Improving AI-assisted conversation with zero-shot learning

Zero-shot learning is a relatively new technique in machine learning (ML) that is already having a major impact. With this method, ML systems such as neural networks require zero or very few “shots” to arrive at the “correct” answer. It has especially gained ground in areas such as image classification and object detection and for Natural Language Processing (NLP), addressing the twin challenges in ML of having “too much data” and “not enough data”.

But the potential for zero-shot learning extends far beyond the static visual or linguistic fields. Many other use cases are emerging with applications in almost every industry and field, helping to spark a new imagination of how people approach most human activity – conversation.

How does zero shot learning work?

Zero-shot learning allows models to learn to recognize things that they have not been introduced to before. Rather than the traditional method of sourcing and labeling huge data sets — which are then used to train models under supervision — zero-shot learning seems little short of magic. The model does not need to be shown what something is 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 offers many benefits to developers in many areas. First, it significantly speeds up ML projects as it reduces the most labor-intensive phases, data preparation, and custom, audited model creation.

Second, once developers have learned the basics of zero-shot learning, what they can achieve is radically expanded. Increasingly, developers appreciate that once a modest initial knowledge gap has been bridged, zero-shot learning techniques allow them to dream much, much bigger about what they can achieve with ML.

Finally, the technique is very useful when models have to walk a fine line between being general enough to understand a wide range of situations while also being able to locate meaning or relevant information within that broad context. In addition, this process can take place in real time.

How zero-shot learning improves conversational intelligence

The ability to extract the right meaning from a broad spectrum in real time means that zero-shot learning is transforming the art of conversation. Pioneering companies, in particular, have found ways to apply zero-shot learning to improve results in high-value interactions, most commonly in customer support and sales. In these scenarios, AI-assisted people are coached to better respond to customer information, close deals faster, and ultimately, improve customer satisfaction.

Generate Opportunities

Conversational AI, developed using zero-shot learning, is already being used to spot upselling opportunities, such as every time a prospect or customer talks about pricing. There are hundreds of different ways the topic can present itself, for example, “I’m on a tight budget,” “How much does that cost?”, “I don’t have that budget,” “The price is too high.” Unlike traditional models supervised, in which data scientists must collect data, train the system and then test, evaluate and benchmark it, the machine can use zero-shot learning to train itself very quickly.

Trackers in real-time streams go beyond just identifying certain topics and can make recommendations in response to certain situations. For example, if a tracker detects that a person is in financial difficulty during a conversation with a customer service or sales agent in a financial services company, it can provide an appropriate response to this information (for example, a loan).

Developing AI-powered 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 ​​works with people and helps them to better fulfill their role.

There are two main ways this works. After a customer-agent conversation ends, the system can generate a report summarizing the interaction, assess how it performed against pre-agreed Key Performance Indicators (KPIs) and make 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 that have until now relied on arduous, expensive training supplemented with sales scripts for staff who want to coach them on the best way to identify and respond to the needs of the business. customer.

Training represents a significant investment for companies, especially in high turnover sales environments. The turnover of the sales staff has been driving around lately 10 percentage points higher. Industry studies suggest that even at the largest companies, salespeople tend to stay on the job for as little as 18 months before churning. It’s 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. Perhaps their most powerful feature is their ability to make on-the-job recommendations that help the salesperson — and the company — succeed.

From training to career coaching

This ability to improve output and performance through AI-enabled coaching not only benefits businesses, it can also be adapted to accelerate an employee’s personal career path. Consider a scenario where a zero-shot learning system works with an employee to help them achieve their personal 360-degree goals. A goal like “Convert X% more leads” becomes more achievable when supported by an ML model prepared to discover and develop opportunities that the employee alone could miss.

Turn conversations into insights

Zero-shot learning is a relatively new technique and we are just beginning to understand the full breadth of applications. Particularly suitable for situations where models must be trained to locate meaning within a broad context, conversational intelligence is quickly emerging as a leading area of ​​development. Data scientists, developers and time-sensitive, cost-conscious business leaders alike don’t need specialized model training for conversational intelligence systems, speeding up processes and shortening lead times.

While conversational intelligence applications are thriving alongside the better-known use cases for image detection and Natural Language Processing (NLP), the reality is that we have barely discovered what zero-shot learning can achieve.

For example, my company works with clients who want to solve problems to radically improve the capabilities of conversational AI, not only when it comes to coaching and training, but also how ML systems improve productivity by compressing and contextualizing business information, how they improve compliance, on harassment or profanity, and increase engagement in virtual events, all through the use of zero-shot learning models.

Toshish Jawale is CTO of Symbl.ai