GRADO ANTE-UP: AGI R&D, Scale Up Self-Learning Network Since The VASE

The inventive MoE (Mixture of Experts) learning approach that empowers DeepSeek directs attention to the industrial evolution behind the transition from ANI (Artificial Narrow Intelligence) to AGI (Artificial General Intelligence). This transition impressively gives birth to the Vase Lounge Chair, which is the very first creation applying the AI drawing tool by GRADO. However, recognized as an enhanced, versatile, and adaptive form of AI that can exhibit human-level cognitive capabilities, AGI arguably excels at analyzing and synthesizing information from diverse datasets, generating smarter solutions to evolving settings. Therefore, we wonder how much of the vase is made from ANI and what exactly can be improved to reach the cutting edge of AGI.

Cam, the designer of the vase, first focuses on training a model based on a specific database that can describe a specific style product, such as business-style stools, which basically sets up on ANI. However, AGI requires feeds from broader domains beyond furniture designs and then integrates generic properties to deliver a smarter solution rather than suggest a normal business stool. A clear reflection of ANI use is generating design proposals through stable diffusion, which is limited to the context of the trained LoRA model. The outputs are basically programmed and formatted, grounded on defined inputs. The AI-generated renderings that help create the prototype also depend on previous outputs instead of generating new ideas. If allowed for adaptive learning to understand user interactions with these prototypes and evolve based on collected analytics, the shift to AGI may be possible. For the same reason, ANI cannot handle internal structuring and product sampling, but experienced human labor back at the production department can suggest practical modifications based on real-life testing.

Although ANI predominates the development of the vase over AGI, it somehow steers our R&D towards something possibly more advanced. Apart from rapid conceptualizing and innovative prototyping, the potential of AGI left to explore includes providing data insights on market trends and customer preferences updated with the evolving environments and custom needs. The auto-responsive AGI system can also help realize cost savings and shorter lead times, always keeping key roles in touch with each other on a collaborative panel.

Technically, for the early phase of training models, ANI relies on supervised learning, reinforcement learning, or unsupervised learning on specific datasets. A machine learning model designed for image classification is just one example trained solely on images, learning patterns within that specific domain. On the other hand, AGI allows for a more sophisticated architecture that generalizes knowledge and leverages multiple training methods, including transfer learning, meta-learning, multimodal learning, or few-shot learning. AGI systems base their approach on scaling up the database by accessing multi-type high-quality data across realms comprehensively. Despite the extent to which AGI may replicate human beings, it can hardly intrude on where we keep the independence to perceive and appreciate emotional nuances, subtle cultural contexts, and sensory experiences.