How do you teach AI and optimization to the next gen?

From my vantage point, it's imperative to view AI and optimisation not merely as tools but as extensions of human thought and creativity. When I've led classes, my primary focus was to demystify AI, stripping away the aura of inscrutability around it. At the outset, it's vital to contextualise AI within historical, ethical, and socio-economic frames, lest we inadvertently churn out technocrats. Students should leave not just with technical prowess, but also with a robust understanding of AI's implications. This holistic approach prepares them to design solutions that are ethically sound, culturally sensitive, and truly beneficial.

The key is that we need ALL students to develop critical thinking and collaboration skills regardless of which educational track they follow – practical development or solution concepts and design. It is important we maintain a multitrack education system to ensure a talented pool of: • Technical experts that can help create and maintain key models and algorithms as well as . It is thought that there are fewer the 1,000 people in the world capable of building robust large language models, and that could rapidly create scenario of dwindling expertise beyond proprietary models. • Solutions Analysts and Designers educated about real life scenarios and use cases. Too much theory will steepen the learning curve and delay productivity.

In AI and optimization education, it's crucial to emphasize their industry potential and foundational principles. As AI rapidly evolves, focusing on core principles over current techniques helps ground learners. The curriculum should prioritize real-world applications, bridging academia and industry. Beyond technical skills, understanding AI's ethical and societal facets is essential. Embracing iterative learning, akin to the tech industry's approach, promotes innovation and resilience. Recognizing AI's influence beyond tech, intertwining with fields like psychology, is key. Ultimately, AI education should balance technical depth with practicality, producing not just skilled coders but also visionary leaders attuned to AI's broad impacts.

I couldn't agree more. Indeed, we can't claim that everyone is capable of coding. However, it's important to inculcate the moral dimension in society- including the next generation- before problems arise due to cognitive prejudices and negligence, given that we're not handling just any lambda tool with regard to AI, but a decision-making tool that affects life, the concrete and the future. The future of the dataset and the people it concerns is in the hands of whoever owns the code. This is why, let us remain responsible and strive to use technology wisely thanks to Explainable AI (xAI) and Responsible AI (rAI).

I have found it helpful to explore real-world applications of AI in addition to understanding the technology and concepts behind it. In my work on commercial AI solutions, there are different factors to consider including governance, cost and technology maintenance, and ensuring a high level of accuracy in open market conditions that are just as important as the technology. Defining real-world use cases that cause students to challenge their thinking around how to apply what the learned is critical.

In the digital age, the choice and structure of educational content, especially in AI, is crucial. The curriculum must go beyond mere theory, reflecting real-world applications and showing how AI principles shape industries like finance and healthcare. While textbooks provide knowledge, the essence of a course lies in hands-on experiences with tools, platforms, and real projects. AI, an interdisciplinary subject, intertwines with ethics, economics, and sociology, urging students to contemplate its societal implications. Given technology's rapid evolution, course content needs constant updating to stay pertinent. Essentially, AI education should craft a narrative aligning with modern challenges, presenting AI as a societal cornerstone.

Remember, it's like constructing a building. Start with a solid foundation using basic concepts, and then add layers, making connections between them. Think of your course as a playlist; each topic is a song. The order matters, and a good mix of theory (the lyrics) with practical examples (the beats) keeps the learners engaged.

In my experience, the first time I ran a workshop, the first thing I did was to gauge the young people's receptiveness. As expected, attitudes towards AI ranged from enthusiasm to distrust. It's great to deduce that, whatever the case, no one is indifferent to its presence, and to use this energy to stimulate curiosity. Then, one piece of advice I've been given is to start from a "funnel" scenario, i.e. go from the known and familiar to what's yet to be discovered. I can attest that it works.

The best way in structuring the content will be to use the practical applications which is in use in today’s world along with theory which will be helpful for the students to understand and imagine the concepts better. The more case studies we incorporate the better would be the understanding which would then be helpful for them to come up with different use cases.

Imagine planning a party where every guest has a unique taste. Just like that, each student has different learning preferences. When structuring a course, it's like setting up a playlist that includes everyone's favorite song. You mix lectures with hands-on labs, sprinkle in some quizzes, and jazz it up with engaging online content like videos.

As I have been preparing for the Post Graduate Program in Artificial Intelligence for Leaders course that I will be attending at The University of Texas at Austin this month, I've found ChatGPT to be an excellent tutor (when the prompt context has been tuned for accuracy and verification). By using ChatGPT, the pre-course study material on statistics and data science was made much simpler and easier to comprehend. I want to emphasize that ChatGPT was not used to "get the answers" but rather to understand the answers and underlying principles. I believe that ChatGPT, and other LLM chatbots, in the right scenario, can help educate the next generation.

One thing what I have seen in recent times is that most gen z prefer online teaching as opposed to offline. Use of technology is already in play ranging from online quizzes, hands on lab, curriculum community , 1-1 mentor support & interview with industry expert after each module of the course. To make it more engaging and interesting students can be made aware of online competitions for Ai like Kaggle and can make them try out these after each course or modules

Apart from the ones mentioned above , 24/7 technical support for issues arriving from accessing / viewing materials and community to help them fix issues or learn from common mistakes / issues that occurs will be helpful. 1-1 mentoring or doubt clearing session with the instructors at end of a module would be highly beneficial to the students.

I provided my dog industry students with a Facebook group to discuss new ideas as well as suggested prompts for a variety of key tasks.

Course development is an evolving journey that prioritizes the continual improvement of the learner's experience. It is not merely a business endeavor but a commitment to updating, revising, and enhancing the course content. This process also includes integrating the latest research advancements and industry insights, ensuring that the course remains relevant and valuable to learners.

Just like incorporating new data and features to enhance the AI models, improving your course requires constantly taking in feedback. In the past, I have collected direct, anonymous feedback from students through platforms such as SurveyMonkey and Poll Everywhere which can solicit more authentic opinions while keeping the responder's identity confidential. I also paid attention to indirect feedback such as the latest trend of application interest through internal communication channels or professional social media platforms like LinkedIn to keep the course content current and relevant.

Tests, quizzes are effective way to evaluate student’s understanding of the concept. To improve the quality of the course or to know if the student is happy with the course taught ,feedback and surveys and also asking them to identify the relevant topics taught under each section will be helpful to the instructors to identify gap if any. Since there are lots of improvements happening in the filed of AI nowadays it would be better to keep the content up to date with market trends as more people are looking forward to learn the latest developments and trends.

Teaching AI and optimization to the next generation involves a multidimensional approach. Start with foundational concepts and theories, gradually introducing real-world applications and case studies to showcase their relevance. Utilize interactive and hands-on activities, such as coding projects and simulations, to engage learners and promote practical understanding. Encourage critical thinking by presenting challenges that require AI and optimization solutions. Additionally, emphasize ethical considerations and societal impacts of these technologies, fostering a well-rounded understanding of their potential benefits and implications.