Duration: One Day
Balancing the use of data with human problem solving requires a nuanced approach that recognizes the strengths and limitations of both. By integrating data with human judgment, fostering cross-functional collaboration, and building a data-literate culture, organizations can solve problems in a more informed, ethical, and sustainable way.
This course introduces Artificial Intelligence (AI) and its role in solving real-world problems. Participants will explore key AI concepts, tools, and technologies such as machine learning, data-driven decision-making, and popular AI platforms. The course covers practical aspects such as building and integrating AI models, ethical considerations such as bias and privacy, and the future impact of AI on industries and employment.
This one-day workshop will help you teach participants how to:
- Understand the fundamentals of AI and its capacity for solving problems.
- Use specific AI tools and technologies that facilitate problem-solving.
- Explain how machine learning models are developed and used in AI solutions.
- Implement AI solutions in real-world problem-solving scenarios.
- Discuss the ethical implications of using AI in problem-solving.
- Speculate on future trends and the evolving role of AI in solving complex problems.
- Apply the knowledge and skills that are learned to a real or simulated problem.
Course Overview
Introduction to AI and Problem-Solving
In this session, students will gain a foundational understanding of how AI can mimic human intelligence to solve problems. They will explore key concepts such as machine learning, natural language processing, and computer vision, and understand how these technologies apply to real-world scenarios.
AI Tools and Technologies
In this session, students will be introduced to popular AI platforms and tools including TensorFlow, PyTorch, and cloud-based solutions from Microsoft, Google, and Amazon. They will learn about the features and capabilities of these tools and how they can be used to build AI applications.
Data-Driven Problem-Solving
In this session, students will explore techniques for data collection, cleaning, and preparation, ensuring that data used in AI models is high-quality and relevant. The session will also cover methods of transforming raw data into actionable insights and visualizing AI outputs for better decision-making.
Machine Learning Models in Problem-Solving
In this session, students will learn about the three main types of machine learning — supervised, unsupervised, and reinforcement learning — and their practical applications. The session will include discussions on how these models can be applied to solve various business problems.
Implementing AI Solutions
In this session, students will focus on integrating AI into existing workflows and systems, with emphasis on aligning AI solutions with business goals, conducting pilot tests, and scaling up AI implementations. They will also learn strategies for managing data privacy, ethical concerns, and potential challenges during integration.
Ethical Considerations in AI Problem-Solving
In this session, participants will delve into the ethical issues surrounding AI, including bias, fairness, and privacy. They will explore how to balance AI-driven decisions with human judgment, ensuring that AI solutions are both effective and ethically sound.
Future of AI in Problem-Solving
In this session, students will examine emerging AI technologies and their potential to transform industries. They will discuss the impact of AI on the future of work, including how AI will shape industries such as healthcare, finance, and retail.
Preparing for an AI-Driven Future
In this session, participants will learn how to adapt to a rapidly evolving AI landscape. They will explore strategies to stay competitive in an AI-driven world, including upskilling in AI technologies and staying informed about the latest AI developments.
Workshop Wrap-Up
At the end of the course, students will have an opportunity to ask questions and fill out an action plan.
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