Futurist: Aware (AURA) Lessons 1 and 2
Initial Aware Assessment:
The following are a series of questions you can use pre-aware. Or, more bluntly, to start your organization's AI journey. Topics and questions can be consumed in a formal or informal setting. They can be a website thrown out for people to interact with. They can also be used as questions in meetings and even hallway conversations. Or they can be sent out as a form to be filled out by email.
No matter what, there are no right answers to these questions. The goal is to find out where the organization is as far as AI understanding.
Assumptions
·??????? Your organization has limited experience and knowledge of AI technologies, techniques, and applications.
·??????? Employees may have heard of AI but lack a deep understanding of its capabilities, limitations, and implications.
·??????? There is a general interest in exploring how AI can be leveraged to improve your organization's operations, products, or services, but the path forward is unclear.
Instructions
1.???? Distribute this assessment to the individuals or teams you would like to evaluate.
2.???? Ask them to complete the assessment independently, without collaborating with others.
3.???? Collect the completed assessments and review the responses to identify strengths, weaknesses, and knowledge gaps.
4.???? Use the results to inform your AI training and upskilling plans.
Assessment Questions
·??????? Definition of AI:
·??????? How would you define artificial intelligence (AI) in your own words? (Provide a brief, high-level explanation, as if explaining it to a non-technical audience.)
·??????? What are your initial thoughts or impressions when you hear the term "artificial intelligence"? Do you have any preconceived notions or concerns about AI?
·??????? AI Techniques:
o?? List at least three common AI techniques or approaches (e.g., machine learning, natural language processing, computer vision). You do not need to provide detailed explanations of these techniques.
o?? Are there any specific AI techniques that you are unfamiliar with or that concern you? What would you like to learn more about?
·??????? AI Applications:
o?? Provide three examples of real-world applications of AI technology. These can be examples from various industries, such as healthcare, finance, or customer service.
o?? Do any of these AI applications raise any particular concerns or uncertainties for you? If so, what are they?
·??????? AI Adoption:
o?? On a scale of 1 to 5 (1 = not at all, 5 = extremely), how would you rate your organization's current level of AI adoption and implementation?
o?? What are the primary factors or barriers that are limiting your organization's adoption of AI technologies from your perspective? (e.g., lack of understanding, concerns about impacts, insufficient resources, etc.)
·??????? AI Skills Gap:
o?? Do you feel you have the necessary AI-related skills and expertise to effectively use or work with these technologies in your role? Why or why not? (Explain the perceived gaps in knowledge or capabilities.)
o?? What are your main concerns or doubts about your ability to leverage AI technologies in your work? (e.g., lack of technical expertise, ethical considerations, potential job displacement, etc.)
·??????? AI Training Needs:
o?? What specific AI-related topics or skills do you think would be most beneficial for you to focus on in terms of training and development? (e.g., machine learning, natural language processing, computer vision, ethical AI, etc.)
o?? What are your primary fears or uncertainties about the potential impacts of AI-related training and upskilling within your organization? (e.g., job security, cultural resistance, technological complexity, etc.)
·??????? AI Vision:
o?? Describe your vision for how AI could be used to improve your work, your team's operations, or your organization's products or services in the future. (This can be a high-level, aspirational response, as the details may not be fully known at this stage.)
o?? What are your main concerns or doubts about your organization's ability to successfully implement and manage AI-powered solutions in the long run? (e.g., data privacy, algorithmic bias, lack of transparency, etc.)
·??????? Scoring and Interpretation
o?? There is no single "correct" score for this assessment. The goal is to gather insights into the current state of AI knowledge and readiness within your organization, assuming a baseline of limited AI experience.
o?? Use the responses to identify areas where employees have a general awareness of AI, as well as areas that require more foundational training and development. Pay attention to the depth of understanding, as well as the specific AI-related topics and skills that are highlighted as areas of need.
o?? Consider the following guidelines when reviewing the assessment results:
·??????? Emerging AI Knowledge: Employees demonstrate a basic understanding of AI concepts, techniques, and applications, but lack depth and specificity in their responses.
o?? Moderate AI Knowledge: Employees have a general awareness of AI and can provide some relevant examples, but still have significant knowledge gaps.
o?? Limited AI Knowledge: Employees have a very superficial or incomplete understanding of AI and its potential impact on the organization.
o?? Use these insights to inform your AI upskilling and training initiatives, ensuring your team is equipped with the necessary knowledge and skills to leverage AI technology effectively and responsibly.
