Well, everyone knows what artificial intelligence (AI) encompasses. But what exactly is generative artificial intelligence or Generative AI or GenAI?
You might have heard about ChatGPT, Bard, DALL-E, and Google DeepMind. Indeed, these are the most common examples of generative artificial intelligence applications. It produces text, images, or audio content as per the prompts.
Generative AI has several use cases, from creative to industrial applications, such as manufacturing.
In this article, we will discuss some of the key aspects of generative artificial intelligence, its benefits, and applications.
What is Generative Artificial Intelligence?
Generative AI is a part of artificial intelligence that can generate a wide range of content in the form of text, images, audio, video, code, and any other data created using algorithms. Generative AI uses machine learning algorithms to generate results based on a training data set.
Furthermore, GenAI produces outputs in the same or different format in which you give the prompts. For example, the prompts give you a text-to-text, text-to-image, or image-to-video.
Though, the technology is not new. In fact, it existed during the sixties. However, it gained much popularity with the advent of generative adversarial networks (GANs). They are known for creating lifelike content based on the prompts.
How Does Generative Artificial Intelligence Work?
Generative AI gives outputs based on prompts in the form of texts, images, videos, and much more. You can train the GenAI models to produce new outputs using large quantities of existing content. It uses neural networks that help manage complex patterns.
One of the peculiarities of neural networks is that they do not need human intervention to differentiate patterns in the training data. Thus, it identifies the patterns within the prompts and produces new results by sampling while relentlessly adjusting the parameters to enhance the chances of delivering precise output. This helps generate accurate results that match user needs.
Difference between Traditional Machine Learning and Generative AI
The fundamental difference between traditional ML and GenAI is that the former is used for analyzing patterns to make predictions. In contrast, the latter becomes useful for creating results similar to the prompts given.
Popular Generative AI Models
GenAI uses several natural language processing (NLP) techniques to learn from large quantities of data. It generates new content that is similar to the training data. In fact, its capabilities go the extra mile to deliver the most desirable results.
A few of the most common examples of GenAI models are as follows:
ChatGPT
Developed by OpenAI, ChatGPT is a text-to-text generative AI. It is an AI-powered chatbot that can interact with users.
For example, you can give input to generate texts on a particular subject/topic in different styles, ask questions, engage in conversations, and so on.
Google Bard
Bard is a text-to-text generative model developed by Google based on the large language model LaMDA (Language mode for dialogue applications).
Similar to ChatGPT, Bard is also an artificial intelligence-enabled chatbot. It can answer questions and produce text based on the prompts.
DALL-E
It also uses neural networks trained on images alongside texts, DALL-E is a type of text-to-text GenAI model. It is trained on a data set of images in several styles and related text content.
You can give text prompts to produce images based on the training data. Furthermore, it can create variations on the generated image in different styles.
Applications of Generative AI
Generative AI serves several purposes. For example, as explained above, you can generate any content using generative AI.
Let’s check out some of the top use cases of GenAI:
- Customer Service Support
- Education and Training
- Content Creation
- Marketing, Advertising, and Search Engine Optimisation
- Software Development
- Investment and Financial Services
- Healthcare and Wellness Services
Benefits of Using Generative AI in Different Fields
Generative AI has several applications in various industries. It can interpret the prompts and generate new outputs accurately.
Here are some advantages of using GenAI in a few business sectors cited below:
Customer Support
AI-enabled chatbots help businesses minimize response times during queries in software development.
Content Generation
Writers use generative AI models to plan, draft, and arrange articles. In fact, they can save time dedicated to in-depth analysis.
Software Development
Generative AI models help developers create and review code, identify bugs, and suggest proper fixes.
Marketing and Sales
Generative AI tools help generate ad copy, product descriptions, and convincing sales scripts suitable for each customer type, which otherwise can consume much time and effort.
Healthcare Services
Generative AI diagnoses diseases, discovers drugs, creates personalized treatments, and conducts healthcare research.
What Are Large Language Models?
A large language model (LLM) is an AI model that can understand text prompts and produce lifelike responses resembling humans. LLMs are made using deep learning techniques with the help of a neural network called Transformer architecture.
How Do Large Language Models Work?
Typically, large language models use a combination of pre-training, fine-tuning, and in-context learning.
Pre-training
During the pretraining phase, it is trained on large datasets with a wide range of texts on the Internet. Moreover, it helps LLM understand the language patterns such as grammar and syntax.
At the time of pre-training, LLM can be trained in several ways and updates the parameters to reduce the errors in prediction. Further, it helps produce relevant, useful texts.
Fine-tuning
Once pre-training is over, LLM is trained on task-oriented datasets. During this phase, the LLM is fine-tuned with the help of supervised learning and receives labeled examples of relevant results.
Above all, it helps LLM use its pre-trained knowledge to cater to the requirements of a specific task.
In-context Learning
In-context training, LLMs can finish tasks with limited training. For example, it does not need any training for that particular task.
Also, the model’s parameters are not updated, which implies that it learns without training. The biggest advantage is that you can use such models to finish new tasks without much training.
Frequently Asked Questions
What are generative AI tools?
Generative AI helps create authentic content using its ability to learn from the training datasets.
How do generative AI tools work?
Generative AI is a subset of artificial intelligence that creates models that can generate new data similar to the patterns from training datasets.
How does generative AI differ from AI?
Conventional AI carries out tasks on the basis of predefined rules, while generative AI can generate new data similar to human content.
The Bottomline
Today, businesses need advanced tools to stay ahead of the competition. Generative AI is one such tool that helps organizations reach new heights. Generative AI automates several tasks and enhances productivity in the first place. To make the most of GenAI, enterprises can rely on prompt engineering and use the AI-enabled tool to suit their purposes.
Look no further; explore ThinkPalm’s AI development services to meet all your requirements for AI-enabled services. Regardless of the industry type, our ingenious AI services can make your business fly high!
Author Bio
Silpa Sasidharan is a content writer and social media copywriting expert working at ThinkPalm Technologies, who aspires to create marketing texts for topics spanning from technology, automation and digital business solutions.
