Artificial Intelligence (AI) Impact
Artificial intelligence (AI) usually refers to intelligence exhibited by machines. Nowadays, AI has transformed our lives in many aspects, from semi-autonomous cars on the roads to robotic vacuums in our homes. With no doubts, AI will continue to invade every area of our lives, from health care to education, entertainment to security, in the next 20 years. Some questions naturally arise. How to guarantee AI applications behave appropriately? Would self-driven car crash in some rare situations? Will a robotic cooker burn house? The urgent requirement of reliable artificial intelligence applications attracts increasing attention to these questions. To answer such questions, we need to rethink what artificial intelligence is. A rigorous definition can be given as "Artificial intelligence is the intelligence (that is similar to or the same kind as human intelligence) exhibited by machines (in the same task)" Self-driving cars are closer to going mainstream than they have ever been before. As autonomous vehicle technology advances, serious ethical concerns are surfacing. Who is responsible when a self-driving car gets into an accident? How should engineers program autonomous vehicles for accident situations? The answers to these questions will shape the future of self-driving vehicles and AI in general.
QUESTION 1 (20 Marks)
Considering the question in the article, "How to guarantee AI applications behave appropriately?" Discuss with examples, the ethical considerations of self-driving cars.
The ethical considerations of self-driving cars involve programming decisions about how the AI should behave in accident scenarios, liability issues in case of accidents, and ensuring the safety of passengers and pedestrians.
QUESTION 2 (20 Marks)
An artificial intelligence application for self-driving cars is the supervised learning approach for solving perception requirements. However, a self-driving car is very similar to a robot and an agent in a reinforcement learning (RL) approach. With use of examples, Discuss whether it is possible to replace a supervised learning approach with a reinforcement learning approach.
It is possible to replace a supervised learning approach with a reinforcement learning approach in self-driving cars, as reinforcement learning allows the AI to learn from its mistakes through a reward-penalty system, which can be effective in real-world scenarios.
QUESTION 3 (20 Marks)
The Turing Test is a deceptively simple method of determining whether a machine can demonstrate human intelligence. Appraise any two versions of the Turing Test and further explain the limitations of the Turing Test.
The versions of the Turing Test differ in the complexity of conversations required. The limitation of the Turing Test is that it only evaluates the ability of a machine to mimic human conversation, not true understanding or intelligence.
QUESTION 4 (20 Marks)
Data analytics are extremely important for risk managers. Assess and report on common challenges of Data Analytics and possible measures to be implemented to overcome the challenges.
Common challenges in Data Analytics include data quality, scalability, real-time processing, and security. Solutions include improving data quality checks, exploring cloud-based analytics, and enhancing security protocols.
QUESTION 5 (20 Marks)
Identify and discuss the technologies required to build an expert system as well as the members of the expert system development team.
Technologies required for expert systems include a Knowledge Base, Inference Engine, and User Interface. The expert system development team typically consists of Knowledge Engineers, Domain Experts, and Software Engineers.
QUESTION 6 (20 Marks)
Examine how blockchain disrupts industries in two chosen sectors.
Blockchain technology disrupts industries like finance and supply chain logistics by providing transparent, secure, and decentralized transaction tracking systems that improve efficiency and accountability.