Landscape of Conversational AI Platforms

In the recent Analytics Magazine from Informs, thoughtful look at this area:

The landscape of conversational AI platforms
How to choose the right platform for your business.
By Abe Raher

Vijay Ramakrishnan (photo, right) is the technical development leader of MindMeld, a conversational AI platform recently open-sourced by Cisco. Ramakrishnan develops the machine learning (ML) and information retrieval (IR) models in MindMeld, and architects conversational applications on top of the platform. Before Cisco, Ramakrishnan built conversational assistants for Fortune 500 companies such as Starbucks and Fast Retailing.

Georgian Partners defines conversational AI as “the use of messaging apps, speech-based assistants and chatbots to automate communication and create personalized customer experiences at scale.” Artificial Solutions describes it as “a form of artificial intelligence that allows people to communicate with applications, websites and devices in everyday, humanlike natural language via voice, text, touch or gesture input.”

Abe Raher, who writes technical documentation at AppDynamics, recently interviewed Ramakrishnan on the topic of conversational AI platforms and how to choose the right platform for particular business needs. Following are excerpts from the interview: 

Describe the conversational AI landscape.

The market is crowded. Players ranging from startups to blue chip companies now offer conversational AI platforms. I think of the major differences between platforms in terms of three tradeoffs: usability versus configurability, cloud versus on-premise and closed source versus open source.

“Black box”-type AI platforms abstract all the internals of machine learning (ML), which makes them easy to use and increases the speed to deploy a prototype application. By contrast, highly configurable platforms expose ML internals to the developer, which makes it easier to fix problems once an application is in production. For example, an application might misclassify some subset or category of queries. This is easier to address with a highly configurable platform, where the developer owns the entire ML stack, than with a “black box” platform.

Cloud-based AI platforms require training data to be uploaded to the cloud. For organizations whose customer data cannot be released to third-party services, uploading data to the cloud can be a deal-breaker. For them, it’s essential to find a platform that can be deployed on-premise, with all training data stored locally.

Platforms that are tightly coupled with consumer products tend to be closed source. The benefits of deep integration must be weighed against the disadvantages of closed source. For example, it might be easier to develop on the recommended AI platform as the device, but support might be limited, and since the code-base is closed, detailed introspection into the code to fix an issue cannot happen.

Open source platforms, meanwhile, are transparent due to their open code-base, so most issues regarding the platform have already been discussed, support is generally faster to provide due to the involvement of the open-source community and any further investigation of an issue can be done by introspecting the code. While evaluating open-source platforms, make sure to consider whether they have integrations to consumer or enterprise devices that you care about, and how vibrant their developer communities are.  .... "