Buenos Aires31 de octubre de 2018

The dawn

of a new eraAI, machine & deep learning

Rachid Chair

@RChair

Sr. Systems Engineer, DellEMC

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NEW NAVY DEVICE LEARNS BY DOING

July 8, 1958“The Navy revealed the embryo of an electronic

computer today that it expects will be able to walk,

talk, see, write, reproduce itself and be conscious

of its existence… Dr. Frank Rosenblatt, a research

psychologist at the Cornell Aeronautical

Laboratory, Buffalo, said Perceptron's might be

fired to the planets as mechanical space

explorers”Arthur L. Samuel, Machine learning pioneer:

In 1953 at MIT he created a revolutionary

learning algorithm which could beat the local

state champion at Checkers. He did this on

IBM’s first commercial computer the 701.

What goes around, comes aroundMachine learning.. As old as computers themselves

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1950 1960 1970 1980 1990 2000 2010 2020

1950 1960 1970 1980 1990 2000 2010 2020

AI ‘Winters’

What makes this time any different?

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1950 1960 1970 1980 1990 2000 2010 2020

1950 1960 1970 1980 1990 2000 2010 2020

Cost of compute

Amount of data

Inverse conditionsenable the possibilities

Timing is everything …What makes this time any different?

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1. Remember, computers don't “hear” or “see” anything, They

just “see” numbers!

2. Deep learning is really just mathematical “pattern recognition”

3. Pattern recognition in data is all about making statistically

accurate predictions based on algorithms

4. Thus, Deep Learning is an evolution of predictive data

analytics!

5. Deep Learning is used when you need to look for patterns in

data that are so complex the algorithms required would be

almost impossible to tune and figure out manually…. so the

machine does it for you!... it experiments until it gets it right!

6. Deep Learning is when the predictive algorithm “tunes” itself in

for greater & greater accuracy!

One more thing - “Machine learning” is a less fancy kind of “deep learning”…

way less complex but the idea of “self-tuning” remains.

0,0,0

247,199,161

15,4,2

255,255,255

The cat picture is comprised of pixels…

each pixel displaying 8-bit values between

0 and 255 of red, green and blue light

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Object (pattern) recognitionAn introduction to supervised learning

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Prediction machine / Self tuning (aka Learning algorithm)

100’000+ nobs & dials to adjust!

Prediction output

(Trained Model)

Organized & Labeled Data-set

DL is when the algorithm “Learns” by labelsIt tunes itself by rewarding & punishing itself

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6.JPG

28x 28 pixels

Back Propagation

“Punishing” or “rewarding”

the weights & bias’s |

Hyper-parameters”

(AKA making adjustments)

Hidden Layers

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Contrast

Proximity Clusters

Edges

Free Open source “Learning Machines”

(Aka Deep learning Models

Convolutional neural network)

Under the hood with Deep learningMore information than is required | Exceeding Human performance!

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Solve ancient riddles….Is Leonardo da Vinci’s Mona Lisa smiling or not?

11https://www.graphcore.ai/posts/graph-computing-for-machine-intelligence-with-poplar

Modeling an AIVisual graph of complex neural networks | Computer vision exceeding human performance

12https://www.graphcore.ai/posts/graph-computing-for-machine-intelligence-with-poplar

Complex? Try inexplicable! Visual graph of complex neural networks | Computer vision exceeding human performance

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Accurately label your surroundingsComputer vision exceeding human performance | Objects & full environmental surroundings

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Identify objects & formsFacial recognition for security & identity management

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Image classification: Pattern recognitionExceeding human performance | Early cancer detection, cataract scanning, genomics

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• “60% of people using voice search have started this

year”

• “Google voice search queries in 2016 were up 3500%

over 2008”

• “50% of all searches will be voice searches by 2020”

• “Nearly 50% of people are now using voice search

when researching products.”

• “40% of adults now use voice search once per day”

MindMeld Search Engine Watch comScore Social Media Today Location WorldSources:

Natural language processingIdentifying & predicting patterns in sound…. Voice recognition continues to improve

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• No labels

• “incentive” based learning

But wait…. There’s moreUnsupervised learning | Truly, a new frontier

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See better, listen better = play better!More potential moves in a game of Go than atoms in the known universe

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Key use cases

Fraud prevention • Risk management • Facial recognition • Video surveillance •

Drug interaction • Cancer detection • Video captioning • Content-based search •

Real-time translation • Language processing • Preventative maintenance •

Product and service quality

Artificial Intelligence Revenue, World markets 2016 - 2025

$40,000

$35,000

$30,000

$25,000

$20,000

$15,000

$10,000

$ 5,000

$ 02016 2017 2018 2019 2020 2021 2022 2023 2024 2025

$ M

illio

ns

CAGR52%

AI workloads become pervasive

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are investing in machine learning in the next 3 years

of CIOs

See the full study: The Global CIO Point of View

Machine learning has captured the imagination due to automated processes

and improved decision-making that could speed up difficult digital transformations

say automation will

increase the speed and

accuracy of decisions

machine learning

is a focus of

those efforts

leading

digitization

efforts

Transforming with machine learning

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http://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#633ea7f67f75

