How the neural network will evolve

[sadiksojib35]

The implementation of the marking could be implemented through algorithms that create unique noise patterns that the neural network can recognize. However, in order for the hidden pattern not to degrade the image quality, it must be embedded in small quantities and irregularly.

Such markings may be more noticeable in small areas of the image, where the pattern appears as a separate element. Large language models (LLM) work on roughly the same principle, embedding markings at the level of predicted tokens.

While companies like OpenAI claim that this does not affect the quality of the text, differences are sometimes visible when comparing labeled and unlabeled text, so such approaches have not yet gained widespread adoption.

Labeling generated images helps differentiate belgium phone number lead between AI-generated content and real images. Social networks, for example, can build in special algorithms to recognize hidden signs.

When loading an image, such algorithms scan the file: if they find a hidden pattern, they add a label; if not, the image remains unmarked.

Without such markers, social networks have few tools to reliably determine whether an image is AI-generated, so the pressure on developers like Midjourney to add such labels is justified.

In the future, major services will likely be required to include hidden labels to combat fake content. This is especially relevant in the context of the spread of fake news, supported by realistic images that many people believe.

There are many local models that run directly on users' computers and generate decent quality images. These local models do not provide for labeling, and no one can force them to do so.

On Topic. New Midjourney 6.0: How to Compose Queries That Are Beneficial for Business



At first, Midjourney focused on the quality of generation, and then gradually began to complicate its product. It is impossible to create a very complex system at once: development must proceed step by step, from simple to more complex. A logical step is a gradual increase in functionality.

It can be assumed that within a year it will be possible to upload small photographs, which the neural network will complete into a full-fledged scene, or even a 3D model.

If you upload a photo of a monument, in theory the model will be able to reconstruct the area around it, creating a sense of presence where the user can look around and explore the space. So far this can only be partially implemented through functions like Resize, but a full 3D model of each object in the photo, where angles can be changed, is the next technological frontier.

By loading an image into a neural network, it will be possible to rotate it to view the scene from a different angle. There are no such implementations yet, but this is a direction that will most likely be reached over time.

The API capability for Midjourney is the next strategic step to enable their technology to be integrated into third-party services, including social networks and apps like Instagram*.

Midjourney doesn't have a public API at the moment, but it wouldn't be wise to give up on that part of the market. If they don't offer an API, platforms like Stable Diffusion could fill that niche by upgrading their products to the right level and opening the door to integrations.