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Zero-shot learning is a relatively new technique in machine learning (ML) that’s already having a major impact. With this method, ML systems such as neural networks require zero or very few “shots” in order to arrive at the “correct” answer. It has primarily gained ground in fields such as image classification and object detection and for Natural Language Processing (NLP), addressing the twin challenges in ML of having “too much data” as well as “not enough data”.
But the potential for zero-shot learning extends well beyond the static visual or linguistic fields. Many other use cases are emerging with applications across almost every industry and field, helping to spur re-imagination of the way humans approach that most human of activities — conversation.
How does zero-shot learning work?
Zero-shot learning allows models to learn to recognize things they haven’t been introduced to before. Rather than the traditional method of sourcing and labelling huge data sets — which are then used to train supervised models — zero-shot learning appears little short of magical. The model does not need to be shown what something is in order to learn to recognize it. Whether you’re training it to identify a cat or a carcinoma, the model uses different types of auxiliary information associated with the data to interpret and deduce.
Assimilating zero-shot learning with ML networks holds many advantages for developers across a wide range of fields. First, it dramatically speeds up ML projects because it cuts down on the most labor-intensive phases, data prep and the creation of custom, supervised models.
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Second, once developers have learned the basics of zero-shot learning, what they can achieve radically expands. Increasingly, developers appreciate that once a modest initial knowledge gap is bridged, zero-shot learning techniques enable them to dream much, much bigger with what they can achieve with ML.
Finally, the technique is very useful when models need to tread a fine line between being general enough to understand a broad range of situations while at the same time being able to pinpoint meaning or relevant information within that broad context. What’s more, this process can take place in real time.
How zero-shot learning improves conversation intelligence
The ability to pick out the right meaning from a broad spectrum in real time means zero-shot learning is transforming the art of conversation. Specifically, pioneering businesses have found ways to apply zero-shot learning to improve outcomes in high-value interactions, typically in customer support and sales. In these scenarios, humans assisted by AI are coached to respond better to information that the customer is providing, to close deals faster and ultimately deliver higher customer satisfaction.
Generating sales opportunities
Conversational AI, developed using zero-shot learning, is already being deployed to recognize upselling opportunities, such as every time a prospect or customer talks about pricing. There are hundreds of different ways the topic could present itself — for example, “I’m tight on budget”, “How much does that cost?”, “I don’t have that budget”, “The price is too high.” Unlike traditional supervised models, in which data scientists need to gather data, train the system, then test, evaluate and benchmark it, the machine can use zero-shot learning, to very quickly begin to train itself.
Going beyond simply identifying particular topics, trackers in real-time streams can make recommendations in response to particular situations. During a call with a customer service or sales agent in a financial services company, for example, if a tracker detects a person is in financial difficulty, it can offer an appropriate response to this information (a loan, for instance).
Developing AI-assisted human interactions
Coaching and training are among the most promising applications for zero-short learning in such conversation-based scenarios. In these cases, the AI is working alongside humans, assisting them to better fulfil their role.
There are two main ways this works. After a customer-agent call is over, the system can generate a report summarizing the interaction, rating how it was conducted according to pre-agreed Key Performance Indicators (KPIs) and providing recommendations. The other approach is for the system to respond in real time during the call with targeted recommendations based on context, effectively training agents on the optimal way to handle calls.
On-the-job training with zero-shot learning
In this way, zero-shot learning systems address an essential, perennial challenge for sales teams who have until now relied on laborious, expensive training supplemented with sales scripts for staff that aim to coach them on the best way to identify and respond to the needs of the customer.
Training represents a hefty investment for businesses, especially in high-churn sales environments. Sales staff turnover has recently been riding around 10 percentage points higher. Industry studies suggest that even among the biggest companies, sales reps tend only to stay in the job 18 months before churning. It is a worrying trend, especially when you consider that it takes an average of three months to train them initially. Zero-shot inference systems don’t just help with initial training. Arguably their most powerful feature is their ability to provide on-the-job recommendations that help the sales rep — and the company — succeed.
Beyond training to career coaching
This ability to improve output and performance through AI-assisted coaching does not just benefit companies, it can be tailored to accelerate an employee’s personal career trajectory. Consider a scenario in which a zero-shot learning system works with an employee to help them attain their personal 360 targets. A goal like “convert X% more leads” becomes more attainable when assisted by an ML model primed to spot and develop opportunities the employee alone might miss.
Turning conversations into insights
Zero-shot learning is a relatively new technique and we are only just beginning to understand its full breadth of applications. Particularly suited to situations where models need to be trained to pinpoint meaning within a broad context, conversational intelligence is rapidly emerging as a leading development area. For data scientists, developers and time-sensitive cost-conscious business leaders alike, conversational intelligence systems require no specialist model training, accelerating processes and cutting lead times.
Although conversational intelligence applications are thriving, alongside the better known image detection and Natural Language Processing (NLP) use cases, the reality is that we have barely scratched the surface of what zero-shot learning can achieve.
For example, my company is working with clients seeking to solve problems to radically improve conversational AI’s capabilities when it comes not only to coaching and training, but also how ML systems improve productivity by compressing and contextualizing business information, how they improve compliance, clamp down on harassment behaviors or profanity and increase engagement in virtual events, all through the use of zero-shot learning models.
Toshish Jawale is CTO of Symbl.ai
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