Introduction to Artificial intelligence.

This lecture introduces Artificial intelligence for beginner for the AI course

1. COMP3071-Artificial Intelligence BY Assistant Professor Dr. Nivine Guler Room 315

2. C ONT E NTS What is Artificial Intelligence? 1 2 What problems can AI solve? 3 How is machine learning related? 4 5 6 Examples of AI Disadvantages of AI How can we use AI responsibly?

3. Artificial intelligence has been remarked as a branch of computer science aiming at transforming a machine or a computer system into more intelligent systems by imitating the human capabilities such as reasoning , acquiring semantic , and learning from past experience WHAT IS AI ?

4. AI Act rationally Always doing the right thing Think humanly The cognitive modelling approach Act humanly The Turing Test approach Think rationally Thinking with logic

5. The ‘Turing Test’ was designed back in 1950 by mathematician and computing engineer Alan Turing to determine whether a machine can show human intelligence . In simple terms; can a computer talk like a human? At the time Turing called it the ‘ Imitation Game ’. As part of the test, a human judge has a text conversation with unseen players and evaluates their responses. If the evaluator could not differentiate the machine from the human, the machine would be said be deemed artificially intelligent

6. H ow does M achine Learning Relate to A I ? Machine learning implies to the technologies and algorithms that allow systems to recognize patterns, make decisions, and improve themselves through experience and data. Although the terms artificial intelligence (AI) and machine learning are frequently used interchangeably, (machine learning is a subset of the larger category of AI. ) Artificial intelligence signifies computers' general ability to mimic human thought while carrying out tasks in real- world environments Norah

7. EXAMPLES Virtual Assistance Autonomous vehicles Chatbots Recommendation systems Navigation apps Facial recognition Text editors AI can be used for various situations, but these are some examples of AI in our daily life. E- commerce

8. SOFIA THE AI ROBOT Sophia is a realistic humanoid robot capable of displaying humanlike expressions and interacting with people. It's designed for research, education, and entertainment, and helps promote public discussion about AI ethics and the future of robotics.

9. AI Technologies AI encompasses a wide range of technologies, including: Expert Systems : programs that use specialized knowledge to make decisions in narrowly defined areas. Natural Language Processing (NLP) : technologies that enable computers to understand and generate natural language. Computer Vision : systems capable of recognizing and interpreting images.

10. What does AI need to LEARN? One of the capabilities of both Artificial and Human intelligence is learning. Learning in the context of AI refers to a system’s ability to improve its skills and performance based on experience and analysis of dependencies in large datasets. This process is fundamental to creating AI systems capable of adapting to new tasks and situations. All AI models do this – if it can’t adapt based on data , then it simply isn’t artificial intelligence. While there are different types, ranging from simple AI models to fully fledged deep neural networks, they all have to meet this initial criteria.

11. Types of Machine Learning Algorithms (ML) AI uses various types of ML algorithms to create advanced, intelligent systems: Machine Learning (ML) : this technique allows computer systems to learn from data without being explicitly programmed. ML algorithms analyze input data, identify patterns, and use this information to make predictions or decisions. Examples include image classification, sentiment analysis in texts, and demand forecasting. Neural Networks : these are mathematical models inspired by the biological structures of the brain, used for pattern recognition and information processing. Deep neural networks consist of layers of neurons interconnected and processing input data through different levels of abstraction. They are used in image and sound processing.

12. Types of Machine Learning Algorithms (ML) Deep Learning (DL): a subfield of machine learning that uses multi-layered neural networks to analyze data. DL is particularly effective in processing large amounts of unstructured data, such as images, sounds, and texts. Applications include speech recognition, language translation, image generation, and medical diagnostics. Robotics: this field involves designing, constructing, and operating robots. In the context of AI, robotics often uses ML algorithms for navigation, object manipulation, and interaction with the environment. Intelligent robots can operate in dynamic and unpredictable environments, making autonomous decisions. Q: How Does Machine Learning Work? Machine learning involves developing algorithms that enable computers to learn from data and make decisions autonomously, similar to human learning through experience. Instead of being explicitly programmed, these algorithms identify patterns in data to predict outcomes and make informed decisions .

13. WHAT PROBLEMS CAN AI SOLVE? Healthcare Research Transportation → self driving cars * medical records * idea generation, finding data Cybersecurity → dectecting spam

14. uses of AI Customer service Customer service teams can get feedback from customers by using AI. For example, AI- powered information can provide agents with information on client intent, language, and sentiment so they are aware of how to approach an encounter. Medical diagnosis Provides more exact diagnoses, detects hidden patterns in imaging investigations, and predicts how patients will respond to specific medications. This leads to better treatment strategies, fewer clinical errors, and more accurate diagnosis. ( AD VANTAGES OF AI) Image and facial recognition It can help make data safer and more secure. For example, face authentication can ensure that only the appropriate person has access to sensitive information that is intended specifically for them. Recommendation systems AI content recommendations help people stay engaged and informed. For example, Virtual(Siri and Alexa.), Personalized content on streaming platforms, Apps that suggest best routes based on traffic.

15. W hat are the disadvantages of AI? Privacy Concerns Ethical Dilemmas Security Risks Concentration of Power Dependence on AI Job Displacement Lack of Transparency → lying about using AI Bias and Discrimination → assumtion based of incorrect information

16. Put People First Consider data and privacy goals Minimize unintended bias Ensure AI transparency People should use their own creativity, not copy off of AI! AI is just a tool for efficiency! H ow can we use AI R esponsibly?

17. Exercise1- Group work To what extent are the following computer systems instances of artificial intelligence : A. Supermarket bar code scanners. B. Web search engines. C. Voice-activated telephone menus. D. Internet routing algorithms that respond dynamically to the state of the network. 1. Prepare PowerPoint to answer the above with your names and IDs. 2. Save it as pdf and upload it to the Moodle. Due date: Wednesday Aug.4 th , 2024

18. HOW AI LEARN? Like humans learn through: E xperiences Experimentation Absorbing new information AI develops its skills by analyzing vast amounts of data . This analogy to the human learning process is particularly evident in the concept of neural networks , which mimic the functioning of the human brain, and in machine learning. Let’s explore how exactly AI learning works.

19. The Process of AI Learning Next Lecture