filip maertens - ai, machine learning and chatbots: think ai-first
TRANSCRIPT
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