How Generative AI Works

Generative artificial intelligence is gradually becoming an integral part of our everyday life. It is of crucial importance to businesses, and many companies have already embraced generative AI initiatives. It already helps to accomplish many business tasks and maximize employee productivity as well as work efficiency.

For instance, generative AI is capable of creating draft documents, preparing replies to incoming emails, generating marketing materials, and much more. Machines are handling routine tasks faster and with greater efficiency, freeing up human workforce resources for far more interesting and meaningful tasks.

Such a rapid increase in the use of generative AI is largely driven by the advancement of modern computers and deep learning. In the future, generative AI will be able to accomplish even more tasks and become even more accessible. Further, we will explore how Generative AI works, its purpose, and why it is so relevant.

Generative AI explained

Generative AI represents a variety of artificial intelligence techniques that can create new content. The data that such AI generates may be completely different, ranging from source code to music.

Generative AI has generative models at its core and these artificial intelligence algorithms learn to generate new content, such as images or text, derived from regularities they have discovered in existing data. For instance, models that predict the next word in a sequence are generally generative since they can produce entire sentences.

For the purpose of creating unique content, effective generative AI models employ deep neural networks. Deep learning generative AI has seen huge development recently as computers have become more accessible and can now process vast amounts of complex data.

Today, immense amounts of data can be processed with the help of so-called foundation models. Such models are the backbone of complicated AI systems and can perform several tasks simultaneously. Foundation models in generative AI consist of neural networks with multiple layers of neurons, which are created in the likeness of the human brain. Due to such architecture, generative AI is considered a deep learning method that can process gigantic sets of data and carry out more than one task at a time.

Such models have already significantly impacted fields such as computer vision and human language processing, enabling anything from creating realistic images to language translation.

Discriminative and Generative AI

Two classes of algorithms are employed in machine learning: discriminative and generative models. As previously discussed, generative AI heavily relies on generative AI models. The goal of a generative model is to produce new patterns from what has already been allocated in the training data. This requires the generative model to grasp the structural basis of the data and analyze a realistic generalized representation of the dataset. This kind of solution produces unique text, realistic images, music, source code, and even 3D models.

The goal of discriminative models, so-called traditional AI, is to master recognizing the desired values from a labeled training set. Simply put, they learn to detect patterns in data sets and apply them to fulfill a task of new patterns prediction or classification.

How does generative AI work?

Sophisticated AI algorithms, which incorporate neural networks, are utilized by generative AI tools to create synthetic data from existing content. Each type of generative AI model works differently and has a unique process of content creation.

Types of generative AI models

Here are the most notable types of generative AI models:

Generative Adversarial Networks (GANs)

The generative adversarial network is composed of two subnets, one that generates values and one that discriminates. They are playing a game called a zero-sum game, which consists in beating each other. Zero-sum signifies that whenever the discriminator successfully identifies real or counterfeit samples, it is rewarded with no modifications for its parameters, meanwhile, the generator gets large updates to its model parameters as a punishment.

The generator produces images, and the discriminator tries to guess if it's a real picture. If the discriminator indicates that it is genuine, then the generator wins, as the picture is not real anyway, although it looks like real, human-generated content.

Thus the mechanism of mutual evolution is implemented. The failures and victories of these two neural networks help them in their training. As a result of successful learning, the generative part of the network may produce a variety of values that fulfill the requirements for the resulting value, for example, producing different images similar to those in the training set.

Here is a summary of the GAN's stages of operation. Two neural networks, a generator, and a discriminator, are trained together:

1. The generator creates a series of samples and these, along with genuine examples from the source dataset, are provided to the discriminator;

2. The discriminator classifies them as genuine or fake;

3. The discriminator is updated if it has failed and misclassified the generated content to improve the recognition of genuine or forged samples in the next training round;

4. The generator is updated based on whether the generated samples tricked the discriminator or not;

Apart from the conventional so-called Vanilla GAN, the network has evolved and acquired improvements over the years. There are several types of GAN:

• DCGAN (Deep Convolutional Generative Adversarial Network) is more stable for training and can generate higher-quality outputs

• Improved DCGAN allows the generation of higher-resolution images

• Conditional GAN uses additional information, such as higher quality outcomes, as well as the ability to have some degree of control over how the generated images will look like

Transformer-based models

Transformer is a model that employs an attention mechanism to increase the speed of training. It is a relatively new type of neural network that can translate text, write papers, and create software code. It is currently one of the most advanced systems in the field of natural language processing (NLP).

Transformer neural networks can be parallelized and trained much faster, as they do not process sequential data in order, like recurrent neural networks, for example. In that way, such network architecture escapes recurrence, i.e., sequential computation. The calculations happen simultaneously, making the network operate faster.

