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Generative AI and its Applications

Generative AI and its Applications

Generative AI has become increasingly popular within the last few years because of how it redefines content creation. With user input, it can automatically put out media that corresponds with and imitates what is provided to it. Despite the novelty of this technology and the potential security or ethical concerns, it’s a technology that shows promise.

What is Generative AI and its Applications?

Generative AI is a type of technology that can synthesize a variety of content, including text, images, and audio.  Though the technology necessary for generative AI has existed since the 60s, it hasn’t been until the mid 2010s that generative adversarial networks (GANs) have unlocked greater potential for this technology and have been applied towards more practical uses.  It combines a number of other AI algorithms to produce  A generative AI model starts with a prompt in the form of some existing text, imaging, video, audio, or any other physical input the AI can process.  After interpreting the data provided, it will proceed to regurgitate the information into the implemented medium.  Even after the most recent developments, generative AI is still in its nascent stages and can at most produce the most basic outputs.

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Model Types

Diffusion Models:  Also known as denoising diffusion probabilistic models or DDPMs for short, diffusion models are generative models that determine vectors in latent space through a two-step process during training: forward diffusion and reverse diffusion.  The forward diffusion process slowly adds random noise to training data.  By comparison, the reverse process reverses the noise to reconstruct the data samples.  Novel data can be generated by running the reverse denoising process starting from entirely random noise.  Multiple iterations can then refine the logic the model uses.

Variational Autoencoders: Variational Autoencoders or VAEs consist of two neural networks typically referred to as the encoder and decoder.  When given an input, an encoder converts the input into a smaller, more dense representation of the data. This compressed representation preserves the information that’s needed for a decoder to reconstruct the original input data, while discarding any irrelevant information.  The encoder and decoder work together to learn an efficient and simple latent data representation to imitate so it can be mapped through the decoder to generate new data.  Though this model generally speaking can generate outputs faster than the Diffusion model, the outputs typically aren’t as refined.

    Generative Adversarial Networks: First conceptualized in 2014, Generative Adversarial Networks or GANs were considered to be the most commonly used methodology of these three models before the recent success of diffusion models. GANs pit two neural networks against each other.  The first network is a generator that generates new examples and a discriminator that learns to distinguish the generated content as either real or generated.

    These two models are trained together incrementally to improve their ability to churn out content and to recognize whether the content is faked or not respectively.  This is repeated until the generated content is determined to be indistinguishable from the original content.  While the content produced is high-quality, the downside is that sample diversity tends to be weak, making GANs better for more specific data generation.

    Making New Content Automatically

    CloudContactAI (CCAI) has been a gradual project of adding more features to make it the most extensive and adaptive multi-media marketing program on the market.  Most recently, we have begun implementing the email editor and email campaigns into the main features of what marketing tools users have access to.  The email campaign maker is also going to be the first feature to take advantage of generative AI functionality with the SMS marketing campaigns expected to be updated to receive the same tools soon.

    After opening the email builder to make a campaign from scratch or to make a template, users will be immediately presented with the option to build an email using a prompt.  If the user doesn’t want to manually construct the email, they can type in a number of concepts as the basic prompt for the model to work with.  After accepting the prompts, the generative AI will then write an abstract which the user can then continue to edit, implement other images, links, and features to the email, or accept as is.

    Closing Thoughts

    For the longest time, we have been looking for ways to implement generative AI into one of CCAI’s features.  It’s a marketing platform where understanding customer trends is made easy and generating content is made to be as simple as possible.  If you’re interested in how generative AI can be implemented into your marketing strategy or are eager to get started with CCAI’s generative AI beta, you can learn more and sign up on the CCAI website.

    Dolan Cleary

    Dolan Cleary

    I am a recent graduate from the University of Wisconsin - Stout and am now working with AllCode as a web technician. Currently working within the marketing department.

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