Recent developments in AI and computation have already put everyone on the path to a whole new world in which each product is one-of-a-kind and possesses unparalleled degrees of complexity.
FREMONT, CA: Despite the fact that technology has taken over many elements of our lives, our product design and manufacturing methods remain substantially unchanged from the industrial age. Companies struggle to build better-performing products while keeping costs down. They arrive at the best designs possible after significant experimenting. The instructions are then sent into production machines, which produce hundreds of similar items or parts, leaving little potential for customisation.
Product customisation necessitates a significant amount of manual labor. Even relatively simple products, such as sporting shoes, generally necessitate assembly lines staffed by dozens of people. However, AI and machine learning will eventually enable far more automatic product customisation.
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Continuing with the sneaker analogy, upcoming technology will allow each pair of athletic shoes to be entirely tailored, increasing the shoe's performance for a specific athlete. Shoe consumers will soon be able to employ new input devices, such as sensors that build pressure maps of their feet and collect data that will lead to uniquely customised designs. Then, generative design tools will automatically synthesize designs and translate them into machine-readable assembly instructions on the basis high-level specifications.
Recent developments in AI and computation have already put everyone on the path to a whole new world in which each product is one-of-a-kind and possesses unparalleled degrees of complexity.
Product designers often have a clear grasp of the outcomes they will achieve with various materials. However, when designers are required to balance multiple desired outcomes, things can quickly become problematic. When building an automobile, for example, designers strive to optimize not only performance but also cost, durability, safety, and fuel efficiency. Using AI and ML tools, design teams may rapidly iterate through dozens or even millions of distinct potential designs, focusing only on those that the algorithms have recognized as having the most potential.
In this context, the term "design" usually refers to performance design rather than aesthetic design. While humans are still better than computers at producing visually appealing products, AI and ML can assess how modest modifications to a product will affect numerous distinct elements of performance. This will be a game changer for design teams, allowing engineers to focus on more creative aspects of their professions rather than countless hours of grueling and inefficient trial-and-error testing. Furthermore, it will result in better products.
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