Evaluation of different types of generative AI applications

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Artificial intelligence includes many techniques for developing software models that can accomplish purposeful work, including neural networks, genetic algorithms, and reinforcement learning. Previously, only humans could perform this work. Now, these technologies can build different types of AI models.

Generative AI models are one of the most important types of AI models. The generative model creates things. Any tool that uses artificial intelligence to create new output — a new image, a new paragraph, or the design of a new part of the machine — includes a generative model.

Various applications of generative models

Generative AI works across a wide range of applications, including the following:

  • Natural language interfaces. In performing both speech and text synthesis, These artificial intelligence systems Digital assistants like Amazon’s Alexa, Apple’s Siri, and Google Assistant, as well as tools that automatically summarize text or automatically create press releases from a set of key facts.
  • Image installation. These artificial intelligence systems create images based on instructions or directions. If asked, they will create an image of a kiwi bird eating a kiwi fruit while sitting on a large padlock key. They can be used to create advertisements, costume designs, or storyboards for film productions. 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 entire city plans, as well as virtual spaces for metaverse-style play or collaboration. Spacemaker is real architecture software, while Meta’s BuilderBot (in development) focuses on virtual spaces.
  • Product design and object synthesis. Now that the public is more aware 3D printingNote that generative AI can design and even create physical objects such as machine parts and household goods. AutoCAD and SOL75 are tools that use artificial intelligence to perform or help design a physical object.

Many tools use both generative and discriminatory AI models. Discriminative models, passively, define things. Any tool that uses artificial intelligence to identify, categorize, discriminate or assess the authenticity of an artifact (physical or digital) that includes a discriminatory model. The discriminative model does not usually say definitively what a thing is, but what it is most likely depends on what it sees.

Schematic diagram of the GAN training method
GAN تدريب training method

How do generative and discriminative models work together

a Generative Adversarial Network (GAN) It uses a generative model to generate outputs and an adversarial discriminant model to evaluate it, with feedback loops between the two. For example, a GAN may be tasked with writing fake restaurant reviews. The generative model will attempt to generate reviews that appear real, and then pass them, along with the real reviews, through the discriminant model. The discriminator acts as an opponent of the generative model, trying to identify counterfeit products.

Feedback loops ensure that the exercise trains both models to perform better. The discriminator, who is then told which entries were real and which ones are fake after being evaluated, adjusts itself to better identify fakes and not flag real reviews as fake. The generator gets better at creating undetectable fakes because it learns which fakes were successfully identified and which original reviews were flagged incorrectly.

This phenomenon is applied in the following industries:

  • finance. AI systems monitor transaction flows in real time and analyze them in the context of a person’s history to judge whether a transaction is genuine or fraudulent. All major banks and credit card companies are using such software now; Some develop their own solutions and some use commercially available solutions.
  • manufacturing. Factory AI systems can monitor the input and output flows using cameras, x-rays, etc. It can identify potentially defective or deviant parts and products. Kyocera Communications and Foxconn both use artificial intelligence for visual inspection in their facilities.
  • Films and media. Just as generative tools can create fake photos (for example, a kiwi bird eats a kiwi on a switch), discriminatory AI can identify fake images or audio files. Google’s Jigsaw division focuses in part on developing technology to make deepfake detection more reliable and easy.
  • Social media and the technology industry. AI systems can look at posts and patterns in posts to help detect fake accounts by misinformation bots or other bad actors. Meta has used artificial intelligence for years to help find fake accounts and to flag or block misinformation about COVID related to the pandemic.

Generative AI may become a widely known buzzword, like automation, and its many applications prove that this emerging branch of AI is here to stay. To meet the modern challenges facing the technology industry, it makes sense for this technology to expand and become deeply ingrained in more and more organizations.


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