What is Generative AI?

Generative AI generates new content by learning from existing data and then creating similar yet original output. It can generate various forms of content, including images, music, text, and even complex data structures.

Generative AI uses various algorithms to create content, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers for text generation.

How is Generative AI used? Generative AI has a wide range of applications including creating realistic images, writing human-like text, composing music, generating 3D models for virtual reality environments, and much more. It is a rapidly evolving field with many exciting possibilities for the future.

Applications of Generative AI

One key application of generative AI is in the field of natural language processing (NLP), where it can be used to generate human-like text or conversation. This technology has enabled advancements in various areas such as chatbots, automated content generation, and even creative writing assistance. By training on large datasets, generative AI models can learn patterns within the text and then generate coherent sentences or paragraphs based on those patterns.

Another important use case for generative AI is in the domain of image generation. Generative adversarial networks (GANs) are a popular technique used in this context. GANs consist of two neural networks: a generator network that creates images from random noise input, and a discriminator network that tries to distinguish between real images and those generated by the generator. Through an iterative process, both networks improve their performance until convincing images can be generated by the generator alone.

Generative AI holds immense potential for creating original content across various domains such as natural language processing and image generation. As this technology continues to advance, we can expect more sophisticated applications that push boundaries in creativity and innovation. However, it also raises ethical concerns regarding ownership rights and authenticity verification which need careful consideration moving forward.

Generative AI is powerful and presents a lot of potential. In particular, AI in supply chain management is already having a massive impact.

One Network's NEO Platform that powers the Digital Supply Chain Network™ uses generative AI to power its NEO Assistant. NEO constantly monitors the supply chain for potential problems, alerts the user, and provides a Smart Prescription™ (a recommended, practical, i.e., executable solution). The NEO Platform includes other powerful AI capabilities, including NEO Hyperbots

One Network's NEO Platform is based on a network approach to supply chain management, as this is the only way to include all trading partners and systems to create a real-time, single version of the truth that can power effective AI (generative or otherwise).

Traditional supply chain technologies that involve standalone systems and hub-and-spoke portals, create disconnects in the supply chain, and make it extremely difficult to reap the benefits of AI in the supply chain.

One Network provides a Digital Supply Chain Network™ which connects all trading partners in near real-time, through a single data model, to a single platform. This enables thousands of member companies to share information, transact, and collaborate in real-time to satisfy customer demand. This network-based approach, with a real-time single version of the truth across all trading partners, enables easy data-sharing, larger datasets, more effective (network-wide) model training. And it doesn't require complex integrations between systems that are costly to implement and maintain.

The real benefit of generative AI in supply chains, is the ability to help solve problems by generating new orders, expedites, dock door schedules, etc. This is a major step towards enabling autonomous supply chain management.