COMPUTER SCIENCE

Taken from the "Computer Science Guide. First Examinations 2014"
"There is no one scientific method of gaining knowledge or of finding explanations for the behaviour of the natural world. Computer science works through a variety of approaches to produce these explanations, but they all rely on data from observations and have a common underpinning rigour, whether using inductive or deductive reasoning. The explanation may be in the form of a theory, sometimes requiring a model that contains elements not directly observable. Producing these theories often requires an imaginative, creative leap. Where such a predictive theoretical model is not possible, the explanation may consist of identifying a correlation between a factor and an outcome. This correlation may then give rise to a causal mechanism that can be experimentally tested, leading to an improved explanation. All these explanations require an understanding of the limitations of data, and the extent and limitations of our knowledge. Computer science requires freedom of thought and open-mindedness, and an essential part of the process of science is the way the international computer science community shares ideas through academic papers, conferences and open forums. The syllabus details sections in the group 4 guides give references in teacher’s notes to appropriate topics where theory of knowledge can be addressed."

"During the course in computer science a number of issues will arise that highlight the relationships between theory of knowledge and computer science. Some of the questions that could be considered during the course are identified in the following list.

• What is the difference between data, information, knowledge and wisdom? To what extent can computers store and impart data, information, knowledge and wisdom?
• Computational thinking includes: procedure, logic, pre-planning (thinking ahead), concurrency, abstraction and recursion. To what extent are these ways of thinking distinct? To what extent can knowledge in different areas (mathematics, ethics, and so on) be analysed in these ways?
• It has been said that human memory is more like an improvised performance than a movie on a DVD. What does this mean? How does human memory differ from computer memory?
• How does a computer language differ from a natural language?
• What are the differences between representing numbers in denary and in binary? In binary, 1 + 1 = 10. Does this tell us anything about the nature of mathematical truth?
• What are the challenges of creating a computer model of some aspect of the world?
• A chess machine can beat the top human chess players. Does a machine therefore “know” how to play chess?
• To what extent does computational thinking challenge conventional concepts of reasoning?
• How do we know if other humans feel emotions? Can a machine ever feel an emotion? How would we know?
• Was Akio Morita correct when he claimed that “You can be totally rational with a machine. But if you work with people, sometimes logic has to take a back seat to understanding”?
• Does information and communication technology, like deduction, simply allow the knower to arrange existing knowledge in a different way, without adding anything, or is this arrangement itself knowledge in some sense?
• What did Sydney Harris mean when he said that “The real danger is not that computers will begin to think like men, but that men will begin to think like computers”? Was he right, or was this statement based on a misunderstanding of either men or computers?
• What do we mean by “holistic” and “reductionist” approaches to knowledge? What are the strengths and weaknesses of each approach?
• To what extent is it possible to capture the richness of concepts such as “intelligence” or “judgment” via a reductionist approach?
• If we attach a camera or microphone to a computer, it can receive data from the world. Does this mean that a computer can “perceive the world”? To what extent might human perception be a similar process?"