Talking about Cognitive Computing Platforms with ���Barcelona GSE Master Data Science Students

Jordi Torres March- 2015

IS BIG DATA A TREND THAT IS HERE TO STAY OR IS IT ONLY A BUZZWORD?

Big Data: NEW ERA OF COMPUTING

SOCIAL NETWORKS

MOBILE TECHNOLOGY

INTERNET OF THINGS

CLOUD COMPUTING

NEW DATA CHALLENGES

Volume Velocity Variety Veracity

NEW MANAGEMENT ENVIRONMENTS ARE REQUIRED

e.g. NoSQL DB

e.g. MapReduce

Ongoing projects in Big Data at BSC

Big Data Application Monitoring

Design and implement a set of tools to monitor and to

trace network traffic for hadoop applications

Software Defined

Env.

Building cloud environments embracing

hardware and network heterogeneity to host a

variety of workloads (JSA-SDN)

Cloud Recom-

mendation

Enhance a decision support system providing guidance in the selection of the right set of Clouds analysing trade-off between cost, reliability, risks and quality impacts

Holistic Integration of Emerging Supercomputing Technologies ERC Starting Grant David Carrera

RM for Green Infrast.

Optimise placement of VM that exploit the usage of green

energy and the interaction with energy supply and cooling

systems

RM for Green

Software

RM for Green

Hardware

Optimise placement of VM with the aim for energy efficiency exploiting the ARM low-power architecture

Optimise placement of VM with the aim for energy efficiency

focusing on the interaction exchange during the whole

service layer

HI-EST project

Ongoing projects in Big Data at BSC

High Performance

Key/Value Stores

Explore the use of high performance key/value

databases for fast persistent memory technologies

Mngt. of Data

Streaming Environ.

Explore novel architectures of the emerging IoT stream processing platforms, that provide the capabilities of data stream composition, transformation and filtering in real time

Extending PyCompSs to NoSQL

DB Integration of COMPSs runtime with Cassandra and exploration of scheduling policies driven by data locality

NoSQL Data

Management Research

Design and implement a software layer to enable NoSQL databases to decouple data

organization from data model and provide NoSQL databases with efficient multi-level

indexing support

Improving Cost-effect. of Big Data

Deploy.

Develop mechanisms for an automated characterization of cost-effectiveness of Hadoop deployments (runtime performance vs software and hardware configuration choices)

BIG DATA IS ALREADY INFLUENCING OUR EVERYDAY LIVES

the technology is often so subtle that consumers have no idea that big data is actually helping make their lives easier

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Example: #OnlineShopping

database of around 250 million customers

Predicts what items you might want and sends it to your nearest delivery hub

Example: #Retail

Target made headlines in 2012 for correctly identifying a pregnant teenager before her family knew about her condition.

Identified 25 items that, when purchased in a particular order …

HOW DO THEY DO IT?

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•  feasible for companies to find interesting patterns hidden in data. •  predictive modeling  attempts to set up a model  to predict the

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Machine Learning Algorithms

ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed

instructions.

New algoritms:

•  the term itself dates from the 1950s. •  periods of hype and high

expectations alternating with periods of setback and disappointment.

Artificial Intelligence

plays an important

role

Computer Vision

Speech Recognition

Natural Language Processing

Artificial Intelligence: enabler of other technologies

http://cdn2.hubspot.net/hub/346378/file-529609748-png/blog-files/kcocco_twitter_data_google_prediction_api.png

Along the explosion of data …

New Algorithms

can be trained

Source: www.blogcdn.com/jobs.aol.com/articles/media/2011/08/statistics-getty.gif & cdni.wired.co.uk/620x413/d_f/dataastonishment.jpg

DATA SCIENTIST

the complexity of current big data platforms forces Data Scientist to waste part of their working time doing non-glamorous tasks such as data preparation, parameters tuning or selecting the most

suitable modeling method.

The goal is to automate predictive analysis

How?

and also preventing users and developers from wasting their time on tedious tasks related to data management and data processing.

Systems will have a new cognitive

abstraction layer in the software stack

offering learning tools, but at the same time,

abstracting lower layers to simplify the

big data software stack. 

“cognitive layer” will enhance the knowledge extraction loop with new features

Source: www.blogcdn.com/jobs.aol.com/articles/media/2011/08/statistics-getty.gif & cdni.wired.co.uk/620x413/d_f/dataastonishment.jpg

… behaving as a “Predictive Modeling Factory”

WILL THE RISE OF PREDICTIVE MODELING FACTORIES ELIMINATE THE NEED FOR

DATA SCIENTISTS?

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they could focus their effort on what really matters: figuring out what are the right questions to ask the predictive models

Source: http://www.muycomputerpro.com/2015/02/20/cinco-claves-para-ser-un-gran-data-scientist

NO !!!

WHY NOW?

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COMPUTING WAVES

The first wave of computing made numbers computable

The second wave has made

text and rich media computable and accessible digitally

We are in the next wave that will also make context computable

Systems that embed predictive capabilities, providing the right functionality and content at the right time, for the right application, by continuously learning about them and predicting what they will need.

