The Internet of the future might be something completely different from what we know currently. But this change will be for the better good or worse. Experts from Copenhagen Institute for Future Studies (CIFS) have raised questions about AI-generated content, and how it might rule digital locations and the much-hyped metaverse. According to CIFS expert Timothy Shoup, 99% or more of the internet's content will be generated by artificial intelligence by 2025 to 2030, especially if models such as OpenAI's GPT-3 witness a wider use.

According to CIFS expert Timothy Shoup, 99% or more of the internet's content will be generated by artificial intelligence by 2025 to 2030, especially if models such as OpenAI's GPT-3 witness a wider use. "The internet would be completely unrecognizable," Shoup told colleague Sofie Hvitved. As the capabilities of AI advance, it could start creating entire online worlds, alongside all the things that inhabit them. This will also include all the online material that humans make use of even now. This could give birth to things that we could only imagine right now.

Shoup: I'm going to share very little of my personal knowledge, in fact, none of it, but I'm going to talk about a cool paper that I really like. Then Gwen [Shapira] is going to talk about another cool paper and Roland [Meertens] is going to talk about yet another cool paper. The one I want to talk about is a paper that's around using machine learning to do database indexing better. This is a picture of my bookshelf at home. A while ago, I bought myself a box set of "The Art of Computer Programming", which has basically all of computer science algorithms written by or assembled by Don Knuth. There's 4a, so he's still working on completing the thing, hopefully, that will happen. When we're choosing a data structure, typically we're choosing it in this way, we are trying to look for time complexity, how fast is it going to run, and space complexity, how big is it going to be? We typically evaluate those things asymptotically, we're not looking as much at real-world workloads, but looking at what are the complexity characteristics of this thing at the limit when things get very large? We're also, and this is critical, looking at those things without having seen the data and without having seen typically the usage pattern. We're doing is we're saying what is the least worst time and space complexity, given an arbitrary data distribution and an arbitrary usage pattern? It seems like we could do a little better than that, that's what this paper is about. What we'd like to be able to ask or to be able to answer is how could we achieve the best time/space complexity given a specific real-world data distribution and a specific real-world usage pattern.