somo ai breakfast briefing

Artificial Intelligence Workshop

20th October

Confidential and copyright of Somo Custom Ltd. October 16 2

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Agenda

1. The relationship between man and machine

Demo #1 – Alexa and Allo

2. A look under the bonnet 3. Machine Learning & Deep Learning

Demo #2 – Image recognition with Cortexica

4. Applications of Artificial Intelligence by Industry

Demo #3 – Pepper the robot with Emotion Robotics

Coffee Break

The relationship between man and machine

There is a change happening between the relationship between man and machine


We are seeing exponential growth where we are used to linear growth

We are in a period of change


We are seeing exponential growth where we are used to linear growth


"I think if we can effectively merge with AI by improving the neural link between your cortex and your digital extension of yourself […] then effectively you become an AI-human symbiote, and if that then is widespread, and anyone who wants it can have it, then we solve the control problem as well. We don't have to worry about some evil dictator AI because we are the AI collectively. That seems like the best outcome I can think of.” Elon Musk - CEO, and CTO of SpaceX; co-founder, CEO, and product architect of Tesla Motors



"The development of full artificial intelligence could spell the end of the human race." Stephen Hawkings



"Ten years from now, it will have even more significant wealth creation, stockholder appreciation opportunities, than what I believe we saw in social networks in 2005 and internet investing in 1995.” Jim Breyer


AI software automation and robotics will fundamentally transform business models

Businesses will need to embrace disruption

Businesses that don’t leverage the technology will likely find it

impossible to compete

General motors 840,000 workers

$11 billion in earnings

Google 38,000 workers

$14 billion in earnings

AI = different business models •  Increased efficiency •  Growth through creativity

AI solves high value recurring problems •  Decrease in resource vs output •  Increased efficiency = increased ouput

1979

2012


AI can be broken down into 3 main groups

AI is a broad term

Artificial Superintelligence (ASI) •  An intellect that is

much smarter than the best human brains in practically every field

Artificial General Intelligence (AGI) •  Refers to a

computer that is as smart as a human across the board

Artificial Narrow Intelligence (ANI) •  Specialises in one

area •  Completes and

solves one problem


AI is already all around us, with some key future trends

How will it effect us in the near future?

•  Smarter Robots

•  Faster Analysis

•  More Natural Interactions

•  Hotter Competition

•  IOT – explosion of connected devices

•  Proactive assistant become the norm

•  Expansion of Deep learning moving more towards machine learning


Who are the main players? We have seen intense acquisition activity in AI market 2011-2016

Source: CB Insights


An open source software library for machine learning

TensorFlow

Google autonomous cars reached a total of

2.7 mn km

Google launches Google Assistant in all Android phones and Google Home

Facebook launches the Moments app

based on facial recognition

Apple launches Siri in iOS

AI is experiencing similar tech advancements and cost reduction curves as we have seen on PCs and smartphones

Rapid productisation of AI

Amazon launches Alexa

Facebook opens Messenger App

to bots

2011 2015 2016

Facebook launches

automatic facial

recognition for photos

2010 2012

Google Now is launched on

Android

2014

Tesla Motors announced its first version of AutoPilot on Model S cars

Google launches Face Unlock in

Android

WeChat launches its bot platform

2013 2mn

500mn People who used an AI-powered service


Roles/Tasks for which using AI According to smartphone users WW

Why do you use an AI assistant US AI assistant users

Frequency of use for voice controlled personal assistant

US mobile phone users

Source: Ericson ConsumerLab, 2016 Source: AYTM Market Research, June 2016 Source: 451 Research March 2016

How has AI re-set customer expectations New behaviours require a change in product and services

73%

64%

57%

44%

43%

41%

35%

Search Engine

Travel guide

Personal Assistant

Teacher

Medical advisor

Financial advisor

Companion to talk

64%

40%

25%

23%

17%

8%

Asking general questions

Getting directions while driving

Making calls

Dictating texts and emails

Checking updates

Other

Don’t have

opinion, 33%

Have opinion

but never tried, 17%

Tried once or twice, 13%

Monthly, 10%

Weekly, 14%

Daily, 13%


With changing customer expectations we are seeing a surge of group use of AI

How is AI responding to customer expectations?

