AT THE used book section of a Salvation Army store in Vancouver, I picked up an interesting book first printed in 2007 called the “The Human–Computer Interaction Handbook Fundamentals, Evolving Technologies, and Emerging Applications,” compiled and edited by Andrew Sears of the University of Maryland, Baltimore County (UMBC) and Julie A. Jacko from the Georgia Institute of Technology.
As I read through the many researches compiled in the book that I realize that it is only now, with the availability of rapid systems of communications and neural networks provided by the hyperscalers, can machine learning be fully utilized to create a semblance on an emotional connection with a machine.
I had just come from a Bentley Systems Infrastructure Conference where the use of artificial intelligence in building and construction was the hottest topics. From that conference, I concluded that in the design of infrastructure—buildings, roads, bridges, cities—are all focused on humans and the needs of people and communities.
The question now is: can AI systems become increasingly sophisticated to a point where it can develop emotions to better understand the needs of the humans it is to serve? This exploration delves into the intricate relationship between AI, human-computer interaction (HCI), and the potential for AI to acquire emotional capabilities.
The seeds of AI and HCI were sown in the mid-20th century, with pioneering work in computer science and psychology. Alan Turing’s seminal paper, “Computing Machinery and Intelligence,” laid the groundwork for the development of AI, while researchers in HCI began to explore the interaction between humans and computers. By the 1980s, HCI had matured into a distinct field, focused on designing user-friendly interfaces and improving human-computer interactions.
Isaac Asimov’s “I Robot” though science fiction brought a deeper angle to human-computer interface. But with the dawn of Co-Pilot, Chat GPT and Gemini, AI has witnessed remarkable progress, particularly in the areas of machine learning and natural language processing. These advancements have enabled AI systems to perform tasks that were once thought to be exclusively human, such as recognizing speech, understanding natural language, and even generating creative content.
One of the most intriguing aspects of AI development is the pursuit of emotional intelligence.
While traditional AI systems were primarily focused on cognitive tasks, researchers have begun to explore the possibility of imbuing AI with emotional capabilities. This involves developing algorithms that can recognize, interpret, and respond to human emotions, as well as generating emotional responses themselves.
The concept of human-computer interaction (HCI) is built upon three fundamental principles: usability, universality, and usefulness. These principles provide a framework for designing technology that is not only functional but also accessible and valuable to users. Sears and Jacob summarizes it in the “3 Us of HCI”—usability, universality and usefulness.
Usability refers to the ease with which a system can be learned, remembered, and used with little or no error. A usable system should be intuitive, efficient, and satisfying for users. In the context of currently publicly available AI systems, usability is crucial in making, for instance, Gemini, Co-Pilot, and ChatGPT accessible to a wide range of users, regardless of their technical expertise.
Universality means that a system should be accessible to as many people as possible, regardless of their age, gender, culture, or disability. A universal system should be inclusive and avoid perpetuating biases or discrimination and should be able to understand and respond to different dialects and accents, and they should be accessible to users with visual or hearing impairments.
Usefulness refers to the extent to which a system helps users achieve their goals. A useful system should provide value to users and meet their needs. In the context of AI, usefulness is determined by the extent to which AI systems can solve real-world problems and improve people’s lives–just like Bentley did with automating infrastructure design and development and what Oracle-Netsuite is doing to quicken and simplify enterprise resource planning platforms. – with Raymond B. Tribdino (to be continued next week).
