Within this processing context, nsfw ai relies on sophisticated Natural Language Processing (NLP) and machine learning algorithms that have been trained to carefully process language as well as interpret the nuances within imagery. Nonetheless, the ability to reach a high level of contextual accuracy is still not quite there as AI today can tell you what about 85% for simple nuances and will then drop drastically with complex cases or ambiguous situations. For nsfw ai to be good at what it does there has to be a way for whatever deciphering algorithms is being used with the data you want them filtered through must understand context — they have to know that this specific word in fact means something other than its explicit use (or similar image, or who knows how many internet things) just because of where and why it appears.
Semantic analysis—where AI models determine the meaning behind sentences—is one of a handful of key challenges in making context processor friendly. NLP models such as OpenAI's GPT-3 take in the context to process text, sentence by sentence and look words or clauses up against an organisation of a bunches data points for seeking this much particular intent. Still, research reveals that AI misreads intent 20% higher than humans in the case of sarcasm or irony (or implied meaning). In the real world, this can be seen with nsfw ai like Facebook's and Instagram' automated systems blocking content that they do not fe timely categorised anatomical parts from slides or models etc., mostely because their art and educational creators often see examples of artistic work mislabelled.
For ImageBased NSFW AI, it also experienced the same effort as well for differentiating visually between nsfw content and non nswf. Stanford researchers discovered that even with models calibrated to human-level performance, accuracy plummets by 15% in abstract or layered images — scenes like multiple people posing together for a photo against complex backgrounds. While AI algorithms examine different shapes, colours and positions to understand context this method fails terribly in the scene that has artistic or symbolic components due to high contextual interpretation which is required.
We already see companies like YouTube using a hybrid human and AI moderation approach to improve contextual accuracy. It reduces errors HUMAN 30 per cent, particularly in borderline cases but is obviously very labour intensive. As such, human moderators are needed to service the contextless AI — but scale quickly becomes a problem as platforms like YouTube see over 500 hours of content uploaded per minute. Consequently, the automated nsfw ai continue to be one of our first proposed solutions limited by its contextual nature.
Artificial-intelligence ethics has also been a talking point of late, with scholars such as Timnit Gebru arguing that “data bias does matter [to the context] and impact[s].”. because AI often relies on singular training data per culture. This creates a lack of standardization in global applications, hence nsfw ai might correctly understand the context one culture but will completely fail another. For example, a phrase or gesture benign in one place could be loaded with meaning in another — throwing yet more variables into the mix when it comes to context.
Although nsfw ai continues to get smarter about context, a complete understanding is still far from completion — especially in fields that require an intimate knowledge of deep cultural references and intricate aspects. For a deeper dive on how nsfw ai tackles context, check out the latest from our series exploring AI moderation technology: nsfw ai.