Phase 0
Assessment
·??????? Assessing an organization's readiness for AI training is a critical step in successfully implementing and benefiting from AI technologies. Here's an expanded overview of the key concepts:
o?? Data Infrastructure
§? A robust data infrastructure is the foundation of any AI initiative. Organizations should evaluate:
§? Data quality: Assess the accuracy, completeness, and consistency of existing data.
§? Data quantity: Determine if there's sufficient data to train AI models effectively.
§? Data accessibility: Ensure data is easily retrievable and usable for AI applications.
§? Data management practices: Evaluate data governance policies, storage solutions, and data integration capabilities.
§? Key actions:
§? Implement data cleansing and standardization processes
§? Establish data governance frameworks
§? Invest in scalable data storage and processing solutions
o?? Technology Stack
§? The existing technology stack must be capable of supporting AI implementation:
§? Hardware capabilities: Assess if current computing resources can handle AI workloads.
§? Software ecosystem: Evaluate the compatibility of existing software with AI tools and frameworks.
§? Cloud infrastructure: Determine if cloud resources are available for scalable AI operations.
§? Key considerations:
§? Identify necessary upgrades to support AI processing requirements
§? Assess the need for specialized AI hardware (e.g., GPUs, TPUs)
§? Evaluate integration capabilities with existing systems
o?? Skill Set and Talent
§? AI implementation requires specific expertise:
§? Current skills assessment: Inventory existing AI-related skills within the organization.
§? Skill gap analysis: Identify areas where additional expertise is needed.
§? Training needs: Determine which skills can be developed internally through training.
§? Recruitment strategy: Plan for hiring AI specialists to fill critical roles.
·??????? Action items:
§? Develop comprehensive AI training programs for existing staff
§? Create partnerships with educational institutions for ongoing skill development
§? Establish a recruitment strategy for attracting top AI talent
o?? Organizational Culture
§? A supportive culture is crucial for AI adoption:
§? Innovation readiness: Assess the organization's openness to new technologies and approaches.
§? Change management: Evaluate the company's track record in implementing significant changes.
§? Leadership support: Determine the level of executive buy-in for AI initiatives.
§? Cultural shifts to consider:
§? Foster a data-driven decision-making culture
§? Encourage experimentation and learning from failures
§? Promote cross-functional collaboration for AI projects
o?? Strategic Alignment
§? AI initiatives should support overall business objectives:
§? Business goal alignment: Ensure AI projects directly contribute to key organizational goals.
§? Use case identification: Define specific, high-value applications for AI within the business.
§? ROI expectations: Establish clear metrics for measuring the success and impact of AI initiatives.
§? Strategic planning steps:
§? Conduct workshops to identify AI opportunities aligned with business strategy
§? Develop a roadmap for AI implementation with clear milestones and success criteria
§? Regularly review and adjust AI strategy based on business needs and technological advancements
o?? Governance and Ethics
§? Responsible AI use requires strong governance:
§? Ethical framework: Develop guidelines for ethical AI development and deployment.
§? Regulatory compliance: Ensure AI initiatives adhere to relevant laws and regulations.
§? Oversight mechanisms: Establish committees or boards to guide AI governance.
§? Key considerations:
§? Create an AI ethics policy
§? Implement processes for ongoing monitoring of AI systems for bias and fairness
§? Develop protocols for addressing ethical concerns in AI applications
o?? Change Management
§? Effective change management is crucial for AI adoption:
§? Resistance assessment: Identify potential sources of resistance to AI implementation.
§? Communication strategy: Develop a plan to inform and engage employees about AI initiatives.
§? Training and support: Provide resources to help employees adapt to AI-driven changes.
§? Change management strategies:
§? Create a change management team dedicated to AI implementation
§? Develop clear communication channels for AI-related updates and feedback
§? Offer continuous support and training throughout the AI adoption process
o?? Financial Resources
§? Adequate funding is essential for successful AI implementation:
§? Budget allocation: Assess if sufficient funds are available for AI projects.
§? Long-term financial planning: Consider ongoing costs for AI maintenance and scaling.
§? ROI projections: Develop realistic financial models for AI investments.
§? Financial planning steps:
§? Conduct a cost-benefit analysis for proposed AI initiatives
§? Allocate budget for both initial implementation and ongoing support
§? Explore potential funding sources, including grants or partnerships
o?? Risk Assessment
§? Identifying and mitigating risks is crucial:
§? Technical risks: Assess potential issues with AI system performance or reliability.
