Artificial intelligence (AI) is powering the fourth industrial revolution. Intelligent machines are tackling new cognitive tasks at scale, leading to enormous economic efficiency gains and disruption across the labour market. But what will be the net impact of AI on society and the ecological environment? In this talk, Alex Housley, founder and CEO of open-source machine learning platform Seldon, explains how the collaborative approach to AI development helps transform industries and provides the macro-scale opportunities for AI to make the world a better and more sustainable place. The event was chaired by David Wood. The camera was operated by Kiran Manam. For more details about this event, see https://www.meetup.com/London-Futuris.... For more information about Seldon, see https://www.seldon.io/. To apply to join the closed beta mentioned in the talk, visit bit.ly/deploy-beta.

1. Open Source Machine Learning
Alex Housley, Founder & CEO
ah@seldon.io @ahousley @seldon_io
The future of AI and sustainability
London Futurists - 1st July 2017

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My Journey into AI
2003 2009 2011 2014
Acquired B2B Pivot MBO ???

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Genome Laser

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• Chairman of the Science & Technology
Committee of the Apollo Moon Landings.
• Discovered the first polyatomic molecules in
space (i.e. H2O)
• Figured out there was a black hole in the centre
of the Milky Way.
• Founder of SETI
• Infrared Interferometry - measure size and shape
of stars
• Advisor to Popes and Presidents
• INVENTOR OF THE FREAKING LASER!
genomelaser.com
Dr. Charles H Townes
(1915-2015)

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Genome Laser

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4th Industrial Revolution

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“The first ultra-intelligent machine is
the last invention that man needs ever make”
Irving J. Good (1965)

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47% of jobs at risk

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Machine learning is everywhere

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Machine learning is changing the world

20. Our Purpose
Seldon's purpose is to help people
predict and shape the future with machine learning

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UN Vision for 2030
Open Source AI is the key to turning this vision into reality.

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What is machine learning?
Traditional Programming Machine Learning
Data
Program
Output
Data
Output
Program

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Data Network Effects
Network Effects
The more people that use the service,
the more useful it is because there are
more people to use it with.
Data Network Effects
The more people that use it, the
better the service actually becomes.

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UnsupervisedSupervised Learning
Types of machine learning
Regression Classification Clustering

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UnsupervisedSupervised Learning
Types of machine learning
Labelled Data Unlabelled Data
Cat on a skateboard

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Types of machine learning problems
• Forecasting (e.g. Energy demand prediction, finance)
• Imputing missing data (e.g. Netflix recommendations)
• Detecting anomalies (e.g. Security, fraud, virus mutations)
• Classifying (e.g. Credit risk assessment, cancer diagnosis)
• Ranking (e.g. Google search, personalization)
• Summarizing (e.g. News zeitgeist, social media sentiment)
• Decision making (e.g. AI, robotics, compiler tuning, trading)
• Transforming sequences (e.g. Translation)
via Nando de Freitas & Nathan Benaich

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The Machine
Learning Canvas
by Louis Dorard

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Types of machine learning
Reinforcement
Learning

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Deep Neural Networks

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Generative Adversarial Networks

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Understanding the physical world
Connecting Images and Natural Language, Andrej Karpathy, PhD Thesis, 2016
http://cs.stanford.edu/people/karpathy/main.pdf

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NLP & Virtual Assistants

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How to add AI to your organisation
Build In-House
3rd Party API
Open-Source
Platform
Control Model Evaluation Time Data Scientists Cost
$$$✔
Too Many
$$
✗
Limited /
Unknown
$✔
Many Algorithms &
Models + Your Own

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Inefficiencies
Shortage of data scientists
Loss of control
Problems

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Technology Control Community
Seldon is open source

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Over 1000 stars and 200 forks on Github
Thousands of data science teams

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Open Source Launch - Feb 2015
Documentation
SaaSInfrastructure
Sales
CommunityLicense

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How we generate revenue
SaaS Support Add-ons

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Go-to-market
Horizontal Vertical
Services
Product

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9 Months Later…
(h/t @NathanBenaich)

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Open Source Community
1. Fix Documentation
2. Help Each Other
3. Fix Bugs

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Containers

45. Data Scientist
● Loads runtime models as
microservice components
● Builds predictive units
● Sandbox Tests
Data Manager
● Creates predictive project
● Defines business KPIs
● Specifies input/output
contract
● Real-time analysis
Data Engineer
● Launch Seldon clusters
● Canary tests new
predictive unit
● Deploys to production
● Monitor clusters
Data Science Workflows

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Advanced Experiments
! Explore: testing of multiple
strategies in parallel using multi-
armed bandit.
! Exploit: The latest winning test
strategy (1...N) is promoted to
best.
! Adaptive to context changes - i.e.
time of day, special event.

