The Conversational AI Landscape & HumanFirst

And How It Covers A Niche Vector In The Chatbot Market


With chatbot development the initial approaches seemed to have been centered around a single vendor or technology framework/stack to address the complete chatbot development and maintenance process, end-to-end.

In most implementations this seems to be still the case.

Setup challenges, including training data and maintenance, were among the top reasons for not implementing chatbots in enterprises, according to a recent Deloitte survey.

There is an approach now of building a chatbot ecosystem using various technologies and components. Following a more open approach and opting for the best of breed for each individual component of the conversational experience.

This principle is evident in the open architecture of Cognigy where each component of their framework can be used and implemented independently.

Building A Sustainable Conversational AI Ecosystems

The initial maturing of the Conversational AI landscape has also lead to the proliferation of various tools. These tools could be divided into three categories:

  • Pre-Conversation
  • In-Conversation &
  • Post-Conversation.

HumanFirst cover both pre and post conversation scenarios. Again, traditionally in chatbot development and maintenance, the focus was always on what happens in conversation. However, there are specific vertical tools available to address the pre and post conversation areas. Of which HumanFirst is one.

As seen above, HumanFirst has an utterance view and also a conversations view. Hence addressing the pre and post conversation scenario.

The HumanFirst interface allows for per utterance view of user input strings or intents. This is analogous to how most NLU interfaces works. With the marked difference that in traditional NLU interfaces the user needs to decide/guess on what intents need to be serviced. And subsequently possible or plausible user utterances associated with this intent need to be crafted.

With HumanFirst customer utterances can be added and with clustering intents are defined automatically.

The conversations tab allows for past dialogs to be interrogated and utterances labeled.

According to Deloitte 20% of patents in conversational AI relate to improving the training process. Innovations focus on automating and accelerating the training process to better understand users’ inputs and improve the quality of responses.

Manually processing user input strings or dialogs, with traditional manual approaches is error prone, inefficient and not feasible long-term.

Seven Vertical Vectors In Conversational AI

Within the Conversational AI landscape there are seven vertical vectors and also a horizontal view. The verticals can be divided into three main groupings: Pre-Conversation, In-Conversation and Post-Conversation.

The seven vertical vectors are listed above, where respond & negotiate is constituted by NLU and Personalization. Vector 5 is very much a niche market, which is very much the case with vector 1. HumanFirst covers both Vector 1, and also vector 7.

As stated in the introduction, it is not uncommon for larger organizations to construct their Conversational AI landscape from various technologies, choosing the best of breed for each area of concern.

“We shape our tools and, thereafter, our tools shape us.” — John Culkin (1967)

Horizontal View Of Conversational AI Ecosystems

In broad strokes, the Chatbot development framework landscape can be divided into four categories.

Making astute technology decisions at the inception of your chatbot journey has a significant impact on what your chatbot’s trajectory will be.

Hence, choose and shape your tools wisely.

Because, later in the process those tools will shape and influence the way you plan, develop, scale your chatbot. Impediments are usually system or framework related.

Chatbot development tools and frameworks can be divided into four categories, roughly…

HumanFirst fits into category 4 as a tool to manage training data and create structure from unstructured data.

Category 1

The open source, more technical NLP tools and chatbot development frameworks. Typically, these tools:

  • Can be installed anywhere
  • Has open architecture
  • Open Source
  • No or limited GUI
  • Configuration file and pro-code focused
  • Machine Learning Approach
  • Higher barrier to entry
  • Scales well
  • Demands astute technical planning for installation & operational management
  • Often used as underlying enabling technology by Category 3 software
  • New features can be developed and the platform enhanced

Category 2

  • Often used by large-scale commercial offerings
  • Cloud based, big tech companies
  • In some instances specific geographic regions can be selected
  • Seen as safe bets for large organizations
  • Solutions range from pro-code, low-code to no-code
  • Lower barrier to entry
  • GUI focused
  • Little to no insight or control as to what happens under the hood
  • Little to no user influence on the product roadmap
  • Rigid rule-based dialog state management
  • Cost is most often not negotiable
  • Collaboration and group-design-development focused

Category 3

  • These are independent, alternatives for Conversational AI, providing an encapsulated product
  • The enabling technology under the hood is often not made known
  • Independent, alternative solution providers
  • Frequently built using open-source NLP tools
  • Often innovative approaches are followed to the challenges of Dialog State Design, development and management
  • Low-code to no-code approach
  • The possibility exist of these companies being acquired
  • Price is often more negotiable
  • Feature requests are more likely to be accommodated
  • Lower barrier to entry and to get going

Category 4

  • Natural Language Processing and Understanding tools
  • Text or conversations can be analyzed for intent, named entities, custom defined entities.
  • Often tasks like summarization, key word extraction, language detection etc. can be performed
  • Data annotation and training data improvement GUI tools are available in some cases
  • Also tools for managing training data
  • Easily accessible, but with a higher technical barrier to entry
  • Ideal for high NLP pass on user input prior to NLU
  • Not a chatbot development framework
  • Does not include features like dialog state management, chatbot response management etc.
  • Focused on wider Language Processing implementations and not just conversational agents
  • Often used for non-real-time, off-line conversational text processing
  • Often used as underlying technology by Category 3 software

Democratizing Access To Conversational AI

As seen below, HumanFirst allows for the upload of utterances or whole conversations. Existing data sets can be re-used or public data can be used. The option to use public data is quite fun for experimenting and prototyping.

The idea, that with HumanFirst the ability exist to easily interrogate data, creating structure from utterance data is core to its purpose. Users can even work their way back from conversation data.

Above, HumanFirst has got a few interesting public data sets which can be used for experimentation and framework exploration. Working with familiar data helps with demystifying some core NLP concepts.

In Closing

After an initial overview a few things came to mind. These might change as my knowledge of the system grows…

A few closing thoughts on HumanFirst

  1. Exceptional tool for exploring & Labeling data. Improving model accuracy and accommodating scaling model scope.
  2. HumanFirst does cover a niche and much required gap in data preparation.
  3. The product might be sold currently as a data discovery tool, and structuring unstructured conversational data. But I see huge potential in implementing HumanFirst as an API within a current chatbot implementation. Especially the NLU portion. Currently aJSON endpoint is exposed to allow running real-time predictions using the model trained within Studio. The input is a single utterance, and the output is a distribution over intents. Adding intent hierarchy and entities will bolster the API immensely.
  4. Using the the NLU API as a first-pass or high-level pass to categorize the user input can be helpful.
  5. HumanFirst is offered as SaaS, but can also be installed in any cloud via containerization. This is advantageous for companies with stringent government-imposed data protection and management regulations.



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