How do DALL-E and other forms of generative AI work?

What is generative AI? Artificial intelligence that creates

Generative artificial intelligence has seen an incredible popularity surge in 2022. Big Think has called it ‘the technology of the year’, and judging from the amount of attention and VC support generative AI startups have been gaining this year, this claim is more than justified. Moreover, tech experts say that in the next few years, not only will the development of generative AI not slow down but will also rapidly increase, conquering new and new fields.

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A generative AI model is designed to learn underlying patterns in datasets and use that knowledge to generate new samples similar but not identical to the original dataset. For example, a generative AI model trained on a dataset of images of cats might be able to generate new images of cats that look similar to the ones in the original dataset but are not exact copies. Since then, progress in other neural network techniques and architectures has helped expand generative AI capabilities.

Quality of Generated Content

Arguably, because machine learning and deep learning are inherently focused on generative processes, they can be considered types of generative AI, too. As concerning as this may sound to some, it is as important for us to keep an open mind about it and be mindful that for a generative model to operate, human input is a must; hence we are still in control. Generative modeling helps reinforcement machine learning models in exhibiting less bias. Generative AI’s abilities go beyond fun mobile applications and avatars, as they are now being used to create art pieces and design, produce code, write blogs, and generate all kinds of different high-quality content. RedShark is a multiplatform online publication for anyone with an interest in moving image technology and craft. With over 50 contributors worldwide, full-time developers, editorial, sales and marketing staff, it is the go-to site for informed opinion and know-how for the quickly changing video, film and content creation industries.

how generative ai works

There are a variety of generative AI tools out there, though text and image generation models are arguably the most well-known. Generative AI models typically rely on a user feeding it a prompt that guides it towards producing a desired output, be it text, an image, a video or a piece of music, though this isn’t always the case. Assisting in drug discovery by generating new chemical compounds that Yakov Livshits could serve as potential treatments for various diseases. By analyzing large datasets of existing drugs and their effects, generative AI can generate new compounds that may exhibit similar or improved therapeutic effects. Moreover, Generative AI offers practical applications for businesses, enabling the creation of product designs, marketing materials, and personalized customer recommendations.

Want to build intelligent applications?

Generative Pre-trained Transformer 3 (GPT-3) is the bleeding edge of AI technology. The proprietary computer code was developed by the misnomered OpenAI, a Bay Area tech operation which began as a non-profit before turning for-profit and licensing GPT-3 to Microsoft. GPT-3 was built to produce words, but OpenAI tweaked a version to produce DALL-E and its sequel, DALL-E 2, using a technique called diffusion modeling. Generative AI promises to simplify various processes, providing businesses, coders and other groups with many reasons to adopt this technology. Also developed by OpenAI, the AI system can generate images from textual descriptions. For example, if you give DALL-E the prompt „an armchair in the shape of an avocado,“ it will generate a completely new image of an avocado-shaped armchair.

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.

  • It requires a deep understanding of ML techniques and their practical applications and the ability to work with large datasets and complex algorithms.
  • This training data is then used to generate text, translate languages and answer questions via natural language processing (NLP).
  • The trade-off between creativity and adherence to training data is a challenge in generative AI.
  • There are several prominent types of generative AI models, each with its pros and cons.

The exploration of these case studies reveals the vast potential of generative AI to influence and improve various aspects of our lives. Whether it’s aiding scientific research or enhancing our leisure time, generative AI is demonstrating its value in practical and often surprising ways. Instead of merely suggesting trending or highly-rated content, the algorithm generates recommendations based on intricate patterns in the user’s viewing history and preferences. This ensures a more personalized user experience, increasing viewer engagement and satisfaction. DeepMind, an AI research lab, made headlines when its generative AI model, AlphaFold, showed unprecedented accuracy in predicting the 3D structure of proteins. This development has the potential to significantly accelerate drug discovery and the treatment of diseases.

The probabilistic approach allows VAEs to capture the uncertainty and variability present in the data rather than focus solely on reconstructing the input data. With generative AI, you can easily generate new outputs similar to the training data. But more often, you’d want to explore variations in the data in a specific direction. They are used when some labeled data is available for training, but the amount is insufficient to train a complete model. The algorithm uses the labeled data along with the unlabeled data to identify patterns and structures within the data. Semi-supervised learning can be considered a hybrid approach between supervised and unsupervised learning techniques.

Evaluation and refinement of generated outputs play a crucial role in the training process of generative AI models. While the models aim to generate new and creative content, it is essential to assess the quality, coherence, Yakov Livshits and relevance of the generated outputs to ensure they meet the desired standards. In this section, we will explore the evaluation techniques and refinement strategies used to improve the generated outputs in generative AI.

Get Hands-On With Data

For instance, on receiving an input the model assigns weights to various parts of the input sequence in parallel. Once it identifies their relationship, it generates output particular to the specific input. In this article, we aim to decode Yakov Livshits, the inner mechanics, and various types of generative AI models. We will also shed some light on how businesses can harness the potential of generative AI for organizational growth and customer satisfaction. Generative AI is a powerful technology that enables the generation of diverse and contextually relevant content, including images, text, and music. However, it also comes with challenges and concerns, including ethical considerations, lack of control over outputs, potential biases, resource requirements, and quality issues.

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A neural network is a type of model, based on the human brain, that processes complex information and makes predictions. This technology allows generative AI to identify patterns in the training data and create new content. Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds. Generative AI models, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), are extensively trained on datasets to understand patterns, structures, and relationships within the data. DALL-E combines a GAN architecture with a variational autoencoder to produce highly detailed and imaginative visual results based on text prompts.

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