Secular Technological Tailwinds

Trends vs. Predictions
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“ A prediction or forecast is a statement about the way
things will happen in the future (…) ” Wikipedia
“Trend: To extend in a general direction : follow a general course ”
Webster Dictionary

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• Concerned about learning the
process, the interactions and the
emerging laws.
Trends Predictions
• Concerned about being right.

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Yes, we may use trends to make predictions.
Steve Ballmer, former chief executive of Microsoft, on the iPhone shortly
after Steve Jobs announced it. Ballmer went on to promote Microsoft's cheaper phones,
saying "right now we're selling millions and millions and
millions of phones a year. Apple is selling zero."

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More information doesn’t imply more accuracy.
There is no free lunch.

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“Trend: To extend in a general direction : follow a general course ”
Webster Dictionary

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“Trend: To extend in a general direction : follow a general course
” Webster Dictionary

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“Trend: To extend in a general direction : follow a general course
” Webster Dictionary
This is what this presentation is about.

Secular Technological Tailwinds
(or trends!)
Dionisio Chiuratto Agourakis
Founder / CEO – J!Quant

11
Dionisio who?
dionisio@jquant.com.br
https://plus.google.com/+DionisioChiuratto/
Facebook: Dionisio Chiuratto
Twitter: josaum
YO!: DIONISIO

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Cloud for what?!
Why should we let things go from our premises?

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Cloud for what?!
IT Overall Hardware Costs are Decreasing...
but change in IT is very expensive.
And it takes lots of time.

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Cloud for what?!
So came outsourcing.

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Cloud for what?!
Outsourcing was the first attempt to have IT-as-a-service.
Whenever there was change, the contractor adapted (or should have).
“If IT isn’t our core business, why have it in-house?”
So the servers, databases, developers and support team were gone.

20
Cloud for what?!
Outsourcing was the first attempt to have IT-as-a-service.
Whenever there was change, the contractor adapted (or should have).
“If IT isn’t our core business, why have it in-house?”
So the servers, databases, developers and support team were gone.
And so there was a bug...
... and the company was unable to invoice customers for a week.

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Cloud for what?!
There was (and there is) a need for reliability in IT, but with tight budgets and control over the process.
There was (and there is) a need for quickly scaling storage, processing, networking and licensing.

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Cloud for what?!
I don’t want someone to take care of my ERP. I have the right guys.
I want someone to take the burden of hosting the infrastructure for my ERP.
I don’t want someone to take care of my DB. I have my DBA.
I want someone to take the burden of hosting the infrastructure for my DB.
And updating them.
And taking care of their maintanance, power shutdowns, backups...

23
Cloud for what?!
The cloud concept can be understood as a evolution from the IT outsourcing desires.
Outsourcing
Infrastructure-as-a-Service (IaaS)
Platform-as-a-Service (PaaS)
Software-as-a-Service (SaaS)
Monitoring-as-a-Service (MaaS)
Communication-as-a-Service (CaaS)
AWS (EC2, RDS, ...)
AWS – Elastic Beamstalk
Office 365
Datadog
Skype

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Got it. Now why so much buzz about it?
Conceptually, both for users and enterprises, the cloud allowed a
detachment from the physical IT.
Before After
My computer with:
my hardware, my apps, my
music, my documents, my
data, my contacts, my e-
mails and my games.
There is this place called
“Cloud” which holds
everything, no matter the
device I’m using to access
it.

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We just need a minute (or twenty) to get
through the evolution of the...
Ok! Hold on about cloud computing!

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Internet
1.0
Read-Only WEB
The Shopping Cart
Company Page
Your own (html-coded) Personal Home Page!

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Internet 1.0
The internet of pages!
Site 1 Site 2
LINK

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Internet
2.0
Read/Write WEB
Blogs
Social Networks

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Internet 2.0
Social Internet / Collaborative
Open-source
Like
Share
Repositories
Crowd-sourcing
Crowd-funding

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Internet 2.0
PROSUMER
It became easy for anyone not only consume, but also produce content over the internet.

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Everybody started producing contents, using
PaaS and SaaS.
Social media emerged.
Ok! We’re back with the cloud!

