A presentation to the tech engineers on how Big Data and AI are impacting the world and how to prepare for it.

1. The$wonders$of$Big$Data$
processing$using$DL
SK#Reddy
Chief#Product#Officer#AI
skreddy99
skreddy99

2. Confidential2
Keepers0jump0toward0the0kicker’s0
left0corner057%0of0the0time,0and0to0
the0right0only041%
“Think0Like0a0Freak.”:0Steven0D.0Levitt0&0Stephen0J.0Dubner.0(2314)0
Can$you$think$bold?

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4. Confidential4https://www.linkedin.com/pulse/crystal9ball9emerging9technologies9s9k9reddy/

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https://www.capgemini.com/wp6content/uploads/2017/09/dti6ai6report_final1.pdf
Key$technologies$classified$as$AI

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https://www.theverge.com/a/luka:artificial:intelligence:memorial:roman:mazurenko:bot
Chatbots=that=responds=like=your=loved=one….
https://mental.jmir.org/article/viewFile/mental_v4i2e19/2
Chatbots=could=be=your=therapist….

7. Confidential7
Consumer0credit0is0one0of0these0areas,0says0Graubner7Müller,0who0cofounded0Kreditech0in02012.0There0are0two0classes0of0consumer0credit,0he0says0– people0with0no0
access0to0credit0and0people0with0access0to0credit.0One0is0middle0class,0well7employed0and0has0a0strong0credit0history.0The0other has0more0flexible0employment0situations,0
for0example,0and0is0underserved0by0traditional0financial0institutions.
Banks0are0not0able0to0cope0with0the0risk0of0these0customers0– in0fact,0in0Kreditech’s0native0Germany,0around0400per0cent0of0customers0are0considered0to0be0
“underbanked”.0“This0was0a0giant0market0opportunity,”0says0Graubner7Müller.
They0use0an0additional020,0000data0points0to0assess
AI#discovers#40%#customers#that#are#
under5banked#in#Germany
http://www.wired.co.uk/article/ai7revolution7alexander7graubner7muller7kreditech

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INTERPRETABLE*COUNTING*
FOR*VISUAL*QUESTION*ANSWERING
https://arxiv.org/pdf/1712.08697.pdf

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Microsoft0researchers0build0
a"bot"that"draws"what"you"tell"it"to
https://arxiv.org/pdf/1711.10485.pdf

10. Confidential10 http://aiweirdness.com/archive
AI:can:help:you:with:
Trendy:beer:names
New:paint:colors:and:paint:names
Design:Halloween:costumes
New:Disney:songs
Harry:Potter:spells
Pub:names
Diseases:you:wont:get
Kitchen:recipes
Star:War:characters
Star:War:plane:names
Broadway:musicals
Music:band:names
Fortune:cookies
Cocktails
Superheros

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AI#is#everywhere
By.8am.you.have.used.AI.at.least.10.times
• Read.your.News
• Checked.the.Weather
• Looked.at.interesting.tickers.in.Stock.market
• Checked.for.traffic.and.got.to.know.the.new.suggested.route
• Responded.to.a.trending.topic.on.your.social.media
• Listening.to.the.new.popular.music.on.your.way.to.work
• Searched.for.the.best.gift.to.your.mom.on.Mother’s.day

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Fowl%Language - AI0Decodes0the0Nuances0of0Chicken0“Speech”
https://www.scientificamerican.com/article/fowl-language-ai-decodes-the-nuances-of-chicken-ldquo-speech-rdquo/

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http://www.aiindex.org/#report
AI#Index#Report#2017#(One9Hundred9Year9Study9on9Artificial9Intelligence9(AI100)9at9Stanford9University)

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https://dupress.deloitte.com/dup7us7en/focus/industry7470/using7predictive7technologies7for7asset7maintenance.html
Predictive=Maintenance=Process

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• Global0anomalies
• Local0anomalies
• Micro0clusters
Basics&of&anomaly&detection

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Predictive1analyses1algos
https://d.dam.sap.com/a/xOXXb/50764_GB_46588_en.pdf

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Recommendations,for,
Unsupervised,anomaly,detection,algorithm,selection,
• Local0Outlier0Factor0(LOF)0
• Connectivity9Based0Outlier0Factor0(COF)0
• Influenced0Outlierness (INFLO)0
• Local0Outlier0Probability0(LoOP)0
• Local0Correlation0Integral0(LOCI)0
• Approximate0Local0Correlation0Integral0(aLOCI)0
• Cluster9Based0Local0Outlier0Factor0(CBLOF/0uCBLOF)0
• Local0Density0Cluster9based0Outlier0Factor0(LDCOF)0
• Clustering9based0Multivariate0Gaussian0Outlier0Score0
(CMGOS)0
• Histogram9based0Outlier0Score0(HBOS)0
• One9Class0Support0Vector0Machine0
• Robust0Principal0Component0Analysis0(rPCA)0
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152173

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Data.Science.and.Machine.Learning.in.the.Internet.of.Things.and.Predictive.Maintenance;.https://d.dam.sap.com/a/xOXXb/50764_GB_46588_en.pdf
Classification+of+Data+Science+Analyses

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15.Million.Battery.Voltages.and.Current
Example:.Anomaly.Detection.of.Sensor.Data.Using.
Distance>Based.Failure.Analysis
Sensor.data.from.the.same.15m.Batteries
Can*you*find*the*anomaly?

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Popular(ML(frameworks
• Tensorflow,3Tensorflow Lite,3TF3Learn,3tf.layers,3
TensorLayer,3TF3Slim,3tf.contrib.learn,3Pretty3Tensor,3
Sonnet
• SageMaker
• CNTK32.0
– Similar3to3TF,3design3ML3solutions3using3a3Directed3
GraphH3FasterH3provides3Java3APIs
• MS3Distributed3Machine3Learning3Toolkit3(DMTK)
– Few3algosH33
• Caffe2
– Vision3problemsH3in3C++H3uses3CUDAH3
• H2O
– Dev3environments3(Python,3Java,3Scala,3R)H3Big3Data3
Systems3(Hadoop,3Spark),3Data3Sources3(HDFS,3S3,3
SQL,3NoSQLH3meant3for3enXtoXend3solutionsH3
• MS3Azure3ML3Studio
• Spark3Mllib
• MXNet
• Theano
• Deep3Learning4j
• ..and3more….
• Keras
• Paddle3(by3Baidu)
• Apache3Mahout
– Algos can3run3on3spark3distributed3clusterH3supports3
CPU3&3GPU
• Veles
– C++3and3PythonH3Distributed3ML3platform3like3TF3and3
DMTKH3employs3flowXbased3programmingH3can3be3
done3using3iPython or3Python3NotebookH3Open3
sourced3by3Samsung3Russia3in32015
• MLpack2
– C++
• PyTorch
• Neon:
– From3Intel3(Nervana)H3Python3and3C++H3a3few3preX
trained3models3in3LSTM,3AlexNet,3GoogleNet,3etc.
• Marvin:3
– Minimal3code,3in3C++3and3relies3on3CUDA3GPUH3
Multiple3preXtrained3models
• Apache3Singa

21. Confidential21 Ref:/https://www.kaggle.com/surveys/2017
What/tools/are/used/at/work?

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Thank&you
skreddy99
skreddy99

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