Artificial (Emotional) Intelligence


There seems to be enough debate about artificial intelligence without adding emotional baggage to it.  But that’s what is happening in the AI community. Emotion has entered as the latest characteristic of what many previously consider a “Spock-like” technology — it could beat you at chess despite not being able to fall in love with the game.

The interesting thing is that there is enough fear of artificial intelligence in some quarters even without the emotional aspects being added. As counterintuitive as it may seem, no greater technology pioneer as Elon Musk feels that robots and artificial intelligence could threaten humanity. And I suppose that with artificial feelings this will lead to them ‘enjoying’ it.

This is a double-edged sword. Do we want AI to be emotionless for some things that require completely independent thinking; or do we feel “artificial” emotion adds to the quality of the decision? I suppose it all depends on the situation and whether the oxymoronic term artificial emotional intelligence makes any sense at all.

As a person professionally obsessed by the neuroscience of buying I must admit I’m very attracted to the thought of an algorithm that would add the proper emotion to my marketing materials as I write them. I envision pulldown menus with all the demographic and psychographic data needed for the target audience and watching the system spell emotion-check my work as I write based on the audience I’ve selected.

Actually, this is not that futuristic. I’ve worked with a firm called Centiment that scours content assets or news stories and provides a report on the emotion therein.  It might find fear, uncertainty or doubt without those words even appearing in the piece. One of the most fascinating aspects of the algorithm is that it will identify emotional patterns that might not even be linked to a known emotion…. it’s a yet-to-be-defined sentiment but it’s embedded in the content nonetheless. I regard this as the emotional equivalent of umami for those that are foodies. It’s more a subtle sensation than a well-defined flavor.

What does stretch the mind is how the algorithm can identify subtle emotions without having something more than a “binary feeling” in the algorithm.

To take it a step further, how can companies scale something as culturally-laden as artificial emotional intelligence internationally? Any student of cultural anthropology knows that emotion can evidence itself in very different ways across cultures. For example, some Asian cultures are much more prone to show dimensions of shame, humility and harmony not found in nearly the same degree in the West. Needless to say, an AI system targeting a market like Asia would be heavily skewed toward that culture by design.

But what happens when the same system is deployed in a culturally diverse environment, like that of an intercity healthcare provider?  Tailoring artificial intelligence to these populations without emotion is difficult enough, but being able to determine the degree to which one’s ethnic influence bubbles up (or doesn’t) is an incredible challenge.

One of the biggest gripes with some cognitive computing products is that they draw from samples and use-cases that are far too small or isolated to make predictive analysis possible. Consider the variations in situational emotion in a single person based on work, family, season, health or relationships — one can see how far emotional artificial intelligence has to go to become a reliable mainstream technology.


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  1. People keep arguing about whether AI will harm human or not. In my opinion, AI can only become harmful if the programmer intends to do so. AI will never have consciousness, they’re created as something that human want them to be. They can’t decide who they are, only human can decide for them.


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