§? Operational risks: Evaluate how AI might impact existing processes and workflows.
§? Reputational risks: Consider potential public perception issues related to AI use.
§? Risk mitigation strategies:
§? Develop a comprehensive risk management plan for AI initiatives
§? Implement robust testing and validation processes for AI systems
§? Create contingency plans for potential AI-related disruptions
o?? Pilot Projects (normally planned in Refine, Built in Apply)
§? Small-scale AI projects can provide valuable insights:
§? Proof of concept: Implement limited-scope AI projects to demonstrate value.
§? Learning opportunities: Use pilots to identify challenges and refine implementation strategies.
§? Stakeholder buy-in: Build support for larger AI initiatives through successful pilots.
§? Pilot project approach:
§? Select high-impact, low-risk areas for initial AI implementation
§? Establish clear success criteria and evaluation metrics for pilot projects
§? Use learnings from pilots to inform broader AI strategy and implementation plans
·??????? By thoroughly assessing these areas, organizations can gain a comprehensive understanding of their AI readiness. This assessment allows for the development of targeted strategies to address gaps, leverage strengths, and pave the way for successful AI implementation and training.
Phase 1
Aware
The Basics of Artificial Intelligence: Understanding its Foundations and Possibilities
Artificial Intelligence (AI) has become a ubiquitous term in modern technology, but its meaning and scope are often misunderstood. To grasp the essence of AI, it is essential to delve into its definition, history, types, and technologies. Additionally, understanding the concepts of intelligence, both human and artificial, and the Turing test, provides a solid foundation for exploring AI's applications and ethical considerations.
Definition and History of AI
Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. The term AI was coined in 1956 by John McCarthy, and since then, it has evolved through various stages, including rule-based systems, machine learning, and deep learning.
Types of AI
There are two primary types of AI: Narrow or Weak AI, designed to perform a specific task, and General or Strong AI, which aims to replicate human intelligence. Narrow AI is prevalent in applications like virtual assistants, image recognition, and natural language processing. General AI, still in its infancy, seeks to create intelligent systems that can reason, learn, and apply knowledge across various tasks.
Key AI Technologies
Machine Learning (ML) and Deep Learning (DL) are crucial technologies driving AI advancements. ML enables systems to learn from data, while DL uses neural networks to analyze complex patterns. Other essential technologies include Natural Language Processing (NLP), Computer Vision, and Robotics.
Intelligence Concepts: Human vs. Artificial
Human intelligence encompasses various aspects, including reasoning, problem-solving, and learning. Artificial intelligence, on the other hand, focuses on replicating these abilities through algorithms and data. The Turing test, proposed by Alan Turing in 1950, assesses a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
AI Applications: Current and Future
Current AI applications include virtual assistants, image recognition, natural language processing, and autonomous vehicles. Future applications may revolutionize healthcare, education, and energy management, among other industries. However, these advancements also raise ethical concerns, such as job displacement, bias in decision-making, and privacy issues.
Ethical Considerations
As AI becomes increasingly integrated into our lives, it is crucial to address ethical considerations. These include ensuring transparency, accountability, and fairness in AI decision-making, as well as protecting individual privacy and security.
Interactive Elements: Demonstrations and Group Discussions
To fully comprehend AI's possibilities and implications, interactive elements like demonstrations and group discussions are essential. These engaging activities facilitate a deeper understanding of AI concepts, applications, and ethical considerations, encouraging participants to think critically about the future of AI.
In conclusion, understanding AI basics, intelligence concepts, and ethical considerations is vital for navigating the rapidly evolving AI landscape. By exploring AI's definition, history, types, and technologies, we can better appreciate its potential and limitations, ultimately shaping a future where AI enhances human life while minimizing its risks.
The driver for “awareness” is two-fold. The first is to build a structured understanding of a topic. To peak the user's or learners' interest in a specific area. Adults learn best when they have a “connection” or framework around which they can hang the information. Put, context and the easiest context to build is a user's interest. That doesn’t represent the organizational “Why” of learning. It represents why the user continues past the introduction. What it is that the user is interested in learning. Most employees of most organizations will not move beyond the awareness phase of this learning plan. That is not a slight or intended comment; it is simply that awareness is a robust step to use AI. Beyond using, users need to move further into understanding, Refine, and Apply, but for the consumer or user level, awareness is a great start.
·??????? Lesson 1 focuses on understanding what AI is.
