Explainer: A deep dive into how generative AI works

That’s why this technology is often used in NLP (Natural Language Processing) tasks. Discriminative algorithms try to classify input data given some set of features and predict a label or a class to which a certain data example belongs. Many interactive applications require fast generation speeds, such as real-time image editing, for content creation workflows. As such, the speed at which a generative model can produce outputs is also important to consider when evaluating its effectiveness. The quality of the generated outputs is crucial, particularly in applications that interact directly with users.

It was not until the advent of big data in the mid-2000s and improvements in computer hardware that neural networks became practical for generating content. Generative AI is a branch of artificial intelligence that focuses on creating unique content based on training data and neural networks. Learning from large datasets, these models can refine their outputs through iterative training processes. The model analyzes the relationships within given data, effectively gaining knowledge from the provided examples. By adjusting their parameters and minimizing the difference between desired and generated outputs, generative AI models can continually improve their ability to generate high-quality, contextually relevant content.

Multimodal models

Whether the original artist should be compensated for such AI-generated works is a complex question that intersects with legal, ethical, and economic considerations. One use of multimodal models is in generating text descriptions for images (also known as image captioning). They can also be used to generate images from text descriptions (text-to-image synthesis). Other applications include speech-to-text and text-to-speech transformations, where the model generates audio from text and vice versa.

• I hope this blog has given you a better understanding of the first 3 steps of the generative AI end-to-end process.
• Generative AI is a powerful technology that enables the generation of diverse and contextually relevant content, including images, text, and music.
• A network is a group of computers that share resources and communication protocols.
• Generative AI is a powerful tool for streamlining the workflow of creatives, engineers, researchers, scientists, and more.
• While generative AI can produce novel combinations of existing ideas, its ability to truly innovate or create something entirely new is limited.

Machine learning is the foundational component of AI and refers to the application of computer algorithms to data for the purposes of teaching a computer to perform a specific task. Machine learning is the process that enables AI systems to make informed decisions or predictions based on the patterns they have learned. One example might be teaching a computer program to generate human faces using photos as training data. Over time, the program learns how to simplify the photos of people’s faces into a few important characteristics — such as size and shape of the eyes, nose, mouth, ears and so on — and then use these to create new faces.

How diffusion models work

Meanwhile, Microsoft and ChatGPT implementations also lost face in their early outings due to inaccurate results and erratic behavior. Google has since unveiled a new version of Bard built on its most advanced LLM, PaLM 2, which allows Bard to be more efficient and visual in its response to user queries. Applying generative AI in simulation and game development to create dynamic and realistic virtual environments.

The core idea of how diffusion models work is they destroy training data by adding noise. Then, the model learns how to remove the noise, applying a denoising process progressively to reconstruct the original data. A real-life example based on GANs is CycleGAN, which is used for image-to-image translation. CycleGAN can convert images from one domain to another without paired training data. For example, it can convert a daytime image to a nighttime image or a horse image to a zebra image.

From Imagination to Reality

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

This ability to learn and mimic patterns provides generative AI with its creative edge. As machine learning techniques evolved, we saw the development of neural networks, which are computing systems loosely inspired by the human brain. These networks can learn from vast amounts of data, making them incredibly powerful tools for tasks like image recognition, natural language processing, and content generation.

These deep generative models were the first able to output not only class labels for images, but to output entire images. Similarly, you can find many other applications, frameworks, and projects in the world of generative artificial intelligence. Conventional AI systems rely on training with large amounts of data for identifying patterns. Generative artificial intelligence takes one step ahead with complex systems and models, generating new and innovative outputs, in the form of audio, images, and text, according to natural language prompts.

Accuracy & harmful content

These networks are the foundation of machine learning and deep learning models, which use a complex structure of algorithms to process large amounts of data such as text, code, or images. Over the following decades, researchers refined generative AI techniques, including the development of neural networks for more advanced data analysis. In 2014, deep learning and generative adversarial networks (GANs) garnered significant attention, enabling the creation of highly realistic images and videos. Generative AI, significantly advanced through models such as variational autoencoder (VAE) and generative adversarial network (GAN), is reshaping multiple sectors with an investment of over $17 billion. Real-world applications span text generation, where AI can produce human-like language patterns, image creation, offering the ability to generate novel images, and audio production, where new sounds can be synthesized.

The process entails training a model on an extensive dataset comprising existing examples, enabling it to discern patterns and relationships within the data. Subsequently, the model employs these learned patterns to generate novel content. Variational Autoencoder (VAE) -The VAEs model uses the neural network to encode the input data into a lower-dimensional representation, then decoded to generate new output data. To develop a condensed representation of the data, known as a “latent space,” a particular neural network class is trained on a dataset. Then, this latent space may produce new data comparable to the original data.

Customer spotlight

Design tools will seamlessly embed more useful recommendations directly into workflows. Training tools will be able to automatically identify best practices in one part of the organization to help train others more efficiently. And these are just a fraction of the ways generative AI will change how we work. Despite their promise, the new generative AI tools open a can of worms regarding accuracy, trustworthiness, bias, hallucination and plagiarism — ethical issues that likely will take years to sort out.

For instance, a generative AI model trained on lots of images of cats could generate a new image that looks like a cat. Or, a model trained on lots of text descriptions could write a new paragraph about a cat that sounds like a human wrote it. The Yakov Livshits generated content isn’t exact copies of what the AI has seen before but new pieces that fit the patterns it has learned. The generator creates new content, while the discriminator evaluates that content against a dataset of real-world examples.