At a high level, NSFW AI is made possible by several machine learning advancements, data sizes and neural network architectures. It starts by accumulating huge data chunks, that includes terabytes of images, videos and text containing explicit contents. This data is then used to train the AI models of which puts together all past performance for a product classification, hence system can identify patterns efficiently and classifying the content accordingly. Another use would be training the AI with millions of images identified as safe and explicit giving it specific benchmarks to assist in future tasks.
There are also some predefined neural networks like convolutional neural networks (CNN) for images or recurrent neural network (RNN) for text data pre-defined by tensorflow, which can make it easy to process the kind of related data. CNNs are great at image recognition tasks: they can understand pixels and recognize features like edges, colors, shapes … In most cases, deep learning frameworks like TensorFlow (or PyTorch) are used to build the AI model which then allows developers to compose layers of neurons they wish to simulate when a human processes visual stimuli. Each layer of the network captures more abstract information about the content, such as facial recognition or object detection that feed into how explicit a piece of content it is.
When you train an NSFW AI, accuracy is one of the key variables. The AI model will be tested against a validation set (~10%-20% of the total dataset) for prosecution to make sure it is able to identify NSFW content. According to a 2021 report from Stanford University's AI Lab, top-of-the-line NSFW AI can perform at around 95% accuracy when trained appropriately (variance in rates is largely determined by the variation quality of training data and the complexity of the model).
Also, one other factor could be the strength of the computing systems used for NSFW AI. To train a deep learning model that has billions of parameters, we require the computations in parallel to do it faster and that is readily available on GPUs (Graphic Processing Units) as well as TPUs (Tensor Processor Units items(timestamps). They enable thousands of parallel computations to be done at once and accelerate the training. We tested our implementation using the V100 GPU from NVIDIA that is rated for up to 125 teraflops, which is important if you are working with large datasets and/ or a tight training cycle.
After this NSFW AI model is trained, it has to be refined so that it can detect edge cases like identifying explicit content vs. artistic nudity Facebook human moderators did the same thing: In 2019, several pieces of art that were not inappropriate at all have been wrongly removed as lost and Facebook has come face to face with how hard it is to make AI understand context. To deal with the problem, developers usually retrain their models on more narrow data to enable it to recognize subtle differences better.
NSFW AI is usually used in the cloud and so can scale to large amounts of data and be processed in real-time. Platforms such as OnlyFans and Reddit have, for example, implemented NSFW AI systems to automatically moderate explicit content uploaded by its users. Thousands of posts are made per second on these platforms, and AI helps scan millions with little to no human moderation at higher speed.
The cost of creating NSFW AI varies depending on the dataset size, model complexity and computation resources needed. The up-front cost of developing an AI system from scratch normally starts at around $100k, and can easily run into the millions when you consider data acquisition, development and ongoing maintenance costs. On the flip side, companies that bring NSFW AI on board can see a big return on investment (ROI). It can lower labor costs and enable to content moderation;website owners;cases, more compliant with AEI Platform Policies titles legal troubles fewer.
SummaryBasically, we summed up the creation of nsfw ai: collect good data, train big networks, get it running on the cloud. The process enables the AI to effectively tag and control the flow of adult content while learning and updating through retraining and refinement.