Demystifying Artificial Intelligence

ARTIFICIAL INTELLIGENCE

DEMYSTIFYING ARTIFICIAL INTELLIGENCE

3 Goals of this presentation

#1 Introduce you to Artificial Intelligence

#2 Provide a high-level overview of today & tomorrow

#3 Help organisations get started with AI

Speaker on Bitcoin & Blockchain

Consultant at Duval Union Consulting

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Sam Wouters

@sdwouters

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Speaker on Artificial Intelligence

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‣ We are a digital consulting company.

‣ Founded in 2009.

‣ We work for large clients across all sectors.

‣ We have a strong vision on the impact of digital and act accordingly.

‣ We are advisors, writers, coaches and entrepreneurs.

‣ Slide 8-22: A brief history of Artificial Intelligence

‣ Slide 23-36: Demystification

‣ Slide 37-44: AI is all around us

‣ Slide 45-63: Going from ANI to AGI

‣ Slide 64-75: Going from AGI to ASI

‣ Slide 76-98: How to use ANI

‣ Slide 99-110: How to start building

Table of contents

How to define AI

intelligence exhibited by machines

How to define AI


DEEP LEARNING

MACHINELEARNING

A building block of AI

A subset of Machine Learning

Intelligence by machines



A BRIEF HISTORY

First AI: The Staffelwalze

The first known calculator to perform all 4 operations: addition, subtraction, multiplication and division.

1672

In the 1940s the Church–Turing thesis was created. It suggested that digital computers can simulate any process of formal reasoning and led researchers to consider the possibility of building an electronic brain.

1940s

AI started getting developed by philosophers and mathematicians in the 19th century. Alan Turing is one of the best known founding fathers of modern AI.

1950s

INTERVIEWER

PERSON AI

The interviewer has a limited amount of time to ask questions to the other rooms.

An interviewer, a person and an AI get put in 3 different rooms. They communicate through text.

If the interviewer can’t figure out who is human before time is up, the AI passes the Turing Test.

AI research was founded at Dartmouth College in 1956.

The founders and their students wrote astonishing programs that were winning at checkers, solving word problems in algebra, proving logical theorems and speaking English.

They expected to have pure AI within twenty years, but underestimated the difficulty and in 1974 all funding was cut off from AI research.

1956-74 Founding of AI & practice

After a few more attempts at revival, AI began to be used more in the 90s in all kinds of areas. In 1997, IBM’s Deep Blue beat Garry Kasparov, the reigning world champion at chess.

1990s

In 2011 IBM’s Watson beat Ken Jennings and Brad Rutter in the TV Quiz Jeopardy, the first sign of AI beating people at non-math.

1990s

In 2016, Google’s Deepmind beat Lee Sedol 4-1 at Go, putting AI 10 years ahead of expectations.

2016

Today AI is booming

Across all sectors

AI Funding since 2012:

$14.9 BILLION 2250 DEALS

MORE DATA & RESEARCH

Why now?

ERROR RATES ARE FALLING

CHEAP NEURAL NETWORKS

“2015 was a landmark year for AI, with software projects using AI at Google increasing from ‘sporadic usage’ in 2012 to over 2700 projects.”

~Jack Clark, Strategy & Communications Director at OpenAI

Why does AI matter?OUR LIVES

OUR BUSINESSES OUR SOCIETY

How will AI impact our purpose?

How will AI change our businesses? How will AI impact our workforce?



DEMYSTIFICATION

Human progress

Time

Where we think we are

Human progress

Time

Where we really are

Human progress

Time

Reason #1: Projection“This is how things went in the past, so that will probably continue but a bit faster”

Human progress

Time

1. Experimentation 2. Exponential growth 3. Level off

Reason #2: Innovation happens in phases

1 23 1 2

3

1995-2007

2008-2015

It’s not about robots It’s about the brain

3 Types of Artificial Intelligence

Artificial Narrow IntelligenceANI

Specialised in ONE area

ANI Artificial Narrow Intelligence


Artificial Narrow IntelligenceANIAGI Artificial General Intelligence

AGI

Specialised in ALL areas

Artificial General Intelligence

Our best work

in progress

AGI

7 ABILITIES:1. Reason 2. Plan 3. Solve problems 4. Think abstractly 5. Comprehend complex ideas 6. Learn quickly 7. Learn from experience

Artificial General Intelligence



ASI Artificial Super Intelligence

ASI

Smarter than humans in EVERY WAY

Artificial Super Intelligence



ASI Artificial Super Intelligence



IS ALL AROUND US

ANI is all around us

5 business applications

Google says its AI catches 99.9% of Gmail spam

Netflix uses AI to go from recommendations based on what you’ve seen, to what you like

A 19-year-old made a free chatbot lawyer

that has appealed $3m in parking tickets

Identifying diseases by comparing huge amounts of data

Freenome, a startup focused on detecting cancer through AI, set up by a 28-year old, landed a $65 million investment by Andreessen Horowitz & Google Ventures

Uber is using AI for Route optimization



FROM ANI TO AGI

We build computers to process large amounts of data

= Hard to humans

Next step: things that are easy to humans

What is a cat?What is a human face?

