Introduction
Generative AI represents a transformative shift in the traditional approach to performing work, opening the way for a renewed focus on the creation of ground-breaking products and services that have the potential to advance human civilization. The ability of generative AI to take over repetitive tasks that are part of the production process can significantly ease the burden on human resources, freeing up talent to work on more sophisticated and imaginative tasks.
This technology allows even those without deep technical or artistic expertise to generate awe-inspiring creations, thereby democratizing the artistic process. Unlike conventional forms of AI, such as discriminative AI used in classifications or reactive machines employed in self-driving cars, generative AI is exclusively designed to produce new content as its primary output.
How Generative AI Works
Generative AI employs models, which are essentially algorithms trained on specific datasets, to generate new content. These models can be interacted with using AI notebooks, such as Google Colab, which is a standard for web-based notebooks. AI notebooks provide a means to input data into the system and receive output. Google Colab is built on the fundamental Jupyter Notebook, a tool that has been used by data scientists and AI researchers for many years.
A prime example of a company leveraging AI and helping others do the same is Hugging Face. They are at the forefront of AI development, creating tools and packages that assist businesses in their AI endeavors.
The Rise of Transformers
Introduced in 2017, transformers marked a significant breakthrough in the field of AI. Initially successful in natural language processing (NLP) applications, the architecture was later adapted for computer vision applications. OpenAI’s GPT (Generative Pre-Trained Transformer), the first pre-trained transformer model, and Google’s BERT (Bidirectional Encoder Representations from Transformers) were early pioneers in this field. These were followed by Image GPT and Google Vision Transformers, both designed to generate images pixel by pixel.
Image vs. Text
While transformers excelled in NLP, images presented a unique challenge. To overcome this, images are divided into 16-pixel square patches, a process known as 2D Convolution. These patches are then arranged in sequence or “flattened.” Position Encoding is used to maintain the relative position of these patches, ensuring that the arrangement of the image is preserved. The patch and its position encoding are fed into the Transformer Encoder, and the first index is passed to the Multilayer Perceptron (MLP) Head, a fully connected network capable of distinguishing between different data classes once trained.
Generative AI in Action
HuggingFace is a valuable resource for those embarking on a journey with generative AI. It offers Python packages for generative AI and machine learning tasks, with access to pre-trained models from their hub. The packages include:
- Transformers: These pre-trained models are trained on well-known datasets and come with a fully-developed model architecture and weights.
- Datasets: A crucial aspect of machine learning is having separate datasets for training, validation, and testing.
- Evaluation: These packages help gauge the accuracy of the models.
- Gradio: This package is used for creating web demos of machine learning solutions.
Most image models are trained on ImageNet, a collection of images in 1000 different classes. Some models are even trained on ImageNet21K, which boasts 21,000 classifications! Vision Transformers from HuggingFace have been trained on both. Pretrained models save significant time and computational power, as they have already been trained on millions of images.
Conclusion
Generative AI, with its capacity to create new, unique content, heralds a paradigm shift in the way we approach creativity and work. With the proliferation of AI tools and resources, the barrier to entry in the AI space has significantly lowered, bringing the immense power of AI to the fingertips of every aspiring creator. Whether it’s creating art, designing products, or solving complex problems, generative AI has become an invaluable tool in our technological toolbox.