Generative AI According to Report Hive Research is projected to expand with
a growth of $1.4 trillion over the next 10 years from a market size of $46
billion in 2023 due to the influx of consumer generative AI programs like
Google's Bard and OpenAI's ChatGPT.
What is Generative AI?
An artificial intelligence that can produce fresh content is
known as generative AI. This is in contrast to discriminative AI, which is used
to classify or predict existing content. Generative AI can be used to create
images, text, music, and other forms of content.
There are many different types of generative AI models, but
they all work by learning from a dataset of existing content. The model then
uses this knowledge to generate new content that is similar to the data it was
The Generative AI Market 2023
The growth of the generative AI market is being driven by the
increasing demand for new content, such as images, text, and music. Generative
AI can also be used to improve existing products and services, such as customer
service chatbots and personalized marketing campaigns.
Growth may accelerate at a CAGR of 44%, propelled initially by training infrastructure and subsequently by large language models (LLMs), digital advertisements, specialised software, and services in the medium- to long-term.
Additionally, growing demand for generative AI solutions might result in an increase in software revenue of around $300 billion, driven by copilots that speed up coding, new infrastructure products, and specialised assistants. The biggest winners as businesses transition increasingly to cloud computing could be companies like Amazon WebServices, Microsoft, Google, and Nvidia.
According to our Analyst, generative AI is expected to increase its influence from less than 1% of the overall spending on IT hardware, software services, advertising, and gaming to 12% by 2030. By 2030, generative AI infrastructure as a service, which is used to train LLMs, will be the largest source of additional revenue, followed by specialised generative AI assistant software ($90 billion) and digital adverts supported by the technology ($195 billion). Revenue from hardware will be driven by conversational AI devices ($113 billion), AI servers ($140 billion), AI storage ($98 billion), and computer vision AI products ($65 billion).
Generative AI Applications
There are several potential uses for generative AI,
Content creation: New music, text, and images can be produced using generative AI. There are several uses for this, including marketing, entertainment, and education.
Generative AI Technology
There are many different technologies that can be used for
generative AI. Among the most popular technologies are
Deep learning: a type of machine learning that
uses artificial neural networks to learn from data.
Generative adversarial networks (GANs): GANs are a type of deep learning model that consists of two neural networks that compete against each other. One network, the generator, tries to create new content that is similar to the data it was trained on. The discriminator on the opposing network seeks to discern between authentic and false content.
Variational Autoencoders (VAEs): VAEs are a type of deep learning model that can be used to encode and decode data. VAEs can be used to generate new content by decoding random noise into data that is similar to the data it was trained on.
Generative AI Future
The future of generative AI is bright. As the technology
continues to develop, it will become more powerful and versatile. This will
allow generative AI to be used for a wider range of applications, such as
creating realistic virtual worlds and designing new products.
Generative AI Examples
Midjourney: Using brief cues, users of this
generative AI platform can produce music, writing, and visuals. Although
Midjourney is still in development, some excellent work has already been
produced using it.
Night Cafe AI: Another generative AI application that
enables users to produce visuals from word descriptions is Night Cafe AI. Night
Cafe AI is renowned for its distinctive and surreal aesthetic.
Stable AI: Stable AI is a platform for generative AI
that specializes in producing realistic visuals. Although stable AI is still
being developed, several remarkable photos of people, animals, and objects have
already been produced with it.
Use Cases for Generative AI in IT
Code generation: Software applications can
be created via generative AI. This can speed up the development process and cut
down on the time and expense involved in developing new applications.
Bug fixing: Software application flaws can
be found and fixed using generative AI. This can aid in enhancing software
quality and lowering mistake rates.
Generative artificial intelligence (AI) can
be used to test software for security flaws. Before attackers take advantage of
security holes, this can assist to find and repair them.
Analysis of huge data sets: Generative AI can
be used to analyze large data sets. In the data, this can assist in seeing
patterns and trends that would be challenging to detect manually.
Generative AI in the Healthcare Industry Use Case
medical images: Generative AI can be used to
produce medical images like X-rays and MRIs. This can be used to develop
virtual simulations of medical operations as well as train machine-learning
models for medical diagnostics.
care: Generative AI can be utilized to give patients individualized care.
This can involve making personalized educational materials, building virtual
assistants, and forecasting patient outcomes, among other things.
Author Name : Report Hive Research