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AI encompasses many techniques for developing software models that can accomplish meaningful work, including neural networks, genetic algorithms and reinforcement learning. Previously, only humans could perform this work. Now, these techniques can build different kinds of AI models.
Generative AI models are one of the most important kinds of AI models. A generative model creates things. Any tool that uses AI to generate a new output -- a new picture, a new paragraph or a new machine part design -- incorporates a generative model.
Generative AI functions across a broad spectrum of applications, including the following:
Many tools harness both generative and discriminative AI models. Discriminative models, adversely, identify things. Any tool that uses AI to identify, categorize, tag or assess the authenticity of an artifact (physical or digital) incorporates a discriminative model. A discriminative model typically doesn't say categorically what something is, but rather what it most likely is based on what it sees.
A generative adversarial network (GAN) uses a generative model to create outputs and an adversarial discriminative model to evaluate them, with feedback loops between the two. For example, a GAN might be tasked with writing fake restaurant reviews. The generative model would attempt to create seemingly real reviews, then pass them, along with real reviews, through the discriminative model. The discriminator acts as an adversary to the generative model, trying to identify the fakes.
The feedback loops ensure that the exercise trains both models to perform better. The discriminator, which is then told which inputs were real and which were fake after evaluating them, adjusts itself to get better at identifying fakes and not flagging real reviews as fake. The generator gets better at generating undetectable fakes as it learns which fakes the discriminator successfully identified and which authentic reviews it incorrectly tagged.
This phenomenon is applied in the following industries:
Generative AI may well become a widely known tech buzzword, like automation, and its myriad applications prove that this nascent branch of AI is here to stay. To meet modern challenges facing the tech industry, it only makes sense that this technology will expand and become deeply embedded in more and more enterprises.
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