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Unlocking Creativity and Predictive Power with AI Generative Models for Digital Twins

Our recent CNY post picture on LinkedIn might have caught your attention. Allow us to reveal that it was generated by the cutting-edge generative AI research lab, "Midjourney". With just a few keywords like "universe", "rabbit", "CNY", and "tech", the system quickly produced a high-quality image for marketing use. Building on this technology, the AI industry has seen a rise in the popularity of advanced generative AI models like Chatgpt and Stability AI. This has prompted large tech companies to invest in a similar model. For example, Google just unveiled their Microsoft-backed Chatgpt rival, Bard AI.


CNY post image created by Virspatial and Midjourney

OpenAI's ChatGPT is a significant player in the ever-evolving field of generative AI. Beginning with Alan Turing's landmark Turing Test in 1950, which aimed to assess a machine's ability to display human-like intelligence, the development of AI has undergone a tremendous transformation. With the release of Nvidia's StyleGAN in 2018, which enabled the automatic creation of high-quality images, and today's widespread use of ChatGPT for generating natural language text, deep learning algorithms continue to push the boundaries of what is possible. These advancements are allowing humans to tap into their creativity like never before, by providing them with the necessary building blocks to jumpstart their ideas. The potential applications of AIGC are actively being explored across various industries. Amazon has already utilized ChatGPT in different functional scenarios such as answering interview questions, writing software code, and creating training documents. Microsoft Teams integrates Chatgpt to do the monotonous note-taking. We are also actively exploring the potential ways in which AIGC can be applied in the enterprise metaverse and digital twin industries.

In the case of Digital Twins, AIGC can help teams generate models and solvers that allow them to create predictive analysis when it comes to generating data based on past records and predicting future trends. One thing that clients always ask is the availability of predictive technology in Digital Twins. This is where AIGC can be of great help, picking up datasets of existing patterns within the Digital Twin whether it is crowd/traffic flow simulations, structural weakness, or accident probability. Once the AI-fed models have enough data to go by, organizations or even cities can make better operational decisions for the future that can reach a high level of accuracy. Making early iterations faster is another benefit of AIGC, said Rahul Nath, our VP of Innovation and Production. AIGC can save a lot of time when it comes to preproduction work. Being able to create variables of concepts quickly and effectively before going into production will help teams work in a lean workflow. This helps in iteration and brainstorming within teams faster, thereby reducing 3-4 days of a timeline. Rost, GM of Meta BU in Digitwin, provides a more specific use case to use generative AI as a tool to generate more data for training purposes. By training an AI generative model using a single photo of a chair, it can generate 200 images of a seated person with clothes on. This opens up the possibility of exploring more similar scenarios, such as different seat fabrics and seat positions, allowing organizations to make better operational decisions for the future with a high level of accuracy.


In conclusion, the advancements in AI Generative models such as ChatGPT and Bard AI have opened up a wealth of opportunities for businesses and individuals to tap into their creativity and drive innovation. But, Rahul also points out that there is still an ongoing debate on the ethics of using AI-generated art, as the technology is based on model sets derived from human artists' work, often without permission. This has led to lawsuits between artists and AI organizations. However, there are potential solutions that can be applied to the music industry or come up with a new revenue model. On the other hand, ChatGPT is not always reliable since its responses are built from billions of online results, making it challenging to determine their sources. Despite this, ChatGPT has the potential to be accurate in the future, and it could revolutionize how we search for information online, and the way we work and live.


Written By: Grace Zhao- The ghostwriter | We write content that's both authentic and engaging for tech people to read.


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