Neural networks, deep learning and analytics (And a demo with Edvard Munch’s The Scream)

NEURAL NETWORKS, DEEP LEARNING AND ANALYTICS(And a demo with Edvard Munch’s the scream)

WINTER IS COMING

• Industry is transforming more and more into analytics and cognitive driven model

• Significant reductions in human workforce will be achievable in near future

• We are entering into era where computer cognitive capabilities in specialized solutions exceed human---by significant margin

• Transformation from owning archive and other backend solutions into analytical and automated decision making is going to be quick…and harsh to those who face it unprepared

WORKFORCE REDUCTION

• It is expected that in developed countries 7 000 000 “white collar” workers will be replaced by AI solutions in next 3 years (World Economic Forum research)

• Pressure is very high in Nordics where labor costs are high, so we are expected to make substantially more savings faster than relatively cheaper southern European countries

• Insurance and banking/finance industries will be leading this change…but will impact on all industries with back office workers

HOW ARE WE GOING TO COPE WITH THIS CHANGE

• There are two significant quickly maturing technologies that will enable Elinar to drive this change together with our customers:

Convolutional neural networks and Hadoop

CONVOLUTIONAL NEURAL NETWORKS

• Enable computer AI to make human-like large scale decisions with extremely high accuracy

• ”Traditional” neural network technology had ”hit the wall” on capacity, all neurons were connected to each other

• Convolutional neural networks contain layers where within layer connections are many but like in human brain layers (brain regions) are interconnected with fewer connections making extremely large networks possible

• For specialized workloads computer AI can already exceed human cognitive skills

• For example you can use neural network to create optimum treatment plan for radiation therapy cancer patients

EXAMPLE: TEXT ANALYTICS USING CONVOLUTIONAL NEURAL NETWORK• Text is processed in phases:

Characters

Syllables

Words

Concepts

Meaning

Layer 1 Layer 2 Layer 3 Layer 4 Layer 5

Fraud

Customer

LoyaltyBusiness optimizati

onWord Vectors

WHY CONVOLUTIONAL NEURAL NETWORKS ARE EMERGING NOW?• Two major advances in industry

• Convolutional neural networks were invented just 5 years ago• Specialized hardware (GPU computing) from 3D acceleration

works extremely well on neural network acceleration as well• GPU computing enables us to dramatically shorten

time-to-value on neural network developement and deployment

• Traditional CPU: 4 – 20 cores / CPU -> GPU: 3096 cores• Neural network training scales extremely well on parallel

computing using GPU technology. In today’s demo time goes from 1,5 hours to < 5 minutes

HADOOP – LARGE SCALE ANALYTICS PLATFORM• Hadoop is interesting; it allows to implement LARGE

scale analytics solutions that go far beyond capabilities of traditional data warehouses

• Data lakes can ingest any kind of data, structured and unstructured

• All major IBM ECM solutions are now capable of using Hadoop as storage natively

• No more separate loading process to Hadoop. When content is created it is immediately stored into Hadoop for advanced analytics

• Elinar is now engaging Hadoop with significant investments on capabilities on hardware and competence

HADOOP AND COGNITIVE – A WINNING COMBINATION• When we combine analytical power of Hadoop with

latest cognitive technologies we will be able to:• Implement effective machine learning systems for faster

and more automated customer service• Provide more in depth understanding of customer needs• Create more and more self-service solutions• Find weak signals that will be important for decision

making much faster than competition• Data lake yields significantly more value from data• Data lake will provide analyzed data for more

advanced cognitive layer for high level of automation

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DEEP LEARNING AND ANALYTICS

Business Data

Customer Behavior

Open Data

Customer Rating

Archives

Social media

Business Systems

emails Hadoop – Data Lake

Deep Learnin

gCluster

Spark

Neural net

Traditional DW/BI

Business Processe

sDeep Decision

making

ProcessedInformatio

nResults

for traditional analytics

DEMO – THE SCREAM

• Demo is running on Elinar Neural Net Development Workstation using MXNet Neural Network Framework

• Utilizes layered neural network that has been teached using ~30 k paintings created by famous artists

• Analyzes ”model” – in this case The Scream by Edvard Munch for ”artistical patterns” that it has learned from teaching material

• Applies patterns found from model into photo (provided by you) in order to synthetize art

DEMO – THE SCREAM

Neural

network

30 000paintin

gs

Syntethic

painting

DEMO – PAINTING:30 ITERATIONS





DEMO – PAINTING:FINAL (CA 400 ITERATIONS – WITH MORE ITERATIONS MORE AND MORE DETAIL IS ACHIEVED)

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