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Continuous Deployment

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Model Explanations
Explaining individual predictions to a human decision-maker. Source: Marco Tulio Ribeiro.
In May 2018 the new General Data Protection Regulation (GDPR) will give consumers
a legal “right to explanation” from organisations that use algorithmic decision making.

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Model Explanations
Original Image Electric Guitar Acoustic Guitar Labrador
Explaining the predictions of any classifier arXiv:1602.04938 [cs.LG]
Model predicts 3 classes – ‘Electric Guitar’, ‘Acoustic Guitar’, ‘Labrador’

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New enterprise product coming soon
More Control. Easier Maintenance.
Better Performance. Highly Scalable. Shiny UI.
● Workflows with Approvals and Audit Trails
● Advanced Experiments
● Continuous Deployment
● Model Explanations
Hybrid SaaS: Seldon-hosted Deploy interface, customer-hosted Core clusters
Seldon Deploy
Machine Learning Deployment for Data Science Teams

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Component Diagram

52. bit.ly/deploy-beta

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Social
"Sustainable development is development that meets the
needs of the present, without compromising the ability of
future generations to meet their own needs."
Environment Economic

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•1st order effects (direct effects) are
detrimental: increase energy, GHG and waste.
•2nd order effects (higher order effects):
decreased ecological footprint (i.e. video conf,
smart grids).
•3rd order effects: both positive and negative -
efficiency gains result in increased usage.

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• Equipment interacts in nonlinear ways.
• Formula-based engineering and human
intuition often do not capture
interactions.
• Cannot adapt quickly to internal or
external changes.
• Each data centre has a unique
architecture and environment.
• Custom tuned model for one system
doesn't apply to another

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• Applied neural networks trained on
operational scenarios and parameters
• Historical data from thousands of
sensors: temperature, power, pump
speed, etc
• Reduced cooling bill by 40%

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North American Electric Grid
• Century-old infrastructure, one of the largest and most complex machines:
• 5 loosely connected AC grids
• 20k generating units
• 7k power station suppliers
• 160,000 miles of high voltage cable
• 150 million meters
• Since 1973 OPEC Oil Embargo: exponential growth in consumption, increased
costs, decreased reliability, more outages, sustainability concerns, increased
risk, decreased investment.

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Clean energy generation and storage
(h/t @azeem)

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Smart Grid
• Energy increasingly generated at the distribution edge:
• 1 in 50 US jobs in Solar, increase 25% YoY in 2016.
• 1/3 of energy generation consumer owned and operated by 2020.
• 10% of consumers deploy on-site generation.
• nodes in grid grow by two orders of magnitude to 15 million assets.
• Distributed, autonomous monitoring, analysis and automation.
• Convergence with 50 billion IoT things (most with multiple sensors) by 2030 -
powerful, resilient, efficient, economical.

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Autonomous Factories
Changying Precision Technology Company
Production of mobile phones and uses automated production lines
Changying Precision Technology Company
90% of human workers replaced with robots
Production rises by 250%
Defects drop by 80%

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Cucumber farmer using deep learning and commodity hardware

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A ZRR2 unit, with two arms, makes up to 4,000 picks per hour.

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Macro AI Model for Sustainability
• Integrate indicators for sustainable
development: dimensions of
environment protection, social and
economic development.
• Teach sustainable goals and strategies
into systems from inception.
• Not simply offloading work to
machines, but helping humans before
better problem solvers at the same
time. (Move 37)
Formation of AI Model of Sustainable Development Environmental Dimension - Navickas, 2008
Sustainable development is a global-scale nonlinear problem.

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Macro AI Model for Sustainability Benefits
• Complexity: Possible to integrate of indicators
of dimensions of environment protection, social
and economic development.
• Simulation: implement multifunctional
simulation operations. Helps experts to achieve
higher level of argumentation on decisions.
• Concentration: More effective distribution of
support from the state and EU.
• Prevention: Anticipatory identification of
environmental problems earlier.
• Transformation: Possible to connect and
disconnect additional events and indicators as
they emerge.
Formation of AI Model of Sustainable Development Environmental Dimension - Navickas, 2008

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1.AI is the key to the UN vision for 2030
2.Micro: optimisation of discrete sustainability problems.
3.Macro: augmentation in human decision and policy making.
4.Progress is driven by open source technology, open data
and global community collaboration across academia,
corporations, start-ups and the government.

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Thank you!
Alex Housley
ah@seldon.io
@ahousley @seldon_io
docs.seldon.io