Through our childhood, we’ve seen dogs counting one plus nine, elephants painting natural landscape, cats pleading for its food, monkey doing acrobat, dolphins clapping their fins (as in human clapping hands), and so on. That’s all are the result of animal habituating, imprinting human behaviour pattern in several aspects of action, as animals won’t do it in autodidact.
This is the concept we would like to embrace about deploying a bot: keep in mind that bot wasn’t innately humane. It manages your answers to be triggered by exact questions. To make them in duplicating human behaviour is to train them. The knowledge to act and talk like human won’t come by its own.
Indeed, Artificial Intelligence in literal meaning is a computer-based product that capable bot to do human chores, even to think like one. AI refers to human intelligence simulation, powered by a programmed machine to imprint human behaviour pattern. Yet although bot is a form of AI we can use to help companies in customer experience, we can’t get rid the human resource responsibility in shaping the mould. The human can’t fatalistically rely on the knowledge for coming on its own and suddenly jumps to the conclusion that AI would replace them.
Instead, AI, moreover bot, urgently needs human participation to put a humane sense, therefore we, the human, took control in determining the depth of bot knowledge. In this case, we encourage the company’s role in teaching what bot needs to know. Someone needs to inherit the company’s knowledge to be internalized in the system; it’s that showman. The bot mentors.
To make things easier, imagine that the bot is a bank of assorted answer pattern to the public. And by pattern, it doesn’t mean the conversation stuck nonchalantly, corresponding each other without humane context, and less attempt to develop it into desirable action. Bot mentor can define how deep and far the bot has to handle for the audience know. As to all customers, they need to talk directly to your alive representative, bot mentor is the key to embodied your representative knowledge to the system.
It takes a profound understanding of, 1) your audience behaviour in seeking information of your brand, and 2) the suggestion they might need from your brand. Both patterns are easier to comprehend within a quantitative approach, such as data about the rating, viewers, closest distance, and so on. This is the importance of supervised learning: parse the tangled pattern to the public acceptable information.
As supervised learning helps us defining bot knowledge capacity, this process helps us deciding the coordinate point of the bot and human agent; answering stakeholders doubt in human-AI collaboration. 3Dolphins based bot users can decide whether they need auto-switch dialogue with a human agent, digital form FAQ, multiple dialogue intention and entity recognition, or external system integration within our Process Flow. As we provide them with all, bot mentor holds the decision of the usage itself.
To have a bot means to have the commitment to maintain and adjust the knowledge, and bot supervisor is the one who does. It’s like to point a responsible person to put the fire ring for the dolphin to jump off the pool, or someone to teach the elephant about the colour and what to paint with that.
Of course, we’d take part in the training itself. If you’re interested in our solution and take part in it, you can reach our sales team.
And for those who do, learn more about how to optimize our bot with 3Dolphins support and delivery team.