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http://articles.economictimes.indiatimes.com/2012-05-27/news/31860969_1_instagram-largest-online-retailer-users

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The cloud enabled us all to be creators in the internet.
The cloud enabled the mobile smart-devices widespread.
The cloud enabled companies to process and store HUGE amounts of
data (more of that soon in the big data section)
The cloud enabled us all to connect with each other and share.
Without buying more hardware or dealing with any IT-specific
problems.
RESPECT THE BUZZ!

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It’s all about context!
The cloud got us covered with the infrastructure, platform and
softwares. Now other questions arise:
“Who’s in the picture?”
Twitter Trending Topics
#hashtag
Apple Siri
Microsoft Cortana
Google Now

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It’s all about context!
People have been producing and interacting
with contents over the internet. We’ve
evolved so much!
...
Meh.

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The problem. Yes there is a problem.
The internet was made for people…
Limited Time
Limited Knowledge
Limited Accuracy
Ambiguous Concepts
…

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How do we search for and fetch information?

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How do we search for and fetch information?
How do we make a potato salad?

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Internet < 3.0
A person browses, searches, filters and reports the results.

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Semantic Web
Linked Data
Ontologies
Internet > 3.0

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Internet > 3.0
A thing browses, searches, filters and reports the results.

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Internet > 3.0
URL = http://www.receitas.com.br/Palmirinha/Batata.html
Page with Recipes
Why only define addresses for pages?
Pages are for people…

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Internet > 3.0
URI
A universal address for…
anything.

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Internet > 3.0
URI http://www.productontology.org/id/Potato_salad
URI http://purl.org/goodrelations/v1#ProductOrService
URI http://purl.org/goodrelations/v1#closes

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Internet > 3.0
The Semantic Web has the purpose of conecting and relating data.
The Semantic Web is based on LINKED DATA.
The Semantic Web is MACHINE READABLE.

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RDF triples?
Sorocaba , belongs to , SP state
Subject Predicate Object
SP state, has climate , Subtropical
RDF – Resource Description Framework
Subject Predicate Object

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SPARQL
SELECT ?city
WHERE {
?city relationship:belongs ?state
?state relationship:hasClimate “Subtropical”
}
List all cities in the state of SP that has the
Subtropical climate.
How to make queries in a knowledge graph.

57http://commons.wikimedia.org/wiki/File:Rdf_graph_for_Eric_Miller.png

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Ontologies
Explicit representation of knowledge –
definitions and relationships
Formal 1st order logic
XML-based
Object-Oriented(?!)
OWL – Ontology Web Language

http://www.inf.unibz.it/~franconi/dl/course/slides/kbs/kbs-modelling.pdf
60

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Ok, so far we’ve seen that:
• Cloud computing gave us a smart(cheap?) and
scalable way of using platforms, softwares and
infrastructure;
• We are producing A LOT of content through;
• Semantic web is giving context, meaning and
relationship to data.
• Semantic web makes data machine-readable.

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Now...The machines! (and other things)
As the cloud concept is interesting because we
detached from physical devices...
...the semantic web is interesting because it will
detach us from the graphical user interfaces. (and
from browsing the internet)

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• Hardware costs are falling. (and are more powerfull)
• M2M (machine to machine) communication is growing fast
• Advanced Software
• Cloud Services
http://www.microsoft.com/en-us/server-cloud/internet-of-things.aspx

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http://www.opensource.com

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Devices,sensors,cameras,....
Knowledge
Base
Processing Results
Devices,sensors,cameras,....
Cloud Services (IaaS, PaaS, SaaS)
Internet...of things

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A lot of things connected through
the internet, talking to each other
and living within the world around
them, creates data...a lot of data...

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http://www.binarylaw.co.uk/2010/11/08/the-hype-cycle/
The hype cycle! (or the buzz evolution chart)
Take my money!!!
Meh. Doens’t work.
Interesting. Should
analyse the pros and
cons!

72
What is Big Data?
• Companies have been producing and storing data since the ERP’s, CRM’s, BI’s,
WMS’s, etc. adoption waves.
• Users have been producing content, giving away personal informations, and (letting
big cloud tech companies) storing it since the PROSUMER feature of the web >= 2.0.
• With the widespread of IoT, billions of new devices are starting to produce and store
data.

