siliconindia | | December 20189Ever partied alone? Well, Alexa did. Amazon's Echo gave a sleepless night to the German cops barging into an apartment following a complaint lodged by neighbors. Reportedly, Alexa was in party mood and blast-ing-out music when nobody was home. As AI is being weaved into mainframe business process and key drivers for success, such dents in the very fabric of its nature is totally un-called for, and raise serious concerns about its arguably rewarding bene-fits. How far does it stand or will it ever emerge to show its human self at all? You see the greatest challenge with artificial intelligence & machine learning is that it is being compared to the most sophisticated, intelligent ma-chine of all time, i.e., humans.So how do you humanize a con-cept like technology that is inherently perceived to be cold and in human? The design of AI experiences must always be rooted in the most funda-mental quality that defines us as hu-mans, i.e., empathy. It is empathy that enables every other core human ability: the ability to learn, to adapt, to compensate, to troubleshoot and to solve problem. The need to create suc-cessful AI experiences that can boast of similar abilities, calls for tools and frameworks that understands hu-man decision and response matrices, while also having a clear demarca-tion of tasks that need automation and augmentation.AI that Automates: Adobe Photoshop's new `Select Subject' tool uses AI to accurately identify and se-lect objects within an image. While it can be written-off as just another use-ful feature integrated into an already useful application, its core is deeply empathetic to the typical Photoshop user who has spent hours painstaking-ly contouring an object with the Wand or Lasso tool.AI that Augments: Google show-cases its intelligent camera Google Clips as an example for human-cen-tered AI design. With the ability to focus on the people that matter and the intelligence to decide what makes for a memorable photograph, it allows the user to be part of his captured mo-ments instead of behind the camera. Troubleshooting Like a Human: The current problem with AI, regard-less of its medium, is that it often doesn't make it very far past the first stumbling block or breakpoint. This is an inherently anti-human trait because humans have the ability to assess an unexpected problem and solve it on-the-go. Maintain Transparency: No user likes the idea that someone somewhere has a database of what books he reads, what brand of sham-poo he uses or by how many inches his waistband may have expanded in the last few months. e-Commerce sites often tell you why they're mak-ing certain recommendations to you - because of a similar item you bought, or because other users like you have bought it too. While seemingly trivi-al, such a move demonstrates trans-parency and gives the user a greater degree of control. The core philosophy is simple, always come back to what a human tries to get-out of his interactions with another human. Detect or predict how the user feels, and create mecha-nisms to counteract those feelings in as human a way as possible. Consid-er the first wave of AI a worldwide prototype. Now the ground has been tested, giving us data to learn from. With this, we're done with Round 1. Our next step is to focus on what can we learn from how people currently respond to AI?
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