The key constituents of Transformers are the encoder and decoder. The encoder and decoder consist of layers. The encoder converts the original input data and transforms it into a vector. The decoder, on its part, interprets it in the form of a new sequence. The basic idea behind Transformer is to use self-adaptive attention to process a sequence of input data. This allows the model to take into account the context of each word and associate it with other words in the sequence. Due to the attention mechanism, the data in Transformer goes through a shorter route in comparison to the recurrent approach. Instead, it focuses on remote yet significant words and processes these words straight away. Consequently, the neural network has improved long-term memory. The renowned Generative Pre-trained Transformer (GPT) text generator, first introduced by the research organization OpenAI in 2018, belongs to this type of generative AI model.

Diffusion models

Diffusion models are a subcategory of deep generative models, which are comprised of stages of forward and backward diffusion. They generate unique data similar to the data they are trained on. They slowly add accidental noise to the data, thereafter learning ways to reverse the diffusion process to create the desired data samples from the noise.

The diffusion models operation includes two major steps:

1. Forward diffusion, which consists of distorting the training data by adding Gaussian noise step by step and eliminating the details until the data is converted to plain noise;

2. Reverse diffusion, which consists of training the neural network to reverse the diffusion process, which means restoring the original image by synthesizing pure noise by gradually reducing the noise until a new clean result is obtained.

In other words, diffusion models are trained on a sample of hundreds of thousands of images. Then at each step a bit of noise of a certain value is imposed on the image from the sample, whereas the model learns to reverse this noise, hence making the quality of the image higher.

Data scientists then proceed to apply the model trained in this way to an entirely new image entirely made up of totally irregular noise, where at each step by reversing the noise, the model will be capable of forming an entirely novel picture, gradually getting rid of the random noise via reverse diffusion.Generative AI tools such as DALL-E, MidJourney, and Stable Diffusion use such models to produce realistic images.

Variational Autoencoders (VAEs)

Variational autoencoders are comprised of two networks called an encoder and a decoder. The encoder transforms the input data to a smaller and denser representation of the data, called the latent space. This condensed representation stores the information needed by the decoder to reconstruct the original data and discards all irrelevant information. The decoder then exploits this information to generate a new image similar to the original one.

One network discovers the better means of encoding raw data into latent space, and the other network, the decoder, figures out the best ways to transform these latent representations into new relevant content. VAEs, unlike conventional autoencoders, have a characteristic that renders them useful in data generation. Their latent space is constructed in a continuous way, enabling random transformations.

Generative AI preparation stages

The process of training and preparation of different generative AI models will vary depending on the algorithm employed, but the basic steps are as follows:

Selection of generative AI model architecture

Various kinds of models propose a range of options and are appropriate for particular purposes. For example, GANs and diffusion models are better for realistic images generation, transformers show great results in natural language processing and so on. The right kind of model architecture is essential to achieve the desired results. The choice of a suitable type of architecture for a model is decisive for generative AI, as it determines how the model explores and produces relevant data.

Data collection and cleaning

A rather large data set is acquired from a certain area of interest. A massive set of training data is collected or located that holds the type of information that the future model will be required to generate. For instance, it could be a massive collection of texts or millions of images as well as audio recordings, etc. At this stage, experts can also clean the data, split it into smaller units, and convert it into a format that the model will understand.

Iterative training and optimization

The stage of generative AI model training is perhaps one of the most fundamental stages of model preparation. The model is trained to generate novel data on its own. The collected set of training data is submitted for model processing, during which the model analyzes all collected data, looking for features, common elements, and structures to learn the essence of the given data. The reduction of the distinction between the model results and the training data to a minimum enables the model to create synthetic content that is akin to that training data.

During the training process, various machine learning algorithms are applied for model parameter optimization. It can happen through such methods as stochastic gradient descent (SGD), forward and backward propagation, regularization, and so on.

To properly train AI models, the model needs to be exposed to training data a considerable number of times. Therefore, training is not a fast process and generally involves multiple stages, referred to as epochs. The more there is data, the greater the number of such stages. Epochs in turn are also partitioned into several iterations. The model undergoes each iteration, thus learning from the data processed in it.

Evaluation and deployment

After training, experts check how well the model has been trained on new test data that was not included in the original training dataset. If the performance of the model is not acceptable, they carry out additional training with modified training parameters. If the model's performance meets the expectations of its creators, the final trained model is applied to create images, text, music, or even program code and other new content.

Training data for generative AI models

Generative AI differs from traditional AI algorithms, among other things, in that it does not require data annotation for training. This means that it entails the application of unsupervised and semi-supervised machine learning algorithms. It is interesting to note, however, that there has to be an immense amount of such data, and these massive sets may not always consist of high-quality data.

