According to the so-called godfather of AI, Nobel Prize winner Geoffrey Hinton, what two factors make artificial neural networks fundamentally more scalable than biological human learning?

Prof. Walker from UCSC told us about an example where she was involved in a pioneering development of a technology (personality detection through NLP). She said it was eventually put to ends that were not her intention. What was her final takeaway for how to deal with such issues?

When setting up an agent-based computer simulation, you can adjust several aspects. Which aspect would you NOT adjust from the outset, before running your model?

What fundamental insight about social emergence connects Adam Smith’s “invisible hand” to Rousseau’s “volonte generale”?

Prof. Frey’s lecture draws a fundamental distinction between “playing for the mean” and “playing for the max.” How does this distinction dictate the optimal strategy for decision-making in the provided model?

In the lecture’s Schelling run, why does increasing the threshold from 75% to 76% illustrate a phase transition in the macro outcome?

What does it mean that a network is scale-free?

Why it is worthwhile for companies to pay celebrities thousands of dollars to post a tweet?

How does the lecture differentiate the primary application of Recurrent Neural Networks (RNNs) from that of Convolutional Neural Networks (CNNs)?

During his lecture, Prof. Frey, from UC Davis, wrote a few lines of code to assist you in decision-making. His initial hypothesis was that it is better to take safe bets for important decisions. Going systematically about it with a computer model, what was the result?