GEIT112 Artificial Intelligence Chapter 4 PDF

GEIT112 | Chapter 4 GEIT112 Artificial Intelligence Chapter 4 CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 LEARNING OBJECTIVES...

GEIT112 | Chapter 4 GEIT112 Artificial Intelligence Chapter 4 CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 LEARNING OBJECTIVES  Understand the evolution of AI, including key historical milestones and advancements.  Define artificial intelligence and differentiate between related concepts like machine learning, deep learning, and large language models.  Analyze current market trends, growth projections, and the economic impact of AI across various industries.  Identify the core components of AI systems and differentiate between various AI fields such as machine learning and natural language processing.  Evaluate practical applications of AI in Industry 4.0 and analyze its advantages in manufacturing, supply chain optimization, and predictive maintenance.  Explore the ethical, social, and practical challenges of AI implementation, including workforce displacement and data privacy concerns. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 CONTENT o Introduction o Applications of AI in Industry 4.0 o History of AI o Advantages of AI in Industry 4.0 o Definition of AI o Challenges and Considerations o AI in numbers: Statistics and trends o AI vs. Human intelligence o Case studies and examples of AI in action o The UAE National Strategy for Artificial Intelligence (AI) 2031 o Components of AI systems o Conclusion: Future of AI in Industry 4.0 o Types of AI (based on capabilities) o Writing effective prompts for OpenAI o OpenAI o Exercises CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 INTRODUCTION CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 HISTORY OF AI CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 DEFINITION OF AI Artificial Intelligence (AI) What is Artificial Intelligence? o Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (acquiring information and rules for using it), reasoning (using rules to reach approximate or definite conclusions), and self-correction. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 DEFINITION OF AI Key Characteristics of AI Adaptability: Ability to learn from data and experiences. Autonomy: Capability to operate without human intervention. Intelligence: Ability to understand complex concepts and make decisions. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 DEFINITION OF AI Capabilities of Intelligent Machines Reasoning and Problem-Solving: AI can use algorithms to analyse situations, identify patterns, and develop solutions. Planning: AI can use algorithms to set goals and create plans to achieve them. Learning: This is a powerful capability of AI. Unsupervised learning allows AI to identify patterns in data without explicit instructions. Supervised learning involves training AI on labelled data sets, enabling it to make predictions or classifications on new data. Social Intelligence: This is a rapidly evolving field of AI. While AI can now recognize emotions from facial expressions and analyse sentiment in text, understanding the nuances of human emotions and social interactions remains a challenge. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 DEFINITION OF AI Differences between AI, Machine A graphic explaining the four types of AI: machine learning, neural networks, natural language processing and robotics. Learning, Deep Learning, and Large Language Models (LLMs) AI: The broad field of creating machines capable of intelligent behaviour. Machine Learning: A subset of AI that involves training machines to learn from data. Deep Learning: A subset of machine learning involving neural networks with many layers. Large Language Models (LLMs): Advanced models designed to understand and generate human-like text, such as OpenAI's GPT-4. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 AI IN NUMBERS : STATISTICS AND TRENDS Current Market Size and Growth Projections The adoption of AI in Industry 4.0 is rapidly increasing. A McKinsey report estimates that AI could contribute up to $12 trillion to global economic activity by 2030. Over 80% of manufacturers are planning to invest in AI solutions in the next five years AI-powered robots are expected to handle 20% of all manufacturing tasks by 2030 CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 AI IN NUMBERS : STATISTICS AND TRENDS Investment in Technology and Training in Gen AI Tools: 85% of Middle East business leaders surveyed plan to increase technology investments in 2024. 93% specifically plan to invest more in AI and Gen AI. The Middle East leads globally in training workers in Gen AI tools. 6% of respondents worldwide reported that 25% or more of their staff are already trained in Gen AI tools. In the Middle East, 11% of companies reported that 25% or more of their staff are trained in Gen AI tools. This percentage surpasses all other surveyed regions. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 AI IN NUMBERS: STATISTICS AND TRENDS The Middle East and Asia-Pacific lead a wave of rising investment in tech and AI/GenAI CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 AI IN NUMBERS : STATISTICS AND TRENDS Key Industries Adopting AI Healthcare Finance Manufacturing Retail Transportation And many more… CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 CASE STUDIES AND EXAMPLES OF AI IN ACTION Predictive analytics in healthcare for disease prediction. Autonomous vehicles in the transportation sector. AI-driven supply chain optimization in retail. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 COMPONENTS OF AI SYSTEMS Machine Learning Algorithms: These algorithms Artificial Intelligence system components analyse data to learn patterns and make predictions. Data: The fuel for AI systems, high-quality data is crucial for effective learning and performance. Computing Power: Complex AI models require significant processing power, often provided by GPUs or cloud computing. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 COMPONENTS OF AI SYSTEMS AI Fields Natural Language Processing (NLP): Focuses on enabling computers to understand and process human language. This Machine Learning: Machine learning is the study of algorithms includes tasks like sentiment analysis, machine translation, and and statistical models that computer systems use to perform specific speech recognition. Used in chatbots, virtual assistants, and voice- tasks without explicit instructions, relying on patterns and inference activated devices. instead. o AI systems that learn from data without explicit programming. Large Language Models (LLMs): LLMs, like GPT-4, are designed o Deep Learning: A subset of machine learning inspired by the to understand and generate human-like text. They are used in structure and function of the human brain. applications ranging from chatbots and virtual assistants to advanced o Computer Vision: Computer vision enables machines to data analysis and content creation. interpret and make decisions based on visual data from the world. Robotics: Robotics involves the design, construction, operation, and Neural Networks: Inspired by the human brain, these are use of robots for performing tasks that are typically carried out by interconnected networks that learn from data. They excel at humans. recognizing patterns and making predictions, enabling applications Cognitive Computing: Simulates human thought processes. like image recognition and speech translation Machines can learn, reason, and understand language to analyse data and uncover hidden patterns. Used in healthcare, finance, and manufacturing. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 AI Fields CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 TYPES OF AI ( BASED ON CAPABILITIES ) Narrow AI (Weak AI) General AI (Strong AI) o Narrow AI is designed and trained for a specific task. General AI refers to systems that possess the o Virtual assistants like Amazon Alexa, Google ability to perform any intellectual task that a Assistant, Rabbit AI are examples of narrow AI. human being can do. This level of AI remains Super-intelligent AI theoretical. o Super-intelligent AI surpasses human intelligence o Deep Learning: Inspired by the brain, these and can perform any task better than a human algorithms are excelling in tasks like image can. This is a hypothetical concept at present. recognition and language processing, potentially paving the way for more general intelligence. o Multimodal Learning: By training on diverse data (text, audio, video), AI could understand the world more holistically, mimicking human capabilities. o Neuroscience and AI: By studying the human brain, researchers might unlock new AI architectures with greater flexibility and adaptability, potentially leading to General AI. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 What is Open AI? o OpenAI is a non-profit research company focused on developing safe and OPENAI beneficial Artificial Intelligence (AI). They work on a variety of projects exploring different aspects of AI, aiming to ensure its responsible development and positive impact on society. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 OPENAI Examples of OpenAI Generative Pre-trained Transformer (GPT): This is a family of large language models (LLMs) developed by OpenAI, known for their ability to generate realistic and coherent text formats, translate languages, write different kinds of creative content, and answer your questions in an informative way. Codex: This is an AI system built on top of GPT-3, specifically designed to assist programmers. DALL-E 2: This is an image generation model that allows users to create realistic images from text descriptions. It can be used for creative purposes, design exploration, or even generating images to illustrate concepts. Gym: This is a toolkit for developing and comparing reinforcement learning algorithms. Policy & Safety Research: OpenAI also conducts research on policy and safety considerations surrounding AI development. This includes exploring potential risks, biases, and ethical implications of powerful AI systems. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 APPLICATIONS OF AI IN INDUSTRY 4.0 AI is transforming various aspects of Industry 4.0, including: Robot Learning: AI-powered robots can adapt to changing environments and perform complex tasks with greater precision. Predictive Maintenance: AI systems predict equipment failures before they occur, reducing downtime and maintenance costs. Quality Control: Machine vision and AI algorithms ensure products meet quality standards by identifying defects in real-time. Supply Chain Optimization: AI improves supply chain efficiency by optimizing inventory management, demand forecasting, and logistics. Autonomous Vehicles: Self-driving cars and trucks leverage AI for navigation, obstacle detection, and decision-making. Smart Manufacturing: AI-driven systems automate manufacturing processes, enhancing productivity and precision. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 APPLICATION S OF AI IN INDUSTRY 4.0 CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 ADVANTAGES OF AI IN INDUSTRY 4.0 Increased Efficiency: AI automates repetitive tasks, reducing human error and increasing speed. Cost Reduction: Automated processes lower operational costs and improve resource utilization. Enhanced Quality and Precision: AI ensures higher consistency and accuracy in production processes. Improved Decision-Making: AI analyses vast amounts of data to provide actionable insights, aiding strategic decisions. Innovation and Competitive Advantage: AI fosters innovation by enabling new business models and improving existing ones. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 CHALLENGES AND CONSIDERATIONS Ethical and Social Implications: AI raises ethical concerns such as bias, privacy, and job displacement. Workforce Displacement and Job Transformation: Automation may lead to job losses, necessitating workforce reskilling. Data Privacy and Security: AI systems must ensure the protection of sensitive data against breaches. