Artificial Intelligence is a hot topic today. There is no doubt AI is making a huge impact not only on daily lives of individuals, but also on the way the companies operate. Businesses of all kinds are now integrating Intelligent chatbots into their business model. In this talk, we will discuss technologies capabilities for building chatbots. The focus will be on solutions which are less trivial than you typically will see in talks like “step-by-step guidance how to build the chatbot” . In this presentation you will learn: - What is the difference between hype and actual conversational technology capabilities; - Why it’s good time to start mastering conversational user interfaces; - How to build a chatbot with AIaaS and what you’ll have to do on your own. https://blog.griddynamics.com/chatbots-in-retail-2017-is-shaping-up-to-be-a-big-year

1. Public
Demystifying AI-chatbots
Just add CUI to your business apps
Anton Ovchinnikov, Technical specialist
10/28/2017

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What it will be about
§ What if you discard all the marketing hype around intelligent chatbots?
§ Why Conversational User Interface (CUI)? And why now?
§ The basis of the use case: technology must simplify life!
§ Where does omni-channel architecture fit in?
§ High Level Architecture of an omni-channel platform for CUI applications

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Who we are
§ Grid Dynamics is an engineering services company for tier-1 enterprises in retail,
finance & technology. A pioneer in enterprise cloud, big data and devops.
§ Known for transformative, mission-critical cloud solutions. We architected 2 of the
top 10 U.S. retail websites & have never had an outage during peak loads.
§ Founded in 2006, headquartered in Silicon Valley with offices throughout USA and
Eastern Europe. Profitable and growing at 30+% YOY.

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What if we discard all
marketing hype around
intelligent chatbots?

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The power of AI scares! Seriously?..
Is AI really so advanced that we should fear of it?
Actually no. The successor of Tay is the polite Zo (www.zo.ai). Try to chat to it.
Unfortunately, there is no that much capabilities in the AI world to power smart chatbots. Yet.

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Sequence-to-sequence model
One correct result forever
Translator to Chinese“What will be the weather today” 会有什么天气呢
Seq-to-seq chatbot“What will be the weather today” 9 C0
One correct result forever???
This is just an illustration how seq-2-seq technology works, real implementations are more complex, of course

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Hype Hype Hype
Virtual Assistants,
maturity in 5-10 years

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Virtual Assistant is…
A virtual assistant (VA) is a conversational, computer-generated character that
simulates a conversation to deliver voice- or text-based information to a user via a
Web, kiosk or mobile interface.
A VA incorporates natural-language processing, dialogue control, domain knowledge
and a visual appearance (such as photos or animation) that changes according to the
content and context of the dialogue.
The primary interaction methods are text-to-text, text-to-speech, speech-to-text and
speech-to-speech.
Source: www.gartner.com/it-glossary/virtual-assistant-va

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Practical refocus: AI-chatbots are CUI applications
§ Modern technologies can’t get to the sweet
point today. All solutions are compromise.
§ AI-chatbots focused more on Dialog control
leveraging ML and lack NLU
§ Practical CUI apps focused on NLU leveraging
ML and Domain Skills, lack Dialog control
AI-chatbot

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Why CUI? And why now?

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“Mobile first” is here, “AI first” is coming
Industry research shows customer spend 85% of their time on mobile devices using
just 5 applications.
“AI-first” era was announced by Google in this year. That means they will be developing the
mobile platform in the direction of catching/processing the majority of initial user requests
expressed in natural language.
Building mobile apps is a dead-end, since you have almost no chance to convince
people to use them after they are downloaded.
The next step will be dispatching those request to perform business tasks. Within a few
years, businesses which doesn’t have AI-based conversational capability will be at the end of
the line.

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Customer’s expectations
Customers want that!
Industry researches shows a great customer’s interest in possibility to contact business
chatbots in messengers because…
§ They’re 24/7
§ Instant answering

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The basis for the use case:
technologies must simplify life

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§ Mobile shopping via CUI-based IM
§ Product recommendations via CUI
§ Conversational search & browse
§ NLP-based home shopping (via Google Home)
Applications in Retail
§ Order management
§ Loyalty
§ FAQ
§ Helpdesk
Shopping assistant Customer service

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Use Case: conversational search
search search
filter1
filter2
filter3
prod1 prod2 prod3
… … …
… … …
Regular search
Even super-smart semantic search works like that

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search search
Use Case: conversational search
Dead-end — merchandizer’s nightmare
Out of stock or brand we don’t carry – many non technological reasons

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Use Case: conversational search
Problem: don’t have a product that customer searches
Solution: a substitute… but… Which one?
“Green cold shoulder mini dress”
is out of stock
But! We have
Green sleeveless mini dress Aqua cold shoulder mini dress
OR

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Use Case: conversational search
§ show all possible options
§ guess what option fits customer best, show it
Regular search engine approach:
§ ask the customer what she prefers
Conversational search engine way:

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Where does omni-channel
architecture fit in?