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What is Big Data?
That’s Big Data.

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What is Big Data?
That’s Big Data. Really.

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What is Big Data?
That’s Big Data. Really.
Just tons and tons of data that are now being created and stored.

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So...?
Big Data by itself is nearly useless.
There were important developments on processing and storing peta/hexa/yota-scale
data.
But as for business and individuals, there were very few outcomes.
Nice solutions were deployed: visualization of twitter hash-tags in real time and geo-
located. Truck-tracking, health-care experiments on collecting patient’s data, and so on.

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So...?
But the killer application for Big Data is yet to come.
And other buzz-words (Analytics? Deep learning?) might take the merit for themselves.

78
So...?
And the reason is that...

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Big Data won’t do anything without “Big Algorithms”.

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Insights do not emerge by themselves.
They need algorithms for Optimization, AI, Data Mining, Graphs, ...
And these requires processing power.

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http://5gnews.org/critique-pure-speed/

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http://5gnews.org/critique-pure-speed/
3-4 GHz

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Where is the 100GHz processor?
CPU’s have been advancing in speed since they were born.
There are a number of factors resulting in the overall speed of a CPU, but by far the
most straightforward is the clock speed (the MHz, GHz !)

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Now it is easy to aknowledge that companies such as Intel or AMD are no longer
increasig the clock speed.
A barrier was hit around 3-4GHz. Why is that?

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HEAT!

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We can’t afford any more frequency increases with silicon boards.

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But...computing power continues to expand, right?
Yes. There are two major avenues of computing power growth.
New Materials
and Hardwares
Parallelism

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New Materials and
Hardwares
Parallelism
Post-SI Computing
• Graphene
• Silicene
• Quantum Computing
materials (nanotechnology)
Coarse-Grain Parallelism
• Distributed Computing
• Hadoop
Fine-Grain Parallelism
• Multi-Core Processing
• Many-Core Processing

• Hadoop
90
New Materials and
Hardwares
Post-SI Computing
• Graphene
• Silicene
• Quantum Computing
materials (nanotechnology)
Under Research! In Stock!
Parallelism

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Parallelism
• Hadoop
Master
Slave Slave ... Slave
Task
Task
Task
MAP
Master
REDUCEPartial Ans.
Partial Ans.
Partial Ans.

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Parallelism
• Hadoop
How many letters in this sentence?
Slave Slave Slave
Counts: 7 Counts: 9 Counts: 12
Master
Sum Reduce: 7 + 9 + 12 = 28

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Parallelism
http://www.techpowerup.com/reviews/Intel/Core_i7-5960X_5930K_5820K_Comparison/2.html

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Parallelism
Massively Parallel Processing Paradigm

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Parallelism

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Parallelism

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Paralellism is the way to go on speeding up applications and data processing.
All major tech companies are using parallelism (GPU’s, Xeon Phi’s, Hadoop) to
analyse, process and store data.
Parallelism is the way to go with Big Data.

98
Paralellism is the way to go on speeding up applications and data processing.
All major tech companies are using parallelism (GPU’s, Xeon Phi’s, Hadoop) to
analyse, process and store data.
Parallelism is the way to go with Big Data.
http://www.networkworld.com/article/2167576/tech-primers/hadoop---gpu--boost-performance-of-your-big-data-project-by-50x-200x-.html

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But even with lots of data and the correct processing
approach through parallelism,
one question remains:

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Where does insights comes from?

102
An algorithm is a set of instructions to be performed.
To be honest, all codes are algorithms by definition.

103
There are, however, non-trivial sets of instructions (i.e. algorithms) that aims to
solve specific problems.

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What is the shortest path between two nodes in a directed graph?

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Graphs 101:
Vertices (nodes) – Circles
Edges – Lines connecting them
If the lines have arrows – Directed Graph (Digraph)
If the lines doesn’t have arrows – Undirected Graph
The numbers over the edges indicates the weight of each one.

106

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http://www.googlemaps.com

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Ok! There’s an app for that!

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Ok! There’s an app for that! algorithm for that!

