Filip Maertens presented this "AI, Machine Learning and Chatbots" at the "Future of IT" seminar on 20th of September 2017 in Brussels. Twitter: @fmaertens Email: filip@faction.xyz

1.
AI, Machine Learning en Chatbots:
Think AI-first
Filip Maertens (Founder, faction.xyz)
Twitter: @fmaertens
LinkedIn: 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

2. AI, Machine Learning, and
chatbots: an AI-First approach
Seminar “The Future of IT” by ITWorks

3. • Learning is the process of improving with experience at some task
• Improving over task, T
• With respect to performance measure, P
• Based on experience, E
Learning how to filter spam
T = Identify spam emails
P = % of filtered spam emails vs % of filtered ham emails
E = a database of emails that were labelled by users/experts
the principles of learning

4. Deep Belief Networks
Computer Vision
Audio Signal Processing
Natural Language (NLP)
many domains in the field of A.I.

5. 5 year old ?
the age of A.I. ?

6. 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
P
Supervised UnsupervisedVS
Target / outcome is known
I know how to classify this data, I just
need you(the classifier) to do it.
Target / outcome is unknown
I have no idea how to classify this data,
can you(the algorithm) create a
classifier for me?
ReinforcementVS
Classification & outcome is unknown
I 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

7. unsupervised deep learning

8. Two sides to the data story
Declared
Observed
Content
Structured, explicit,
self-declared, and static
Context
Unstructured, time-series,
observed, and dynamic

9. “ 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.

10. “ … but I also need enough data! “
UNDERFITTING
Using an algorithm that cannot capture the full complexity of the data

11. “ … and data should also be diverse enough! “
OVERFITTING
Tuning the algorithm so carefully it starts matching the noise in the training data

12. “ training vs test data “
20%
Test data
80%
Training data
TESTING IS A
HUGE FIELD

13. intelligent process automation

14. data fusion & predictive maintenance on cars
Enablement of new business, worth US$ 1.1 billion (of US$ 31 billion) over next 5 years

15. prediction on ocean to coast currents
We did it for ecological reasons. Better predictions, mean better care of our coastal regions and humans. Oh, and surfing!

16. automating 50% of a support center
Savings already 75% over target. Bonus points because support agents can now do better work
Natural language understanding
Natural language generation
Voice and text
Profiling and analytics

17. automated damage classification
Saving already 1 million / year (estimated to increase savings tenfold over next five years)

18. early cancer detection on ct images
Surpassing efficiency and accuracy of radio specialists in the next few months

19. Artificial Intelligence Ÿ Affective Computing
Rethinking the ambient intelligence paradigm
a pervasive computing principle that is sensitive and responsive

20. 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

21. Experiential challenges
Acceptance of pervasiveness
Social and psychological elements in
engineering serendipity
Privacy (GDPR) and Ethics
Morality Systems
Decision-support vs. Autonomous
systems

22. 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 ?

23. a final thought before we part…

24. zooming in on chatbots

25. Difficult to ignore the
conversational
opportunity
With 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”.

27. Over 90% understanding
Technology 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

29. 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.

30. 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

31. Building the frictionless
customer 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.

32. The evolution of shrinking interfaces
Size of a room
Mainframe
Fits in your hand
Smartphone
Fits in a bag
Desk & Laptops
Fits on your wrist
Wearables
Pervasive interfaces
Invisibles

33. The types of conversational interfaces
Dedicated
Messaging
Voice HUBs
Appliances
Integrated
Smartphone
Existing Channels
Traditional

34. The conversational channel strategy

35. 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

36. The support business case
Lowering 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.

37. The user experience & brand case
Increase brand visibility and proximity through new and innovative conversational user
experiences. Reduce churn, increase conversions or raise brand awareness. Built on vision.

38. AI, Machine Learning, and
chatbots: an AI-First approach
Seminar “The Future of IT” by ITWorks