·??????? Lesson 2 focuses on
·??????? Lesson 3 focuses on the tools (this one will change the most as new tools are released)
1.???? Learning Goals for Awareness (Aware):
o?? During the awareness phase, consider the following learning objectives:
§? Existing AI Tools: Understand how to use AI tools available in the market.
§? Organizational Tools: Explore how to utilize AI tools developed within your organization.
§? Job Enhancement: Identify AI-related enhancements for your role
§? Introducing the topics as an introduction (broad)
§? Aware training is not deep training, it is introductions to the core concepts
Awareness is the foundational knowledge or understanding of a subject, issue, or situation that forms the basis for further learning and skill development. In training contexts, awareness typically involves:
The Awareness Training Model, as described by William Schutz, emphasizes:
Key aspects of awareness training include:
It's important to note that while awareness training is often used as a starting point for various topics (e.g., diversity, cybersecurity, workplace safety), it is generally considered a preliminary step. True behavioral change and skill development typically require more in-depth training and practice beyond simple awareness.
领英推荐
Introduction to AI
Organizational Callout 1
Start off your AI learning journey with a conversation. Before launching the AURA Aware module, have a guest speaker talk about the impact of AI on your organization (positive).
Please note that phase 1, Aware, has many videos. Multiple videos are used because not all presenters appeal to all viewers. Pick and choose the ones that make sense to you!
Lesson 1: Independent study
Before you start, consider these questions and your answers.
·??????? What is your current level of understanding about AI? (e.g., beginner, intermediate, advanced)
·??????? What specific areas of AI are you most interested in learning about?
·??????? Do you have any concerns or fears about AI and its impact on society or your job?
·??????? What are your main goals for taking this training? What do you hope to achieve?
·??????? Do you have any experience with programming or data science? If so, in what capacity?
·??????? Are you familiar with any AI tools or applications currently used in your industry?
·??????? What are your thoughts on the ethical implications of AI?
·??????? How do you think AI might affect your specific role or industry in the coming years?
·??????? Are there any particular AI-related myths or misconceptions you'd like to explore or debunk?
·??????? Do you have any experience with machine learning algorithms or neural networks?
·??????? What sources do you currently use to stay informed about AI developments?
·??????? Are you more interested in the theoretical aspects of AI or its practical applications?
·??????? Have you ever worked with or implemented any AI solutions in your professional life?
·??????? What are your expectations for the pace and depth of this training program?
·??????? Are there any specific AI-related skills you're hoping to develop through this training?
The topics listed here are addressed through Lessons 1-5. For an organization embraking on AI training, the first three are the ones that the majority of empooyes have considered, or hold as their opinion now.
§? Job displacement: There's worry that AI will automate many jobs, leading to widespread unemployment across various sectors.
§? Privacy and surveillance: Advanced AI systems may enable more pervasive monitoring and data collection, raising concerns about personal privacy.
§? Bias and discrimination: AI systems can perpetuate or amplify existing biases if not carefully designed and trained, potentially leading to unfair treatment of certain groups.
§? Safety and control: As AI systems become more advanced, there are concerns about maintaining human control and ensuring they remain aligned with human values.
§? Existential risk: Some worry about the potential for advanced AI to surpass human intelligence and potentially pose an existential threat to humanity.
§? Misinformation and manipulation: AI-generated content could be used to create convincing fake news, deepfakes, or other forms of misinformation.
§? Economic inequality: There are concerns that AI might exacerbate wealth inequality by concentrating economic benefits among a small group of tech companies and AI experts.
§? Ethical decision-making: Questions arise about how to program AI to make ethical decisions, especially in complex scenarios like autonomous vehicles.
§? Dependence on technology: As AI becomes more integrated into daily life, there are worries about over-reliance on these systems and potential vulnerabilities.
§? Lack of transparency: The "black box" nature of some AI systems makes it difficult to understand how they arrive at decisions, raising accountability concerns.
