Francisco Webber is the CEO and Founder of Cortical.io, a company that develops Natural Language Processing solutions for Big Text Data. Francisco’s medical background in genetics combined with over two decade’s of experience in Information Technology, inspired him to create a groundbreaking technology, called Semantic Folding, which is based on the latest findings on the way the human neocortex processes information.

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Semantic Folding
co-Founder and General Manager
Francisco De Sousa Webber
Language Intelligence made easy
f.webber@cortical.io

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What is Cortical.io ?
We explore & expand Semantic Folding Theory
We spread & sell Semantic Folding Technology
We build & grow Cortical.io as the “Oracle” for
Semantic Processing

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NLP Market Problem
• All systems are based on statistics - low differentiability
• Hard to build - high level of expertise needed
• Inaccurate compared to humans - low precision
• Have complex tuning procedures - hard to deploy
• Slow and inefficient compared to humans - hard to scale
Natural Language Processing Technology:
• Human metadata management - for differentiability
• Human specialists - for expertise
• Human correction - for precision
• Human generated gold-standards - for tuning
Weakness Compensated with:
Business-NLP is currently very expensive.

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Solution:
Language Intelligence
• By Jeff Hawkins (Silicon Valley, California)
• numenta.com technical implementation & IP
• Processing algorithm of the human brain (neo-cortex)
Hierarchical Temporal Memory Theory
• By Francisco De Sousa Webber (Vienna, Austria)
• cortical.io technical implementation & IP
• Processing language-data like the human brain
Semantic Folding Theory
+

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Cortical Constraints
• Neocortex is a 2D sheet of repeating Modular Assemblies of neurons with
binary inputs.
• Neocortex is a Memory System not a processor.
• Neocortex stores Pattern Sequences.
• Neocortex is an Online Learning system
• Neocortex is only Trained by Exposure to real-world data
• All data fed into the neocortex must have Sparse Distributed Representation
(SDR) format:
• SDRs are very long Binary Vectors with max. 2% of “1”.
• Every SDR-bit is a self-contained Semantic Feature of the world (via sensorial input).
• Every SDR-bit is an Explicit Part of the signal.
• Similar “things” have similar SDRs.
• The Union of SDRs maintains all information of its members.

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Virtual Word Layer
hear see touch
word (SDR)
…..Wordsensorstream
Wordproductionstream
Symbolinput
Muscles
Motor output
Symboloutput
Virtualization into Retina

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Offline Process
RetinaDB Generation
Retina Training defines the Semantic Universe.
Training Collection specifies all vocabulary, linguistic properties and knowledge.
The Semantic Folding Engine generates a Semantic Map.
Every utterance is positioned within the Semantic Space.
Every term is defined by its distributed selection of utterances/contexts.
A topographic bit-vector is generated for each term of the corpus.
Training
Collection
Preprocessin
Semantic
Folding
Engine
Fingerprint Generation

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Retina-API Operation
The generated topographic bit-vectors are called Semantic Fingerprints
The Semantic Fingerprints are stored in the highly performant RetinaDB
The RetinaDB is a complete Language Model
The Retina-API provided functions: convert, compare, dissect, classify and extract text
The user application interacts via a REST Interface
Functions out
Fingerprint out
Compare out
RetinaDB
Retina API
User
Application
REST call
Online ProcessOffline Process
Training
Collection
Preprocessin
Semantic
Folding
Engine
Fingerprint Generation

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Tuning The Retina
“cholecystitis”

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Aligning Semantic Spaces
philosophy philosophie filosofía философия ‫ﻓﻠﺳﻔﺔ‬
Concepts and their representations are stable
across languages.
EN FR ES RU AR ZH

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The Cortical.io
Retina Technology …
… converts any text into
a semantic fingerprint.
teens like playing good
music with their mobile
phones
Fingerprint Generation

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organ
Step 1: Word Fingerprints
piano
church
liver

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aggregation
+
sparsification
Step 2: Text Fingerprints
teens like to hear music on their mobile phones
teens like to hear
music on their mobile
phones

