Generative AI, or generative adversarial networks (GANs), has found excessive implementations in various fields. Here are some technologies that utilize generative AI. Generative AI is raising the GDP of world economy. A statement released by McKinsey that half of the work will be done by this GAN technology between 2030 to 2060.
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Generative AI could add up to $4.4 trillion a year to global economy: McKinsey
The UK’s GDP in 2021 was $3.1 trillion. Mckinsey has observed 850 occupations and 2100 elaborated work activities and gave a report. In this report it is assumed that to the global economy only Generative AI can add from $2.6 trillion to $4.4 trillion, which is equivalent to the GDP of a new country approximately equivalent in size and which generates productivity somehow related to UK’s productivity . Here is the link to forbes magazine regarding the content.
What are the implementations of GAN or Generative AI ?
Here are few executions of generative adversarial neural networks. These usages up lift the productivity of creative and generative efforts.
Image Generation:
Generative AI is widely used for image generation tasks, such as creating realistic images from scratch or transforming existing images. It has contribution in digital graphics, art, technical and nontechnical design, and also generating synthetic training data points for data science models.
Video Synthesis:
Generative AI can generate new video content by synthesizing frames based on existing videos or images. This technology is used in video editing, VFX , and deep fake generation.
Text Generation:
Generative models can generate similar data if any prompt is given with contextually relevant information. This technology has applications in natural language processing, creative writing, chatbots, and automated content generation.
Music Composition:
For different generes generative AI can create music with different style. Copying a famous music director or creating an original composition now became easy with this. It needs train the model on huge amount of music.
3D Object Generation:
It can create 3D models of objects based on limited input or transform existing 3D models. This AI technology finds applications in virtual reality (VR), computer-aided design (CAD), and engineering visualization.
Style Transfer:
Such Generative learning models can transfer the style of one image or artwork onto another, creating visually stunning combinations. This technology has been used in various creative domains, including graphic design, fashion, and visual effects.
Game Development:
Generative AI can help in procedural content generation for video games, generating levels, characters, terrains, and other game equipment. It helps developers create vast and diverse game worlds dynamically.
Data Augmentation:
Generative models can generate synthetic data to augment existing datasets, improving the performance and robustness of machine learning models. In limited training data this method is used often.
Drug Discovery:
Generative AI is applied in the field of pharmaceuticals to generate new drug candidates. By learning from existing compounds and their properties, models can suggest potential molecules that meet specific criteria.
These are just a few examples, and generative AI continues to be explored and applied in many other fields, demonstrating its versatility and potential impact.
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Which software use generative AI to generate art?
There are several software tools and frameworks available for AI art generation. Here are some popular ones like Midjourney and bluewillow and these two you can use in discord social site also.
- Runway ML: Runway ML is a user-friendly software platform that allows artists and designers to create AI-generated art. It provides a visual interface and supports various AI models and techniques, including image synthesis, style transfer, and text generation.
- DeepArt: DeepArt is an online platform that uses deep learning algorithms to transform photographs into artistic styles inspired by famous artists. It offers a simple and intuitive interface for generating AI-based artworks.
- NVIDIA DeepArt: NVIDIA DeepArt leverages deep neural networks to transform images into artistic styles. It provides pre-trained models and allows users to customize and apply different artistic filters to their images.
- OpenAI’s DALL-E: DALL-E is a neural network model developed by OpenAI that generates images from textual descriptions. It can create unique and imaginative visuals based on specific input prompts.
- Google’s DeepDream: DeepDream is a computer vision program that uses deep learning to generate psychedelic and surreal images. It amplifies patterns and features in images to create visually striking and dream-like results.
- Magenta: Magenta is an open-source project by Google that focuses on the intersection of AI and art/music. It provides various tools and models for creating AI-generated music and artworks.
- Adobe Photoshop with NVIDIA AI plugins: Adobe Photoshop, a popular image editing software, can be enhanced with NVIDIA’s AI plugins like NVIDIA GauGAN and NVIDIA DeepArt Effects. These plugins utilize generative AI models to create or transform images in artistic styles.
- Corel Painter: Corel Painter is a digital art software that incorporates AI-based tools for creating unique artistic effects. It offers features like AI style transfer, auto-painting, and pattern generation.
- Prisma: Prisma is a mobile app that uses AI algorithms to transform photos into artworks with different artistic styles. It provides real-time rendering and allows users to adjust various parameters to achieve the desired results.
Conclusion
These are just a few examples, and the field of AI art generation is rapidly evolving with new tools and software being developed. Artists and designers can explore these platforms to experiment with AI and create captivating and imaginative artworks.