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What is Generative AI? How does it Work?

August 11, 2023

Table of Contents

    It’s just a matter of time. Artificial Intelligence has been through a tremendous roller-coaster ride. It set its foot in the market with an AI model like ChatGPT to expedite its advancement to CRM-based AI models like Generative AI.

    Terms like “ChatGPT” and “Generative AI” have undertaken the market and created a disruption one would’ve never considered. The magic of generative AI through its conversational chatbot and image-generating capabilities is recklessly crafting emails, social media captions, Excel formulas, codes, and even websites, which grabbed the attention of millions.

    On the horizon, AI’s enterprise embrace is projected to rocket with a 38.1% yearly surge from 2022 to 2030. The call is clear—time to equip and embrace Generative AI for every business pro.

    But what is this recurring term: Generative AI, and how is it different from any other AI?

    In the following read, we’ll paint a vivid picture of the AI landscape. This piece is tailor-made if you’ve engaged in talks but need clarification on the concept.

    What is Generative AI?

    Generative AI is algorithms that generate new and human-curated content from images, text, or audio data. Consider it as an algorithm built on different foundation models, which is further trained on a wide array of information trained in a way to uncover underlying patterns. Just as an artist might create a variety of paintings from a single stroke of inspiration, Generative AI crafts text, images, or audio based on its insights.

    Foundation models are robust AI systems that can learn from large amounts of data and be adapted for various tasks and domains. GPT-3.5 and DALL-E are two examples. GPT-3.5 is a foundation model capable of processing natural language and producing text. It can be used for various tasks, including question-answering, text summarization, and sentiment analysis.

    DALL-E is a foundation model that can combine text and image inputs and generate images. It can be used for creative tasks, such as image creation, enlargement, or variation.

    Let’s understand it with an example:

    There is a healthcare service provider who leveraged the capabilities of Generative AI to enhance patient care. For instance, a hospital aims to improve patient treatment plans.
    By inputting patient medical history and symptoms, Generative AI can swiftly generate personalized treatment options, considering factors like drug interactions and effectiveness.

    This innovation streamlines the decision-making process for doctors, leading to quicker and more accurate treatment recommendations. As a result, patients receive tailored care that enhances their outcomes.

    This integration of Generative AI showcases the healthcare provider’s commitment to utilizing advanced technology for improved patient well-being and underscores their position as a leader in innovative healthcare solutions.

    How Does Generative AI Work?

    Generative AI uses neural networks that are mathematical models. It creates a replica of the human brain to understand the structures and patterns of the data.

    Another advantage is that you can train neural networks using various learning methods, such as unsupervised or semi-supervised. This helps organizations build foundation models and general-purpose AI models that can perform multiple tasks.

    Some examples of foundation models are GPT-3 and Stable Diffusion, which are based on natural language processing. They can generate text or images from a given input.

    For instance, ChatGPT, powered by GPT-3, can curate an article from a short text command. Similarly, Stable Diffusion can produce realistic images from a text description.

    You can read about Generative AI for Sales here, and how it can help you transform your sales strategy.

    What does Machine Learning have to do with Generative AI?

    Generative AI falls under machine learning and is capable of crafting fresh content resembling what already exists. We educate models to fashion items akin to those they’ve encountered earlier.

    Subsequently, these models employ their acquired knowledge to produce novel content akin to the examples. These models put their developed understanding to work by creating unknown content resembling the given criteria.

    This process is facilitated through various methods, including utilizing techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These tools employ machine learning to generate new content mirroring established patterns.

    Machine learning helps it to make new things that follow patterns from the old stuff. It’s like copying a style to create new music, pictures, or words.

    What Are the Types of Generative AI Models?

    Different kinds of Generative AI Models:

    Text Models

    1. GPT-3
      GPT-3 (Generative Pretrained Transformer 3) is like an intelligent writer who learns from lots of text to make sentences. It can translate languages, summarize things, and answer questions.
    2. LaMDA
      LaMDA (Language Model for Dialogue Applications) is trained on conversations, so it’s good at making realistic chat responses.
    3. LLaMA
      LLaMA is a smaller model that’s still good at understanding and creating text, but it’s made to be efficient.

    Multimodal MODELS

    1. DALL-E
      DALL-E turns words into pictures. It’s like an artist that draws images based on what you describe.
    2. Stable Difusion
      Stable Diffusion is similar to DALL-E but uses a remarkable process to slowly make a picture more transparent based on a text description.
    3. GPT-4
      GPT-4 is a big model that knows both text and images. It guesses what words come next in a text and improves how factual and well-behaved its answers are.
    4. Progen
      Progen creates proteins based on what you describe in words. It’s trained on a lot of protein data to make accurate molecules.

    What are the Applications of Generative AI?

    Generative AI is a potent asset in optimizing the processes of creators, engineers, researchers, scientists, and beyond. Its applications span diverse industries and individuals.

    These models can process inputs like text, images, audio, video, and code and produce novel content in any form. For instance, it can transform text prompts into images, convert images into musical compositions, or transcribe video content into written text.

