Humanizing AI

Building empathy and trust are the two most critical factors for humanizing AI

Imagination is a uniquely human ability. When the human brain imagines, it activates millions of neural networks. Well, machines to are trained to perform tasks that involve some human imagination. But what they lack is the human touch. There are many examples of Artificial intelligence’s positive impact but for sure they fall short of delivering human-like interaction. Therefore, researchers are working towards replicating the human ability of acting and generating near approximate human insights by humanizing AI. Businesses are realizing the benefits of having a more human AI and the impact it can make. This is a specialized field in AI called acculturated AI. When the question of humanizing robots is looked at from a vantage point it becomes imperative to think, why to create human-like machines when millions of humans exist. In this context, the two very essential human traits that are essential for humanizing robots should be considered– empathy and trust ­– which make humanizing AI sound more meaningful.

Interaction and optimum data key to developing empathy

AI algorithms run based on extrapolated data. When large samples are fed, AI generates a new sample retaining the essential traits. However, given the access we have to enormous data, there is a tendency to overdo the process of humanizing AI. In a sense, it results in burning out AI machines in an effort to feed them with huge data. The ideal path can be integrating data and user experience, so as to let your machine know every touchpoint of a human experience.

Why is trust a missing factor?

As many experts consider building trust in AI would be the most challenging issue for scientists for humanizing AI, it is imperative to build systems that are free from a programmer’s bias. Indeed, it is a complicated process and the most crucial problem to address for humanizing AI, in the years to come. Given the diverse value system of the human race, the question remains which value system a machine should follow. Perhaps the best approach to humanize AI would be to have different value systems for AI machines and humans. Even if machines are trained for a certain cultural interaction, they can fail to understand the consequences, let alone react to it. IBM researchers are working on this front through a method called inverse reinforcement learning. According to a statement by Murray Campbell, IBM researcher, this method helps to understand what holds importance for people in different circumstances, helping systems to make consistent decisions.

While AI acting with integrity is one thing, making people put faith in it is quite another. AI applications in the medical field and self-driving cars are the best examples. In this direction, AI researchers are exploring explainable AI (XAI). The idea is to make people understand what is happening inside the black box to make the interaction make more human.