The development of Artificial Intelligence (AI) through knowledge engineering aims to create systems that can perform tasks like humans, such as financial advising or decision-making. Replicating the human mind in computational form is an ambitious goal, especially since its workings remain largely mysterious. Progress has been made with models like neural networks, which draw from cognitive theories, psychology, and human behavior, pushing AI beyond traditional approaches.
However, as AI systems grow more complex, they require increasing computational power and resources. There’s also a risk of oversimplifying human intelligence, assuming it can be fully replicated by machines. AI systems are tools that appear to think, but they do not possess true consciousness. This can shift responsibility from creators to machines, which is problematic when trying to find accountability.
The use of AI to replace human jobs raises ethical and social concerns, such as job displacement and the reduced role of humans in certain sectors. These challenges highlight the need for careful consideration of AI’s potential benefits and risks, particularly its environmental and social impact. Responsible development requires balancing technological progress with the broader implications for society.
Sources
Knowledge Engineering: What it Means, Examples
New Scientist Essential Guide No.23 The AI Revolution