Self Paced Learning
o?? Kingdoms of Amalur Guide to dispelling chests. by TDKPyrostasis
o?? A brief history of AI by Plattform Lernende Systeme
o?? The History of AI: From Beginnings to Breakthroughs by Mr. Singularity
o?? The History of Artificial Intelligence [Documentary] by Futurology – An Optimistic Future
o?? History of AI | VOANews by Voice of America
o?? history of the entire AI field, i guess by bycloud
·??????? What is the impact of AI on jobs today?
o?? The Impact of A.I. on Jobs | Rutika Muchhala | TEDxDSBInternationalSchool https://www.youtube.com/watch?v=_U2YobRC8OY
·??????? Working with AI Chatbots in call centers
o?? What is a chatbot? Types of chatbots & how they work by Zendesk
o?? ChatGPT for customer service is here by Intercom
o?? AI Customer Service Demo Chatbot Customer Support AI Automation With Knowledge Base Update Feedback by Chatic Media
o?? How to build AI Customer Service Chatbot (Complete Tutorial) by Sandeep Kaistha | Flipbytes
·??????? What is AI?
?? What Is AI? | Artificial Intelligence | What is Artificial Intelligence? | AI In 5 Mins |Simplilearn by Simplilearn
?? What is AI? - AI Basics by LearnFree
?? What is AI? by Museum of Science
?? What Is AI? This Is How ChatGPT Works | AI Explained by howtoai
·??????? What do we mean by intelligence
o?? Trust, Transparency & AI | William Lobig | Cognizant by Cognizant
o?? How will AI change the world? by TED-Ed
o?? AI and Human Augmentation: Enhancing Our Capabilities | Artificial Intelligence | AI by The Intelligent Web
Lesson 1: A History of AI in person
·??????? Brief History of Artificial Intelligence
o?? Artificial intelligence (AI) is a field of computer science that has fascinated researchers and the general public alike for decades. The quest to create intelligent machines capable of performing human-like tasks has a rich history spanning centuries. Let's explore a brief overview of the key milestones and developments in the history of AI.
·??????? The Origins of AI
o?? The concept of artificial intelligence can be traced back to ancient Greek mythology, where tales of human-made intelligent beings, such as Hephaestus's robots, were told. However, the modern field of AI began to take shape in the 1950s, when computer scientists and mathematicians started to seriously explore the possibility of creating machines that could "think" and "learn."
·??????? The Pioneering Years (1950s-1960s)
o?? In 1956, a group of researchers, including John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, organized the Dartmouth Conference, which is widely regarded as the birthplace of AI as a field of study. During this time, early AI systems were developed, such as the Logic Theorist, which could prove mathematical theorems, and the General Problem Solver, which could find solutions to a variety of problems.
·??????? The AI Winter (1970s-1980s)
o?? Despite the initial excitement and enthusiasm, the 1970s and 1980s saw a period of disillusionment and funding cuts for AI research, known as the "AI winter." This was due to a number of factors, including the inability of early AI systems to live up to the high expectations, as well as the realization that the task of creating truly intelligent machines was much more complex than initially thought.
·??????? The AI Resurgence (1990s-2000s)
o?? In the 1990s and 2000s, AI experienced a resurgence, fueled by advancements in computing power, the availability of large datasets, and the development of new techniques such as machine learning and deep learning. This led to significant breakthroughs in areas like natural language processing, computer vision, and game-playing AI systems.
·??????? The Modern Era of AI (2010s-present)
o?? In the current decade, AI has become increasingly integrated into our daily lives, with applications ranging from virtual assistants and autonomous vehicles to medical diagnosis and financial decision-making. The rapid progress in AI has also raised important ethical and societal questions, leading to ongoing discussions about the responsible development and deployment of these technologies.
·??????? Conclusion
o?? The history of AI is a story of human ingenuity, perseverance, and the constant pursuit of understanding the nature of intelligence. From the early pioneers to the cutting-edge researchers of today, the field of AI has evolved and continues to shape the way we interact with technology and solve complex problems. As we look to the future, the possibilities for AI seem limitless, and the impact it will have on our lives and society is sure to be profound.
Lesson 2, what do we mean by AI?
After lesson 1 and before lesson 2 consider these questions
·??????? What is your current level of understanding about Artificial Intelligence?
·??????? Have you worked with or used any AI tools or applications before? If so, which ones?
·??????? What do you know about Large Language Models (LLMs)? Have you interacted with any, like ChatGPT?
·??????? Do you have any experience with programming or data science? If yes, to what extent?
·??????? What is your understanding of Machine Learning? Can you differentiate it from AI?
·??????? Are you familiar with any ethical concerns surrounding AI? What are your thoughts on them?
·??????? What are your main goals for taking this training? What do you hope to learn or achieve?
·??????? Are you more interested in the technical aspects of AI or its practical applications?
·??????? How do you think AI might impact your specific job role or industry?
·??????? Do you have any concerns about AI's impact on society or the workforce?
