The most significant aspect of a virtual agent is how fast it is able to learn. With a human in the conversational loop, training AI goes much faster: your bot learns and changes, keeping knowledge up to date.
Gartner suggests companies working on AI and ML should employ human-in-the-loop crowdsourcing as an enabler of AI solutions since this approach gives a wider access to problem solving, model training, classification and validation capabilities, in comparison with traditional ML processes. Therefore, when rules are too complicated for automation or the ML algorithm cannot get more accurate results is time to resort to humans.
Take a look at the following example of an e-commerce bot with a human in the loop:
If you AI isn’t trained for a request yet, it will let the customer know it’s getting help from a human. Your team will be on the stand-by ready to moderate in such situations. This way, your sales representative will get a notification from your bot on your channel preferences. Those human agents will help their bot co-worker by answering the question. Then, your bot will release the answer to the customer and even confirm whether it was helpful or not.
After that, the bot confirms that the question and answer should be retained as training and your bot automatically learns more utterances for the newly-trained intent via Bitext Artificial Data Generation service. Customers will love it and your employees only had to help once since next time the whole thing will be automated without needing help from another human agent. That’s how your AI training will be driven from specific users’ needs and how it will take less effort to implement.
In the following chart, you will see how a bot falls back on a human agent when it doesn’t understand a query from a user:
After this process, some tests must be made to see if a model is properly working, especially when the algorithm must face more complex issues and may not be confident enough to make the right decision on its own.
Human-in-the-loop (HITL), as mentioned in our previous post 'When Humans and Machines Come Together', is a concept combining both human and machine intelligence in order to improve the performance of ML algorithms. In this process, humans participate actively in training, tuning and testing data for those algorithms. ML algorithms do not work as traditional algorithms. They must be fed with great amounts of data to increase their accuracy. Here is where Bitext NLG services for artificial training data come to the fore. Try Bitext API now to check all core NLP bot building packages at disposal: