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One of the biggest setup challenges artificial intelligence (AI) teams face is manually training agents. Current supervised methods are time-consuming and expensive, requiring manually labeled training data for all classes. In a survey by Dimensional Research and AIegion, 96% of respondents say they have encountered training-related issues such as data quality, labeling required to train the model and building model confidence.
As the domain of natural language processing (NLP) grows steadily through advances in deep neural networks and large training datasets, this problem has moved to the center of a number of language-based use cases. To address that, Yellow AI recently announced the release of DynamicNLP, a solution designed to eliminate the need for NLP model training.
DynamicNLP is a pre-trained NLP model, which provides the advantage that companies do not have to waste time training the NLP model continuously. The tool is built on zero-shot learning (ZSL), which eliminates the need for companies to go through the time-consuming process of manually labeling data to train the AI bot. Instead, this allows dynamic AI agents to learn on the fly, setting up conversational AI flows in minutes while reducing training data, cost and effort.
“Zero-shot learning offers a way around this problem by letting the model learn from intent,” said Raghu Ravinutala, CEO and co-founder of Yellow AI. “This means the model can learn without having to be trained on each new domain.”
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In addition, the zero-shot model can also reduce the need to collect and annotate data to increase accuracy, he said.
Barriers to conversational AI training
Conversational AI platforms require extensive training to effectively provide human-like conversations. Unless utterances are constantly being added and updated, the chatbot model fails to understand the user’s intent, so it cannot provide the correct response. In addition, the process must be maintained for many use cases, which requires manual training of NLP with hundreds to thousands of different data points.
When using supervised learning methods to add utterances (a chatbot user’s input), it is critical to constantly monitor how users write utterances, incrementally and iteratively flag those that were not identified. Once they have been marked, the missing utterances must be reintroduced into the training. Several questions may be de-identified during the process.
Another significant challenge is how utterances can be added. Although all the ways in which user input is recorded are considered, there is still the question of how many the chatbot will be able to detect.
To that end, Yellow AI’s DynamicNLP platform is designed to improve the accuracy of seen and unseen intent in utterances. Removing manual tagging also helps eliminate errors, resulting in a stronger, more robust NLP with better intent coverage for all types of conversations.
According to Yellow AI, the model agility of DynamicNLP enables businesses to maximize efficiency and effectiveness across a wider range of use cases, such as customer support, customer engagement, conversational commerce, HR and ITSM automation.
“Our platform comes with a pre-trained model with unsupervised learning that allows companies to bypass the tedious, complex and error-prone process of model training,” Ravinutala said.
The pre-trained model is built using billions of anonymized conversations, which Ravinutala claimed helps reduce unidentified utterances by up to 60%, making the AI agents more human-like and scalable across industries with broader use cases.
“The platform has also been subjected to many domain-related utterances,” he said. “This means the subsequent sentence embeddings generated are much stronger, with 97%+ appropriateness.”
Future trends and challenges for conversational AI
Ravintula said the use of pre-trained models to improve conversational AI development will undoubtedly increase, encompassing various modalities, including text, voice, video and images.
“Companies across industries will require even less effort to fine-tune and create their unique use cases as they will have access to larger pre-trained models that will provide an enhanced customer and employee experience,” he said.
A current challenge, he pointed out, is to make models more context-aware since language, by its very nature, is ambiguous.
“Models capable of understanding audio inputs consisting of multiple speakers, background noise, accent, tone, etc., would require a different approach to effectively deliver human-like natural conversations with users,” he said.
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