Steve and I discuss his book, How to Motivate Your Students to Love Learning, which is both a memoir and a guide for teachers and students to optimize the learning experience for intrinsic motivation. Steve taught neuroscience and engineering courses while running his own lab studying the activity of live cultured neural populations (which we discuss at length in his previous episode). He relentlessly tested and tweaked his teaching methods, including constant feedback from the students, to optimize their learning experiences. He settled on real-world, project-based learning approaches, like writing wikipedia articles and helping groups of students design and carry out their own experiments. We discuss that, plus the science behind learning, principles important for motivating students and maintaining that motivation, and many of the other valuable insights he shares in the book.
We made it to the last bit of our 100th episode celebration. These have been super fun for me, and I hope you’ve enjoyed the collections as well. If you’re wondering where the missing 5th part is, I reserved it for Brain Inspired’s magnificent Patreon supporters (thanks guys!!!!). The final question I sent to previous guests:
Do we already have the right vocabulary and concepts to explain how brains and minds are related? Why or why not?
In the 4th installment of our 100th episode celebration, previous guests responded to the question:
What ideas, assumptions, or terms do you think is holding back neuroscience/AI, and why?
As per usual, the responses are varied and wonderful!
Part 3 in our 100th episode celebration. Previous guests answered the question:
Given the continual surprising progress in AI powered by scaling up parameters and using more compute, while using fairly generic architectures (eg. GPT-3), do you think the current trend of scaling compute can lead to human level AGI? If not, what’s missing?
It likely won’t surprise you that the vast majority answer “No.” It also likely won’t surprise you, there is differing opinion on what’s missing.
In this 2nd special 100th episode installment, many previous guests answer the question: What is currently the most important disagreement or challenge in neuroscience and/or AI, and what do you think the right answer or direction is? The variety of answers is itself revealing, and shows how many interesting problems there are to work on.