. Acting rationally
● Rational behavior involves doing the right thing to maximize goal achievement based on
available information.
● Rational action does not always involve thinking but should serve the purpose of acting
ethically.
8. Rational agents
● An agent is an entity that perceives and acts based on formal rules.
● Rational agents aim for the best performance in a given environment and task.
● Perfect rationality is limited by computational constraints.
9. AI prehistory
● Various disciplines have contributed to the development of AI, including philosophy,
mathematics, psychology, economics, linguistics, neuroscience, and control theory.
10. Potted History of AI
● Highlights key milestones in the development of AI, including the Boolean circuit model
of the brain, Turing's work on machine intelligence, expert systems, neural networks, and
the resurgence of human-level AI.
11. Reinforcement machine learning
● Reinforcement machine learning involves replicating human learning patterns of rewards
and punishments to train AI systems.
12. Steps towards creating artificial intelligence
● Know the domain and the problem you are solving for.
● Study the data through data mining.
● Cleanse and normalize data, develop tools.
● Choose a model and compare different models.
● Shortlist the optimal models and pick the best one.
● Train, fine-tune, and test the model.
● Monitor errors, record learning, and assess positive impact.
13. Unintentionally funny stories from AI
● AI-generated stories that exhibit unintentional humor due to limitations in understanding
context and generating coherent narratives.
14. Definitions:
● Intelligence: The ability to acquire and apply knowledge and skills.
● Artificial Intelligence: Any task performed by a program or machine that requires
intelligence if a human were to carry out the same activity.
● Agent: An entity that perceives and acts based on rules or programming.
15. Types of AI:
● Reactive Machines: AI systems that react based on present actions and cannot use
previous experiences to form decisions.
● Limited Memory AI: AI systems that can store and use past experiences to make current
decisions.
● Theory of Mind AI: AI systems with advanced understanding of emotions, people, and
the real world.
● Self-Aware AI: AI systems that possess human-like consciousness and self-driven
actions.
16. AI classifications:
● Artificial Narrow Intelligence (ANI): General-purpose AI used in virtual assistants like
Siri.
● Artificial General Intelligence (AGI): Strong AI with human-like intelligence and
capabilities.
17. State of the art in AI
18. The internet offers numerous advantages but also presents security risks.
19. Personal information is vulnerable when accessing online services like banking.
20. Banks take measures to protect transmitted and processed data.
21. The internet provides opportunities but also exposes users to serious threats.
1. What Is the Internet?
● The internet is a network of networks, connecting computers worldwide.
● It facilitates email, bulletin boards, file archives, hypertext documents, etc.
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TCP/IP protocols and packet switching enable data transport.
It spans from local networks to global connectivity.
Brief History of the Internet:
In 1968, DARPA contracted with BBN to create ARPAnet.
The first five nodes were UCLA, Stanford, UC Santa Barbara, U of Utah, and BBN.
In 1974, Vint Cerf specified TCP, and by 1984, the internet had 1000 hosts.
The Creation of the Internet:
The internet solved challenges in digital networking, infrastructure, and messaging
reliability.
4. Tribute to the Internet Pioneers:
● The internet's development required the contributions of many individuals.
● Their technologies and standards made today's internet and World Wide Web possible.
5. Internet Security: What it is:
● Internet security encompasses browser security, data protection, and authentication.
● Securing computers is essential, just like securing physical office spaces.
● Users must protect themselves from risks and threats associated with internet use.
6. Why do you need Internet Security?
● Malicious programs can record online activities and compromise personal information.
● Criminals can steal private information, money, or hijack computers for illegal activities.
7. What is Hacking?
● Hacking involves penetrating a system with permission to secure it.
● Hackers are skilled individuals exploring system details, sometimes with illegal
intentions.
8. What is Cracking?
● Cracking involves unauthorized penetration of a system for fun or malicious purposes.
● Crackers lack ethics and engage in harmful activities.
9. Difference between Hacking and Cracking:
● Hacking is legal and aims to improve technology, while cracking is illegal and malicious.
10. Methods of Hacking:
● Hacking can occur over the internet, LAN, locally, offline, or through theft, IP addresses,
telephone, or email.
11. Types of Hackers:
● Black Hat Hackers are malicious and violate computer security.
● White Hat Hackers are ethical hackers working to improve security.
● Grey Hackers offer their services to improve systems for a fee.
● Blue Hat Hackers perform system tests before launch.
● Hacktivists utilize technology for political or social causes.
● Script Kiddies lack technical knowledge and use others' tools for hacking.
● Elite Hackers are highly skilled and deceptive.
● Bots are software tools used by hackers.
12. Ethical Hacking:
● Ethical hacking helps fight cyberterrorism, prevents unauthorized access, and identifies
system vulnerabilities.
