Research questions from the list below and give your answer, with reasons and examples. If you are reading this chapter as part of a class – either at a university or in a commercial course – work in pairs then report back to the class.
1) Define technology utopianism? What is the technology singularity? Give examples from movies. What is the big assumption behind the claim that computers will take over from people? Do you think computers will supersede people by 2025? Give reasons for your view.
2) What technology advances did the last century expect by the year 2000? Which ones are still to come? What do people expect robots to be doing by 2050? What is realistic? How successful are mimic robots like Asimo and the Sony dog? How might social-technical design improve the Sony dog? How will robots evolve in the social-technical paradigm? Give promising examples, like elderly care.
3) If super-computers with many processors can equal one human brain, are many brains together more intelligent than one? Look at the psychology of crowds to argue whether people are dumber or smarter together. Does adding more programmers to a project always finish it quicker? What, in general, affects whether a system of many parts performs as the sum of those parts? Is a super computer, with as many transistors as the brain has neurons, necessarily its processing equal? What is the difference?
4) How do today’s super computers increase processing power? How many of the top ten processor cores use NVidia graphic board cores? How is this power utilized in real computing tasks? What decides whether many processing cores can operate in parallel on a problem? (computer science students only).
5) Review the current state-of-the-art for fully automated vehicles, like car, plane, train, etc. How many cases are implemented? Compare fully automated with remotely piloted vehicles. When does full computer control work and when not? (Hint: consider how phone help systems evolved). When might full computer control of a vehicle be useful? Suggest how computer control of vehicles will evolve, with examples.
6) What is the 99% barrier? Why is the last 1% of accuracy a problem for productive tasks? Give examples from language, logic, art, music, poetry, driving and another. How common are such tasks in the world? How does the brain handle them?
7) What is a human savant? Give examples past and present. What tasks do savants do easily? Can they compete with modern computers? What tasks do savants find hard? What is the difference? Give examples of areas where savants need support. If computers are like savants, what support do they need?
8) Find three examples of software that, like Mr. Clippy, thinks it knows best. Give examples of: 1. Acts without asking, 2. Nags, 3. Changes secretly, 4. Makes you work.
9) Think of a relationship issue you would like advice on and form a clear question, like “Should I argue with my mother when she criticizes me?” Now ask the same question in these three ways:
a) Go to your bedroom alone and put a photo of family member you like on a pillow. Ask the question out loud then imagine their response.
b) Go to an online computer advisor like Cleverbot and do the same.
c) Ring an anonymous help line and do the same.
Compare and contrast the results. Which was the most helpful?
10) A rational way to decide is to list all the options, assess each one and pick the best. How many options are there for these contests: 1. Checkers, 2. Chess, 3. Civilization (a strategy game), 4. A MMORPG, 5. A debate. Which ones are computers good at? What do people do if they cannot calculate all the options? Can a program do this? How do online gamers rate human and AI opponents? Why? Will this always be so?
11) What is the difference between syntax and semantics in language? Which are programs better at? How successful are text-to-speech systems like NaturalReader for some text like a poem? What is the computer doing? Now with a friend who knows another language, try language-to-language translators like Google Translate on the same poem. How good is the computer at semantic level transformations? Discuss using John Searle’s Chinese room thought experiment.