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Reasons Why AI Needs Arts Students Just as Much as Engineers

There has been a huge shift in focus on AI and Machine Learning – and specifically – the importance of data. Everyone’s eyes have been on engineers to make the impossible possible but have often neglected the area of studies that can make artificial intelligence more human-friendly than anything – the humanities. But it’s even more elaborate than that.

There are so many components to the way humans think to merely boil them down to numerical algorithms. It starts as simple (but really, not that simple) as language and ends with the endless number of factors that go into how people communicate and cognitively process information.

So, AI creators are starting to look to Arts students of all areas of expertise to tailor AI and then make it relevant in ways that codes and algorithms haven’t learned to yet.

Making Sense of the Unfathomable

AI is very intimidating to many people right now. It’s hard to socially accept that we are trusting something that’s inhuman to do tasks we are so accustomed to people performing. However, what people haven’t come to realize is AI is only being created to better human life – to increase human productivity, not take it over.

Part of the problem is the layman can easily get lost in the technical mumbo-jumbo related to how it works. Thankfully, the solution is fairly simple: using marketers and communication professionals to find the right language to explain the benefits of AI. After all, communication is key to every adoption.

An example of this is IMI’s AI, Rhonda. Rhonda is a workforce management AI that monitors morale in the workplace. Weekly, Rhonda asks IMI employees how they are feeling on a scale of 1-5. The hurdle in their implementation was communicating to their employees that first, Rhonda is not a person and then second, Rhonda is not in place to micromanage, but rather to ensure they are happy on the job.

Ethical Decisions

Right now, the AI technologies being developed are using calculations to make decisions. Technology like self-driving cars are calculating which scenarios cause the least damage mathematically, or workforce management AI uses the value of communicative answers to screen or monitor employees. However, there’s a big difference between numerical value and face value.

The learning curve is to teach AI how to recognize emotion and to look beyond the data. It comes down to sociological, psychological, and anthropological information for AI to adapt to how a human may process decisions or processes.

Back End Design

Another important feature to consider about AI is the (user experience). People like using technology that is user-friendly – meaning everything flows well and frankly, the sexier it is, the better.

AI developers are using graphic designers to make AI technology attractive and seamless to its users. Not only is it a selling feature for clients looking to infuse AI into their business, but it brings that accessibility to humans and it lets them feel more in control of a much bigger picture.

AI everywhere is still very much in the developing stages. There’s so much that makes up human intelligence to even fathom technology taking over anytime soon. Sure, it’s up to engineers to find the right codes to punch in, but think of AI like a baby, it doesn’t learn to be who it is without the learning curves of life, trial-and-error, and other brains shaping it into what it intends to be.

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