Generative Artificial Intelligence or Generative AI is a set of algorithms capable of generating text, images, or other media in response to prompts. It performs beyond simple classification problem. It can create new text, images, video, audio, code, or synthetic data. Whenever any AI technology is generating something on its own, it can be referred to as “generative AI.” This umbrella term includes learning algorithms that make predictions as well as those that can use prompts to autonomously write articles and paint pictures. In the recent years, the realm of AI has expanded a great deal. It has the capability to revolutionize industries, spark innovation, and shape the future of business.
How does Generative AI work
In previous versions, Generative AI submitted data via an API or other process. Developers familiarized themselves with specialized tools and wrote applications using languages such as Python. Nowadays, results can be customized with feedback about the style, tone, and other elements. Generative AI models combine various algorithms to process content, generate new text, transform characters into sentences, parts of speech, etc. NLP and other multiple encoding techniques is the key to its success.
Techniques like GANs and variational autoencoders (VAEs) – neural networks with a decoder and encoder — are suitable for generating realistic human faces or even synthetic data for AI training purposes. Recent progress in transformers such as Google’s Bidirectional Encoder Representations from Transformers (BERT), OpenAI’s GPT and Google AlphaFold have also resulted in neural networks which can not only encode language, images and proteins but also generate new content.
Types of Generative AI Models
Generative AI models can be classified into text models and multimodal models.
Under text models we have:
• GPT-3, or Generative Pretrained Transformer 3. This is an auto-regressive model pre-trained on a large set of text data to generate high-quality natural language text. GPT-3 is flexible in design and can be fine-tuned for a variety of language tasks. Some of them include language translation, summarization, and even question answering.
• LaMDA, or Language Model for Dialogue Applications, is a pre-trained transformer language model that generates high-quality natural language text, like GPT. However, the approach towards training of LaMDA was quite different. It is trained on dialogue with the objective of picking up nuances of open-ended conversations.
• LLaMA is a smaller natural language processing model compared to GPT-4 and LaMDA, with the goal of a performant. This is also an auto-regressive language model based on transformers. LLaMA is trained on more number of tokens which improves performance having lower numbers of parameters.
Under multimodal models we have:
• GPT-4 is the latest release of GPT class of models. It is a large-scale, multimodal model which can accept image and text as inputs and produce desired text outputs. GPT-4 is a transformer-based model that is pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behaviour.
• DALL-E is a multimodal algorithm that can operate across different data modalities and create novel images or artwork from natural language text input. It connects the meaning of words to visual elements. It enables users to generate imagery in multiple styles driven by user prompts.
• Stable Diffusion is a text-to-image model like DALL-E, but uses a process called “diffusion” to gradually reduce noise in the image until it matches the text description.
• Progen is trained on 280 million protein samples to generate proteins based on desired properties which are specified using natural language text input.
Challenges in Generative AI
Nevertheless, Generative AI has a few issues attached to it. One being the ethical implications surrounding the technology. Deep fake videos are some examples in this category. It becomes easier to claim that real photographic evidence of a wrongdoing was just an AI-generated fake. Data source leads to another type of nuance. Generative AI collects huge amount of data from the internet to train its model and make predictions. But there is no guarantee that the predictions will be correct. There is no way to validate the presence of any kind of bias in the models. For instance, currently there are numerous chatbots present in the market providing incorrect information or simply making things up to fill the gaps. It can promote new types of plagiarism that ignore the rights of content creators and artists of original content.
Final Notes
Thus, this new wave of Generative AI systems such as ChatGPT, Bard and Dall-E possess the potential to significantly accelerate AI adoption, even in those organizations lacking AI or data-science expertise. While proper customization still requires expertise, adopting a generative model for a specific task requires relatively low quantities of data or examples through APIs or by prompt engineering. Not only that, improvements in AI development platforms will help accelerate research and development for text, images, video, 3D content, drugs, supply chains, logistics and other business processes. Organizations will start to customize generative AI on their own data to help improve branding and communication. Vendors can integrate generative AI capabilities to streamline content generation workflows. Hence, all of this together will boost productivity.
