Generative AI models use complex algorithms and neural networks to autonomously generate new, original content such as images, text, videos and music. These models include variational autoencoders, generative adversarial networks, transformer-based models and neural radiance fields. Generative AI has applications in industries like art, design, healthcare, cybersecurity, and gaming where it can supplement operations by producing images, text, code documentation and more. However, challenges remain around preventing the generation of harmful content, addressing bias, attributing sources, protecting privacy, and reducing energy consumption of these technologies.

1. Generative
AI Models
& Their
Applications
Generative AI models are highly
scalable artificial intelligence (AI)
solutions that utilize complex
algorithms, language modeling and
neural networks to generate new,
original material.
Generative AI models are able to
supplement and transform various
business operations by producing
data, images, text and other forms
of content autonomously.
Generative AI Models
DEFINED
TYPES OF
Generative AI Models
APPLICATIONS OF
Generative AI Models
Image
Generation
AI-generated
images applied
in art, design and
entertainment
industries
Text/content
Creation
Human-like
text used in
natural language
processing,
chatbots and
content creation
Code
Documentation
Synthetic
code applied
with human
and artificial
programming
languages
Video, Music
Sound
Generation
Generation and
completion of video/
sound synthesis
for use across
entertainment
industries
•
Variational autoencoders (VAEs
Combines the power of autoencoders and
probabilistic modeling for enhanced image,
audio and video content creation
•
Generative adversarial networks (GANs)
Utilizes a generator network and a discriminator
network to create highly realistic samples that
resemble real data
•
Transformer-based models
Generates coherent and contextually
appropriate language that expands AI
capabilities in text generation
•
Neural radiance fields (NeRFs):
Produces novel views of complex 3D imagery
based on 2D image inputs
•
Deep Reinforcement Learning (DRL) Models
Leverages the power of deep neural networks
and reinforcement learning to enhance machine
learning capabilities (decision-making and adaptive
behaviors) in dynamic and complex environments
Gaming
Development
Creation of
highly immersive
storytelling and video
game experiences
Medical Research
Healthcare
Design of
proteins and drugs;
optimization of
imagery in diagnostics
Cybersecurity
Risk Management
Automation
of digital security
processes and
improved visibility
Sources: xenonstack.com/blog/generative-ai-models | eweek.com/artificial-intelligence/generative-ai-model/
webagesolutions.com/blog/generative-ai-engineering | ucsd.libguides.com/c.php?g=1322935p=9734831
CHALLENGES
THE FUTURE OF
Generative AI
•
Organizations must implement
generative AI and machine learning
training procedures to keep pace with
ongoing advancements.
•
Care must be taken to prevent AI
generation of illegal, inappropriate
or harmful material.
•
AI-generated content must be
evaluated for bias as it may not have
equal knowledge of all topics.
•
AI does not currently cite or otherwise
indicate sources, which opens up
liability for copyright infringement,
unreliable narrator, etc.
•
Organizations must protect the privacy
of personal data being accessed by AI.
•
AI technologies consume significant
amounts of energy and must be made
more sustainable going forward.