Events

PubNub BLOCKS: Streaming Data Enhanced with Watson

Share this post:

If you’ve had to deal with managing streaming data, maybe you’ve heard of PubNub. Now it’s easy to add Watson-powered machine intelligence to those streams with BLOCKS, a feature of the PubNub Data Stream Network (DSN) that makes the network programmable. Using BLOCKS, developers can easily deploy functions on the PubNub network to modify messages without the need to manage their own infrastructure.

In a new episode of the Building with Watson webinar series, Josh Marinacci, Head of Developer Relations at PubNub demonstrates how he used the Watson Conversation PubNub BLOCK to build a geology-themed chatbot called Mr. Rockbot.

When you’re building a chatbot, you need to remember that a chatbot involves constant communication between the user and your bot. To tie these elements together, you’ll need a real-time, low-latency and high security infrastructure. The PubNub programmable network is designed for developing real-time applications like chatbots. It’s like a global Content Delivery Network (CDN), but for the streaming web (also known as a Data Stream Network or DSN). While a CDN is designed to serve static content, you can use a DSN like PubNub to conduct real-time communication.

A chatbot may need to interact with other platform proxies and access web services that provide the knowledge the chatbot needs to function successfully. Chatbots also require some level of artificial intelligence that can range from a phone tree, to full natural language processing or a rich neural net for the back-end.

Creating the example chatbot

Mr. Rockbot, the geology-themed chatbot example in the webinar demonstration, uses a serverless infrastructure and a number of third-party services. The client can be a phone or webpage. It then talks to the real-time network provider (PubNub) and computes in the network’s serverless compute system (PubNub BLOCKS). Here’s how the process works with Watson:

  • The message goes to the network compute block, which uses the IBM Watson Conversation API to add natural language processing insights to the input text.
  • You train the Conversation API to allow it to identify relevant entities and intents. Intents are possible chatbot actions and entities are targets of an intent, such as questions the user might ask.
  • After you have entities and intents set up, you can then create dialogs, which are the workflows a user can follow. Using dialogs, you can specify what the bot actually says to the user in different circumstances.
  • After you teach Watson Conversation, you can call the API from your serverless code using a simple HTTP POST.

Note that Watson Conversations is a stateless API, which means that to understand the context of a conversation, you have to provide this context on each request using a context structure.

To learn more about how you can add Watson-powered machine intelligence to your streaming data, be sure to check out the Building with Watson webcast.

 

Learn more about how you can add Watson-powered machine intelligence to your streaming data.

Add Comment
No Comments

Leave a Reply

Your email address will not be published.Required fields are marked *

More Events Stories
August 10, 2017

Building better chatbots: Two questions to ask before you get started

Chatbots are the new apps, offering scalable, instantaneous, 24x7 interaction that is difficult to achieve with human agents. Learn how an IBMer built a chatbot that could answer question about herself — an interactive, conversational resumé of sorts. Through trial and error, she found two important considerations to take into account when building a chatbot.

Continue reading

July 31, 2017

How enriched and faster news discovery fuels business development and growth

Watson Discovery News is like having a large team of tireless researchers vigilantly seeking opportunities and threats in news and blog content, only faster, and with more precision. Watson's NLP-enriched news search sources from more than 100,000 outlets, with 300,000 new articles added daily, in real-time. Build powerful news queries in under five minutes.

Continue reading

July 26, 2017

Meet the 13-year-old prodigy taking IBM and artificial intelligence by storm

ABC recently profiled 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at five, launched his first app at nine, and has been working with IBM's cognitive APIs for a few years now. In 2013 he built "tTables," an app to help kids learn multiplication, a huge achievement for a child who loves to code but is largely self-taught.

Continue reading