{"id":1825,"date":"2019-10-23T01:48:32","date_gmt":"2019-10-23T01:48:32","guid":{"rendered":"http:\/\/beyondconference.org\/?p=1825"},"modified":"2020-09-09T14:35:37","modified_gmt":"2020-09-09T14:35:37","slug":"human-vs-things-how-ai-is-disrupting-the-design-process","status":"publish","type":"post","link":"https:\/\/beyondconference.org\/news\/2019\/human-vs-things-how-ai-is-disrupting-the-design-process\/","title":{"rendered":"Human vs Things: How AI is disrupting the design process"},"content":{"rendered":"\n[et_pb_section][et_pb_row][et_pb_column type=”4_4″][et_pb_text]\n

It’s a pleasure to share this article by Prof Chris Speed<\/a> who is Chair of Design Informatics at the University of Edinburgh. Over to Chris… <\/p>\n\n\n\n

A great deal has changed since the Creative Informatics<\/a> R&D Cluster began in October 2018. Data-Driven Innovation as a term is becoming common place, and AI has moved from an imaginary based upon the images and tales of science fiction, and in to practical examples that give the public an idea as to their limitations. We now know that Gartner was almost right in 2015 when they predicted that \u201cby 2017, a significant disruptive digital business will be launched that was conceived by a computer algorithm<\/a>\u201d although it wasn\u2019t a business, it was politics, and we are all familiar with the distortions that algorithms are making to the news feeds that inform our decision making. <\/p>\n\n\n\n

As designers we have seen the slow creep of AI tools, and this year all of the big creative application developers have extolled the virtues of their systems from Adobe Sensei<\/a> to Autodesk\u2019s 3D and VFX Content Creation<\/a>. Central to the \u2018sell\u2019 of these tools is their role in complimenting the creatives skills and not usurping them, such as Autodesk\u2019s aim to produce \u2018Trusted Collaborators\u2019 as the third level of their product intelligence (beyond smart tools and intelligent assistants): \u201cTools that understand the context in which we [the human] work. These systems turn data into contextual insights.\u201d (Evan Atherton, Senior Research Engineer).<\/p>\n\n\n\n

Whilst Adobe and Autodesk are keen not to challenge the creative control of the creative, colleagues and I developed the international ThingTank<\/a> project that pursued the idea that it wouldn\u2019t be long before things would start designing things themselves, and they would no longer require humans to help. The project was funded to explore the potential for identifying novel patterns of use within data that is streamed through the interaction between people and things, and things and things. Through an understanding of what data can tell us about how we use domestic objects in practice, the project posited that new models of use would emerge and reinvigorate the role of things and people within design and manufacturing. <\/p>\n\n\n\n

In the past, many Internet of Things projects have used the network connection of artefacts to identify cost saving and process efficiencies (e.g., vehicle manufacturers), or to track goods within large networks (e.g., logistics companies), or to monitor the health and safety of systems (e.g., aircraft manufacturers). Such projects look for regular patterns within datasets which suggest efficiencies that will reinforce the identity of a product or service by making its function easier to use or more economical. By contrast, the ThingTank project proposed that looking for anomalies and outliers in datasets could suggest more radical design opportunities. During studies, the research team developed non-anthropocentric methods by gathering and streaming data from both material objects and humans that were involved in a domestic relationship, to better understand how machines could identify practices that went unidentified by human researchers (Giaccardi et al 2016<\/a>).<\/p>\n\n\n\n

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