A new study published in Communications Psychology (May 2025) demonstrates that large language models can now outperform humans on standardized emotional intelligence (EI) assessments. Researchers evaluated six generative AI systems—GPT-4, Claude, Gemini, and others—using five validated tests designed to measure the ability to understand, interpret, and reason about emotions in context.

Picture this: you’re on the phone with a customer service AI agent, stuck in an endless loop of “I’m sorry, I didn’t catch that.” Your frustration mounts with each repetition of the same request. We’ve all been there, and it’s a far cry from a great customer experience. The missing ingredient? Emotional Intelligence. Words alone often fail to convey a customer’s true feelings. This is where Emotion AI comes in – technology that allows AI agents to “listen between the lines” and detect the tone behind the words.

I’ve always believed that some of the best ideas come from the most unexpected places. As Autism Acceptance Month winds down, I find myself reflecting on how neurodiversity has shaped my journey in technology. Valence started in a hackathon building consumer accessibility tech for autism. That experience profoundly shaped the way I think about neurodiversity: people who think differently often see problems—and solutions—that the rest of us overlook. The experience wasn’t just an exercise in coding; it was a lesson in empathy and creativity.​

It’s important to keep up with the quickly changing voice technology landscape. Here are the top ten terms to know.

In the evolving landscape of artificial intelligence (AI), the ability to recognize and understand human emotions has emerged as a critical frontier. Emotion classification, the process of identifying and categorizing human emotions, plays a pivotal role in applications ranging from customer intelligence to accessibility to mental health support.

Emotions are difficult to understand, even for humans. Because of this, building AI systems to accurately identify emotions is also a hard problem to solve. Many things can factor into how someone reads another person’s emotions: tonality, facial expression, context, words said, nonverbal cues, etc.

Data is king. At Valence Vibrations, we follow the view of Andrew Ng’s data-centric AI in building robust systems based off a strong data engineering foundation.

Tonal analysis in machine learning has emerged as an essential tool for extracting valuable insights from vast amounts of audio and text data.

Affective computing is a multidisciplinary field that merges computer science, psychology, and neuroscience which has emerged as a groundbreaking area of research with the potential to revolutionize the way we interact with technology.