Self-Supervised Learning

Implementation of artificial intelligence in various business areas is one of the most important details of running any deals related to the development of innovative intellectual products in the IT industry. Self-supervised Learning is a modern technique that allows you to significantly save on hiring teaching staff since the user has the opportunity not to supervise the machine. If you are interested in mastering this fundamentally new learning system using artificial intellect, we will be happy to help you understand its nuances.

What are AI technologies of the future?

Of course, any achievements in the field of development, improvement, and implementation of AI in the applied field can be called real innovations. However, if you look deeper into such technologies, behind the stunning results is a simple digital algorithm and a combination of primitive puzzles. That is why machine learning is becoming more and more accessible every year for users all over the world.

The architecture of the process is straightforward – neural systems are encoded into each input, after which ordered digital commands form the basis of a lot of complex functions, which gives a large number of iterations of the machine in response to user actions.

Such decentralized systems can scale an infinite number of times, thanks to cloud storage integration. Thus, if the machine’s instruction set is not sufficient, and it stops responding, each cluster can be modified by loading new commands through the global network from the outside.

Modern AI graphics editors

Every advanced Internet user or just a PC has repeatedly faced the problem of identifying an image of interest. Unlike text formats, graphic characters have more variations and their classification is much more difficult.

The modern ImageNet system allows you to recognize a variety of common features on a graphic picture – more than 1000 different criteria, which leads to their multi-stage classification.

It must be said, this technology is considered truly innovative because before it appeared, no developer could accurately control the classification of images.

With the advent of new techniques, the search for graphic files is much faster, since any user can set boundary conditions for a number of similar features, and AI will give a set of approximate results.

Are there similar methods?

Modern artificial intelligence is being developed in such a way as to move away from the typical contest recognition by typing text characters. When abstracting from symbolism, the AI begins to divide each symbol into layers with extreme precision, which increases the efficiency of the result.

If it is necessary to work with text formats, the machine recognizes not only the characters themselves, but also refers to the built-in cloud storage or databases to predict the future structure of the text. To do this, all commands are paired into vector symbols, each of which is a modular cluster for a complex nonlinear dependency and graphics.

How can this Principle Be Applied to Visual Problems?

Self-supervised Learning makes it possible to predict an intermediate context by building a non-linear structure not only for a set of characters but also for distinct words of a graphic image.

Modern visualization search is inherently linked to the SSL principle, where the machine encodes the semantic meanings of characters and compares them to potentially acceptable context variants.

The most common examples of Self-Supervised Learning in practice

Experts identify the following basic Self-Supervised Learning systems that can be found from different developers:

• Rotation. The simplest example of this technique. The machine recognizes the image, after which the same image, but rotated by a certain angle.
• Forced scaling of the image. In this case, the classic similarity rule is applied, when the machine can recognize the same image, but at different scales, accurately defining the distortion index. Such techniques provide many opportunities for scaling the most complex tasks.
• One of the most complex and innovative technologies is the recognition of image details after its arbitrary defragmentation.

Segmentation is performed in three-dimensional space, which complicates the task. When developing applications, an additional coding technique is used, by adding noise effects, picture vibrations. In such situations, AI works according to non-standard schemes and significantly expands the user’s capabilities.

• Assembly of an arbitrary defragmented puzzle. Each patch of the overall graphic structure can have any shape, size, or position. The only thing that distinguishes it from others is the unique location of graphic pixels on each fragment. AI, regardless of external obstacles, recognizes all coordinates. rotation angle, as well as subplot fragment for further fragmentation.
• Fill with color with the selected palette. This takes into account such a feature of AI as the work of neural networks for image compression, as well as its subsequent restoration. Previously, such procedures were always accompanied by the loss of a certain amount of important data. Despite the fact that the input and output data belong to different clusters, distortion does not occur, since the system accurately determines all parameters.

Many developers argue that neural networks have been greatly upgraded in recent years, and today artificial intelligence provides a solution to the most complicated problems. At the same time, the modeling of a nonlinear relationship between symbolic semantics and graphic texture cannot yet be carried out according to a well-developed algorithm. This entails a certain percentage of distortion and errors, but the quality of data processing is constantly improving.

Expected results from implementing Self-Supervised Learning based on AI

With the correct use of Self-Supervised Learning algorithms, specialists can achieve a number of impressive results:

• The machine can perform high-quality classification of complex images, regardless of size and color range.
• AI can scale any data, without being tied to structured clusters, using vector units that are passed through a non-linear layer.
• More and more types of objects are being marked, and the accumulated data, for which huge investments are spent, can be used for the further development of technologies.

The main goal of Self-Supervised Learning based on AI is to train the machine to independently analyze incoming data and create a set of commands without human participation. When such goals are achieved, machines will be able to replace entire office structures, and any business processes will proceed with exceptional algebraic precision. In a word, the spoilers seek to exclude the understanding of human fate.

If you want to get a detailed consultation on the work and possibilities of using AI, contact our company, and our consultants will explain in detail all the principles of integration, as well as provide multifunctional solutions for your business.