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Fixed Automation Approach

Focusing machine learning scope to mimic creative activities, for example Pix2Pix oil paintings, confines the innovation potential of AI into simply fixed automation. These AI approaches generally have the intention of using some form of machine learning to find an algorithm that reproduces creative applications. Deep Art Effects is an example that uses AI to reproduce the painting styles of famous artist as filters for personal photos. It’s like if a creative spark might be inherent in the algorithm, simply waiting for the machine to find the right combination to create, as it learns to mimic.

This fixed automation approach is visible beyond apps. One interesting industry that’s finding ways to apply AI is Fashion. Several interesting presentations around technology took place during New York’s Fashion week that highlight how fixed automation may be seen as innovation in the industry. Database projects offered the promise of a bigger picture of opportunity in fashion retail and brand merchandising through data mining of visual images of products and their structure.

A demo of a fabric weave presented an ironic overlap of old/new tech, though not on purpose. This student’s project of a material that contracted when exposed to heat, touted as self-altering; and, therefore, any garment made by it could fit any body type by fitting it to a plaster cast of the wearer’s body. Here’s the catch with innovation development. The Maker’s personally influences the product. The feedback about the fabric was that not everyone wants to wear clothes that gloss over your body too closely, like high-end couture, which defeated the problem it aimed to solve. A video demo of the shrinking of sections of the garment with a hair dryer can serve as a reminder that even in this alteration innovation experiment, experienced craftsmanship plays a factor: someone needs to have mechanical control over the dryer and exercise continuous aesthetic criteria.

Growth automation as an AI innovation approach

AI projects can vary in reaching across disciplines to inspire and challenge the technology, growing beyond a fixed automation approach. Beyond the analysis of the data set is the analysis of the problem and the how the end user and subject matter expert can complement and enhance a product’s AI approach. These areas complement a product’s development and does not prevent, for instance, a data or computer scientists scope of activities that require a deep understanding of their domain. Apple and Pixar are examples of how AI innovation can produce prolific explorations that lead to innovation by growth automation. With both the tech acumen and creative craftsmanship they are continuing to grow and change their industries. Fashion, and any other industry applying AI innovation, can view the benefit of a new breed of craftsmanship that inherits traditional training and creative sensibilities and complements new innovations to push their exploration of prototype solutions to enhancing strategies or creating the possibility of different business structures like vertical models with in-house creative teams and automation equipment that both up speed time-to-market.