    Looking at the current landscape of Artificial Intelligence’s growth, Generative AI is emerging as a potent resource to streamline the processes of creators, engineers, researchers, scientists, and various professionals. All industries and individuals can benefit from its capabilities and opportunities. It is generative AI, the science of making something new from something old.

    Examples of Generative AI applications

    Generative AI finds its foundation in text, making it one of the most advanced domains. A prime example is large language models (LLMs), widely used for tasks like essay creation, code development, translation, and decoding of genetic sequences.

    In the world of generative artificial intelligence, there’s a focus on audio and music. Models can craft tunes and audio clips from text inputs, identify objects in videos while generating accompanying sounds, and even compose custom music.

    Generative AI’s impact shines in the visual realm, creating 3D images, avatars, videos, graphs, and more. It offers versatility by generating images with diverse styles and editing techniques. It crafts chemical compound graphs for drug discovery, produces augmented reality visuals, develops game-ready 3D models, designs logos, and enhances images.

    Synthetic Data
    This form of artificial intelligence addresses data scarcity with synthetic data, which is especially vital for training AI models. It’s a potent solution for data challenges, achieved through label-efficient learning. This approach reduces labeling costs by generating augmented training data or learning data representations, enabling AI models to excel with minimal labeled data.

    Generative models have a profound influence, with applications continually expanding. Here are a couple of instances that demonstrate how generative AI is driving progress in different industries:

    Finance: Generative AI generates synthetic financial data to simulate market scenarios, helping analysts predict trends and develop effective investment strategies.

    Marketing: Marketers leverage generative AI to create personalized content, tailoring advertisements and product recommendations to individual consumer preferences.

    Fashion: In the fashion industry, generative models create unique clothing designs and patterns, fostering creativity and offering fresh styles to customers.

    Architecture: Architects employ generative models to generate innovative building designs, streamlining the design process and enabling the creation of efficient and aesthetically pleasing structures.

    Energy: In renewable energy, generative AI optimizes the design of wind turbine blades, improving energy efficiency and harnessing cleaner power.

    What are the Benefits of Generative AI?

    Businesses can expect a few of these common advantages from Generative AI:

    Making new and original ideas

    It can produce many different kinds of outputs that are unique and creative. It can help people who work in art, fashion, or product design create new and exciting content.

    Giving personal recommendations

    It can learn what people like and suggest things that suit their preferences. It can power systems that recommend movies and music on streaming platforms, making users happy and engaged.

    Solving problems faster

    Generative AI can quickly generate solutions to problems. In healthcare, it can help find new drugs by testing different chemical compounds, saving time and money compared to traditional methods.

    Creating more data

    Generative AI can make fake data that looks real to train machine learning models. This is useful when real data is not enough, improving the accuracy and reliability of the models.

    Doing boring tasks

    It can do repetitive tasks like making content and reducing the work for human resources. For example, it can write articles, make music, or design graphics, allowing professionals to focus on more critical tasks.

    What is the Future of Generative AI?

    Generative AI is gaining traction and significance globally. Grand View Research indicates that the revenue attributed to it is projected to surge from $44.89 billion in 2023 to $109.37 billion by 2030. This denotes an impressive annual growth rate of 35.6%. By 2023, it is predicted to contribute around 10 percent of the total revenue generated by artificial intelligence overall.

    By 2030, this proportion will rise from 10 percent to 25 percent due to diverse industries adopting the potential of generative AI, like healthcare, finance, manufacturing, and entertainment.

    Below, the table illustrates the market share of generative AI in comparison to other AI technologies for the years 2023 and 2030:

    TechnologyMarket Share (2023)Market Share (2030)
    Generative AI 10%25%
    Machine Learning 60%50%
    Natural Language Processing20%20%
    Computer Vision10%15%

    It is a form of Artificial Intelligence, that can craft unprecedented creations. Its benefits extend to businesses and individuals both.

    While generative AI is relatively new, its progress is swift. It’s swiftly grasping the art of creating novel items resembling prior observations.


    Generative AI emerges as a captivating technology with boundless potential to revolutionize our lifestyles and professions. Where AI was traditionally confined to specialists, the power to effortlessly communicate with software and swiftly craft new content extends its accessibility to a broader spectrum of users.

    Nevertheless, like any technological advancement, applying it requires many considerations. As this technology is embraced and refined, receiving an ongoing series of questions regarding its multifaceted implications is inevitable. Adopting these technologies will foster efficiency, productivity, improvement in customer services, and whatnot.

    For comprehensive insights, reach out to our seasoned experts. We’ve been at the forefront of integrating Generative AI in businesses even before its models gained widespread traction. Our professionals advise on the optimal deployment of this rapidly advancing technology and execute its implementation tailored to your preferences.

    Schedule a call today.

    Frequently Asked Questions

    AI stands for Artificial Intelligence, whereas Generative AI is centered on crafting fresh content, such as images and text. Unlike general AI, Generative AI excels in producing imaginative outputs using learned patterns from available data.

    ChatGPT and DALL-E are examples of generative AI models created by OpenAI. ChatGPT generates human-like text, while DALL-E generates images from textual descriptions. Generative AI generally produces content like text, images, or music using machine learning, often based on patterns learned from existing data.

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