The global visual content market is expected to reach a value of $162.4 billion by 2023.

It is expected that the dataset market will continue to grow rapidly in the coming years due to increasing demand for data-driven insights and decisions. According to one estimate, the global dataset market could reach $54 billion by 2025.


AI generated content is content that is generated by an artificial intelligence algorithm. It can include anything from articles and blog posts to images and videos. AI generated content is often used to create large amounts of content quickly, such as for marketing and advertising campaigns.

Synthetic media (also known as artificial media or computer-generated media) is media content that is created or modified through the use of computer technology. Examples of synthetic media include computer-generated images, audio, video, and text.

In the past, humans had to have sufficient means in terms of talent and economic power in order to create outstanding visual content. The content created was in most cases static – meaning, discrete packages of fixed information which could be modified only with specialized software like for example Adobe’s Photoshop.

This is the past.

In the future, AI will create unique content dynamically and on the fly for a production cost of almost zero.

AI generated media could have a major impact on the content industry, allowing for greater automation and increased efficiency in content production. Automated content generation could open up new opportunities for content creation, allowing for more efficient and cost effective production of media. AI generated media could also be used to create more personalized and targeted content, allowing for a more tailored approach to content creation. Additionally, AI generated media could be used to identify trends and create more engaging content, helping to drive viewership and engagement.

A content gap is an area or topic that isn’t being covered by existing media supply channels.This can be an opportunity for businesses to create content that will be beneficial to their audience, as it will fill a need that is not being met by other sources. Content gaps can be identified by researching what content is already available. Often content gaps are provoked by subjects which are expensive to produce and cover or where the main subject in terms of location, actors or production is hardly or not at all available to content producers. AI generated media can cover content gaps without further investment of time or money.

Visual generative AI is a type of artificial intelligence (AI) used to generate new images and videos from a set of data. This type of AI is typically used to create realistic images and videos from a given set of input data. The AI is trained on a large dataset of images and videos. It then uses deep learning algorithms to generate new images and videos that appear to have been created by a human. The AI can also be used to manipulate existing images and videos in order to make them more realistic or to add special effects. Generative AI can be used in a variety of ways including creating animations, generating art, and creating virtual characters.

Synthetic datasets are available on datasetshop.com. The API to deliver on the fly generated humans will be available in Q2/2023.


A image dataset for AI training is a collection of digital images used to train an AI model.

vAisual’s datasets offer high quality biometric data. All AI models qhich require photos of humans can make use of our dataset. Examples are facial recognition, synthetic media generation or emotion detection.

Yes. VAIsuals proprietory as well as legally clean third party datasets are available to be licensed under datasetshop.com .

A legally clean dataset is a dataset that does not contain any personally identifiable information (PII) or any other data that may be legally restricted. This includes data that is subject to copyright, requires a specific license for use, or is otherwise not allowed to be shared. Such datasets typically include only data that is publicly available or that has been scrubbed to remove any sensitive information.

PII (Personally Identifiable Information) is any type of data that can be used to uniquely identify an individual. Examples of PII include a person’s full name, photo, address, social security number, and date of birth.

The consequences for violating US privacy regulations can vary, depending on the violation. Generally, the penalties for violating US privacy regulations can include fines, civil lawsuits, criminal prosecution, and revocation of licenses. In addition, non-compliance with US privacy regulations can result in reputational harm, loss of customers, and other financial losses.

GDPR (General Data Protection Regulation) is a regulation in European Union law on data protection and privacy for all individuals within the European Union. It sets guidelines for the collection and processing of personal data of individuals within the EU, and also addresses the export of personal data outside the EU. It aims to give EU citizens control over their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU. Photos of a recognizable person is onsidered personal data and is protected under the GDPR.

The fines for violating GDPR regulations can range from €10 million or 2% of the violating organization’s annual global turnover, whichever is greater, for infringements of data subject rights and obligations, to €20 million or 4% of the violating organization’s annual global turnover, whichever is greater, for infringements of the GDPR’s core principles.


The latent diffusion algorithm is a machine learning algorithm that aims to uncover latent patterns in a dataset. It works by using a series of steps to discover hidden correlations and patterns in the data. First, the algorithm starts by randomly sampling points from the data. Then it computes the similarity between each pair of points by using a similarity measure such as the Euclidean distance. After computing the similarities, the algorithm then constructs a graph of the points, connecting the points with edges that represent the similarity between them. Then, the algorithm uses a diffusion process to propagate information through the graph. This diffusion process is based on a random walk, and it works by stepping through the graph and updating the weights of the edges to reflect the similarity between each pair of points. Finally, the algorithm uses the weights of the edges to cluster the points together into groups that represent the latent patterns in the data.

Generative Adversarial Networks (GANs) are a class of unsupervised deep learning algorithms that use two neural networks to create a generative model. The first network, called the generator, is responsible for generating new data samples, while the second network, called the discriminator, is responsible for distinguishing between real and generated samples. During training, the two networks compete against each other, with the generator attempting to create samples that are indistinguishable from real data, and the discriminator attempting to identify generated samples. The two networks are trained simultaneously, with the generator gradually improving its ability to generate realistic samples, and the discriminator gradually improving its ability to identify generated samples. Eventually, the two networks reach equilibrium, resulting in a generative model that can create realistic data samples.