As I look back over 2017, I see that it’s been quite the year for content on artificial intelligence, machine learning, and cognitive solutions. This technology is everywhere these days, and it can be a little overwhelming with the amount of information available to you. To help you sort through the massive amount of content that we’ve published this year, I’ve compiled a list of the most-read content on the developerWorks Cognitive computing hub. This content covers everything from introductory concepts of artificial intelligence and machine learning to specifics of creating and extending your own chatbot.
- “A beginner’s guide to artificial intelligence, machine learning, and cognitive computing” by M. Tim Jones explores some of the important aspects of AI and its subfields.
- “Create a translation application by using Watson services, Eclipse, and IBM Cloud” by John Andersen explains how to create a simple translation application that uses two IBM Watson services: Language Translator and Speech to Text.
- “Create a news chatbot to deliver content through Facebook Messenger” is a series by Michael Yuan that shows how you can create a news chatbot through two messaging applications: Facebook and Slack.
- “Build your chatbot with Watson Conversation explains how to extend the chatbot to return weather information by using expanded entities from Watson Natural Language Understanding.
Along with how-to tutorials, several blogs caught lots of attention with their information on training chatbots,
- 10 Steps to Train a Chatbot and its Machine Learning Models to Maximize Performance
- Build your chatbot with Watson Conversation and entities from Watson NLU
- Watson machine learning within IBM Data Science Experience
If you’re already familiar with the technology and just want to jump right in, IBM also offers code patterns, which are road maps that will help you connect the dots by answering the question, “How do I build it?” Each pattern includes overviews, code, components, architecture info, additional reading, and more. Giving you direct access to the code, they help you jump-start your chatbot development. Some popular code patterns are:
- Create a “cognitive” retail chatbot
- Analyze Twitter handles and hashtags for sentiment and content
- Perform a machine learning exercise on IBM Data Science Experience (DSX) by using Apache SystemML
I hope that you’ve enjoyed reading this content and that you find it useful in helping you do your job. If there’s an artificial intelligence or machine learning topic that you’d like to see covered on developerWorks, send me an email.