How Generative AI Is Changing Creative Work

How generative AI is redefining image search

The video below is generated by AI and shows its visual potentials to be used for marketing purposes. When a customer sends a message, ChatGPT or other similar tools can use this profile to provide relevant responses tailored to the customer’s specific needs and preferences. Generative AI can help forecast demand for products, Yakov Livshits generating predictions based on historical sales data, trends, seasonality, and other factors. This can improve inventory management, reducing instances of overstock or stockouts. The utilization of generative AI in face identification and verification systems at airports can aid in passenger identification and authentication.

As far as text-to-image models are concerned, text symbols are just combinations of lines and shapes. Since text comes in so many different styles – and since letters and numbers are used in seemingly endless arrangements – the model often won’t learn how to effectively reproduce text. Ian Goodfellow demonstrated generative adversarial networks for generating realistic-looking and -sounding people in 2014. Now, pioneers in generative AI are developing better user experiences that let you describe a request in plain language. After an initial response, you can also customize the results with feedback about the style, tone and other elements you want the generated content to reflect.

Top 7 Generative AI Tools for Image Generation: Reviews

Additionally, the encoding and decoding processes used by VAEs have a probabilistic component, which enables them to produce a wide range of new pictures from a single input image. Training involves tuning the model’s parameters for different use cases and then fine-tuning results on a given set of training data. For example, a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return.

In conclusion, developing a generative AI model for picture synthesis requires a blend of technical proficiency, originality, and in-depth knowledge of the technologies involved. However, GANs require significant training to deliver high-quality results, which can be challenging. Despite these difficulties, GANs continue to be a widely used and successful method for image synthesis across various industries. In other words, the huge diversity of associations within the training data impacts the accuracy of quantities in outputs. Generative AI tools such as Midjourney, Stable Diffusion and DALL-E 2 have astounded us with their ability to produce remarkable images in a matter of seconds.

Advantages of AI: Using GPT and Diffusion Models for Image Generation

We focused on real-world applications with examples but given how novel this technology is, some of these are potential use cases. For other applications of AI for requests where there is a single correct answer (e.g. prediction or classification), read our list of AI applications. AI tools like AI Art Generator spark creativity and automate Yakov Livshits drudgery while easy-to-edit templates empower anyone to create device mockups, social media posts, marketing images, app icons, and other work graphics. Generative AI models, enable the creation of visually appealing and conceptually coherent images from text, opening up new possibilities in advertising and digital content creation.

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.

Here, we will discuss some of the most popular generative AI model types used for picture synthesis. Generative AI models have the potential to revolutionize various industries, such as entertainment, art, and fashion, by enabling the creation of novel and unique content quickly. When a user provides a natural language prompt, the AI considers this and will generate extending pixels that match the original and take the instructions into account. Generative Fill uses a process known as outpainting, which enables the expansion of images by adding content around the edges. This content smoothly blends with the existing picture, preserving the style and details of the original, resulting in a coherent and extended image.

DreamStudio (Stable Diffusion)

For example, a 512×512 resolution image contains around 260,000 pixels (or features). An embedding model tries to learn a low-dimensional representation of visual data by training on millions of images. Image embeddings can have many useful applications, including compressing images, generating new images, or comparing the visual properties of different images. DALL-E is an example of text-to-image generative AI that was released in January 2021 by OpenAI. It uses a neural network that was trained on images with accompanying text descriptions. Users can input descriptive text, and DALL-E will generate photorealistic imagery based on the prompt.

While AI image generators can create visually stunning and oftentimes hyperrealistic imagery, they bring several limitations and controversies along with the excitement. The synthetic data generated by DALL-E 2 can potentially speed up the development of new deep-learning tools in radiology. They can also address privacy issues concerning data sharing between medical institutions. Notably, this marked the first time an AI-generated image was used as the cover of a major magazine, showcasing the potential of AI in the creative industry.

Minimalist line art illustrations are generated in muted colors and continuous, fluid lines to match the ever-trendy minimalist aesthetic. Modern and minimalist, imagine if you could illustrate your artwork with a simple line or two. This style brings a jolt of energy and spontaneity to your images, with vibrant splashes of color that mimic exactly what the filter is named after. These client testimonial images can be accompanied by quotes and used to promote products and services. For example, if you apply the 3D Neon image style, your resulting images will be bright and bold.

However, like many other sophisticated GAN models, BigGAN requires significant computational resources for training and inference. GANPaint Studio takes a unique approach to image generation by enabling users to edit existing images using semantic labels. Powered by GANs, this tool allows users to manipulate objects in images by simply adding or removing labels. For instance, users can turn a sunny day into a rainy scene or remove specific objects from an image entirely.