Humans still better than AI for hotdog or not (www.humanprotocol.org)

Ainews247 evaluated Google Cloud Vision’s Safe Search Detection as an automated image-moderation layer by running a 500-image subset (from a 2,000-image mice dataset) with a few intentionally injected explicit or violent images. GCV reliably flagged the injected harmful images with high-likelihood labels (e.g., VERY_LIKELY/LIKELY for adult and violence), but it also produced 15 false positives—safe images of mice wrongly labeled as violence, racy, or adult (≈3% false-positive rate). The authors conclude GCV is useful as an initial filter but not accurate enough to be a standalone moderator. The analysis highlights practical technical constraints that matter to ML practitioners: Safe Search scales efficiently only when images are in Google Cloud Storage (GCS) where asyncBatchAnnotateImages can be used, and even then requests are limited to 100 images each, forcing batching logic for large datasets. Images hosted outside GCS require synchronous, one-by-one calls that severely limit throughput. Overall, the takeaway for the AI/ML community is clear—cloud-safe-search APIs can reduce human workload, but operational complexity, nontrivial false positives, and precision requirements mean a human-in-the-loop (or additional validation models) remains necessary for trustworthy moderation.