Virtual Personal Assistant

Automation Chatbots


Amazon Alexa

The ecommerce giant is using the Echo speaker and the personal assistant Alexa to push the Prime service into all homes and create a bridge online-offline

Google Assistant

Google Assistant is now available on Google Home and on Pixel phones. Google was the last to launch its personal assistant but has significantly more data about consumer behaviours compared to its rivals

Apple Siri

Apple was the first to launch its voice assistant Siri integrated in iOS to provide a faster access to apps and features. In 2016 Siri has been extended to Car and TV OS and has been opened to third party apps

Big tech firms have packaged AI as a Personal Assistant

Alexa and Allo

Demo #1


Alexa, do you use AI?

A look under the bonnet


A system that can sense, analyse, learn and act

Sense and Feel Learn and Act

•  Natural language processing

•  Sensors processing

•  Audio recognition

•  Image recognition

•  Gesture recognition

•  Machine learning

•  Deep learning

•  Expert systems

•  Decision support systems

•  Robotics

•  Contextual Automation


How are AI and Deep Learning related?

Deep Learning Deep Learning breakthroughs drive AI boom

Machine Learning Machine Learning begins to flourish

Artificial Intelligence Early AI stirs excitement

1950’ 1960’ 1970’ 1980’ 1990’ 2000’ 2010’

Machine Learning & Deep Learning

A very shallow introduction @Andrew_Wyld


What is deep learning? Deep learning gets a lot of headlines for super cool applications:

•  Image recognition

•  Speech recognition

•  Language processing

It’s called “deep” because it’s a simple technique applied multiple times.

•  Each layer takes input from the last

•  Each layer gets feedback from the next

•  Layers can model more and more complex concepts as they get deeper


How do we get to that?

We will define a “shallow” learning technique using several inputs to predict one output.

Then we need to work out a way to apply it repeatedly. Since each layer is trained by comparing it with “correct” output, we need to find a way to get the training into the hidden layers.

We will combine several of these into a layer.

1 3 2


Training a classifier

How we get this:

Classifiers are trained using what’s called maximum likelihood estimation. We find a form for the classifier that produces results closest to the ones we expect.


Training a classifier (1)

A classifier at its simplest takes input data, combines it and provides a yes/no prediction.

Each input positively or negatively affects the prediction. In training, the positive and negative effects are summed and compared with a known value.

1 2

Inputs are strengthened or weakened or even reversed depending on their effect on the prediction. This is done till the prediction is correct.

3

prediction

true value



In practice, there will be a large number of different sets of input and output predictions. They will not tend to agree in their predictions all the time. Here, a direct relationship between input and output is present except in one case. The ideal mapping would be wrong here in some cases.



For this reason, the weight of inputs that don’t predict the outputs well will be reduced. Even then, there will be some errors (not pictured, because I’m awesome). The aim is to reduce the error as much as possible while still having a model that generalises well to new data.


Deep learning Hugely powerful. Nobody knows

what’s inside it.

Yet.

The “deep” in deep learning refers to the fact that several classifiers are stacked, one in front of the other. Each one can learn more sophisticated things by building on the previous layer. Nobody really knows what goes on in the middle layers (although we’re beginning to research it).


Stack high the classifiers!

A neural network is just a sequence of classifiers in a stack. Each layer can use the output of the previous layer as input; thus, by the end, features can be very sophisticated, based on complex combinations of other, simpler features. The hidden layers make the technique powerful but inscrutable.


Back propagation

Just as in simple classifiers, the last layer is trained by comparing predictions with reality. However, the inputs to the last layer are then scored on how “good” their predictions were. This score is used to train the next layer in … … and so on.


Transfer learning

The early layers of (for example) a cat recognition system will probably pick up general image features—corners, colour transitions, diagonals and so on—that would be useful for any image recognition task. If you want to make a dog recogniser but don’t have a lot of data, you could simply cut off the last layer, steal these early features, keep them the same, and glue a simple classifier on the end in place of the old last layer. This works surprisingly well.


