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4. Are there similar patterns for intents and training data within the same industry?

Same companies within the industry get asked about similar topics. For example telecom companies get asked about invoices and banks get asked about transactions and loans. There is definitely overlap.

However, the phrases used by customers to ask things from the companies within the same industry still differ. This is because each company offers different services and products. And these nuances make the phrases used slightly different company by company.

3. I'm building a bot for mental health. Can I use

Yes. You can use to build a bot in any domain.

2. What is the best way to measure a chatbot?

It is advisable to first determine the business case or use case for your bot. From there you get your Key Performance Indicators (KPIs) or key metrics. These are the ones you should measure. As there are bots for different use cases, there will be different metrics. Customer service bots will measure self-service resolution rates (number of customers helped by the bot without human support agents).

Sales bots are measured by the amount of leads the capture from the website visitors. For Natural Language Understanding bots a good measure is the intent detection rate where you measure the number of answers as a percentage of total answers where the bot have the correct answers to customer questions. You can read more on bot metrics here.

1. Where will Conversational AI be in 2 years time?

No one knows exactly is the honest answer. Our take is that there will be improvements in intent detection. However, we are still far from Artificial General Intelligence. Even with larger models, intent detection remains a tough problem. Though trained virtual assistants in a narrow domain are becoming more accurate in their answers and as a result useful for users. Aside from algorithms focus will be on product development and no code tools to make Conversational AI easier to use for people without the need of technical skills.

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