A.I. & demystifying conversational agentsSetting expectations on the current and future state of affairs

Filip Maertens filip@faction.xyz @fmaertens

Hi, there. I am @fmaertens

Were accessible. Twitter @factionxyz or filip@faction.xyz

Faction XYZ as applied A.I. partner to Fortune 500 companies.

Building enterprise platform chatlayer.ai

Were hiring 10 ML people (NLP and computer vision) before summer.

Where are we with A.I. ?

Where do we come from ?

1950. Marvin Minsky

built first neural net

1960s. Alexey Ivakhnenko

first works on deep neural

networks

1986. Geoffrey Hinton

backpropagation algorithm

in its current form

2006. Geoffrey Hinton coins

deep learning

Larger datasets, GPU / multi-core processors, efficient training

Hard to train, low computational resources, small datasets

2012. Hinton on

computer vision

2011. Microsoft on

speech recognition

Whats the paradigm ?

Deep Belief Networks

Computer Vision

Audio Signal Processing

Natural Language (NLP)

How intelligent is artificial intelligence today ? 5 year old ?

So, where does this leave us ?

1. Chatbots are an old topic but a nascent business. Accidents ahead! Intelligence comes through solid integrations.

2. Deep learning continues to give results in computer vision, expands into other domains. Machine learning becomes a scarce commodity.

3. ML/DL expectations vs. reality can leave a bitter taste. Not a silver bullet. Controlled vs. real environments.

4. Forget about MLaaS. Too complex. Bridging the competence gap.5. AI doesnt mean anything when not applied. Vertical solutions is where the

money is.

So, lets bring some perspective on chatbots.

Conversational interfaces are an emerging channel. Or are they?

Command Line

MS Dos, Unix

Text Input

Native apps

Client side

GUI

Mac OS, MS Windows

Window based

Native apps

Client side

Web

Mosaic, Netscape

Hypertext

Web pages

Server side

Mobile

iOS, Android

Touch based

Native apps

Client side

Conversation

FB Messenger, Slack

Message based

Bots

Server side

Always useful ?

The many shapes and forms a chatbot can be brought into your life

Chat is here to stay ...

60 Billion

20 Billion

1 Hour

More than 60 billion messages per day onFacebook and Whatsapp.

LINE users exchange 20 billion messages per day.

55% of WeChat users spend over 1 hour per day on the service

Expectation

Reality

Many Oops ahead !

Maturity ?

Some things we learned while building chatbots.

Before we begin, lets get the semantics right. Some commonly used terms and definitions when dealing with a chatbot.

1. Intent. In simple terms, when a user interacts with a chatbot, what is his intention to use chatbot/what is he asking for.

2. Entities. An entity can be nominal, which means it's a common thing like fish or movie. Entities are extracted using Entity Extraction (a common theme in all NLP engines).

3. Named Entities. This entity is a proper noun, a name, such as Antwerp or Ermelinda. Semantic ambiguity can arise, which Entity Resolution resolves. Or hopes to.

4. Regex. A regular expression. This is codified manner to perform pattern matching on text. Its a very basic but efficient way of normalizing text, or match a predefined pattern or keyword.

5. Context. Maintain the Context and its state with all parameters received during the single Session in order

to get the required result to the user.

Type of chatbots

1. Three levels of conversation

a. Command & response

i. Stateless bots with some basic NLP (Wit, Luis, Watson, ...)

b. Hard coded conversation flows

i. Users navigate a flow chart defined by the developers / bot builders.

ii. The bots state corresponds directly to a particular block of code being executed.

c. Continuous stateful flows

i. Human conversations dont follow a template

ii. Hard-coding conversations as flow charts wont work forever

The logical components of a regular chatbot

UI AI SI

The UI of a chatbot is text, not graphics. The UX is tonality and

style, not buttons.

0

1Keep the scope and train a

chatbot narrow at first. Solve one use case, gain trust, then

expand.

Dont try to impersonate humans. The uncanny valley effect will make humans feel

cheated.

2

Getting stuck in more than three repetitive questions is going to

p*ss off the user. 40% drops off in first interaction, 20% more in a

second step

3

Any end to end flow you can do faster in an app or a website isnt

worth building a chatbot for.

4

You better have a damn good reason to ask more than five

questions.

5

Chatbots are just the presentation layer. NLP and

backend integration provide the real intelligence.

6

Be ready prepared to hand over to a human agent. Many

conditions apply (emotion, confidence score, timing,

manual, etc.)

7

Chatbots are just another channel next to web, mobile and

others. Treat it as such.

8

Sometimes you cant replace a human because users just want

to vent their anger. Its psychology, stupid.

9

Is there a business case ?

Example business cases: lowering the support cost

Example business cases: increasing revenue

What we learned through our enterprise platform ?

The Chatlayer.ai Functional Framework. Highlight of functional components that make up for an intelligent chatbot.

PresentationLayer

Language Processing Layer

Web (API) App (SDK) Facebook Skype Telegram WeChat

Flow Control & Business Logic

Intelligence & Profiling Layer

Sentiment Analysis Profile Classification Contextual Analysis

Natural Language Processing

Audience Analytics A/B Testing Module

Spell Checking & Translation Natural Language Generation

Business Logic Layer

Dialogue Management

REST API & Webhook IntegrationsRegular Expression Parsing Keywords And Aliases

Message Components Chat Emulator

Natural Language Context/Memory

Challenges with regards to understanding human language

Semantic understanding Search trees Bag of words Wordnet Word-embedding (word2vec)

Contextual understanding

Memory recall

Couple Word2Vec to a CNN for full contextual understanding, ignoring small errors and variances in wordings.

Implement a word-based LSTM to remember relevant, and forget irrelevant information

Some learnings on building an enterprise platform. Clients asked us ...

Manual overrides on NLP. Conditional flows.

Easy to build custom API integrations

Analytics. Analytics. Analytics.

English is OK. Dutch ? Arabic ? Urdu ?

Multi-tenant management system

Easy to use training and retraining

BYOL of third-party NLPs

Some learnings on building an enterprise platform. Clients asked us ...

Conversational Management Platform

Verticalization of business use

Context-aware chats

Capable of forgetting chats

Clients requested memory networks: situational understanding of text

Clients requested image captioning

But most client requests are moving into non-NLP or chatbot domains

A.I. & demystifying conversational agentsSetting expectations on the current and future state of affairs

Filip Maertens filip@faction.xyz @fmaertens