Percentage of time devoted to categories of work in ML & DL

20

40

60

80

DATA SETSDevelop • Construct •

Organize • Maintain •

Test • Clean • Filter

A day in the life of a data engineerSeventy-nine percent of data science has nothing to do with data science

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ANY combination of primitive &

custom transformations

Filtered

Filtered & Grouped

Filtered, Grouped, Windowed or labeled

1000’s of Databases, files

volumes, logs & streams!

Data EngineeringNot as easy as it sounds!

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54%

37%35% 35%

30%27%

23%

Lack of necessary staff skills

Defining our AI strategy

Identifying use cases for AI

Funding forAI initiatives

Security or privacy concerns

Complexity of Determining how tointegrating AI with measure value from

our existing usingAI infrastructure

Base: n = 83 Gartner Research Circle Members

Q08. What are the top three challenges to the adoption of artificial intelligence within your organization?

Challenges to overcome: Adopting AI

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World’s first purpose built service products for AI

• Standardized for speed & quality

• Built from real world best practices & techniques

• Specifically to accelerate ML/DL results

• Elite, Proven Data engineers & scientists

• Data use-case focused

• Knowledge transfer with REAL project (no POC)

• Sized for small to large

• Globally available & extensible

Run on purpose built platforms & technology Powered by Intel® Xeon® Scalable Processors

• Purpose built ETL platforms / Optimized & scalable

• #1 Industry leading Scale-Out Data lake

• Design

• Data

• Models

• Tools

• Communication

Accelerate data engineeringWith a powerful combination of world class services & technology

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Traditional DIY infrastructure for ML/DL

Pe

op

le G

lue

20% of the challenge 80% of the challenge

The people to work on the infrastructure

(Data Scientists & Engineers)

Customers being weighed

down disproportionately

Focus your efforts where it countsCustomers often struggle on the “easy” part | Keep your data scientists productive!

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Full control

Slow design and deployment

High cost

DIY-On-premises stack

Complex • Hard to maintain

Rapid deployment

Data proximity challenges

Lock-in and security risks

Public Cloud solution stack

Simple • Limited control

Workspace options for data scientists

Dell EMC Ready Solutions

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Introducing Ready Solutions for AI

Validated stack built to handle most demanding AI workloads

Deep Learning with

Machine Learning with

Simpler AI Experience Faster, Deeper AI Insights Proven AI Expertise

30%Improved data

scientist productivity Up to 2.9X Performance vs.

competition 98% Lower training time

Self-service for data scientists

Selection of AI frameworks & libraries

Industry-leading, scale-out architecture

Single point of support

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• Alerts and diagnostics from real-

time patient data

• Disease identification and

risk stratification

• Patient triage optimization

• Proactive health management

• Healthcare provider sentiment

analysis

Healthcare & Life Sciences

• Predictive maintenance or condition

monitoring

• Warranty reserve estimation

• Prosperity to buy

• Demand forecasting

• Process optimization

Manufacturing

• Risk analytics and regulation

• Customer segmentation

• Cross-selling and up-selling

• Sales and marketing campaign

management

• Credit worthiness

evaluation

Financial Services

Retail

• Predictive inventory planning

• Recommendation engines

• Upsell and cross-channel marketing

• Marketing segmentation

and targeting

• Customer ROI and lifetime value

• Cyberattack intrusion attempt

detection, analysis

• Smart power, transportation

design for resiliency

• Terrorist threat prediction

• Socioeconomic trends

and population planning

Government

• Power usage analytics

• Seismic data processing

• Carbon emissions and trading

• Customer specific pricing

• Energy demand and supply

optimization

Energy

• Vehicle crash avoidance systems

• Smart traffic routing

• Public transportation planning for

maximum mobility

• Smart service vehicles for optimal

routes, autonomous

navigation

Transportation

• Aircraft scheduling

• Dynamic pricing

• Social media-consumer feedback

and interaction analysis

• Consumer complaint resolution

• Traffic patterns and

congestion management

Travel & Hospitality

Boundless use casesThe potential to transform the world

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Keep Data Scientists

Productive

Accelerate

Data Engineering

Accelerate with

purpose-built services

Accelerate with

purpose-built solutions

AccelerateResults

SimpleFast

Proven

The full value chain, with modularityFor the full spectrum of customer needs | From data to insights to results

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