Large language models (LLMs), such as the GPT-4 model underpinning the widely used conversational platform ChatGPT, have surprised users with their ability to understand written prompts and generate suitable responses in various languages. Some of us may thus wonder: are the texts and answers generated by these models so realistic that they could be mistaken for those written by humans?Large language models (LLMs), such as the GPT-4 model underpinning the widely used conversational platform ChatGPT, have surprised users with their ability to understand written prompts and generate suitable responses in various languages. Some of us may thus wonder: are the texts and answers generated by these models so realistic that they could be mistaken for those written by humans?Computer Sciences[#item_full_content]

Unlike “traditional” AI that relies on predetermined rules and patterns, generative AI is able to produce novel content—like text, video, images, and music. In a way, unlike old AI, generative AI can think outside the box. Its implications are profound and sprawling, with the potential to reshape virtually every branch of society.Unlike “traditional” AI that relies on predetermined rules and patterns, generative AI is able to produce novel content—like text, video, images, and music. In a way, unlike old AI, generative AI can think outside the box. Its implications are profound and sprawling, with the potential to reshape virtually every branch of society.Machine learning & AI[#item_full_content]

The performance of artificial intelligence (AI) tools, including large computational models for natural language processing (NLP) and computer vision algorithms, has been rapidly improving over the past decades. One reason for this is that datasets to train these algorithms have exponentially grown, collecting hundreds of thousands of images and texts often collected from the internet.The performance of artificial intelligence (AI) tools, including large computational models for natural language processing (NLP) and computer vision algorithms, has been rapidly improving over the past decades. One reason for this is that datasets to train these algorithms have exponentially grown, collecting hundreds of thousands of images and texts often collected from the internet.Robotics[#item_full_content]