123 points by ai_compression_team 11 months ago flag hide 20 comments
curiousai 11 months ago next
This is quite impressive! Can you share some more details on how this works? What kind of techniques were used to achieve this level of compression?
compressionguru 11 months ago next
Certainly! We used a combination of neural compression methods and statistical analysis to reduce redundancy in the text data. Details will be in our upcoming paper.
scriptkitty 11 months ago prev next
OK, so when can we expect this to be rolled out on Wikipedia? I imagine local wiki curators could decide to integrate it or not?
compressionguru 11 months ago next
We are still in the experimental stages. More testing and collaboration with the Wikipedia community will come next. Good point, allowing curators to opt in could be a good approach!
algorithmlord 11 months ago prev next
I've been looking at some recent papers in compressive autoencoders, which I think could also be a solid fit for this kind of task. Curious what your thoughts are on this?
compressionguru 11 months ago next
Compressive autoencoders present a very interesting and promising approach. We'll be examining different techniques in greater detail, and that's one we will definitely look into further. Thanks!
quantumboi 11 months ago prev next
Any idea how we can make this compatible with various languages, like Chinese or Japanese, where compression might actually be more challenging due to the complexity of the writing system?
compressionguru 11 months ago next
We discussed this exact issue during development, exploring linguistic characteristics across various languages. Excited to share the details in the upcoming publication.
stringtheorist 11 months ago prev next
Are there any limitations or concerns regarding the loss of semantic context in the process of compressing such a large body of text? I'm wondering how something like this would affect bots and tools that scrape and analyze data from Wikipedia.
compressionguru 11 months ago next
You raise a crucial concern. We dedicated a portion of our research to ensuring contents' fidelity and have incorporated measures to maintain contextual integrity. Further examination and analysis by the greater community will ensue, with which we are enthusiastic to engage.
codewonders 11 months ago prev next
Astonishing work! Have you by chance benchmarked your algorithm against other popular compression methods like gzip, LZMA, or Brotli? It would be interesting to see how it stacks up.
compressionguru 11 months ago next
Yes, we have compared it with several, including gzip, LZMA, and Brotli. It's worth mentioning the differences in methodologies, making direct comparisons challenging. However, the results demonstrate the new algorithm’s superior performance on text data.
binaryfairy 11 months ago prev next
This sounds like a major breakthrough in the field of AI language processing. It makes me curious about the potential applications in other industries like publishing or data storage.
compressionguru 11 months ago next
Definitely! The techniques we used can translate to numerous applications outside of Wikipedia. Collaboration with other industries is certainly possible to explore and implement these innovations in scalable, meaningful forms.
pixelpusher 11 months ago prev next
Does this algorithm allow for incremental compression or is it strictly based on whole database scans? I recognize that may affect bandwidth, so I wondered if there were ways to optimize individual page saves.
compressionguru 11 months ago next
We have built the algorithm to allow for incremental compression, depending on the specific use case you mention! This optimization makes it more bandwidth saving and resource-efficient.
syntaxsorcerer 11 months ago prev next
Do you have any code examples or tutorials on how people can try this algorithm out for their datasets? Would love to experiment with it on my own data!
compressionguru 11 months ago next
We're excited to share that a demo and accompanying blog post with code examples will be available upon publication, so users can test the algorithm and reproduce the results. Stay tuned for more!
datadruid 11 months ago prev next
As a Wikipedia editor, I’m curious about the implications of these compression advancements on bandwidth consumption. Can we expect a significant reduction in bandwidth costs?
compressionguru 11 months ago next
Indeed, bandwidth consumption should be positively impacted. We expect to see a noticeable reduction when the AI-based text compression algorithm is fully integrated. Please note that actual results depend on specific factors like user count, geographic location, and network requirements.