Most artificial intelligence applications cannot figure out the intricate nuances of natural language and facial recognition.

Most artificial intelligence applications cannot figure out the intricate nuances of natural language and facial recognition. These intricate nuances represent a major persistent challenge to most recent artificial intelligence applications such as Apple Siri, Amazon Alexa, and Google Assistant etc. For instance, artificial intelligence applications often cannot decipher puns, jokes, sarcastic remarks, and other more complex conversations. Artificial intelligence applications often cannot distinguish delicate human facial expressions such as surprise and confusion, fear and anxiety, or hubris and hysteria.

Many artificial intelligence machines learn from big data to predict specific human emotions, actions, and interactive outcomes via neural networks. Primary emotion recognition technology analyzes facial expressions to infer how humans feel, and this technology can create $25 billion business opportunities by 2025. Tech titans such as Facebook, Apple, Microsoft, Google, and Amazon (F.A.M.G.A.) lead these tech advances in artificial intelligence. New lean specialty startups such Kairos and Affectiva also take part in this fresh unique direction. Emotion recognition can often help promote products and services, and this new technology can be useful in job recruitment, fraud, and crime prevention. Several lean enterprises seek to capture this tech niche, and these enterprises have yet to close the gap between artificial intelligence and universal intelligence.