● Advantages include improved security, preventive actions, and protection of financial
settlements.
● Disadvantages include potential misuse of data and theft of sensitive information.
13. Phases of Hacking:
● Hacking involves reconnaissance, scanning, gaining access, maintaining access, and
covering tracks.
14. Types of Attacks:
● SQL Injection: Exploits vulnerabilities to execute malicious SQL commands.
● DDOS Attacks: Overwhelm a server with traffic, making it inaccessible to genuine users.
● Social Engineering: Manipulating people to divulge confidential information.
● Malware and Viruses: Malicious software that steals data or disrupts operations.
15. Preventing Attacks:
16. Introduction: How is Technology Changing the Workplace and Society?
● Technology's influence on the workplace and society
● The changing nature of work due to technological advancements
2. Uses of Computers:
● Various applications of computers in different fields:
● Medical
● Business
● Manufacturing
● Home/personal
● Entertainment
● Government
● Education
● Sales/marketing, etc.
3. Computers and Today's Lifestyle:
● The pervasive presence of computers in modern life
● The ease with which children adapt to technology
● Challenges faced by older generations in using computers
4. Example: Uses of Technology in Business, Medical, Home, Institutions, etc.
● The impact of technology in various sectors
● Benefits of technology in improving efficiency and reducing costs in business operations
5. Impact of Computers on Our Society:
● Overview of the impacts of computers on different levels:
● Personal impact
● Community impact
● National impact
● Global impact
● Negative impact
6. Impact of Computers on Our Society: Personal Impact
● Effects of computers on individuals:
● Privacy and personal rights
● Data security
● Employment and job opportunities
● Business transactions
● Consumer spending
● Automation and job replacement
● Human-machine interaction and ergonomics
7. Impact of Computers on Our Society: Community Impact
● Influence of computers on communities:
● Employment
● Traffic control
● Urban planning
● Law enforcement
● Entertainment industry
● Medical facilities
● Local business operations
8. Impact of Computers on Our Society: National Impact
● Impact of computers on a national scale:
● Communications media
● Information control
● Voting and census
● Electronic funds transfer
● Stock market transactions
● Defense and surveillance
● National data banks
9. Impact of Computers on Our Society (Continued)
● Additional areas of impact:
● Telecommunications
● Satellite broadcasting
● Fraud detection and prevention
● Standards for computer hardware and software
10. Impact of Computers on Our Society: Global Impact
● Computers' influence on a global level:
● Reporting of current events
● Communications media
● World government policies
● International standards
● Space and sea exploration
● Worldwide access to data
11. Impact of Computers on Our Society: Future Impact
● Anticipated impacts of computers on various aspects of life:
● Technology at home and new devices
● Gaming
● Learning
● Electronic mail
● Shopping
● Business transactions
● Information processing and storage
● Home as a work center
● Effects on the family unit and familiar patterns of life
● Travel
12. Impact of Computers on Our Society: Future Impact (Continued)
● Further future impacts of computers:
● Computer communication as a replacement for travel
● Potential disappearance of hard copy as a medium of communication
● Cashless society
● Effects on the formal education system
● Use of robots in industry and home
● Communications networks
13. Impact of Computers on Our Society: Negative Impact
● Negative consequences associated with computers and technology:
● Vision problems
● Physical health issues
● Exposure to radiation
● Addiction
● Computer crime
● Data security
● Unemployment
● Time wastage
● Protecting and Enhancing Our Humanity in an Age of Machine Learning
● Overview of the course and its relevance to society.
2. What is Machine Learning?
● Definition of machine learning as the study of algorithms that improve performance at a
task with experience.
● Components of machine learning: performance (P), task (T), and experience (E).
● Examples of machine learning applications, such as Snapchat filters and personalized
recommendations on Netflix.
3. Machine Learning in the 2010s
● Overview of machine learning advancements in the 2010s, including deep learning,
learning for big data, Bayesian methods, and more.
● Applications of machine learning in various domains, such as vision, speech, social
networks, and genomics.
4. Traditional Programming vs. Machine Learning
● Comparison between traditional programming and machine learning approaches.
● Explanation of the input-data-program-output flow in both paradigms.
5. When Do We Use Machine Learning?
● Scenarios where machine learning is beneficial, including cases where human expertise is
limited, models need customization, or large amounts of data are involved.
● Examples of tasks that are best solved using machine learning algorithms.
6. Classic Examples of Machine Learning Tasks
● Illustration of tasks that are challenging to solve without machine learning, such as
recognizing patterns, generating patterns, recognizing anomalies, and making predictions.
7. Machine Learning Algorithms Used by Companies
● Examples of machine learning algorithms used by companies like Uber for real-time
comparison data and predictive modeling.
● Application of these algorithms in analyzing demand, availability, weather conditions,
and traffic patterns.