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Dijkstra Algorithm (1956)
http://en.wikipedia.org/wiki/Dijkstra%27s_algorithm

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Dijkstra Algorithm (1956)
Shortest path is considered solved by the academia.
The computational time complexity is
This means that if you run the algorithm in your
computer with say, 5 vertices, and it takes 10s to
run.
When you run it with 10 vertices (2x), it is
guaranteed that in the worst case it will take 40s
(4x) to run.

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Nice, but not every problem is under our control.

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What is the shortest path in a directed graph, leaving from node A, passing
through all vertices and returning to A? (TSP = Travelling Salesman Problem)
A A
http://mathworld.wolfram.com/TravelingSalesmanProblem.html

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This is called NP-Complete. No one knows the time complexity of the problem,
and there is no known analytical solution to it.
A A
http://mathworld.wolfram.com/TravelingSalesmanProblem.html

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And there are the so-called “Learning Algorithms”

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How do algorithms learn anyway?

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How do algorithms learn anyway?
2 steps:

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Number of Ed. Institutions in the city Ipads sold (x100)
1 1
2 4
3 4
… …
10 20
Trying to learn the law relating Ipad sales with educational institutions

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Here, learn is the process in which we seek the best red-dotted line.
Because once we find it, we’ll know a mathematical formula
relating the two variables.
Best can mean anything you like. In most cases, we are looking for
the best fit, i.e. the line that minimizes the error across the training
set.

122
Looking at past data, applying some black-box “learning algorithm”
and inferring a mathematical relationship between variables.
The trainning phase is the most time and resource-consuming part of the process.

124
Once we’ve learned the relationship, let’s say it is:
Ipads sold = 2*ed.Inst
If now we want to predict the number of ipads sold in a city with
20 educational institutions, we only need to do a few operations.

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Running already-trained algorithms is lightweight (compared to
trainning).

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That’s how Siri (and Cortana, and Google Now) talks to you.
They’ve been trained for long hours on powerful hardwares, and
they now can rely on the smartphone hardware to execute the
algorithm and “understand” what you meant with “I am hungry”.

127
Trainning
• Heavyweight
• Time and resource-
consuming
• Seeks minimize
errors and
maximize
generality
• Take place in huge
processing clouds
Processing (predicting,
classifying, …)
• Lightweight (relative)
• Real-time (or almost)
• Only apply the pre-
determined operations
• Might take place even
in mobile devices

128
Trainning
• Heavyweight
• Time and resource-
consuming
• Seeks minimize
errors and
maximize
generality
• Take place in huge
processing clouds
Processing (predicting,
classifying, …)
• Lightweight (relative)
• Real-time (or almost)
• Only apply the pre-
determined operations
• Might take place even
in mobile devices
Both processes can be severely sped-up with parallel computing.

129
http://www.gputechconf.com/

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(really) nice
evidences
Drones, 3D-Printers, Self-Driving Cars, 4.0 Manufacturing, ...

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Drones (UAV)
http://nypost.com/2015/04/12/war-against-isis-shows-limits-of-drones/

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3D-Printing
http://www.3dprinters.nl/wat-is-een-3d-printer/

133
http://www.telegraph.co.uk/motoring/road-safety/10570935/Autonomous-cars-is-this-the-end-of-driving.html
Self-driving cars

134
The 4th Industrial Revolution

138
Algorithms will be the new standard.

139
For people’s jobs.

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http://www.forbes.com/sites/roberthof/2015/01/31/now-even-artificial-intelligence-gurus-fret-that-ai-will-steal-our-jobs/
“The U.S. took 200 years to get from 98% to 2% farming
employment,”
“Over that span of 200 years we could retrain the
descendants of farmers.”
“With this technology today, that transformation might
happen much faster,”
Self-driving cars, he suggested could quickly put 5 million
truck drivers out of work.
Andrew Ng

141
For companies' competition.

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For companies' competition.
For countries’ sovereignty.

146
50 billionsNew devices will be connected by 2020
http://share.cisco.com/internet-of-things.html

147
14 trillionDollars added to the global economy by 2030
http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Industrial-Internet-of-Things-Positioning-Paper-Report-2015.PDF

Thanks!
149
Dionisio Chiuratto Agourakis
dionisio@jquant.com.br

Secular Technological Tailwinds

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