Which of these is a tree?

A partially lit-up 3D rock to humans Grey & black 2D shapes to a computer

Heavy investments to achieve AGI

“I truly believe the computing Singularity is coming, that’s why I’m in a hurry – to aggregate the cash, to invest.”

~Softbank CEO Masayoshi Son on their $100B tech fund

How to achieve AGI

CHEAPER COMPUTER

POWERBECOMING

SMARTER

Goal: $1000 for human brainpowerCHEAPER COMPUTER

POWER

1015 calcs/second

20 watts of power

0.00126M3

Our best computer: $390M Tianhe-2CHEAPER COMPUTER

POWER

1034 calcs/second

24 MW of power

2160M3

We should have enough cheap computing power by 2025

CHEAPER COMPUTER

POWER

How to achieve AGI

CHEAPER COMPUTER

POWERBECOMING

SMARTER

3 Strategies

#1 Reverse Engineering

#2 Stimulating Evolution

#3 Autonomy

BECOMING SMARTER

#1: Reverse-EngineeringBECOMING SMARTER

Replicating human brains in a digital way

#2: Replicating EvolutionBECOMING SMARTER

Networks of hardware & software learning from each others mistakes

#3: Autonomy

Letting the computer write its own rules and learn from experience

BECOMING SMARTER

Wolfpack gameGathering game

Experts expect we’ll achieve AGI between 2030-2060

BECOMING SMARTER

We could be missing 1 pieceBECOMING SMARTER

Example: digital money

David Chaum - 1983 - Digital Cash Satoshi Nakamoto - 2008 - Bitcoin



AGI TO ASI

AGI doesn’t existLevel of AI

Time

Where are are right now

Human intelligence AGI

ANI

An AGI has max skills in everything

0

25

50

75

100

Programming Pricing products Catching objects Fraud detection Identifying diseases

Human AI

And it can re-engineer its brain

AGI doesn’t existLevel of AI

Time

<1 day

Human intelligence AGI

ANI

ASI

What will an AI trillions of times smarter than us do?

“AI is likely to be either the best or worst thing ever to happen to humanity.” ~Stephen Hawking

“If I had to guess at what our biggest existential threat is, it's probably AI.” ~Elon Musk

“When a few people control a platform with extreme intelligence, it creates dangers in terms of power and control.” ~Bill Gates

But if we get it right…

Cure diseases? Solve energy problems?

Elon Musk launches Neuralink, a venture to merge the human brain with AI

An Open Letter Research priorities for robust and beneficial Artificial Intelligence

The potential benefits (of AI) are huge, since everything that civilization has to offer is a product of human intelligence; we

cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of

disease and poverty are not unfathomable.

Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls.

futureoflife.org/ai-open-letter/

https://futureoflife.org/ai-open-letter/

Open AI’s “Universe”, a platform to train AI's across games, websites and other apps



HOW TO USE ANI

How to use ANI

There are 10 building blocks in AI

COGNITIONSENSORY PERCEPTION

MACHINE LEARNING

DEEP LEARNING

IMAGE ANALYSIS

NATURAL LANGUAGE

GENERATION

NATURAL LANGUAGE

PROCESSINGSPEECH

RECOGNITIONROBOTICSKNOWLEDGE ENGINEERING

10 building blocks of AI


MACHINE LEARNING

DEEP LEARNING

IMAGE ANALYSIS

NATURAL LANGUAGE

GENERATION

NATURAL LANGUAGE

PROCESSINGSPEECH



A process to understand and represent human knowledge in data structures.

Knowledge Engineering can be used in applications to solve complex problems that are generally associated with human expertise. IBM Watson Health uses engineered

knowledge in combination with over 290 medical journals, textbooks, and drug databases to help oncologists choose the best treatment for their patients.

KNOWLEDGE ENGINEERING


A physical manifestation of an AI, allowing it to interact with the physical world.

Robots are mostly used to automate repetitive tasks in controlled manufacturing environments across all industries. Applications are things such as transporting

goods, assembling products, quality checks, sorting objects,… Amazon employs over 45.000 logistic robots in its warehouses and Tesla has a fully automated factory.

ROBOTICS


A process to convert speech to text, to allow AI to listen to the physical world.

Speech recognition can be used to allow applications to take commands from humans, to transcribe conversations or even participate in them.

Popular examples are Apple’s Siri, Google Now and Amazon’s Alexa.

SPEECH RECOGNITION


A process to understand the meaning of words in text.

Natural Language Processing can be used to analyse any text to extract topics, sentiment, meaning and ultimately to gain knowledge. It is used to read the

sentiment in financial markets, to analyse product reviews, monitor social media,…

NATURAL LANGUAGE

PROCESSING


A process to express stored information in an understandable way for humans.

