Cam Wang: From the initial concept to the final product, AI has brought new possibilities to Vase. Through nearly a hundred iterations, we have finally selected the most satisfactory result. With practical adjustments, Vase has evolved into a piece of furniture with its own unique character. The minimalist and smooth curves, combined with the precise and comfortable human-machine dimensions, emphasize the elegant personality of this product. The automatic reset and rotation function allows Vase to be used in public areas without worrying about the orientation becoming messy. As it literally unveils, the Vase symbolizes delicate flower arrangements, adding a touch of new color to the space.
Revitalizing Classic Chair Design with Generative Algorithms
One way AI is making an impact in the design industry is through the use of generative algorithms. These algorithms can generate new content and products, ranging from text to images. AI is involved in creating a classic seat, highlighting the potential for AI to contribute to the design of the legacy category.
To achieve this, the generative design software underwent a learning process. It analyzed vast amounts of data related to material properties, structural integrity, and aesthetic preferences. Through this collaborative effort between human expertise and AI capabilities, the software was able to generate numerous design iterations that adhered to the specified criteria. This collaborative relationship between humans and artificial intelligence can amplify the creative and engineering abilities of designers.
By leveraging the computational capabilities of AI, designers can explore a vast range of design possibilities, optimize structural integrity, and minimize material usage, ultimately pushing the boundaries of what is achievable in design and engineering. Designers only provided a digital model of a chair as inspiration and entered parameters for the new design. The software then generated potential designs based on these inputs. The designers had the freedom to choose and refine these generated designs. This collaboration between designers and algorithmic design tools showcases the potential for efficiency and innovation in furniture design.
Based on the training data model LoRa, Cam analyzes vast amounts of data related to material properties, structural integrity, and aesthetic preferences to nail down the final style.
VAS-LC-01
W650 D610 H725 SH425
Base: the furniture is equipped with 20*7mm adjustable feet.
OPTION2
VAS-LC-02
W650 D610 H740 SH440
Base: 450mm diameter, 8mm thick steel plate, with a 5mm felt pad on the bottom. It has a 180-degree rotation capability and an automatic reset function.
PACKAGE INFO
W715 D665 H775
1 PCS/1 CTN
Double wall corrugated board outer packing / Wrap up PE pack/ Assembled
FEATURES
AI-assisted design has allowed us to achieve a sleek and elegant aesthetic with a touch of sophistication suitable for a business setting.
The option for a self-resetting base with a rotation function ensures convenience and ease of use.
The compact and petite dimensions of the Vase make it the perfect choice for filling up any space, regardless of its size or layout.
AI-driven Optimization of Office Space Layout
AI is emerging as a powerful tool in redefining workplace design to meet the needs of hybrid employees. By analyzing data on employee movement and interaction, AI can suggest designs that promote efficient layouts, and enhance collaboration, productivity, and well-being, ultimately leading to increased employee satisfaction and reduced costs. AI achieves this by collecting and analyzing vast amounts of data, such as occupancy data, to devise space programming strategies that ensure efficient and purposeful space allocation.
Additionally, AI-driven visualization tools, such as AI-generated 3D visual walkthroughs, allow remote team members to experience proposed office designs as if they were physically present, fostering greater engagement and a stronger sense of belonging. Looking ahead, AI is envisioned to play a significant role in creating hybrid workspaces that accommodate both in-person and virtual collaboration by analyzing data on workforce habits and preferences. By aligning shared spaces with desired company culture and enhancing employee collaboration, AI helps optimize office designs.
In the hybrid work model, the office becomes a central hub for fostering a sense of belonging and company culture. AI-driven climate control systems adapt temperature and lighting preferences based on individual habits to ensure maximum comfort and energy efficiency. AI-powered feedback systems gather anonymous employee input, helping identify pain points and driving necessary improvements. Furthermore, AI's data analytics capabilities empower organizations to make informed decisions about office space usage by analyzing historical data on employees' in-office presence to predict future workspace requirements.
Controllable AI within Industrial Design
The podcast “PRISM” featuring Dan Harden, the Principal Designer of Whipsaw, and Jared Windham, an associate professor of industrial design at Auburn University points out the uniqueness of human designers. They acknowledge generative AI design has the potential to disrupt the field of design. Generative design involves using raw data and human input to quickly generate multiple concept options; however, the current capabilities of AI in industrial design are still relatively primitive. While AI can process large amounts of data rapidly, it struggles to perceive the dynamic nuances of user interaction within the physical world, yet which is crucial in product design.
Comparing AI in industrial design to self-driving cars, unpredictable usage factors and the inability of AI to replicate the embodied cognition of human designers are also confronted. Embodied cognition refers to the influences of physical presence and action on the thinking process. The human-led design process involves a combination of pondering, sketching, making, and other physical interactions that inform the creative process. AI software would need to replicate this connection between mind and body to truly excel in industrial design.
Harden suggests that while AI may eventually overcome these obstacles and make products more efficient and functional, it is unlikely to replace designers entirely. Design, in his view, involves mining the soul and encompasses aspects of individuality and life experiences that are hard to replicate with AI. Instead, he envisions AI augmenting the design process, serving as a powerful tool alongside other advanced software and technologies.
The conversation recognizes the potential disruption of AI in industrial design and the current limitations it faces in fully replicating the unique creative abilities of human designers. While AI can process data quickly, its lack of embodied cognition and connection to human experiences and individuality makes it unlikely to replace designers completely. Instead, AI is expected to enhance and augment the design process, allowing for more efficient and functional designs.
Compounding Bias Underlying AI
London-based startup StabilityAI, which distributes Stable Diffusion, acknowledges that all AI models have inherent biases representative of the datasets they are trained on. Generative AI tools like Stable Diffusion have the potential to transform various industries. By 2025, these tools are estimated to produce 30% of marketing content, and by 2030, AI could be creating blockbuster films using text-to-video prompts.
Bloomberg also conducted an experiment using Stable Diffusion to generate images and found that it perpetuated biases and stereotypes related to gender, race, and occupations. The model depicted the underrepresentation of women in high-paying occupations and overrepresentation in low-paying ones. It also misrepresents racial demographics within occupations, often overrepresenting people with darker skin tones in low-paying fields.
Stable Diffusion also generated biased images related to criminal stereotypes. For example, it depicted people with darker skin tones as inmates, drug dealers, and terrorists, amplifying stereotypes and ignoring the fact that these crimes are committed across all racial groups. The use of text-to-image generative models in policing and suspect sketching has the potential to exacerbate bias in the criminal justice system.
The bias in AI-generated imagery can have significant educational and professional barriers for underrepresented groups. Black and Brown women and girls, already facing discrimination in tech and AI systems, can be further affected by biased representations. AI tools, such as facial recognition, frequently misgender women of color, while being more accurate with lighter-skinned individuals.
The responsibility for addressing and mitigating bias in AI models is a complex issue. Data providers, model trainers, and creators all have a role to play. The presence of biased images in training data can lead to compounding bias in future AI models.
Designers Introduction
