Data scarcity is one of the major bottlenecks for Artificial Intelligence (AI) to reach production levels. The reason is simple: data, or the lack of it, is the number one reason why AI/Natural Language Understanding (NLU) projects fail. So the AI community is working extremely hard to come up with a solution.
Bitext’s is industrializing training data production for any voice-controlled device, chatbot or IVR using artificial training data to accelerate customer support automation. At Bitext we solve data scarcity and legal risks with Multilingual Synthetic Training Data to enhance Conversational AI and to derive insights from text-based and unstructured data such as contact center interactions, chat-bot and live chat transcripts, product reviews, open-ended survey responses and email. We can natively analyze text in up to 80 languages.
One of the most significant aspects of a virtual agent is how fast it can learn. With a human in the conversational loop, training AI goes much faster: your bot learns and changes, keeping knowledge up to date. Plus, users never get the “Sorry, I did not understand your request” response, your brand will be able to solve the problem right away.
Reducing complicated, confusing processes down to a natural conversation is potentially a huge business opportunity for anyone willing to jump headfirst and create a great user experience. Chatbots are only as smart as the words you feed them. If a bot is too rudimentary, people will lose trust in the company and will feel ignored and unappreciated. UX problems appear when user deviates from the designed linear flow.
Most customer service and contact center executives are honing in on bots because they can handle large volumes of queries. Thus, their service center staff can focus on more complex tasks. As the technology behind bots has improved in terms of natural language processing (NLP), machine learning (ML), and intent-matching capabilities, companies are increasingly willing to trust them to handle direct customer interaction.
According to Gartner, companies working with AI should stop combining training and learning activities, one of the reasons being the slowing down of conversational agents’ learning processes. The most recommended course of action for data managers is exploring emerging middleware tools that allow them to use the same training data set for multiple AI service providers.