34 AI content generators to explore in 2024 GPT-3

Assessing different types of generative AI applications

Learn how industries use generative AI models in content creation and alongside discriminative models to identify, for example, instances of real vs. fake.

AI encompasses many techniques for developing software models that can accomplish meaningful work that only humans could perform previously. AI developers build different AI models embodying a variety of techniques, including neural networks, genetic algorithms, deep or machine learning and reinforcement learning.

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 -- is a generative AI tool.

Many applications for generative models

Generative AI functions across a wide range of use cases, including the following:

  • Natural language interfaces. In performing both text generation and speech synthesis, these AI systems power digital assistants such as Amazon's Alexa, Apple's Siri, Google Assistant, Jasper and other tools that can auto-summarize text, generate meeting minutes and answer a customer service line or questions in response to human input.
  • Image synthesis. These AI systems create images based on instructions or directions. They will, if told to, create an image of a kiwi bird eating a kiwi fruit while sitting on a big padlock key. They can be used to create images for ads, ideas for fashion designs, and basic movie or video game character designs and production storyboards. Dall-E, Midjourney and Wombo Dream are examples of AI image generators.
  • Space synthesis. AI can also create 3D spaces and objects, both real and digital. It can design buildings, rooms and even whole city plans, as well as virtual spaces for gameplay or metaverse-style collaboration. Spacemaker is a real-world architectural program, while Meta's BuilderBot (in development) will focus on virtual spaces.
  • Music synthesis. While still nascent, game developers and others are beginning to use AI for music generation, providing new background music to newly generated scenes and spaces.
  • Product design and object synthesis. Now that the public is more aware of 3D printing, it's worth noting that generative AI can design and even create physical objects like machine parts and household goods. AutoCAD and SOL75 are tools using AI to perform or assist in physical object design.
  • Pharmaceuticals. Pharmaceutical companies and academic researchers can use generative AI in drug design and drug discovery, especially to predict the folding of proteins. Amgen, Insilico Medicine and others use generative AI in drug research and development.

How generative and discriminative models function together

Generative models make things while discriminative models 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.

Many tools harness both generative and discriminative AI. A generative adversarial network (GAN) uses a generative model to create outputs and a discriminative model to evaluate them. Using them as adversaries, with feedback loops between the two, accelerates training.

Diagram of GAN training method
GAN training method

For example, a prominent class of GANs are large language models like ChatGPT. Such a model might, for example, be tasked with writing fake restaurant reviews. The generative model, when fed a base of real reviews as training data, would attempt to create seemingly real reviews and 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 discriminator, which is told which inputs were real and which were fake only after evaluating them, then 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. The feedback loops ensure each exercise cycle trains both models to perform better.

This phenomenon is applied in the following industries:

  • Finance. AI systems watch transaction streams in real time and analyze them in the context of a person's history to judge whether a transaction is authentic or fraudulent. All major banks and credit card companies use such software now, some developing their own and others using commercially available solutions.
  • Manufacturing. Factory AI systems can watch streams of inputs and outputs using cameras, x-rays, etc. They can flag or deflect parts and products likely to be defective. Kyocera Communications and Foxconn both use AI for visual inspection in their facilities. Companies are also experimenting with using AI to generate product descriptions, but the health, safety and legal implications of doing so are making them cautious.
  • Film and media. Just as generative tools can create fake images (e.g., a kiwi bird eating kiwi on a key), discriminative AI can identify faked images or audio files. Google's Jigsaw division focuses in part on developing technology to make deepfake detection more reliable and easier.
  • Social media and tech industry. AI systems can look at postings and patterns in postings to help spot fake accounts by disinformation bots or other bad actors. Meta has used AI for years to help find fake accounts and to flag or block COVID misinformation related to the pandemic.

Generative AI has become a widely known tech buzzword, and its myriad current and potential applications mean the technology is here to stay. Although current hype considerably outstrips reality, generative AI is certain to become deeply embedded in more and more enterprises.

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