Generative AI: What Is It, Tools, Models, Applications and Use Cases
You can modify the generated pictures by changing parameters, styles, or additional features. We know AI picture generators are very powerful tools, but we need to merge human creativity with AI benefits to make the best use of them and gain more effective results. These are not replacements for genuine expertise but aid in giving a more advanced touch to human creativity. The specifics of using them might vary based on the latest advancements, but here is a general approach as of AI technology 2023. Organizations will use customized generative AI solutions trained on their own data to improve everything from operations, hiring, and training to supply chains, logistics, branding, and communication.
After the incredible popularity of the new GPT interface, Microsoft announced a significant new investment into OpenAI and integrated a version of GPT into its Bing search engine. Our text encoder just learned how to map from the textual representation of a woman to the concept of a woman in the form of a vector. Above we saw that there exist interpretation schemas in which a vector can be considered to capture information about the concept that a given word references. In particular, we have learned to map from words to meaning, now we must learn to map from meaning to images.
Exploring popular AI image generators
For instance, “a portrait of a woman holding a book on the left side of her office desk.” Toy around with this style to get inspiration for mascots, avatars, or characters in your own content. Applying this AI art style can render results that look inspired by the likes of 3D animated classics like Toy Story or Despicable Me. If you can’t find the right stock image for your blog post, the blog illustration style can help. Generate minimalist-inspired illustrations that are perfect to complement blogs or use as icons. Splatters and drips of multicolored paint will adorn your AI art when you apply the splatter paint style.
DALL-E 2 is a variant of DALL-E, an image generation model developed by OpenAI. It uses a transformer-based architecture to create high-resolution images with fine details. With DALL-E 2, you can generate a wide range of images, including photorealistic images, stylized illustrations, and even images that are similar to existing images but with some variations. This makes it a powerful tool for tasks such as art, design, and animation. It can generate new images by interpolating between existing images, using text prompts as a guide, it can generate any imaginable image.
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If you see inaccuracies in our content, please report the mistake via this form. By default, every image you generate is posted publicly in Midjourney’s Discord. It gives everything a cool community aspect, but it means that anyone who cares to look can see what you’re creating. While not necessarily a problem for artists, this might be a dealbreaker if you’re looking to use Midjourney for business purposes.
Exaggerated features, big bright eyes, and thick character outlines are all familiar features of cartoons. Users of the AI image generator can apply this illustration technique to their creations by applying the cartoon style under the Art section. As the optimization progresses, the generated image takes the content and style from different images. The end result is an appealing blend of the two, often bearing a striking resemblance to a piece of art. Finally, the model can use what it learned in the reverse diffusion process to create new data. Alongside, it takes in a text prompt that guides the model in shaping the noise.
Types of generative AI models
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.
These details are not crucial and are just placed here to highlight how these lofty maximum likelihood objectives become tenable as we impose assumptions by imposing restrictions on our model and how it is trained. For a full treatment of this subject, see our dedicated Introduction to Diffusion Models. The objective is maximum likelihood, meaning that the goal is to find the parameters theta of the denoising model that maximize the likelihood of our data. You can use terms like “vibrant,” “pastel,” or “monochrome” to influence the overall color scheme. Describe the arrangement of elements within the image, such as the positioning of objects or subjects.
Subsequent research into LLMs from Open AI and Google ignited the recent enthusiasm that has evolved into tools like ChatGPT, Google Bard and Dall-E. OpenAI, an AI research and deployment company, took the core ideas behind transformers to train its version, dubbed Generative Pre-trained Transformer, or GPT. Observers have noted that GPT is the same acronym used to describe general-purpose technologies such as the steam engine, electricity and computing. Most would Yakov Livshits agree that GPT and other transformer implementations are already living up to their name as researchers discover ways to apply them to industry, science, commerce, construction and medicine. We all have seen amazing AI artworks created with it and you still can use it, despite Mid Journey v5 being live. To use it you either use it publicly on discord (limited free access or with one of the paid plans) or have access to stealth mode through PRO paid option.
StyleGAN2 is a remarkable AI tool developed by NVIDIA, known for its exceptional capabilities in generating high-resolution images. This tool leverages a progressive growing approach and style-based synthesis to produce photorealistic images. What sets StyleGAN2 apart is its ability to control various aspects of the generated images, including the age, pose, and appearance of human faces. Additionally, users can fine-tune the generated output by manipulating specific attributes. While StyleGAN2 shines in producing lifelike images, it requires considerable computational resources and may not be suitable for real-time applications.
Text-to-image synthesis has numerous practical applications, including creating custom book illustrations, generating product images for e-commerce, and aiding in architectural design. In this case, a generative model (e.g., Stable Diffusion or DALL-E) creates the embedding for your query and uses it to create the image for you. Generative models Yakov Livshits take advantage of joint embeddings models such as CLIP and other architectures (such as transformers or diffusion models) to transform the numerical values of embeddings into stunning images. In recent months, generative artificial intelligence has created a lot of excitement with its ability to create unique text, sounds, and images.
Deep Dream Generator
From creating innovative styles to refining and optimizing existing looks, the technology helps designers keep up with the latest trends while maintaining their creativity in the process. This can be done by a variety of techniques such as unique generative design or style transfer from other sources. Another use case of generative AI involves generating responses to user input in the form of natural language. Generative AI models can generate realistic test data based on the input parameters, such as creating valid email addresses, names, locations, and other test data that conform to specific patterns or requirements. On the other hand, variational autoencoders (VAEs) are also leveraged in image generation technology. It works by encoding images into a lower-dimensional space and then decoding them back into images.
Generative AI is a type of machine learning, which, at its core, works by training software models to make predictions based on data without the need for explicit programming. Conversational AI tools can be trained on a variety of languages, and it can translate messages from one language to another in real-time. Generative AI can be used in sentiment analysis by generating synthetic text data that is labeled with various sentiments (e.g., positive, negative, neutral).
- Experiment with different prompts and be open to revising them based on the AI’s outputs.
- With these tools, it is possible to generate voice overs for a documentary, a commercial, or a game without hiring a voice artist.
- However, another type of generative AI model, called Diffusion model, has gained popularity in recent years.
- Traditional AI, on the other hand, has focused on detecting patterns, making decisions, honing analytics, classifying data and detecting fraud.
- This limits your search options to information that has been explicitly registered with the images.
The TTS generation has multiple business applications such as education, marketing, podcasting, advertisement, etc. For example, an educator can convert their lecture notes into audio materials to make them more attractive, and the same method can also be helpful to create educational materials for visually impaired people. Aside from removing the expense of voice artists and equipment, TTS also provides companies with many options in terms of language and vocal repertoire. In this article, we have gathered the top 100+ generative AI applications that can be used in general or for industry-specific purposes.