AI, The Enterprise, and You
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Using artificial intelligence (AI) to solve real work problems is no longer science fiction. AI is something that is becoming more common in our daily lives, and it has the potential to enhance the way people use technology in their daily activities. The University of Manitoba is just one such enterprise that is already looking into how AI can improve the student experience and aid our staff in doing their day to day tasks. > But where do we start? > In this session, we’ll start with a primer on the types of AI available today that can be used to build modern applications the enhance the user experience for our clients. Then, we’ll take a deep dive by following up with a post-mortem on IST’s own chatbot initiative to explore how the University of Manitoba could use chatbots to provide a robust scalable solution for helping students. Delivered at Prarie Dev Con in Winnpeg, MB, Canada on June 5th, 2018.
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AI, The Enterprise, and You
- 1. AI, the Enterprise, and You A Primer and Post-Mortem David Wesst University of Manitoba Prairie Dev Con | Winnipeg, MB | June 2018
- 4. Goals • Briefing on the AI Landscape • Primer on Chatbot Development • Post-Mortem on UManitoba Chatbot Development Project
- 5. Agenda • What is this talk? • How do we do this? • How did your team do this?
- 6. What is this talk?
- 7. What is this talk? • AI • The Enterprise • And You
- 8. AI in the Enterprise Scenario: Your enterprise manages hundreds of support tickets day that need to be routed to the appropriate support team. Create a utility that provides this functionality.
- 9. You • AI apps are trained to understand rather than programmed to understand • AI enhances our work tools, rather that being our work tools
- 12. How do you do this?
- 15. • Vocabulary for your chatbot Entities • Action the user wants to perform Intents • Sample dialog used to train the chatbot Utterances • The map of the conversation Dialog Tree
- 16. Send a message to David Wesst Entity @member: David Wesst Intent #send_message
- 17. Drop a line to DW Entity @member: David Wesst Intent #send_message
- 18. Photo by Amy Skyer on Unsplash
- 19. Dialogflow • Google • https://dialogflow.com • Highlights • Intuitive UI • Development Hooks • Pre-built integration Google Cloud & Google Assistant
- 20. LUIS • Microsoft • https://luis.ai • Highlights • Improved UI • Large number of pre-trained sets • Multiple pre-built integrations with Azure services
- 21. Watson Assistant • IBM • https://www.ibm.com/watson/ • Highlights • Most Mature • Industry focused • Integrates with BotKit and Bot Framework
- 26. How do you this? (Host App) • BotKit • Microsoft Bot Framework
- 27. How do you this? (Host App) BotKit • Open Source • Tool to integrate directly with some platforms • BotKit Studio aids with AI development • Hosted Development Environment • Integrates with MSFT Bot Framework Bot Framework • Largest number of native integrations • Includes Azure Integrations • Pay for “premium” connections • Not an actual framework, rather a platform
- 29. How do you this? (Host App) Microsoft Bot Framework BotKit
- 31. How did your team do this?
- 32. UM Student Chatbot • Proof of Technology Project • Determine if the university can use AI to provide business value to clients • Solution needs to provide business real value direct to clients • Integrate with existing client facing systems
- 33. Photo by Dane Deaner on Unsplash
- 34. How did your team do this? • Coaching & Support • Defining entities, intents, and using the Watson development environment • Controlling scope • Software Development 101 • Bot Testing • Built the Host
- 37. Photo by Ken Treloar on Unsplash
- 38. Goals • Briefing on the AI Landscape • Primer on Chatbot Development • Post-Mortem on UManitoba Chatbot Development Project
- 39. Photo by Emily Morter on Unsplash
- 40. Thanks! • David Wesst • WesternDevs.com • Email • david.wesst@umanitoba.ca • contact@davidwesst.com • @davidwesst • GitHub • Twitter
Editor's Notes
- Who am I?
- What is this talk AI Landscape How do we do this AI Development Primer What could go wrong Post-Mortem / Lessons Learned
- Hard problems when you need to define a finite set of business rules and limited amount of sample data
- Transition – So what sort of AI is available for enterprise solutions?
- Trained AI – You pay for access to AI trained on large datasets Conversational AI – NLP communication, which combines different trained AI to derive user intent Custom AI – You train it on your data
- Part 1 -- AI
- Photo Credit: https://pixabay.com/photo-2356433/
- Vendors provide similar offering and work in a similar way
- Hosted AI Development Environment Photo by Amy Skyer on Unsplash
- Watson Assistant for UM Student Chatbot
- Web, Slack, Cisco (Jabber/Spark), Teams, Messenger, Twillio …and Microsoft Bot Framework Photo Credits: https://upload.wikimedia.org/wikipedia/commons/7/75/Crystal_Project_web.png
- Web, Slack, Cisco (Jabber/Spark), Teams, Messenger, Twillio …and Microsoft Bot Framework Photo Credits: https://upload.wikimedia.org/wikipedia/commons/7/75/Crystal_Project_web.png
- https://github.com/watson-developer-cloud/assistant-simple Watson Assistant (IBM Cloud) Host on Azure Web App
- Part 1 -- AI
- Business Units (not IST) defines value IST facilitates development
- We didn’t, we just provided guidance and support to the business
- Can we trust the AI to respond for us?
- Goal was to determine if the university could provide business value, and we are not the business experts. We are technologists. Goal was to
- Trained, Conversational, Custom Entities, Intents, Utterances, Dialog Trees Get SMEs building AI instead of developers, they are the experts
- Thanks!