There is always a fine line between a good user experience and unnecessary programming – in relation to budget and overall weight of file size and required processing speed – depending upon how and where the concept is being utilized. People have been shouting at their TV screens for decades and, more recently, delighting in insulting disembodied call centre voices. Whereas victims of cold calling consider the practice to be a form of abuse, perhaps made easy because of the comparative anonymity of its victims. They’re not seen as real people because they’re different, separate, and also invisible.
Using Natural Language Understanding, an IBMer had Watson interpret emotion from her favorite Great Gatsby passage
Voice interface is just a fraction of the complete human interaction with his/her environment. Sometimes we say something, to elicit a particular response; sometimes, a particular movement of ours eyes, hands, body, face can tell more of how we feel, than words. We are far from making a precise opinion about that. And hope we’ll choose the best for us and for our earth and the ecosystem too, but we are not optimistic about the capitalist way of making choices. Where people are displaced by automation, they will find other employment as the nature of work changes. The desire to preserve our own and collective existence is the driving force and the brain is our most important feature which we have been leveraging to achieve this, to date. These natural capabilities will continue to merge with artificial biology and supercomputers, and as a result more and more facets of human life and our abilities will be recreated/improved artificially. The Blue Brain Project is as good an example of this process as any.
Contextual Business Data for Analysis is Possible Thanks to Natural Language Generation Platforms
Natural language generation dynamically increases the volume and value of insights and context in data analytics. It automatically generates a specialized narrative for each user in context, to explain meaning or highlight key findings. In a research by Gartner’s 2016 Hype Cycle for Emerging Technologies Identifies Three Key Trends That Organizations Must Track to Gain Competitive Advantage, Analysts at Gartner referred to this blurring of the lines between humans and machines as creating “transparently immersive experiences” when they highlighted these concepts as key to their 2016 Hype Cycle recently – “The combination of NLG with automated pattern detection and self-service data preparation has the potential to drive the user experience of next-generation smart data discovery platforms, and expand the benefits of advanced analytics to a wider audience of business users and citizen data scientists”.
Hype Cycle for Emerging Technologies
Voice recognition has a long way to go, judging by the difficulty customers have in getting through to call centers. But technology is certainly changing society and the way we use language. Merging artificial intelligence with virtual reality, augmented reality and motion sensor technology is where we are placing most of our focus and we are emphasizing efficiency of user experience over the politeness and demeanor of making a true companion.
Body language is also a large part of communication, it is likely we will communicate with avatars on screens where an inbuilt camera can “see us” and determine further what we really mean. Also, Google are trying other AI learning techniques as seen through Atlas. Reminding of “The Two Faces of Tomorrow” by James P Hogan where people teach a computer system to become self-aware by bullying it. When it achieves this it doesn’t turn out well for the bullies.