As we navigate through 2024 and beyond, the undeniable influence of generative AI reshapes not just our workflow but the very essence of work in the 21st century. Embracing these advancements not only promises enhanced efficiency and innovation but also fosters a future of work that is more inclusive and human-centered.
As AI adoption increases across all industries and data is leveraged for making informed decisions, organizations are more willing to invest in and adopt generative AI solutions.
From information to entertainment, from solutions to practical applications- Artificial intelligence-powered generative AI model-based services are used extensively by diverse users for multiple objectives. As per Bloomberg Intelligence, generative AI is estimated to become a $1.3 Trillion Market by 2032.
Before we start having an in-depth discussion about generative AI’s application, models, tools, and frameworks, let’s have a brief introduction to generative AI.
What Is Generative AI?
Generative AI is a subset of artificial intelligence that utilizes advanced deep learning models and large language models (LLMs) to generate data or content. Generative AI solutions facilitate generating content like text, audio files, images, videos, and other content based on trained data and user inputs.
The question that hits many minds is how generative AI differs from other AI techniques. Traditional AI is based on receiving input and producing an output where inputted data gets analyzed and used further. Generative AI models go above and beyond the capabilities of the traditional AI in generating new information or content.
Different Types Of Generative AI Models
There are various generative AI models, each with its own approach to generating new data. These diverse models ensure delivering generative AI solutions to businesses of all scales and verticals meeting their unique needs and objectives.
Here are some of the most common types of generative AI models:
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Autoencoders
- Transformer-based Models
- Markov Chain Models
- Large Language Models
- Diffusion Models
- PixelRNN and PixelCNN
- Flow-based Models
Popular Libraries and Frameworks for Generative AI:
Here are popular libraries and frameworks widely used by data scientists for implementing generative AI solutions and models.
- TensorFlow
- PyTorch
- Keras
- GANs Libraries
- Scikit-learn
From automating content creation to streamlining design processes, generative AI offers a myriad of benefits that can revolutionize your operations and give you the edge over competitors. Dive deeper into the world of generative AI and unlock its full potential by reading our full blog now: https://bit.ly/49zdvON
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