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Similar meanings …
… look similar
37% overlap
teens like using itunes on their iphone he consumes chart hits on his notebook

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Different meanings …
… look different
the fishermen
are sailing out of
5% overlap
teens like using itunes on their iphone the fishermen are sailing out of the harbor

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Evaluation
There are very few comparable algorithms: a couple
of academic ones that cannot be readily used for
production purposes and Google’s Word2Vec.
The MEN Test Collection: http://clic.cimec.unitn.it/~elia.bruni/MEN.html
The RG-65 Test Collection: http://www.aclweb.org/aclwiki/index.php?title=RG-65_Test_Collection_(State_of_the_art)
The WordSimilarity-353 Test Collection: http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/
Yu&Dredzde 2014: http://arxiv.org/pdf/1411.4166.pdf
Distributed representations of words and phrases: http://papers.nips.cc/paper/5021-di
MEN-3K RG-65 WS-353
word2vec (Google) 55,2 44,8 54,7
Yu&Drezde (2014) 50,1 47,1 53,7
cortical.io Retina 67,4 71,3 62,2
% better word2vec 18,1 37,2 12,1
% better Yu&Drezde 25,7 33,9 13,7

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Semantic Folding Products
• Document Retrieval
• Expert Finding
• Knowledge Management
Enterprise Semantic Search
• Semantic Streaming Text Filter
• (Social) Media Monitoring
• Business Intelligence & Analytics
Big Text Data
• Natural Language based Automation
• Content Personalisation
• Semantic Profiling
Semantic Matching
similarity engine
example
document
most similar
documents
ordered along
the users
information need
query document index result set ranking
#finance
#markets
#mobile
#movies
#products
#trend Topic of interest
Analytics
Match
Making
EnterpriseApplicationWebApp

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Cloudera Integration

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Semantic Search
similarity engineexample
document
most similar
documents
ordered along
the users
information need
query document index result set ranking

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Semantic Content Filter
real-time, across languages, intelligent, meaning based
#finance
#markets
#mobile
#movies
#products
#trend
Topic of interest
Analytics

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Example: Twitter Filter
The State of the Art
desired topic
Every tweet related to
smart phones
200 catch words
mobile phone
Iphone
cell
Android
…
sim-card
text message
network
Verizon
Apple
Google
…
5 words per tweet
Required throughput
for one filter
200 X 5 X 20,000 = 20,000,000 comparisons per second
20,000 tweets/sec

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The State of the Art
Cost per Filter: $ 10,000+ per Month

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Example: Twitter Filter
Semantic Fingerprinting
stream of
semantic fingerprints
twitter firehose
realtime
content sub-stream
Filter
Fingerprint
not
matching
matchmatchmatch

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Cost per Filter: $ 10 per Month
Cortical.io
Streaming Text Filter
convert 100.000+
tweets per second
1.000+ semantic filters
+
one per firehose scalable with number of
Filters

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Dynamic Topic Pattern Analysis
Topic Monitoring
Unseen topics or sudden topic jumps are detected
Compliance Monitoring
Ongoing e-mail conversation Time >
Appearance of unseen
topic clusters

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Similar meanings “look” similar
Special “Financial Retina”
Bridging the Vocabulary Gap
fraud
Words
corruption AND mafia
Expressions
“anti human trafficking”
Idioms
Money laundering is the process
of transforming the proceeds of
crime into ostensibly legitimate
money or other assets.
Text

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Combine Fingerprints with AI Algorithms
Text Anomaly Detection
7. Enabling Artificial Intelligence Applications
email
chat
Message Forums
Blog Posts
Facebook Posts
Realtime Anomaly Detection in Text Streams
any Text Stream

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Combine Fingerprints with AI Algorithms
http://www.cortical.io/demos/semantic-anomaly-detection/

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website: cortical.io
product: https://aws.amazon.com/marketplace/pp/B00T5794P6/
twitter: http://twitter.com/cortical_io
video: https://www.youtube.com/watch?v=g3ZxJokDpds
demos: http://www.cortical.io/demos.html
API: http://api.cortical.io
Numenta: http://numenta.com