AI, Machine Learning en Chatbots:

Think AI-first Filip Maertens (Founder, faction.xyz)

Twitter: @fmaertensLinkedIn: https://www.linkedin.com/in/fmaertens/

Presented at “The Future of IT” - Organised by @itworks on the 20th of September 2017 in Parker Hotel Brussels Airport, Belgium

AI, Machine Learning, and chatbots: an AI-First approach

Seminar “The Future of IT” by ITWorks

• Learningistheprocessofimproving withexperience atsometask

• Improving overtask,T

• Withrespecttoperformancemeasure,P

• Basedonexperience, E

Learning how to filter spam

T =IdentifyspamemailsP =%offilteredspamemailsvs%offilteredhamemailsE =adatabaseofemailsthatwerelabelledbyusers/experts

the principles of learning

Deep Belief Networks

Computer Vision

Audio Signal Processing

Natural Language (NLP)

many domains in the field of A.I.

5 year old ?

the age of A.I. ?

Sensors, cameras, databases, etc.

Measuring devices

Noise filtering, Feature Extraction,

Normalization

Preprocessing

Feature selection, feature projection

Dimensionality reduction

Classification, regression, clustering, description

Model learning

Cross validation, bootstrap

Model testing

PSupervised UnsupervisedVS

Target / outcome is knownI know how to classify this data, I just

need you(the classifier) to do it.

Target / outcome is unknownI have no idea how to classify this data,

can you(the algorithm) create a classifier for me?

ReinforcementVS

Classification & outcome is unknownI have no idea how to classify this data, can you classify this data and I'll give you a reward if it's

correct or I'll punish you if it's not.

machine learning, the basics

unsupervised deep learning

Two sides to the data story

Declared

Observed

ContentStructured, explicit, self-declared, and static

ContextUnstructured, time-series,

observed, and dynamic

“ don’t worry. we have lots of data! “

Data can be unlabeled

Data usually is dirty

Data is sometimes not

relevant

Over 80% of data is not, wrong or insufficiently

labeled

Resolutions, sampling rates,

special characters, hidden values, NULL values, …

Sometimes the data is simply not

fit for purpose!

I don’t need a lot of data. I need good data.

“ … but I also need enough data! “

UNDERFITTINGUsing an algorithm that cannot capture the full complexity of the data

“ … and data should also be diverse enough! “

OVERFITTINGTuning the algorithm so carefully it starts matching the noise in the training data

“ training vs test data “

20%Testdata

80%Trainingdata

TESTING IS A HUGE FIELD

intelligent process automation

data fusion & predictive maintenance on carsEnablementofnewbusiness,worthUS$1.1billion(ofUS$31billion)overnext5years

prediction on ocean to coast currentsWediditforecologicalreasons.Betterpredictions,meanbettercareofourcoastalregionsandhumans.Oh,andsurfing!

automating 50% of a support centerSavingsalready75%overtarget.Bonuspointsbecausesupportagentscannowdobetterwork

Natural language understanding

Natural language generation

Voice and text

Profiling and analytics

automated damage classificationSavingalready1million/year(estimatedtoincreasesavingstenfoldovernextfiveyears)

early cancer detection on ct imagesSurpassingefficiencyandaccuracyofradiospecialistsinthenextfewmonths

Artificial Intelligence � Affective Computing

Rethinking the ambient intelligence paradigma pervasive computing principle that is sensitive and responsive

Technical challenges

Battery and power consumption

Distributed & Edge Computing

On-Chip classifiers

A.I. on time series data (Reservoir, LSM, DL)

Homomorphic cryptography (Privacy)

Pervasive data collection and storage

Experiential challenges

Acceptance of pervasiveness

Social and psychological elements in engineering serendipity

Privacy (GDPR) and Ethics

Morality Systems

Decision-support vs. Autonomous systems

GDPR: When laws clash with machine learning

Right to be forgotten Right to explanation

Automated individual decision making

Hard to explain. How can decisions (predictions) be explained, when they

are the result of complex neural networks, which are black boxes ?

a final thought before we part…

zooming in on chatbots

Difficult to ignore the conversational opportunityWith billions of users exchanging messages and interacting with each other over messaging platforms, a business can no longer ignore the potential and opportunity of getting hands-on with “chat bots”.

Over 90% understandingTechnology maturity

New and improved methods for natural language understanding have produced unprecedented levels of accuracy in understanding and dealing with natural language.

Channel maturity

With over 1 billion users, exchanging over 60 billion messages per day on Facebook and WhatsApp, and spending over 1 hour per day on messaging platforms,

Over 60 billion messages / day

A brief history of conversational agents

Personal assistants, virtual agents, chat bots or conversational agents. However you want to call this technology, they all hint for the need for humans to interact with machines in a more natural and frictionless manner when dealing with complex interactions.

1966, ELIZA by MIT AI Labs

1972, PARRY by Stanford University

1988, Jabberwacky by Rollo Carpenter

1992, Dr. Sbaitso by Creative Technology

1995, ALICE by Richard Wallace

2006, Watson by IBM2008, Siri by Apple

2012, Google Now by Google

2015, Alexa by Amazon

2015, Cortana by Microsoft

1950 Alan Turing on Computing Machinery and Intelligence

1957 Noam Chomsky on Syntactic Structures

1969 Roger Schank on conceptual dependency theory for NLU

1970 William Woods on augmented transition networks

1990s General use of machine learning boosts NLP methods

> 2006 Use of deep learning, increased CPU and data

Building the frictionlesscustomer experience

A seamless user experience between machine and human is the general objective for any company that is using technology to scale their business or deliver a competitive service to their constituents.

While mobile has trumped web in terms of usability by using tactile interfaces, conversational interfaces might trump mobile by using natural language.

The evolution of shrinking interfaces

Size of a roomMainframe

Fits in your handSmartphone

Fits in a bagDesk & Laptops

Fits on your wristWearables

Pervasive interfacesInvisibles

The types of conversational interfaces

DedicatedMessaging

Voice HUBsAppliances

IntegratedSmartphone

Existing ChannelsTraditional

The conversational channel strategy

The types of conversations

AGENT

Genesys, etc.

SOCIAL

SparkCentral, etc.

INTELLIGENT

Chatlayer, etc.

One to one manual conversations between

user and agent

Supporting users through social

channels

Using A.I. to automate

conversations

The support business caseLowering the support cost through natural language processing (NLP) and automating the

conversation, so that the bulk of the load is handled by automated and intelligent platforms. Built on ROI. Reach an ROI in less than a year (*), making a positive business case.

The user experience & brand caseIncrease brand visibility and proximity through new and innovative conversational user

experiences. Reduce churn, increase conversions or raise brand awareness. Built on vision.

AI, Machine Learning, and chatbots: an AI-First approach

Seminar “The Future of IT” by ITWorks