Emotion AI has been described by some as the future of artificial intelligence. Also known as affective computing or artificial emotional intelligence, the field uses AI to study the non-verbal cues of humans, like body language, facial expressions, gestures and tone of voice to detect their emotional state.
The somewhat controversial field has experienced an explosion of development over the past year. According to some players in the space, it has the potential to impact the future of work and influence a number of industries as development continues during the next few years.
“Within the next five years, you’ll see some really amazing experiences come out,” Rana Gujral, CEO of Behavioral Signals, told Builtin. His company aims to transform the customer experience using AI-mediated conversation technology.
Geared toward the financial and payments industries, Behavioral Signals uses voice intelligence analytics to measure and quantify human emotions and behaviors and provide businesses with actionable information. Analyzing voice and audio emotion is one of the three key categories of emotion AI, useful for call center and other customer service environments. Behavioral Signals' solution analyzes voice only, while other offerings also focus on analyzing conversation content.
Text emotion AI is another category, and uses natural language processing (NLP) and "sentiment analysis" to detect and measure human emotions. Sentiment analysis applies NLP to text to gauge whether sentiments are positive or negative and how much so. One of its main applications is to analyze company reviews of products and services.
"A company might have a solution for the hotel industry that contains a certain set of taxonomies," said Seth Grimes, a NLP consultant who is the founder of Alta Plana, which specializes in business intelligence and analytics, text analytics and sentiment analysis. "It understands that a hotel has rooms, service, a restaurant — it understands the structure of the thing being analyzed. But it doesn’t necessarily understand that, say, Hilton Hotels has certain branding, like the reward system is Hilton Honors, and so on. That kind of last-mile training used to be via customizing taxonomies and role sets. Nowadays, it’s transfer learning."
The third category of emotion AI is video and multimodal emotion AI. In this instance, video is analyzed for facial movements, gait, physiological signals or a combination of some or all of those behaviors.
"When you get into something like health, you’re often looking at individuals’ behaviors, trying to understand if what you’re observing are perhaps symptoms, or effects of a medication," said Daniel McDuff, principal researcher at Microsoft AI. "For instance, you may have someone with Parkinson’s disease taking medication that controls tremors. Say you were using video to track how quickly the medication wears off. You need to personalize that because different individuals might have different magnitudes and manifestations."
To learn more about how emotion AI is being used in applications throughout a variety of industries as well as how it stands to impact the workplace of the future, TMC is hosting its its Future of Work Expo from June 22-25 at the Miami Beach Convention Center. The show will examine how AI and machine learning are impacting healthcare, customer service, financial services and a host of other industries.
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