Module Check: Toward More Capable AI
You have traveled the full arc of Module 3. You started by mapping the dimensions along which AI grows more capable — data, compute, and smarter algorithms. You explored how scaling became the dominant strategy of the past decade. You examined reasoning models that think step by step, agents that pursue goals autonomously, and AI systems that extend their reach through tools. You wrestled with the concept of AGI, looked honestly at where AI surpasses humans and where humans maintain advantages, and considered why greater capability demands greater responsibility. This check lesson locks those ideas in place and connects them into one coherent picture.
Key Terms Review
Flashcards — click each card to reveal the answer
Module Quiz
A researcher doubles the number of parameters in a language model, doubles the training data, and doubles the compute budget. According to the concept of scaling laws, what should they expect?
A student uses a language model to solve a ten-step logic puzzle. The standard model gets it wrong, but a reasoning model with chain-of-thought gets it right. What most likely explains the difference?
An AI agent is given the task of booking travel for a three-city business trip. It books the first flight correctly, but misidentifies the second city. It then books a hotel and car in the wrong city. Which concept describes what happened?
An AI assistant has access to two tools: a web search tool and an email-sending tool. Which tool requires much greater caution, and why?
AlphaFold solved the protein structure prediction problem at a level no human team had achieved. At the same time, robotic hands still struggle with tasks a two-year-old can do. What does this pattern reveal about AI capability?
A company builds an extremely capable AI system and deploys it to automatically handle all customer service interactions, make refunds, and update account records — without any human review. Which responsibility concept is most directly at stake?
AI capability grows along multiple dimensions simultaneously — more data, more compute, better algorithms — and this growth is producing systems that can do things that seemed impossible just a few years ago. Understanding what drives that growth, what it enables (reasoning, agents, tools), and what it demands of us (alignment, oversight, equitable access) is the intellectual foundation for engaging responsibly with the most transformative technology of your lifetime.
Synthesis: Letter to a Future Student
- Imagine a student taking this same course five years from now. Write them a letter — roughly three paragraphs — that explains the most important things you learned in this module.
- Paragraph 1: Explain what makes AI more capable and why scaling mattered so much in the early 2020s. Use at least two specific terms from the module.
- Paragraph 2: Explain what reasoning models and AI agents can do that earlier AI could not, and describe one genuine risk that comes with these new capabilities.
- Paragraph 3: Explain what AGI means and why you think the question of AI matching humans is more complicated than a simple yes or no. End with one piece of advice for that future student about how to think carefully about AI capability claims they will encounter.
- Write clearly and precisely — as if your future reader knows a little about AI but has not yet taken this module.