Don't depend on just one AI checker No single checker can guarantee complete confidence. We integrate with leading AI content detectors to give unparalleled confidence that your content appears to be written by a human. One-click, Eight Checks Demo link story.screenspace.io/2161397443/0cb05324
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Content Guardian is the first multi-AI Content Detector to connect to multiple content checker services simultaneously. 8 checks in 1 click.
Content Guardian AI's answer
AI is a very fast-paced environment with new advances happening weekly. Not all AI Content Detector models can keep pace. This is where Content Guardian comes in. You can get a much higher level of assurance about the accuracy of the results as we check up to 8 checkers and provide an aggregate score based on our algorithm. We update this regularly so it is reflective of the latest developments in AI LLM.
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We have 3 target audiences. Writers, Media publishers and Education.
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Content Guardian AI's answer
Content Guardian was born out of necessity. We all work with external collaborators and we wanted to have confidence that the content was authentic and original.
We felt having to rely on one AI checker left the business open to a wider margin of risk that it was solely generated by AI, so we started to use multiple AI Content Checkers.
This became very cumbersome for the team, so we decided to create a tool that seamlessly integrates into our workflow whilst connecting to multiple AI Content Checkers. We wanted to build a tool that provides convenience and higher assurance that we can spot unauthentic content.
The tool is ready to be released to the wider world. So here it is!
We will be actively developing the tool with new features and tweaks to make it the best tool it can be.
Based on our record, WordNet seems to be more popular. It has been mentiond 28 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
TL;DR: The authors pretrain the model to classify images into Wordnet synsets[a] that appear in the caption, using a standard Cross Entropy loss. They keep the number of classes relatively small by removing any synsets that don't show up in captions at least 500 times in the dataset. It seems to work well. My immediate question is: Why not classify among the entire hierarchy of all Wordnet synsets? --- [a]... - Source: Hacker News / 17 days ago
To operationalize this intuition, the Microsoft and UC Berkeley researchers use WordNet and Wiktionary to augment the text in image-text pairs. The concept itself is augmented for isolated concepts, such as the class labels in ImageNet, whereas for captions (such as from GCC), the least common noun phrase is augmented. Equipped with this additional structured knowledge, contrastively pretrained models exhibit... - Source: dev.to / 2 months ago
If you like this, definitely check out WordNet (https://wordnet.princeton.edu/). - Source: Hacker News / 5 months ago
I didn't understand well what you meant, but maybe this site can help you: https://wordnet.princeton.edu/. Source: over 1 year ago
What I'd do is work with a huge database like WordNet and then try to "extrapolate" BIP39 to 4096 words by creating queries against WordNet to obtain words meeting the constraints you'd like to keep. Source: over 1 year ago
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