Introduction to Artificial intelligence test fully solved & updated

Session 1: What are the big AI questions 1. Can we build machines that think? 2. Can we build machines that learn? 3. Can we build machines that are more intelligent than us? 4. Can we build machines that are creative? 5. Can we build machines that have emotions? 6. Can we build machines that are conscious? Session 1: What is AI? Artificial: Computational agents, either robotic or software. Intelligence: Agents capable of achieving goals in the world. "the science of making machines do things that would require intelligence if done by men" Brainpower Read More Session 1: What are the goals of AI? Strong AI vs Weak AI General AI vs Narrow AI Science AI vs Enginereeing AI Session 1: What is Strong AI The goal is to understand the human mind/brain as a computational device. Specific in the sense that we are focused on the human mind/brain. General in the sense that humans can solve an astonishingly wide range of problems. can construct machinery capable of thought, emotions, and any other quality we attribute to humans. Session 1: What is Weak AI The goal is to develop intelligent machinery that is not necessarily like us, but nevertheless solves some problem. Specific in the sense that a single, isolated problem is usually considered. General in the sense that any kind of problems/techniques might be entertained. We are interested in machines that solve tricky problems in ways perhaps not found in nature, independent of whether "strong AI" is possible Session 1: What is general AI Real progress is likely to come from viewing intelligent systems as whole. Set human level performance as the goal, and abandon efforts incapable of reaching this goal. Seek early integration of AI's subfields. Session 1: What is narrow AI Real progress is likely to come from breaking the big problems down, and tackling smaller problems. Seek incremental improvements. Rome wasn't built in a day. Let each subfield solve it's own problem first. Session 1: What is Science AI Reverse-engineer the mind/brain. The object of interest exists in nature. Investigate the hypothesis that humans are machines. AI as a science could fail. Session 1: What is Engineering AI Engineer clever machines. The objects of interest are unknown, and are to be discovered/created. Independent of human nature, can we construct clever machines? AI as engineering Has already succeeded. Session 1: What examples illustrate the diversity of AI? in goals of AI. Session 2: What is the origin of computers The workers carrying out these calculations were called "computers". De Prony lead this huge effort. Session 2: What did Alan Turing contribute to cognition and computation Alan Turing asked the question "Can machines think?" rather than "Is the mind a computer? I Turing considered the similarity "very superficial". Session 2: When did the first programmable computers arrive? ww2 1939 Session 2: What did Newell and Simon hypothesize and what was it called? The physical symbol system hypothesis: a physical symbol system has the necessary and sufficient means for general intelligent action. Session 2: How did Newell and Simon view computers? as symbol manipulators Session 2: Who was the person that said to replace people with a machine as computers? 1812 Charles Babbage Session 2: Who was the one to notice the resemblance between mind/brain in computation and cognition Alan turing Session 2: What is computation? Defining it is arguably impossible There are different models of universal computation Session 2: What does computation rely on? The Turing machine Session 2: What is Computational intractability? Session 2: What are computational Intractable problems? are those that demand infeasible resources to solve. Key resources are time ( the solution takes too long to compute) and space (the solution required too much memory). the time required to solve instances of the problem grows exponentially with the size of the instances. Session 2: What are computational Uncomputable problems? problems that cannot be solved by any computer. Some problems are provable uncomputable. Session 2: What are the limits of computation? Computability and intractability. Session 2: What kind of problems are computationally intractable? problems that require superpolynomial time to solve Session 2: How do humans deal with computationally intractable problems? On the Traveling salesperson problem, very well. Session 2: What do we mean by computational tractability? Session 2: Do humans struggle with intractable problems? Session 3: What is Turing's proposal to replace the question with the imitation game Session 3: What question did Turing want to address? "Can machines think?" Session 3: What is the problem with what Turing wants to address? the terms "machine" and "think" are too vague