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How to use Slurm to scale up your ML/Data Science workloads 🚀

While AI has progressed in classifying content, human moderators remain key for online platforms. AI lacks nuanced understanding of context and culture needed for some moderation decisions. Laws also mandate human oversight. However, AI will likely take a larger role, requiring responsible implementation with transparency and accountability.

Old moderation methods are not keeping up with the rise of video. Current analysis lets harmful content slip through, failing to account for nuance and context. We need advanced, multimodal solutions, to ensure online safety for brands and users alike.

How to set up and use an HPC Cluster to scale up ML experiments 🚀

This article underscores the challenges in detecting Child Sexual Abuse Material (CSAM) online, spotlighting the limited efficacy of hash matching and the prospective advancements of AI classifiers. As legal frameworks like the UK's Online Safety Bill evolve, they may propel enhanced detection technologies, despite prevailing privacy and technical hurdles. Overall, this progression may lead to mandatory technology adoption and elevated live video content detection, marking a significant stride towards combating CSAM.

The implementation of watermarking for AI-generated content poses several challenges. This Deep Dive explores the complexities of watermarking for different media types, including images, audio, video, and text. And highlights the difficulties in creating robust watermarking techniques that can withstand various attacks.

In discussions about AI regulation, there has been relatively little of direct relevance to trust & safety. Most of the focus is on use by regulated organizations rather than misuse by platform users. However, as large platforms play increasingly significant roles in providing access to AI models, we might expect future regulatory efforts to more directly impact T&S.

Concerns about bias in social media content moderation have prompted the drafting of laws and the need for better explanations. Studies show users perceive automated moderation as more impartial with human oversight and that trust in AI moderation increases with transparency.