New generation of Artificial Intelligence: What is generative AI?

The spread of AI has already driven significant technological advances and increased operational efficiency. Among the notable applications of AI is the generation of top-quality pictures, photographs, concept art, music, and various texts, including abstracts and fiction, as well as videos and animations.

Generative AI possesses such capabilities. It represents a creative tool that produces something that has never existed before. It is likely to fundamentally change the processes associated with art and creativity as well as business processes. Below we take a closer look at what generative AI is and why it has recently gained so much public attention.

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The difference between Traditional and Generative AI

The fundamental difference between traditional and generative AI lies in their capabilities and applications.

Traditional AI systems are mainly employed in data analysis and making predictions. It is referred to as narrow AI, which is tailored to process a particular set of input data. Such systems are capable of learning from the data and making decisions or predictions based on it. It is important to note that they require labeled training data.

Traditional AI tools are capable of making decisions within the sets of rules they have been taught. Voice assistants, search algorithms, and recommendation systems are some common applications of traditional AI. Such traditional AI algorithms are quite effective and help make life easier, although they cannot create something new, which is not the case with generative AI. This type of artificial intelligence differs from traditional systems in the fact that it creates completely new content.

What is generative AI?

Generative AI has been rapidly developing over the past few years. Therefore, you can think of it as the next generation of artificial intelligence. It represents a form of AI technologies that are empowered to create new content.

Generative AI creates new content such as realistic images, photos, pictures, texts, videos, etc. by mimicking already existing pieces of content.

As mentioned above, unlike traditional AI, generative AI creates something entirely novel out of the information it has been given. It can create original, creative content, be it text, images, music, or computer code. Generative AI models are trained on a huge set of data and, by picking up the underlying patterns, produce fresh pieces of content that replicate, but don't exactly copy, the training data set. That is, they generate unique data as if it were created by a human.

Popular generative AI interfaces involve, for instance, one of the large language models GPT-4, which employs a neural network trained on gigantic sets of existing data from the Internet. It is designed for natural language processing.

DALL-E, MidJourney, and Stable Diffusion are machine-learning models that can create images based on text prompts.

Using generative AI

Turning raw data into an entirely new experience that can be applied across a wide range of fields, from automating routine tasks to creating pieces of art. By producing fresh content, generative AI serves as an aid but not a replacement for writers, graphic designers, visual artists, and musicians, as well as other professions.

Generative AI is an immensely influential and powerful instrument that can be employed to generate aesthetically appealing and very convincing content. Some of its typical applications by content type and field of use include:

Text

Generative models are capable of easily producing unique texts on a variety of topics. In business, such tools can assist with composing marketing collateral, reports,business documentation, creating articles drafts, and preparing presentations.

Generative AI can transform natural language into code, and also fix bugs. It can help developers reduce the time needed for software development, at the same time improve the quality of code and simplify the development process.

Images

Generative AI transforms textual prompts into life-like images, building new scenes and modeling never-before-seen artwork. For instance, some AI-generated graphics might be almost undetectable from live photo realistic images.

Music

Generative AI models may create authentic music with AI algorithms generating original tunes and beats. The system creates a new piece of composition by analyzing existing music.

Video

Generative AI produces high-quality videos resembling the real ones. In addition to the automated production of video from text, generative AI tools can assemble short videos based on available images.

Marketing

Advertising companies utilize generative AI models to quickly create unique promotional materials, which include AI-generated images and texts. It helps marketing teams to facilitate their work and accomplish projects faster. AI can generate dozens or hundreds of unique and eye-catching photos and illustrations, imaginative product descriptions, articles, and other promotional content in a short time. Thus, machine learning models save the company's resources on the manual creation of original content.

Design

Generative AI helps marketing specialists and designers to create multiple new possible product design options based on past designs. Also, AI tools may provide inspiration for creating illustrations based on the content suggested by the system.

How Does Generative AI Work?

Generative AI is a form of machine learning that, at its essence, combines deep learning neural network algorithms and techniques to create content based on patterns it observes in existing content. A huge amount of available content is uploaded to generative AI models in order to train these models to generate novel pieces of data. They learn to recognize fundamental patterns in a dataset and, when prompted, create content based on those patterns.

The output of generative AI is most commonly original material, as mentioned earlier. However, generative AI models utilize machine learning to create content that mimics previously man-made creative work. Generative AI technologies utilize huge repositories of data and use this collected information to generate content. Many criticize AI for being able to replace humans in this way, but AI algorithms are more about aiding people, not replacing them.

Foundation models in generative AI

Foundation models represent powerful artificial intelligence-trained models that employ tremendous data sets and processing power to create any kind of content, from text to visuals. Foundation models are at the core of generative AI.

