Why UX Design for Machine Learning Matters
Why UX Design for Machine Learning Matters
In this post, discover why user experience (UX) design matters and how it is changing machine learning (ML) in end product designs and products.
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Machine learning (ML) is profoundly significant and alongside it, is the user interface (UI), which denotes wearable parts of a gadget, display screens, texts, graphics, physical look, or appearance. However, as developers begin to take on more complex design tasks to match postmodern technologies such as artificial intelligence, the bone of contention is whether people will unquestionably accept output signals from machines as true reflections of human reasoning. It is to say, machine learning remains a rather complex and opaque process for the layman who doesn’t understand how UX superimposes UI.
In this post, discover why UX design matters and how it is changing ML in end product designs and products.
First things first...
How are app developers, programmers, webmasters, decision makers, and businesspersons adapting to the need for UX designers? Well, new technologies spur competition for profitability, thus forcing businesses to look beyond product design. Deeper aspects of user experience such as attitudes, emotional satisfaction, and practicality now matter more than ever; lending credence to the need for UX designers in a developer’s office.
Important Questions About Machine Learning
To begin with, have you ever wondered why when you type a search phrase into search engines such as Google, many suggestions come up even before one can strike the search button? What about animations popping up on a Smartphone’s keypad every time you save a new word to the dictionary?
Do you always wonder why specific content from a particular social media account keeps appearing on your news feed with phrases like, "You might also like this," or, "Related stories," as triggers for further reading? Moreover, when you stream Netflix movies or view films on YouTube, you must have noticed phrases like "Recommended for you," and you keep wondering how the app/system knows the kind of content you like to view.
Does it mean machines are endowed with emotional intelligence reminiscent of humans? Well, behind machine learning are complex algorithms that undertake data mining to enhance the user experience. These algorithms learn behavioral attributes based on one’s activity (data mining) so they can be able to predict and suggest posts one is likely to read in future. They will frequently show items from a user whose content you like reading. It is the same case when you save a new word to computer’s dictionary. ML makes sure "do you want to save this word" won’t appear next time you type the same word.
More Examples of Machine Learning
- Email spam filtering. Messages you mark as spam will always go into the spam box as long as you mark the source or certain phrases as such.
- Detection of threat in network security. Login fails and bugs detection has long been an indication of how machines learn to not only keep systems safe from hackers but to also hinder unauthorized access
- With the advent of AIs like Alexa from Amazon, machine learning has gone a notch higher to include UX in sound interfaces (SUI)
- AI human bots like Sophia from Hanson Robotics are changing the game of machine learning in ways many never imagined.
Understanding the Role of UX Design in ML
From understanding how machine memory works, recommendation engines in developer’s servers to unsupervised machine learning, UX designers factor in a range of things to give app and product users a seamless, unintuitive, and immersive experience. Thus, the following are reasons why UX design matters.
Accuracy of Feedback
For purposes of accuracy, machines learn input signals and keep them in memory servers so that next time you type something into search engines, and it is a wrong spelling such as "Recieve," you get accurate feedback with phrases like "Did you mean ‘Receive’" or display results for….instead of…
UX design makes it possible for systems to predict behaviors of users. For example, the moment you start typing the first two or three letters of a phrase, the user experience is enhanced by ML that straightaway predicts what you are about to type by displaying half-complete to complete spelling/sentence suggestions. Also, machines are able to rely on their algorithmic ability to mine data and undertake predictive modeling to suggest content for app users.
UX makes it possible for machines to come up with adaptive content for app or internet users. If you like to read the news every time you go online or you always download news apps, ML adapts to that behavior so that it can give you a better experience.
The Bottom Line
Whether it is a supervised ML — where human input, vetting feedback for accuracy and application of learned data must take place — or unsupervised ML, a case of deep learning involving advanced algorithms, UX design helps bring boost satisfaction and accuracy that comes with product look and feel.
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