Today, every business sector and industry is incorporating artificial intelligence (AI) to automate processes, improve efficiency, and reduce costs, including the industry of digital transformation, which is using artificial intelligence specifically to improve the user experience.
In this article, I’ll discuss some impacts of artificial intelligence on user experiences, what UX processes artificial intelligence can make more efficient, how artificial intelligence can help UX designers, and how artificial intelligence might affect UX design jobs in the future. Read More
The insurance industry is both characterized by and driven by data. The industry continually generates and processes large sets of data in delivering effective insurance services, including providing policy pricing, customizing insights, meeting company expectations, and analyzing market trends.
With the world continuously evolving and witnessing extraordinary events such as the global COVID pandemic that inflicted over $55 billion in losses on the insurance industry, the industry’s emphasis on the vitality of existing technology solutions has surged. Plus, the executives and chairpersons of almost three-fourths of insurance-industry verticals are now pushing for innovations. Nearly half of them plan to upscale their expenditures on the integration of artificial intelligence (AI) and machine learning (ML) to enhance predictive analytics and neural networking and deploy robotic process automation. McKinsey has estimated that AI investments could potentially catapult the annual value of the insurance industry to $1.1 trillion! Read More
Remember the computer-science maxim Garbage in, garbage out? So goes big data in artificial intelligence (AI). The historical data we use to train the machines in AI research continue to reflect biases that many of us have hoped to relegate to the past. But, if you ask the machines, women belong in the home and black men belong in prison. So what should you do if your company requires you to design systems that rely on big data that might be faulty or technologies such as voice or facial recognition that have proven to perpetuate gender and racial biases? Start by understanding the problem so you can avoid the mistakes of the past.
Voice Recognition: The Can-You-Hear-Me-Now? Problem
Most of us now have voice-recognition tools in the palms of our hands. Many of us have them on our kitchen counters, too. Every time you tell Alexa to play music, ask Siri to set a timer, or request directions from Google, you are relying on your Echo, iPhone, or Android phone to recognize your speech. Your device could respond with either perfect accuracy or seemingly capricious inaccuracy. Because these are low-stakes requests, you might either persist or give up, depending on how badly you want to listen to “Blush” by Wolf Alice, while stirring your risotto. Read More