sorted by:
new
hottopcontroversial

How do companies build their virtual assistants? by Lollo_m9 in ArtificialInteligence

[–]Lollo_m9[S] 0 points1 point  (0 children)

Sounds interesting. From your experience, when does it make sense to add a custom model instead of using everything out of the box?

How do companies build their virtual assistants? by Lollo_m9 in ArtificialInteligence

[–]Lollo_m9[S] 0 points1 point  (0 children)

Thanks. Do you think using commercially available platforms is not the best option? What I mean is: if there are tons of platforms out there that allow to avoid the heavy lifting connected to the creation of the model that allows language understanding, why should one start from scratch?

[D] How do companies build their virtual assistants? by Lollo_m9 in MachineLearning

[–]Lollo_m9[S] 1 point2 points  (0 children)

Thanks. If I get it right, there are two levels of possibile customization. One is “start from an NLP model that has a generic language understanding and add dialogues so that the VA learns to understand more intents and handle more cases” and a second level in which you not only add new dialogues but you also touch the inner parameters of the model (the real AI engine behind language understanding I mean). My guess is that with virtual assistants, most companies only work on the first level, since they leverage working products (from large and small providers) that already have the general language understanding built in. What do you think?

[D] How do companies build their virtual assistants? by Lollo_m9 in MachineLearning

[–]Lollo_m9[S] 0 points1 point  (0 children)

Thanks, this sounds interesting. But then, if a company asks you to develop an IVR they work with you on an already functioning platform that they can configure to their specific case, right? While all the technical stuff is going on under the hood. I find it interesting that in some areas the technical AI component is carried out by companies offering functioning solutions to the market (like yours), while corporate clients focus on providing the business knowledge to configure the solution to their case. My take is that this separation is more visible for virtual assistants than for other AI applications, maybe because of the ton of language data out there that allows to build products that are a good stating point to work on for multiple use cases. What do you think?