 

computers that address complex situations are necessary

Source: http://21weeks.com/wp-content/uploads/2012/08/96376749.jpg

AMBIGUITY AND UNCERTAINTY

These new systems will raise the potential to augment our reasoning capabilities  

GIVING COMPUTERS A GREATER ABILITY TO UNDERSTAND INFORMATION, AND TO LEARN, TO

REASON, AND ACT UPON IT

instead of instructing a computer what to do, we are going to simply throw data at the problem and tell the computer to figure it out itself.

Source: http://financeandcareer.com/wp-content/uploads/2013/03/webProgrammingInternship.jpg

PROGRAMS DATA

WHY NOW?

#1: now algorithms can be “trained” by exposing them to large data sets that were

previously unavailable.

#2: the computing power necessary to implement these algorithms are now available

•  Multicore Technology

•  Increased network bandwidth

•  Flash Technology

next technological shift

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COGNITIVE COMPUTING *

(*) Others use Smart Computing, Intelligent Computing, …

its meaning is not clear yet…

DATA  

WE LABEL THIS NEW TYPE OF COMPUTING

Supercomputers  Research  

Big  Data  Technologies  

Advanced  Analy:c  

Algorithms  

•  to the continuous development of supercomputing systems

•  enabling the convergence of advanced analytic algorithms

•  and big data technologies

COGNITIVE COMPUTING IS ALREADY IN BUSINESS

IBM was able to double the precision of Watson’s answers in the few years leading up to its famous victory in the

quiz show Jeopardy. 

COGNITIVE COMPUTING IS ALREADY IN BUSINESS

Just closed on $12.5 M in venture capital funding.

Booking a flights …

“I want a flight to MWC Barcelona with a return five days later

via London.”

“COGNITIVE COMPUTING” RESEARCH IN BARCELONA

HTTP://www.bsc.es/autonomic  

Ongoing projects

Probabilistic Ggraphical

Models

Deep Learning

Multimodal Big Data Analytics

Scalable Indexing

of Image/Video

to enhance neural network algorithms using high performance computing platforms to make them more suitable to be used in tasks which involve unstructured types of data and unlabeled datasets.

1. DEEP LEARNING

II. PGM & BAYESIAN INFERENCE

Explores an alternative methodology for applying ML, using high performance computing platforms, in which a bespoke solution is formulated for each application, based on PGM and Bayesian inference for RT applications.

Case study: user as a sensor (social and sensor networks)

for data-driven city management

III. REAL-TIME SCALABLE INDEXING OF IMAGE/VIDEO

Using high performance computing platforms for distributed real-time process of indexing and searching near replica detection over massive streams of shared photos (visual spam detection, copyright infringement)

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PATCH 1

PATCH 2

PATCH 3

PATCH 4

KP1

KP2

KP3

KP4

feature    detec*on  

feature    descrip*on  

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IV. MULTIMODAL BIG DATA ANALYTICS

social network

relationships

audiovisual content metadata

Three types of data  

Analyzing the content  

object  recogni:on      

clustering  

near  replica  detec:on  

face  detec:on+  verificacion  smage  style  recongiton  

scene  undrestanding  

concept  annota:on  

WHAT CAN WE LEARN FROM THE PHOTOS?

(1) THE WORLD OBSERVED

WHAT CAN WE LEARN FROM THE PHOTOS?

(2) THE WORLD

WHAT CAN WE LEARN FROM THE PHOTOS?

(1) THE OBSERVER

Case Study: DESIGUAL

2 DATASETS #desigual #lavidaeschula

#mydesigual followers

30.000 photos 100 photos x 2K followers = 200K Photos (100 GB)

Case Study: DESIGUAL

•  Desigual followers characterization •  multimodal classification (SVM, logistic regression)

desigual  followers  &  taggers  (#mydesigual,  #lavidaeschula)  

Case Study: DESIGUAL

•  Recommendation à collaborative filtering •  Multimodal image clustering (k-means) •  Alternate least square

Master project at BSC?

CATWALK: Social Media Image Analysis for Fashion Industry Market Research ���

Multimedia Big Data Computing platform that operates over freely available online images from sources such as Instagram or Twitter.

CATWALK: Social Media Image Analysis for Fashion Industry Market Research

•  The platform includes tools for the continuous analysis of the harvested images, generating a range of social and behavioral metrics in real-time.

•  Current Research: high performance image analysis and pattern matching algorithms over a distributed and scalable stream processing framework.

•  Proof of concept: carried out with Spark over MareNostrum supercomputer.

•  Also: Market research, IPR prot. & Business Plan

CONCLUSIONS ? So

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•  We are entering into the NEXTS COMPUTING WARE that will make context computable.

•  COGNITIVE COMPUTING Systems will be the next generation of Big Data Platforms.

•  Systems will have a NEW COGNITIVE ABSTACTION LAYER in the software stack.

•  “Cognitive layer” will enhance the knowledge extraction loop with new features behaving as a PREDICTIVE MODELING FACTORY.

•  DATA SCIENTISTS could focus their effort on what really matters: figuring out what are the right questions to ask the predictive models.

Thank you for your attention! JORDI TORRES @JordiTorresBCN

www.JordiTorres.eu

#CognitiveComputing & #Barcelona