Natural Language Generation is the opposite of NL Processing. It allows AI’s to communicate back to humans about the information they processed. It is mostly used in virtual personal assistants such as Siri, Google Now and Alexa, but also in

customer service chatbots.

NATURAL LANGUAGE

GENERATION


Converts objects to text, to allow AI to see the physical world.

A technology that identifies and understands what can be seen in images and video. It assigns labels to objects and situations. Best known applications of Image Analysis

are facial recognition, quality controls and self-driving technology.

IMAGE ANALYSIS


Machine Learning consists of tools and algorithms to analyze data.

Machine learning can be used in a large variety of ways, such as predictions, identifying patterns, recommendations,… Today it is used to better detect diseases, recommend content, improve products and services and many other applications.

MACHINE LEARNING

10 building blocks of AIThe creation of an artificial brain to handle large amounts of data.

Deep learning is a branch of machine learning that focuses on algorithms to create artificial neural networks. These networks are more efficient at handling data at large scale and are used by large Internet companies to organise information, analyse and

predict behaviour, improve search and products & services and more.

DEEP LEARNING

10 building blocks of AIA process to convert physical characteristics to text to provide context.

Sensors measure and collect information about people, places and all kinds of objects to provide context. Examples of this are location, weather, sound, presence, volume and pressure. Sensory perception is used to predict equipment or material

failures before they happen, so maintenance can be adjusted to it.

SENSORY PERCEPTION


Turing-complete applications, where we can’t tell AI apart from other humans.

Cognitive applications have a mind of their own and are able to perceive, interact, learn, act and evolve by themselves. Cognition is a missing piece that,

in combination with the other building blocks, will create pure AI.

COGNITION

How to use ANI



MACHINE LEARNING

DEEP LEARNING

IMAGE ANALYSIS

NATURAL LANGUAGE

GENERATION

NATURAL LANGUAGE

PROCESSINGSPEECH


5 maturity phases

Phase 1: In research phase but not in practical use yet

Phase 2: Used in commercial applications but not accurate and consistent enough

Phase 3: Accurate enough for applications, but still has technical challenges to overcome

Phase 4: Has overcome the challenges in phase 3 but requires perfection

Phase 5: Pure AI, indistinguishable from human intelligence

The maturity of AI in 5 phases

CognitionPhase 1: In research phase but not in practical use yet

Watch for promising future opportunities




Knowledge Engineering

Deep Learning

ImageAnalysis

Natural Language Generation

Pioneering a market before the value is created






Deep Learning

ImageAnalysis


Speech Recognition

Natural Language Processing

Machine Learning

Adds some value for your business today

Dealing with phase 3: AI+Humans


Cognition


Speech Recognition

Sensory PerceptionRobotics





Deep Learning

ImageAnalysis



Machine Learning

Adds serious value for your business today


Cognition


Speech Recognition

Sensory PerceptionRobotics






Deep Learning

ImageAnalysis



Machine Learning

Doesn’t exist yet

How to use ANI



MACHINE LEARNING

DEEP LEARNING

IMAGE ANALYSIS

NATURAL LANGUAGE

GENERATION

NATURAL LANGUAGE

PROCESSINGSPEECH


5 maturity phases






And countless applications…

SENSORY PERCEPTIONROBOTICS

SPEECH RECOGNITION

SENSORY PERCEPTION

NATURAL LANGUAGE

PROCESSING

NATURAL LANGUAGE

GENERATION

KNOWLEDGE ENGINEERING

NATURAL LANGUAGE

PROCESSING

IMAGE ANALYSIS

MACHINE LEARNING

IMAGE ANALYSIS

DEEP LEARNING

A few ANI applications

Do you repetitively deal with physical objects? e.g. moving objects, translating physical to digital, maintenance

Are there interfaces you can improve? e.g. chatbots, voice commands, face detection

Do you need data to be analysed? e.g. categorisation, predictions, recommendations



HOW TO START BUILDING

How to start building

DEVELOPERS PLATFORMSCOMPUTING POWERDATA

Everyone needs AI talent, build a team!


DEVELOPERS PLATFORMSCOMPUTING POWERDATA

You need vast amounts of training data to perfect your algorithms


PLATFORMSCOMPUTING POWERDEVELOPERS DATA

You can start smaller than this :-)


PLATFORMSCOMPUTING POWERDEVELOPERS DATA

Platforms to get started

tensorflow.org

ibm.com/watson

deepmind.com

deeplearning.net/software/theano/

https://www.tensorflow.org/

https://www.ibm.com/watson/

https://deepmind.com/

http://deeplearning.net/software/theano/

Facebook Messenger has 34.000 chatbots

Or leverage the crowd through Kaggle

Like other technology, AI is an open-source focused community

Get startedon the next level of business!



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