Unsupervised learning

In this kind of learning, the machine does the learning itself without human intervention and the given data is unlabeled. The point of it is that there are no correct and straightforward answers in the data, the machine has to find patterns and dependencies between the entities. However, even for unsupervised learning, the training input data the model will be trained on often has to be prepared and cleaned.

Semi-supervised learning

Semi-supervised learning is applicable when there is only a small amount of labeled data available or the model is incapable of learning without any labeling. Thus, data scientists possess a data set that consists of two parts: the labeled and the raw data. They feed both parts of the data to machine learning models to train them.

Key applications of generative AI

Text creation with Large language models

Advances in deep learning have led to the widespread use of various neural networks for natural language processing (NLP) tasks. Currently, nothing does a better job of composing human-like text than large language models (LLMs). The term large language model usually refers to deep language-based generative AI models having about a billion or more parameters. They cope with a wide range of tasks, as opposed to learning one specific one.

An LLM consists of a language model composed of a neural network containing a multitude of parameters. It is typically trained on a substantial amount of unlabeled text via unsupervised learning. A language model is essential for understanding and generating content in natural language. Besides having the capability of learning a great deal of the syntax and semantics of the human language, LLMs also demonstrate considerable general knowledge of the world and can recall a wide variety of facts while learning. For instance, the aforementioned GPT is a large language model with a transformer architecture.

Image creation

A recent breakthrough in AI image creation has been possible due to the development of incredibly powerful generative AI models. They may render an image from a text prompt, a source picture, a rough sketch, or references. Certain generative AI models can even complete unfinished artwork. Other services specialize in face generation.

In the 2020s several popular generative AI interfaces have been created that specialize in creating images and art in a variety of styles that in most cases cannot be distinguished from those created by a person. Such as the Midjourney image-generating tool, for instance, belongs to the class of diffusion networks, i.e. it consists of two networks: one is responsible for text detection, the other for image generation.

Another image-generating tool is called DALL-E. It generates images based on text requests and knows how to produce versions of pictures guided by examples suggested by the user. It relies on three different networks, each serving its own function. The first one, called CLIP, produces sketches based on the pre-recognized text submitted by the user. The sketch is then converted into a low-resolution image by the GLIDE network. And the third network increases the resolution of the final resulting image in the output.

Another image generation tool called Stable diffusion is an open-source service unlike the ones mentioned above. Yet it offers high-quality generated pictures as well. Stable Diffusion is similarly built on diffusion models, which means the gradual transformation of noise into the target image.

Benefits of Generative AI

Generative AI, when used appropriately and correctly, is a powerful instrument to help produce new insights, find answers to solve existing issues, and discover innovations, all of which may be a tremendous asset in the workplace. There are numerous pros to applying generative AI in business, such as:

Faster deliverables, with increased efficiency and productivity

Generative AI helps your employees complete tasks faster and with greater efficiency. AI is capable of working independently or alongside your staff, replacing some routine operations. For example, generative AI can create high-quality document templates, product descriptions, articles, or eye-catching graphic elements and videos faster than a human can. With such a powerful and fast system, multiple content pieces may be generated at the same time.

Reducing costs

Generative AI is capable of automating many mundane processes in a company. Also, for example, a company may save funds when creating promotional content and developing program code. This can be achieved through the implementation of professional generative AI tools that are comparatively cheaper than doing the tasks on your own.

Competitive advantage

Generative AI helps companies make better and faster decisions. It means that they will be able to, for example, rapidly increase their social media presence. One of the ways to accomplish this is to maximize the amount of content posted with the help of generative AI and publish it earlier than competitors. Such an approach gives a company an edge over its rivals by attracting more attention as well as reaching more customers.

Higher originality and lower error rates

Fewer errors mean less time for revisions and improvements, enabling the generation of more original content and products. Meanwhile, the increase in the quantity of content does not affect the quality and originality of the data, as generative AI always provides unique and lifelike content that can be even further refined using generative AI.

Conclusion

Generative AI is a technology that employs complex generative models that can produce new data, like audio, code, images, text, and video. This type of AI, in contrary to discriminative AI capable of performing tasks like identification and classification, builds new pieces of information using foundation models, which are deep AI models that solve complex problems simultaneously.

It's safe to say that we currently live in the prime era of generative AI models. Even today AI solutions propose the top-quality results. The future holds the rise of even more advanced AI systems that will be able to generate unique and realistic content faster and with minimal to zero errors. The generative AI tools will become more accessible and more intuitive over time. They will have the power to change almost every aspect of our lives and business.