One foundation model can accomplish different goals, unlike traditional AI, where one model is trained to handle one concrete job, like detecting objects in a photo or forecast customer churn. In other words, foundation models may also perform tasks beyond content generation; they may also handle traditional AI tasks such as classification and prediction.

Generative AI applications are commonly based on foundation models. Such models consist of artificial neural networks, which in turn consist of neurons that mimic the human brain. Layers are the underlying elements of neural networks. The more the number of layers, the higher the training capabilities of the network.

The foundation models are categorized under such a machine learning algorithm as deep learning, since the mentioned neural networks are very extensive and include many deep layers. The foundation models that generative AI applications are based on can handle extremely vast and versatile sets of unstructured data and carry out more than one task.

Generative AI models

Below are some generative AI models and the way they function:

Generative Adversarial Networks (GANs)

Generative Adversarial Network (GAN) refers to a class of neural networks that create new pictures from a given dataset. GANs consist of two neural networks: a generator and a discriminator. The generator attempts to generate images that are similar to real images and the discriminator determines whether the generated content is real or not.

GANs function by training the generator and discriminator in conjunction through what is known as a zero-sum game, where the gain of one network is treated as the loss of the other. The generator is trained to create images that best favor the discriminator. As a result, the generator learns to generate images that resemble real-life ones, allowing it to produce novel examples of pictures and expand the dataset based on existing images.

Variational Autoencoders (VAEs)

Variational Autoencoder (VAE) is a deep neural network that is used to create synthetic data from existing data. VAE employs two kinds of neural networks. One network discovers the optimal ways to encode the original data into latent space, and the second network, the decoder, figures out appropriate ways to transform these latent representations into new content.

Transformer-based models

Transformer is a type of neural network architecture. Transformer-based models are primarily utilized for tasks that involve data sequences, meaning information that shares particular semantics or has correlations with each other, such as natural language processing. It is perfect for determining how a few bits of information are associated with each other, for instance, how various words in a phrase are linked, or how different parts of an image match with each other.

The transformer AI system handles the whole text at once, which means it can comprehend the entire text rather than just individual words. Therefore, it can take better account of the context of a word. This generative AI model is trained on extremely large text models and then fine-tuned for specific tasks such as translation, responding to queries, or text generation. The large language models that they produce are capable of delivering strikingly cohesive and meaningful contextualized sentences, passages, or even entire articles.

Diffusion models

Diffusion models are the tools utilized to generate images from random noise. Such a model learns on a random sample of thousands of images bit by bit, where at every step a certain amount of noise is applied to the image from the sample, while the model learns to inverse this noise, thus improving the quality of the image. Thus, the trained model can generate completely novel data, gradually getting rid of the random noise on a picture by means of reverse diffusion.

Benefits of generative AI for business

Generative artificial intelligence (AI) has many benefits for companies adopting it, including:

New ideas generation

Generative AI is capable of suggesting new ideas, concepts, and solutions that can be applied not only in business but also in various fields such as science, technology, art, medicine, etc. For example, AI can help develop new products or services, improve manufacturing processes, or suggest new marketing strategies.

Improving customer experience

Generative AI can process large amounts of customer data, which helps businesses gain a better comprehension of their clients' needs and preferences. This improves customer service and satisfaction.

Increased efficiency

Generative AI is able to automate routine processes such as data processing and information analysis, which frees employees from performing these tasks. This allows them to concentrate on more complex data studies and more critical challenges, increasing overall productivity.

Cost reduction

Generative AI can substitute routine manual labor and lower production costs. This is especially important for small and medium-sized organizations which are often challenged with limited resources. It can significantly reduce production costs and improve product quality.

Solving complex problems

Generative AI can be helpful in solving challenging issues that could not be or were extremely difficult to be resolved. It may be utilized, for example, to discover new medications, materials, or technologies.

Software security

Generative AI technology may be utilized to guard a company against cyberattacks and other security threats. It can detect and evaluate large amounts of data to detect suspicious activity and prevent attacks. Large Language Models (LLMs) may also be employed to review and analyze email contents to identify phishing or other types of security threats.

Why is generative AI vital today?

Artificial intelligence technology and Generative AI in particular have numerous advantages and play an essential role in the contemporary world. This technology is the one that utilizes machine learning algorithms to generate various types of new data such as pictures, texts, music, etc. Generative AI reduces human intervention in routine tasks and allows the creation of new products, services, and ideas that can enhance the quality of our lives.

From the ability to create unique works of art to improving customer service, generative AI provides a multitude of tools that can boost our imagination, speed up and simplify routine tasks. In addition, it offers the potential to explore and create innovative solutions to various problems. Overall, generative AI is a significant technology for the development of various fields, including business and society as a whole.