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 AI VS. HUMAN INTELLIGENCE Feature Artificial Intelligence (AI) Human Intelligence Learning Learns from data through algorithms Learns from experiences, emotions, and social interactions Strength Excellent at data analysis, pattern Strong in reasoning, creativity, problem- recognition, and repetitive tasks solving in novel situations, and understanding emotions Limitations Lacks general intelligence, struggles with Can be biased based on experience and tasks requiring context or human-like emotions, susceptible to fatigue and understanding distractions Speed Processes information much faster than Processing speed varies based on task humans complexity Adaptability Can adapt to changes in data patterns Can adapt to entirely new situations with retraining through flexible thinking Creativity Can generate creative text formats Highly creative in generating new ideas, within defined parameters concepts, and solutions CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 THE UAE NATIONAL STRATEGY FOR ARTIFICIAL INTELLIGENCE (AI) 2031 The United Arab Emirates (UAE) has established a comprehensive National Strategy for Artificial Intelligence (AI) 2031. This strategy aims to position the UAE as a global leader in AI by 2031, fostering economic growth and improving the lives of its citizens. Here's a breakdown of the key aspects: Vision: o Transform the UAE into a world leader in Artificial Intelligence. o Create a prosperous digital economy among digitally developed countries. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 THE UAE NATIONAL STRATEGY FOR ARTIFICIAL INTELLIGENCE (AI) 2031 Objectives: Attract and train talent for future jobs enabled by AI: The UAE Build a reputation as a global AI destination: This involves recognizes the need for a skilled workforce and aims to develop attracting top AI talent, creating research facilities, and establishing a educational programs and training initiatives to bridge the skill gap. supportive regulatory framework. Bring world-leading research capability to work with target Increase the UAE's competitive assets in AI sectors: The industries: Collaborating with leading researchers and universities is strategy focuses on specific industries like logistics, transportation, crucial for advancing AI development and addressing industry-specific healthcare, and tourism, aiming to integrate AI for improved efficiency challenges. and innovation. Provide the data and supporting infrastructure essential to Develop a fertile ecosystem for AI: This includes fostering become a test bed for AI: A robust data infrastructure is necessary entrepreneurship, promoting research and development, and creating for training AI models. The strategy emphasizes creating a secure and a collaborative environment for different stakeholders. accessible data ecosystem. Adopt AI across customer services to improve lives and Ensure strong governance and effective regulation: Developing government: The strategy emphasizes using AI to enhance ethical guidelines and regulations for AI deployment is crucial to government services, citizen interactions, and overall quality of life. ensure responsible use of this technology. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 THE UAE NATIONAL STRATEGY FOR ARTIFICIAL INTELLIGENCE (AI) 2031 CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 THE UAE NATIONAL STRATEGY FOR ARTIFICIAL INTELLIGENCE (AI) 2031 CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 Emerging Trends and Technologies: o AI integration with IoT and blockchain. o Development of explainable AI CONCLUSION: (XAI). o AI-driven cybersecurity solutions. FUTURE OF AI The Future Landscape of AI in Industrial Applications IN INDUSTRY o AI will continue to revolutionize industries, leading to smarter, more efficient, and innovative operations. 4.0 Strategic Steps for Integrating AI into Industrial Operations o Invest in AI research and development. o Foster partnerships with AI technology providers. o Implement AI training programs for employees. CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 WRITING EFFECTIVE PROMPTS FOR OPENAI: CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 WRITING EFFECTIVE PROMPTS Strategic skills to help you craft better prompts: 1. Understand the Model's Strengths and Limitations 2. Be Clear and Specific 3. Use Structured Prompts 4. Provide Context 5. Iterative Refinement 6. Experiment with Different Phases 7. Ask for Multiple Options or Perspectives 8. Use Examples 9. Leverage the Model’s Knowledge 10. Be Polite and Courteous CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 WRITING EFFECTIVE PROMPTS Example Prompts Here are some practical examples of well-crafted prompts:  Simple Explanation: "Explain quantum computing in simple terms suitable for a high school student."  Detailed Response: "Describe the key benefits and potential risks of implementing AI in financial services."  Creative Task: "Write a short story about an astronaut who discovers a new planet."  Comparative Analysis: "Compare and contrast the economic policies of the United States and China."  Step-by-Step Instructions: "Provide a step-by-step guide to setting up a WordPress blog." CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 WRITING EFFECTIVE PROMPTS Common Mistakes to Avoid  Vagueness: Avoid prompts that are too broad or lack detail. o Example of a vague prompt: "Tell me something interesting."  Overloading: Don't ask for too much in one prompt. o Instead of: "Explain AI, give examples, and discuss its future," break it down into separate prompts.  Assuming Knowledge: Don’t assume the model knows exactly what you're referring to without context. o Example: Instead of "Discuss the recent event," specify: "Discuss the recent event of the Mars rover landing in 2021." CIT Instructor: Basheir Al-Rei  GEIT112 | Chapter 4 The End These slides are based on the PDF reading files CIT Instructor: Basheir Al-Rei