Human vs. Artificial Intelligence: Both Can Wind Up Winning
Artificial intelligence (AI) technologies are increasingly having an impact on many aspects of daily life. Referring to a machine’s ability to mimic or approximate the capabilities of humans, AI can include tasks such as recognizing spoken words (speech recognition), visually classifying and perceiving (computer vision), understanding user meaning (natural language understanding), and conducting a conversation (dialogue). Systems combining constellations of AI technologies that previously were found only in research prototypes are coming into daily use by consumers in applications such as mobile and in-home virtual assistants (e.g. Siri, Cortana, and Alexa).
Despite these successes, significant challenges remain in the application of AI—especially in conversational applications as we scale from simpler information-seeking-and-control tasks (“play David Bowie”; “turn on the lights”) to more complex tasks involving richer language and dialogue (e.g., troubleshooting for technical support, booking multipart travel reservations, giving financial advice). Among enterprise applications of AI, one approach that is gaining popularity is to forgo the attempt to create a fully autonomous AI-driven solution in favor of leveraging an effective blend of human and machine intelligence.
Human intelligence has always played a critical role in machine learning. In supervised learning specifically, human intelligence is applied to assign labels, or richer annotations, to examples used for training AI models, which are then deployed in fully automated systems. Effective solutions are now emerging that involve the symbiosis of human and artificial intelligence in real time.
These approaches vary in whether a human agent or an artificial agent is the interactions’s driver. In the case of an artificial agent fielding calls, text messages, or other inputs from users, human intelligence can be engaged in real time to provide live supervision of the behavior of the automated solution at various levels (human-assisted AI).
For example, human agents can listen to audio and assist with hard-to-recognize speech inputs, assigning a transcription and/or semantic interpretation to the input. They can also assist with higher-level decisions, such as which path to take in an interactive dialogue flow, or how best to generate an effective response to the user. In these cases, the goal is to contain the interaction to what appears to the customer as an automated solution—albeit one that leverages human intelligence just enough to be robust and have a high quality of interaction.
In contrast, in AI-assisted human interaction, the driver of the interaction is a human agent, and the user believes he is interacting with a person. The role of AI in this case is to help the human agent optimize and enhance her performance. For example, an AI solution assisting a contact center agent might suggest a possible response to return in text form or to read out to a customer.
Several companies have recently explored the application of sequence-to-sequence models using deep neural networks to formulate a response or multiple responses that an agent can adopt or edit. One of the great advantages of this setting for applying new machine learning algorithms is reduced risk of failure, as the human agent maintains the final say on whether to adopt the suggested response or use another. In addition, human decisions to adopt, reject, or edit suggested responses provide critical feedback for improving the AI models making the suggestions.
Another example of an AI-assisted human interaction is the application of predictive models based on user profiles and interaction history; these can be used to help financial advisers make suggestions to clients, or to assist sales reps in determining the optimal strategy for upselling a product. AI could also help human agents via within-call analytics, to track customer or agent emotions and provide live feedback to agents on their customers’ emotional states or their own.
Perhaps the best solutions for customer care will combine both humans assisting AI and AI assisting humans: Customers will first engage with automated virtual assistants that respond to their calls, texts, and other inputs, and human assistance will play a role in optimizing performance. Then, if a call requires transfer to a human agent, that agent will be supported by an AI-enabled solution that quickly brings him up to speed on the interaction’s progress and assists him in real time as he responds to and engages with the customer.
Michael Johnston is the director of research and innovation at Interactions Corporation.
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