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As a matter of fact, customers do use many channels
Device Application Business
...And they expect seamless experience
User

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Omni-channel challenge
What if customer asks to show women’s running shoes
and we have… dozens…
In the web browser we show products – 30-40 product per page is ok.
But what we can do in a messenger window? In smart-speakers?

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High Level Architecture
of an omni-channel platform
for CUI applications

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High Level Architecture

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Conversational search

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Natural Language Understanding
In our case, we understand if we:
§ identify a task, that customer asks us to perform
§ extract all parameters, mentioned in the customer’s statement in NL
What does it mean – to understand?
NLU
Named Entity
Recognition
Part of speech
tag.
Semantic roles
labeling
Actions and
parameters
extraction
Whatever else
analysis
Typo correction
Colocations
finding
Action + parameters
User’s statement
in natural language
Many “boxes” require language model
Coreference
resolution

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Language model
A model describes all possible sentences of the language
There is no one-size-fit-all language model yet
“Shopping fashion” English?
NO
Plain English?
YES
Silver Jeans:
silver – metal, color??? Silver Jeans: brand

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Train: samples (+combination of features) +
Intent: product-search
Do you have white athletic fit dress shirt?
Natural Language Understanding – challenge
SaaS NLU
User typed: Do you have green cold shoulders mini dress?
color
silhouette
product
The language model to be provided to SaaS NLU might be pretty big and dynamic
Perfect expected result
Intent: product-search
green: color
cold shoulder: silhouette
mini dress: product
Taxonomy (products, colors, etc)
dress shirt
mini dress
…

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green: color
cold shoulders: silhouette
mini dress: product
Limited, static language model has to be provided to dialogflow.com, SaaS NLU
User typed: Do you have green cold shoulders mini dress?
dialogflow.com
Phase 1
coarse grained
understanding
Intent: product-search
search-terms:
green cold shoulders mini dress
Search engine
metadata
processing
Phase 2
fine grained
understanding
ResultPhase 1 result
Natural Language Understanding – solution

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Natural Language Generation
Bot’s language characteristics
§ pretty limited by cases of asking use case related questions or presenting results
§ might be somewhat “simplified” as people expect that from a robot
§ operates with concepts mentioned by user
Solution – set of templates that looks like this
Template Render
Search didn’t find exactly #search-terms
Would you like to see our selection of
{#available-options-1} or {#available-options-2}
Search didn’t find exactly green cold shoulder mini dress.
Would you like to see our selection of
{green mini dresses} or {cold shoulder mini dresses}
Note: brackets {green dresses} denote clickable options

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Omni-channel – challenge
Use Case: customer uses messenger, we found 30 products.
What we can to do now?
§ paginate by 7 product a page, show page by page with next / prev navigation
§ suggest customer a filter say by brand to narrow down the result set
Dialog manager shouldn’t care about every specific channel
This behavior will work for Facebook messenger, Kik, Telegram but won’t
for Web-agent integrated to a web-store, for smart speakers.

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Abstraction layer: chatbot channel capability profile
Facebook Messenger adapter
Kik adapter
WEB-agent adapter
UnitedAPIwithCCCP
Facebook
Messenger API
Kik API
WEB-agent API

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Chatbot Channel Capability Profile
# Capability name Value string
1 Product grid size 1x7
2 Integrated product grid Yes
3 Natural Language Generation profile Messenger
Note: example for the messenger channel
Profile for the conversational search use case

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Chatbot Channel Capability Profile – examples if use
MessengerWeb-agent embedded in the web-store

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Wrap up
Today we discussed:
§ practical chatbots – what chatbots can be valuable rather than just “for fun”
§ how chatbots can be build with AIaaS and why in-house chatbot platform is preferable
to relying entirely on AIaaS
§ how to efficiently implement Natural Language Understanding leveraging existing assets
such as domain knowledge residing in the search engine
§ an architectural approach to address omni-channel challenges

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Q & A time
Anton Ovchinnikov
linkedin.com/in/ovchinnikovanton
Read more in the company technical blog
blog.griddynamics.com
Thank you!