Bangkok Bank PLC announced the launch of its Thai language Artificial Intelligence (AI) powered chatbot that was built in-house in a joint collaboration with Singapore-based fintech startup Pand.ai. The chatbot called TT01 was a result of Bangkok Bank's innovation programme, InnoHub. The development began earlier this year and the chatbot was fully completed in October. The solution will be available to clients of Pand.ai. Meanwhile, Bangkok Bank will use this as a digital sales assistant on LINE for their sales and relationship managers, scheduled to launch in Q1 2021.

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.