The Sembang AIOT live session start with the curious questions how Generative AI is able to integrate with the Industrial IoT. Yes. That is the way that we would like to present Generative AI with the focus of industrial application.
The session started with the quick introduction of Axiomtek AMR Controller with the AMR Builder Support package where there will be a ” industrial Transformation event” hosted by Intel Malaysia in Penang next month on the 5th of December 2023 and we are going to showcase the AMR Builder Support package during the show.
An announcement goes for the ” Free AIOT Workshop ” for Fresh Graduate. ( Watch the detail in the youtube channel) 🙂
Generative AI refers to a class of artificial intelligence algorithms and models designed to generate new content, typically in the form of images, text, or other data types. Unlike traditional AI models that are trained to classify or recognize existing patterns, generative AI has the ability to create entirely new and original content based on patterns it has learned during training.
There are several approaches to generative AI, and one of the most notable is Generative Adversarial Networks (GANs). GANs consist of two neural networks—the generator and the discriminator—that are trained simultaneously through adversarial training. The generator creates new data instances, and the discriminator evaluates them. The competition between the two networks leads to the improvement of the generator’s ability to create realistic data.
In the context of the industry, generative AI can be applied in various ways to enhance efficiency, creativity, and problem-solving. Here are some examples:
Product Design and Prototyping:
Generative AI can assist in the design process by creating numerous variations of a product based on specified criteria. This can help in optimizing designs for performance, cost, or other factors.
In manufacturing, generative AI can analyze and optimize processes for efficiency and resource utilization. It can suggest improvements to workflows based on the data it has been trained on.
Generative AI models can be trained to recognize normal patterns in industrial processes. Any deviations from these patterns can be flagged as anomalies, helping in the early detection of faults or issues.
By analyzing historical data, generative AI can predict when equipment is likely to fail. This enables proactive maintenance, reducing downtime and avoiding costly repairs.
Natural Language Processing (NLP) Applications:
In industries where textual data is prevalent, such as customer service or documentation, generative AI can be used for automatic summarization, translation, or even generating human-like responses.
Simulation and Training:
Generative AI can be employed to simulate various scenarios for training purposes. This is particularly useful in industries like aviation or healthcare, where realistic simulations can enhance training programs.
Supply Chain Optimization:
Generative AI can analyze complex supply chain data to optimize inventory levels, predict demand fluctuations, and identify potential risks in the supply chain.
The application of generative AI in industry is a rapidly evolving field, and its potential is vast. As technology continues to advance, we can expect even more innovative applications that leverage the creative and problem-solving capabilities of generative AI in industrial settings.
Demonstration of Generative AI
There are total of 3 Adhoc Generative AI production during the Live session. First one is the Valley in the Alps image, second being the a CAR image with different ingredient from different brand of car’s manufacturer. The last one, please watch it live at our Axiomtek malaysia youtube channel.
The Generative AI example that we were running are based on the ” Stable Diffusion” where it is a Generative AI model used for producing unique photorealistic images from text and image prompts. In the session, we also discussed how importance of the prompt and there is a subject namely ” prompt engineering” which it is a method or practice to tell the AI model more precisely or more understandably given to the NLP ( Natural Language Processing).
To learn more about our live session. Do visit our live sharing on YouTube channel and remember to subscribe if you find this relevant.