Blockchain and Artificial Iintelligence

How will this impact your Business, the Industry and how we live?

1Mervin van der Spuy

AGENDA

● Introduction

● An Introduction to Blockchain

● Crypto Currency and Smart Contracts is only the beginning….

● Artificial Intelligence Primer

● Terminator/Matrix vs Star Trek (or somewhere in the middle)

Blockchain – It’s in the name

Blockchain – A Distributed Ledger

Blockchain – How Transactions Work

Blockchain – How do you scale?

Blockchain – What makes this so exciting?

Blockchain – What do you use it for?

Blockchain – Gartner’s Hype Cycle

What is AI?

Fiction?

What is AI?

What is AI?

AI “HIERARCHY OF NEEDS”

1

ARTIFICIAL INTELLIGENCEIs the theory and development of computer

systems able to reproduce and perform tasks

which normally require human intelligence; such

as: visual perception, speech recognition,

decision-making, and translation between

languages.

3

DATA SCIENCEApplying “math” to data. Data science is an

interdisciplinary field that uses scientific

methods, processes, algorithms, and systems to

extract knowledge and insights from data in

various forms, both structured and unstructured,

similar to data mining.

5DATA PROCESSINGOur ability to collect, store, and process

extremely large data sets - both structured &

unstructured - at scale to address business

problems (and opportunities) which

organizations wouldn’t have been able to tackle

before.

2

MACHINE LEARNINGMachine learning is a subset of artificialintelligence, in the field of computerscience, which often uses statisticaltechniques to give computers the abilityto "learn" with data, without beingexplicitly programmed.

4

ANALYTICSData analysis is a process of inspecting,

cleansing, transforming, and modeling

data with the goal of discovering useful

information, informing conclusions, and

supporting decision-making.

INITIATIVE INDEXInternal executions across 3 core business outcomes / themes

Automation

Robotic solutions tackling automatic processing

enabling people to complete actions & get banking done via self-

service

Augmentation

Machine learning, natural language processing, and computer vision

serving as recommendation engines & insight

discovery to augment human interactions.

Innovation

At the individual level, in the future we will look

less like terminators and more like cyborgs; less like isolated individuals,

and more like a vast network of humans and

machines creating an ever-more-powerful AI.

COMMON ML APPLICATIONS IN CONSUMER ENTERPRISE

Recommendation SystemsCompare actions of consumers to infer similar taste or suggest a nity between consumers and products based on attributes and actions

Audience SegmentationSeparate consumers into groups that look like one another in a way that is relevant for marketing or product performance

PersonalizationModify the experience of a product, marketing message, or channel to best resonate with a consumer at a scale too large for human teams to execute

ChatbotsHelp customers answer questions, resolve problems, or identify the right product mix to redirect human resources to higher-value interactions that require judgment

Risk AssessmentsModify o er and pricing on an insurance or banking product according to predicted risk or likelihood to default

Anomaly DetectionIdentify a shift in customer behavior that could signal opportunity for upsell or risk of churn, or a shift in network or system behavior that could signal malicious activity

Anti-money Laundering and ComplianceIdentify suspicious behavior or attributes and automate compliance reporting work ows using natural language generation

Data ProductsUse algorithms to identify useful insights about consumer behavior that are packaged and sold to other businesses for targeted marketing

AI & ML – Hype Cycles

THE FUTURE OF AI

Artificial Intelligence maybe the biggest and most

disruptive technology advance we see in our lifetimes

Long-term sustainability of any enterprise is predicated

on trust. For any data driven organization, the

respectful and ethical treatment of data is core to to this

trust model.

faster computation smarter algorithms exponential growth in data

Power and reach of ML is rapidly expanding into many areas of everyday life.

BIG PICTURE

Economic Impact

Investors have poured over $28B into AI over the past 3 years

AI is estimated to create up to $15.7 trillion in gains by 2030, with the greatest economic contributor being China with a 26% increase to GDP, followed by North America with an increase of 14.5%

Employment Impact

The share of jobs requiring AI skills has grown 4.5x since 2013

Machine Learning, Deep Learning & Natural Language Processing (NLP) are the 3 most in-demand skills

20% - 40% impact on productivity gains (f/m/bo) - and / or -unemployment, depending on your vantage point

Ethical Impact

There are countless examples of bias being implemented into AI programs- in response numerous governments, universities, and companies have established dedicated staff, researchers, and teams to work on the ethics of AI including: France, UK, Microsoft, DeepMind, and the University of Toronto

THE BIG PICTUREGlobal Perspective

Regulatory Impact

In 2017, three bills were proposed to US Congress, two were about self-driving cars and the third being the “Future of AI Act” which would create a committee for AI issues.

