Learning Plan Builder

One task large language models do well is crafting personalized learning plans. Copy the below text (everything in gray) and paste it into ChatGPT (https://chat.openai.com). Be sure to complete the “About the plan” fields at the top before submitting.

You should receive a week-by-week plan using the content provided. Try to use the most recent large language model you can access (e.g., GPT-4 vs. GPT-3.5). Here’s a demo video if helpful.

Happy learning!

Create a learning plan for automation and AI based on the below information. Include items from the below content and tools list where it makes sense given the time commitment and learning objectives. Guide the learner step-by-step on where to find the various content and tools included in the plan. When suggesting tools, provide a few examples of how the learner could use the tools to build understanding. Assume the learner reads at a pace that is typical for deep understanding.

About the plan

  • Number of weeks: _______________

  • Hours per week: _______________

  • Learning objectives: _______________

  • Learning style: _______________

Content - Videos

  • Humans Need Not Apply (CGP Grey) - 15 minutes

  • The Rise of the Machines (Kurzgesagt) - 12 minutes

  • The Danger of AI is Weirder Than You Think (Janelle Shane) - 10 minutes

  • What Will Future Jobs Look Like (Andrew McAfee) - 14 minutes

Content - Books

  • A World Without Work - Technology, Automation, and How We Should Respond, Daniel Susskind - 320 pages

  • The Second Machine Age: Work, Progress, And Prosperity in a Time of Brilliant Technologies, Andrew McAfee, Erik Brynjolfsson - 336 pages

  • What To Expect When You're Expecting Robots: The Future of Human-Robot Collaboration, Laura Major, Julie Shah - 304 pages

  • On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines, Jeff Hawkins, Sandra Blakeslee - 272 pages

  • You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place, Janelle Shane - 272 pages

  • The Alignment Problem: Machine Learning and Human Values, Brian Christian - 496 pages

  • Drive: The surprising truth about what motivates us, Daniel Pink - 256 pages

Content - Papers

  • Acemoglu, D., & Restrepo, P. (2021, June). Tasks, Automation, and the Rise in US Wage Inequality. National Bureau of Economic Research. - 44 pages

  • Syverson, C., et al. (2017, October). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. National Bureau of Economic Research. - 46 pages

  • Brown, T. B., et al. (2020, July 22). Language models are Few-Shot Learners. OpenAI. - 75 pages

Content - Articles

  • Roose, K. (2020, April 16). Welcome to the ‘Rabbit Hole.’ New York Times. - 2 pages

  • Bessen, J. (2016, January 19). The automation paradox. The Atlantic. - 5 pages

  • Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: Automation, employment, and productivity. McKinsey Global Institute. - 28 pages

Content - Online Courses

  • AI For Everyone, Andrew Ng, Coursera - 10 hours

Tools to gain practical experience (only include tools relevant to learning goals)

  • Large language model (e.g., ChatGPT)

  • Image generator (e.g., DALL-E)

  • Video creation model (e.g. Lumen5)

  • Music generator (e.g., Soundraw)

  • Text-to-speech (e.g., ElevenLabs)

  • Presentations (e.g., Beautiful.ai)