·??????? Are you familiar with concepts like data quality and bias in AI systems?
·??????? What questions do you have about the limitations of AI and large language models?
·??????? Are there any specific AI applications or use cases you're particularly interested in exploring?
·??????? How comfortable are you with technical terminology related to AI and machine learning?
·??????? What experience, if any, do you have with data-driven decision making in your work?
·??????? Components of Lesson 2
o?? What is a Large Language Model
o?? What is a Large Action Model
o?? What is Machine Learning
o?? What are the Benefits and Challenges?
§? How Large Language Models Work by IBM Technology
§? Introduction to large language models by Google Cloud Tech
§? What are Large Language Models (LLMs)? by Google for Developers
§? Large Language Models: Application through Production by Databricks
§? Large Language Models (LLMs) - Everything You NEED To Know by Matthew Berman
o?? What is a Large Action Model (LAM)
?? Unleashing the Power of Large Action Models: The Future of AI by The Conference Board
?? Large Action Model - LAM by KYFEX
?? "VoT" Gives LLMs Spacial Reasoning AND Open-Source "Large Action Model" by Matthew Berman
?? Next Big AI Wave: Rabbit's Large Action Model which will change the way we interact with Computers?? by Execute Automation
o?? What is Machine Learning?
?? Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2024 | Simplilearn by Simplilearn
?? Machine Learning Explained in 100 Seconds by Fireship
?? All Machine Learning Models Explained in 5 Minutes | Types of ML Models Basics by Learn with Whiteboard
?? AI vs Machine Learning by IBM Technology
o?? What jobs will have the biggest impact from AI
?? What jobs are safe from AI? by CBS News
?? How much does an AI ENGINEER make? by Broke Brothers
?? ai robots hit sofi stadium by Los Angeles Chargers
?? The AI Revolution: Will Robots Take Your Job? by Valuetainment
Section 1 follow-up questions
·??????? What are the different tasks you can perform with a large language model?
·??????? Prompts and AI
·??????? Which AI should I use?
·??????? How do you access and process information to answer questions?
·??????? Are you able to learn and improve over time?
·??????? What are the limitations of large language models like yourself?
·??????? How do you think large language models will be used in the future?
Lesson 2 in person
Section 1: Introduction to AI
1.???? Show the video "What Is AI? | Artificial Intelligence | What is Artificial Intelligence? | AI In 5 Mins" by Simplilearn.
2.???? Discuss the definition and types of AI.
3.???? Introduce the concept of Large Language Models (LLMs) and their applications.
Section 2: Large Language Models
1.???? Show the video "How Large Language Models Work" by IBM Technology.
2.???? Explain the architecture and training process of LLMs.
3.???? Discuss the benefits and limitations of LLMs.
Section 3: Machine Learning
1.???? Show the video "Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2024" by Simplilearn.
2.???? Explain the types of Machine Learning and their applications.
3.???? Discuss the relationship between Machine Learning and AI.
Section 4: Benefits and Challenges
1.???? Discuss the benefits of AI, including efficiency, data-driven decisions, and personalization.
2.???? Explore the challenges of AI, including data quality, bias, ethical concerns, and transparency.
Section 5: Follow-up Questions
1.???? Distribute handouts with follow-up questions.
2.???? Have students answer questions in small groups or individually.
3.???? Encourage discussion and sharing of thoughts.
Assessment:
·??????? Participation in class discussions and activities
·??????? Written answers to follow-up questions
·??????? Group presentation on a selected topic related to AI, LLMs, or Machine Learning
Extension:
·??????? Have students research and present on a specific application of AI, LLMs, or Machine Learning.
·??????? Invite a guest speaker to discuss real-world applications and challenges of AI.
·??????? Conduct a debate on the ethics of AI and its impact on society.
?
Lesson 1 and 2 discussions in person
·??????? Discussion Groups
§? Applications: Discuss real-world applications of AI across various domains:
·??????? Natural Language Processing (NLP): AI models that understand and generate human language (e.g., chatbots, sentiment analysis).
·??????? Computer Vision: AI systems that interpret visual data (e.g., image recognition, object detection).
·??????? Recommendation Systems: AI algorithms that suggest personalized content (e.g., Netflix recommendations).
·??????? Autonomous Vehicles: AI-powered self-driving cars.
§? Impact: Highlight how AI transforms industries like healthcare, finance, manufacturing, and transportation.
§? Benefits and Challenges: