Free chatbot xxx

However, whatever technology is used, I’m a big advocate of narrowing down the scope of a chatbot for many reasons I have explained in this blog post (in a nutshell: things like polysemy, neosemy, humour, irony are easier to tackle within a limited scope).

So, if you concurr with this philosophy, 80 intents is more than enough to build a single chatbot.

With LUIS, I used an entity of type incident & another one of type request.

Then, I trained LUIS the following way: by tagging the items in bold to either incident or request.

Granularity may be achieved with Bluemix by creating multiple containers and spreading your Watson services over them but that’d require you to use multiple browser tabs to manage them all.

Side note before we get started: both offerings are moving targets, so today’s truth isn’t yesterday’s nor tomorrow’s one, so you should consider this blog post as a snapshot of today’s situation and always re-consider what is being said below as it might have changed by the time you read this blog post. This works already differently between Bluemix and Azure.

While this offers greater accuracy, it is first and foremost a limiting factor because it’s completely static here.

There is no so-called intelligence in this way of extracting entities.

Bluemix requires you to create a container in a region and create your services inside of that container while Azure is more granular since you can associate any service to any location.

Azure comes with many more locations than Bluemix (only 5 at the time of writing), Best offering: Microsoft, rationale: more granular and much more datacenters, meaning that the likelihood of finding a location close to your apps & users is higher.

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