Although not directly about AI, Europe’s new GDPR, which imposes strict rules on how to handle customer data, has created a challenge for AI development there

AI Chips- A Technology to Watch

As of the start of 2018, there were at least 45 startups focused on AI chips, with at least five having raised over $100 million dollars.

Venture capitalists have invested over $1.5 billion into startup chip companies in 2017.

Google has announced that they will be selling their tensor processing units (TPUs) as a hardware piece for programmers as of October. TPUs are currently available through Google’s cloud and accelerate AI tasks like understanding voice commands or recognizing objects in photos.

THE BIG PICTUREGlobal Perspective

International Outlook

● China has started to see traction towards their goal of being the world leader of AI and growing their industry to be worth 1 trillion yuan ($158 billion) by 2030

○ Chinese AI startups accounted for 48% of the world’s AI investment in 2017 surpassing the US for the first time

○ In 2017, there were 652 patent publications for “deep learning” in China compared to 101 in the United States, 641 for “artificial intelligence” compared to 130, and 882 for “machine learning” compared to 770

● The US has the most AI companies with over 1000 companies and $10 billion USD in VC● The European Commission (the executive arm of the EU) addressed that they need to

invest 20 billion euros ($24 billion) in AI research by 2020● Although facing many challenges and unlikely to catch up with the US or China, an Indian

government-appointed task force released recommendations as to how the country can boost their AI sector

● The countries with the most AI/ML published research papers (in order) are: China, the US, Japan, the UK, and Germany

THE BIG PICTUREGlobal Perspective

Commit to beneficial

intelligence for happiness

PRINCIPLES

Responsible treatment

of data to reinforce

trust - at every step.

ML assumes the future

will look like the past. If

you want the future to

look differently, you

need to design systems

with that in mind.

We are all individuals.

When you deal with

abstractions and

groupings, you run the

risk of treating humans

unethically.

Privacy is not just about

personal data, consent

forms, or a set of

controls to minimize

data use. It is about

appropriate data flows

that conform to social

norms and expectations

Accountability is a

marathon. Govern the

optimizations. Patrol

the results.

AI & ML EthicsResponsible AI & Reinforcing a Model of Trust

RESPONSIBLE AI & REINFORCING A MODEL OF TRUST Stereotyping. Recognition of a person’s humanity. Denigration. Under representation.

It’s not just about models. It’s about the whole system.Where does the data comes from? Do data sources lack diversity? .Open source tools we utilize (Google’s Sentiment Analyzer / NLP API, Google Translate, Google Photos)What we can / can’t measure.

Bias can exist in all sorts of places (and across all levels of the AI Hierarchy):How we define bias - can be biasedTraining data (quantities for minorities, social conventions, labels)How we select & engineer product features Target selection variablesProxies (future outcomes)UX / UI process Metrics / measuring outcomes

Bias & Explainability in Machine Learning

Identifying and addressing

bias throughout the

product development

process

AGILE ETHICS Developing a “cultural competency”

Agile ethics is a process to operationalise values, iteratively identify and address ethical challenges of our initiatives before we send them out in to the world,

It allows us to adapt / refine processes so that as the capabilities of technology evolve, so does our ability to diagnose and prevent harm before it happens.

Agile ethics requires 4 things:

Decentralisation of critical, ethical thinking in ATB / teams

Iterative development of process that supports decentralised consideration of the implications of any given idea.

Dedicated lead and team member time to manage the process

Inclusion of diverse and (when appropriate) external voices

It is the explicit application of agile methods to ethical assessment, adaptation, and learning that allows for a team to mature its practices as it works at the bleeding edge.

It employs agile methods to tackle ethical challenges, and inculcates ethical approaches within an agile development process.

Questions?