Date: 14th November 2018 Location: AI Lab Theatre Time: 15:50 - 16:20 Speaker: Patrice Neff Organisation: Squirro About: Machine learning and AI need huge amounts of data to train good algorithms. Even in today's Big Data landscape companies still struggle to get access to enough data to train systems. Squirro solves this problems in two ways: easy data access and Pragmatic AI. Squirro's pragmatic AI approach allows companies to very quickly gain value from their data, without having to spend weeks on training machine learning models.

1. Shaping an AI-Driven Future with
Augmented Intelligence for
Enterprises
Patrice Neff, Squirro – The Insights Company

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The Importance
of Data
Augmented
Intelligence

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WHO AM I?
Speaker’s Introduction
Technologist and entrepreneur with a passion for building user-
friendly applications. Also a father, runner, cyclist and language
nerd.
Patrice Neff
Founder

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MEET SQUIRRO
The Augmented Intelligence Solution
A leading AI-platform
A self-learning system keeping you in the
know and recommending what’s next
AI-driven Apps
Customer Insights
Financial Services Corporate Insurance Other industries
Service Insights
Support
Cognitive Search
Investors
Company
Augmented Intelligence solutions provider
Global presence in Europe, US and Asia
Partners
Global Network of over 20 certified partners
IB CIB AM CI REI
IT-
SM
CS
CI

5. Squirro Applications

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Bankruptcy Topic Detection Bankruptcy Events are defined as any event related to a company either going bankrupt or an update on
their bankruptcy proceedings
Debt Topic Detection Debt Events are defined as any event related to issuing bonds, loans, or any other debt instrument.
Divestiture Topic Detection Divestiture Events are defined as any event related to a company divesting a portion of its current
operations or a subsidiary
Earnings Topic Detection Earnings Events are defined as any event related to quarterly earnings releases, forecasts, or analyst
estimates about a companies financial performance
Equity Topic Detection Equity Events are defined as any event related to issuing stock, options, or any other equity instrument.
Expansion Topic Detection Expansion refers to the launch of a new business unit, opening a new location, or entering a new market.
IPO Topic Detection IPO Events are defined as any event related to Initial public offerings of stock in a company
M&A Topic Detection M&A Events are defined as any event related to a company merging with, or buying at least a portion of
another company.
Management Change Detection Management Changes are defined as any announcement of changes in employment or leadership at a
company, organization, or group.
Forward Interest Detection Statement about something in the future. Not speculation about things outside of the speaker's control.
Has to be linked to an action that the speaker or their group will take directly
Models

8. The Importance of (Training) Data

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PROGRESSION OF CLASSIFICATION MODELS
Input
data
Predict
Classify
Recommend
Trigger Automation
Engine action
Ad-hoc
search
From simpler to more complex methods
Naive
Bayes
SVM Random
Forest
Deep
Learning
Smart
Filters
Squirro Machine Learning Service
Ample data
Strong models
SquirroMLS
Enterprise learning cycle
Little data
No models
Models
Libraries
Custom
Models

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IMMEDIATE OR ACCURATE?
Bag of Words / Smart Filters
Less accurate long term
Faster learner
SVM, etc.
More accurate long term
Slower learner
Time
Accuracy
Time
Accuracy

11. More Data

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EXHIBIT 1
Unsupervised Word Sense Disambiguation Rivaling Supervised Methods, 1995
Plant
Photo Source: Wikipedia

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EXHIBIT 1
Unsupervised Word Sense Disambiguation Rivaling Supervised Methods, 1995
In this current work, the one-sense-per-
discourse hypothesis was tested on a
set of 37,232 examples (hand-tagged
over a period of 3 years)

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EXHIBIT 1
Unsupervised Word Sense Disambiguation Rivaling Supervised Methods, 1995
More impressively it achieves nearly the
same performance as the supervised
algorithm given identical training contexts
vs and in some cases actually achieves
superior performance.

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EXHIBIT 2
Scaling to Very Very Large Corpora for Natural Language Disambiguation, 2001
We collected a 1-billion-word training
corpus from a variety of English texts.
We show learning curves for each
learner.
The curves appear to be log-linear
even out to one billion words.

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EXHIBIT 3
Scene Completion Using Millions of Photographs, 2007
It takes a large amount of data for our method
to succeed. We saw dramatic improvement
when moving from ten thousand to two million
images. But two million is still a tiny fraction of
the high quality photographs available on sites
like Picasa or Flickr.

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EXHIBIT 4
Revisiting the Unreasonable Effectiveness of Data, 2017
Performance increases increases
logarithmically based on volume
of training data. We find there is
a logarithmic relationship
between performance on vision
tasks and the amount of training
data used for representation
learning.

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EXHIBIT 5
Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large
Corpora, 2018
We report evidence that the inferior quality quality of
German text data and its small size are the major
reasons for the observed lack of progress. To tackle
this problem, we use a larger corpus for training

19. Training effort

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IMMEDIATE OR ACCURATE?
Bag of Words / Smart Filters
Less accurate long term
Faster learner
SVM, etc.
More accurate long term
Slower learner
Time
Accuracy
Time
Accuracy

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FLEXIBLY CHOOSE THE BEST OPTION
Time
Accuracy

22. FROM SMART FILTERS…

23. … TO MODEL TRAINING

24. Augmented Intelligence
Pragmatic AI

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PRAGMATIC: APPLYING THE 80-20 RULE
Task
complexity
Model complexity
Science fiction
Trivial
80%
80%
The 80%
Zone

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PRAGMATIC AI IN REAL WORLD
Taskcomplexity
Model complexity
Science fiction
Artificial General
Intelligence
Lead recommendation
based on statistics
Lead recommendation
based on support vector
machine model
Chatbot with predefined
responses
Generic virtual assistant
(generated responses)
Automatically discover
main topics in a new
dataset
Self-driving car
Trivial

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HUMAN INTELLIGENCE
Excels at causation

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AUGMENTED INTELLIGENCE
Combine causation with correlation

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Pragmatic or
Practical AI
Implementing Artificial
Intelligence in ways that can be
easily deployed today, with real
data available inside an
enterprise

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SQUIRRO AUGMENTED INTELLIGENCE STACK
Vertical applications for rapid results
Cloud / On-Premise Multi-Instance / Scalable
Corporate
Financial
Services
All verticals All verticalsCorporate
Insurance
Other verticals
IB
CIB
REF
REI
CI IT Service
Management
Customer
Service
Management
SquirroAIPlatform
Admin / Security
IAM
Internal data sources External data sources
Enrich Identify Discover Predict Recommend Automate VisualizeLoad
Gather ActAnalyze
Other industries
Manufacturing
Customer Insights Product Suite Service Insights Product Suite Cognitive Search Product Suite
Enterprise
Search
Web Portal
Search
SquirroApplications

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Thank you!