gabipurcaru
Joined 14 years ago
Joined 14 years ago
"The 2 May 2023, 6 months later, the regulation started applying and the potential gatekeepers had 2 months to report to the commission to be identified as gatekeepers. This process would take up to 45 days and after being identified as gatekeepers, they would have 6 months to come into compliance, at the latest the 6 March 2024.[8][32] From 7 March 2024, gatekeepers must comply with the DMA. [33]"
Legitimate scrapers, maybe. Everyone else does it to circumvent the API limitations, by posing as real traffic. APIs imply API keys which can be traced and banned.
The paper mentions accuracy i.e. (true positives + true negatives) / total examples. And it's actually 100% accurate i.e. there are no false positives _or_ false negatives.
But the big caveats are:
1. this was tested only on 180 examples, which is a very very small dataset to draw conclusions on, and
2. this is obviously an adversarial space so any classifier will be obsolete with the next training run
I'm bearish on any attempt to distinguish real content vs. AI generated content (on any medium, text, image or anything else). This is an adversarial game and the AIs can incorporate your fancy algorithm to fool you better. In the end these projects only end up improving the AI models in terms of realism.
- calorie counting or diets: just write down what you ate, and it should be good enough to compute calories, macros etc.
- gym logging: write down when you went to the gym and what you did, and it should give you tips, help you maintain your routine and other helpful things
We did no specific training for these exams. A minority of the problems in the exams were seen by the model during training, but we believe the results to be representative—see our technical report for details.