Part II: Ethics in Machine Learning and Data Science
8. Introduction to Ethics in Machine Learning and Data Science
● Discussion on the ethical considerations and societal impact of machine learning and data
science.
● Topics include fairness, accountability, transparency, privacy, bias, and the responsible
use of AI.
9. Sample Application Domains
● Overview of various domains where machine learning and data science find applications,
such as web search, computational biology, finance, e-commerce, space exploration, and
more.
● Explanation of how machine learning contributes to advancements in these areas.
10. Machine Learning in Games: Example of Checkers
● Historical perspective on machine learning in games, referencing Arthur Samuel's
definition of machine learning.
● Application of machine learning in playing checkers.
11. Popular Machine Learning Algorithms
● Brief introduction to popular machine learning algorithms, including decision trees,
neural networks, probabilistic networks, nearest neighbor, and support vector machines.
12. Defining the Learning Task
● Explanation of how a learning task is defined based on the improvement in performance
(P), the task itself (T), and the experience (E).
● Examples of learning tasks in various domains.
13. State-of-the-Art Applications of Machine Learning
● Discussion on cutting-edge applications of machine learning, such as autonomous cars,
deep belief nets for face recognition, speech recognition, and more.
14. Machine Learning and Automatic Speech Recognition
● Overview of machine learning techniques used in automatic speech recognition systems.
● Impact of deep learning on speech technology.
15. Types of Learning
● Explanation of supervised learning, unsupervised learning, semi-supervised learning, and
reinforcement learning.
● Examples and applications of each type.
16. Designing a Learning System
● Steps involved in designing a learning system, including choosing the training
experience, defining the target function, representing the target function, and selecting a
learning algorithm.
17. Machine Learning in Data Science
● Introduction to data science and its relationship to
Simpler form:
Study Guide: What is AI?
I. Introduction to AI
A. Definition of AI: Systems that perform tasks requiring human intelligence
B. AI vs. HI (Human Intelligence)
1. AI aims to replicate human intelligence behaviors
2. Behaviors include planning, learning, problem-solving, perception, reasoning, and more
II. Approaches to AI
A. Thinking humanly: Cognitive Science
1. Exploration of how humans think and understanding brain activities
2. Approaches: introspection, psychological experiments, observation, brain imaging
B. Acting humanly: The Turing test
1. Alan Turing's test to determine if a machine exhibits human-like intelligence
2. Involves an imitation game to fool a person into thinking they are interacting with a human
C. The Turing test and system capabilities
1. System capabilities for passing the Turing test
- Natural language processing
- Knowledge representation
- Automated reasoning
- Machine learning
D. Achieving ethics through rational behavior
1. Ethics achieved by using logic and reasoning in actions
E. Acting rationally
1. Rational behavior: maximizing goal achievement based on available information
2. Acting ethically serves the purpose of rational action
F. Rational agents
1. Agents perceive and act based on formal rules
2. Rational agents aim for optimal performance in a given environment and task
3. Perfect rationality limited by computational constraints
III. AI Prehistory and History
A. Various disciplines contributing to AI development
- Philosophy, mathematics, psychology, economics, linguistics, neuroscience, control theory
B. Potted History of AI
- Highlights key milestones in AI development: Boolean circuit model, Turing's work, expert
systems, neural networks, and human-level AI resurgence
IV. Reinforcement Machine Learning
● Replicating human learning patterns of rewards and punishments to train AI systems
V. Steps towards creating Artificial Intelligence
1.
2.
3.
4.
5.
6.
7.
Know the domain and the problem
Study the data through data mining
Cleanse and normalize data, develop tools
Choose and compare different models
Shortlist optimal models and select the best one
Train, fine-tune, and test the model
Monitor errors, record learning, and assess positive impact
VI. Unintentionally Funny Stories from AI
● AI-generated stories with unintentional humor due to limitations in understanding context
and generating coherent narratives
VII. Definitions
● Intelligence: The ability to acquire and apply knowledge and skills
● Artificial Intelligence: Tasks performed by machines that require human-like intelligence
● Agent: An entity that perceives and acts based on rules or programming
VIII. Types of AI
A. Reactive Machines: AI systems reacting based on present actions
B. Limited Memory AI: AI systems using past experiences to make decisions
C. Theory of Mind AI: AI systems with advanced understanding of emotions and people
D. Self-Aware AI: AI systems with human-like consciousness and self-driven actions
IX. AI Classifications
A. Artificial Narrow Intelligence (ANI): General-purpose AI like virtual assistants
B. Artificial General Intelligence (AGI): Strong AI with human-like intelligence
X. State of the Art in AI
● Internet advantages and security risks
● Protection measures for personal information in online services
XI. Internet Basics
A. What is the Internet?
- A network of networks connecting computers worldwide