Cool stuff: a very non-exhaustive list

Stanford machine learning course https://www.coursera.org/learn/machine-learning/ did it, loved it. University of Washington machine learning specialisation https://www.coursera.org/specializations/machine-learning/ doing it now. Tensorflow online neural network http://playground.tensorflow.org/ have fun! Google/Udacity deep learning course https://www.udacity.com/course/deep-learning--ud730 want to do it!

Any questions?

Andrew Wyld [email protected]

@Andrew_Wyld

How your business can benefit from Artificial Intelligence


Actionable insights and new value creation

Insights Usability

Gap

Deep learning!

AI bridges the insights usability gap

The data value chain

Advanced analytics and data

visualisation

Exponential growth of data

•  Smartphone penetration and usage

•  Social Media

•  Internet of Things

•  Digital payments

•  Big Data

•  Dashboards

•  Balanced scorecards

•  Infographics

•  Data Visualisation

•  Machine Learning

•  Deep Learning

•  Artificial Intelligence

•  Personal assistant (Alexa, Google assistant)

•  Proactive and contextual recommendation engines (Google now)

•  Decisions in the moment •  Tactical automation


AI allows companies to push customer value

expectations and deliver a compelling customer

experience

AI can be used to re-invent the way you deliver traditional

services and products

AI helps leaders constantly seeking ways to reduce

overhead costs and streamline business

processes

3 ways to realize business value

Reinventing business models

Creating a compelling customer experience

Optimising operations and efficiency


Creating a compelling customer experience

easyJet is using Artificial Intelligence to analyse all

different aspects of the 1.5bn hours flown each year to help identify ways to improve the

customer experience, predict customer needs and avoid

flight delays

1.5bn hours of flight

BMWi Genius is a customer assistant chatbot which

responds to questions on all BMW models

3,500 product features


Source: Bloomberg technology

Optimize operations and efficiency

Google cut its electricity bill by 15% using DeepMind-powered AI. Saving a few

percentage points of electricity usage means major financial gains for companies

like Google

In Sweden, Swedbank launched a AI web assistant

called Nina. In 2016 it achieved an average of 30,000 conversations per month and first-contact

resolution of 78% in its first three months

30,000 conversations/month

15% energy saving


Reinventing business models

AI-powered autononomous cars could be deployed by

2020 or sooner. Driverless cars are expected to completely

disrutp the automotive industry radically changing the business models from

ownership to mobility service

2020 or sooner

The raise of chatbot banking and AI money-managing assistants is expected to

change financial assistance business models. Companies like Digit and Cleo are taking advantage of conversational interface to deliver financial

assistance

$4 bn


Image recognition with Cortexica

Demo #3

Applications of Artificial Intelligence by Industry


Almost USD 1 billion has been invested in AI startups since 2013

New deal activity by industry

2012 2013 2014 2015 2016 (Q2)

Financial Services

Health

Advertising, Sales and Marketing

Travel / Transports

IoT / Wearables

Education

Retail / Ecommerce

Source: CB Insights


Applications of AI by Industry

Personal Assistants

Customer Assistants

Automated Robots

Algorithms Deep Learn.

Financial Services

Health

Advertising, Sales and Marketing

Travel / Transports

IoT / Wearables

Education

Retail / Ecommerce


Health > Personal Assistant

•  Training apps struggle to provide useful recommendations

•  UA integrated IBMs Watson to create a Cognitive Coaching System

•  Launched January 2016

•  10m+ downloads on iOS + Android to date

Under Armour cognitive training assistant


Health > Algorithms and Deep Learning

•  Launched DeepMind Health division to work with NHS

•  Research how AI can support NHS to improve performance and become more cost efficient

•  One of the first projects announced is Streams, an app that detects cases of acute kidney injury

•  Will develop open and interoperable technology while protecting patient data confidentiality

DeepMind to tackle clinician and hospital issues


Transports > Autonomous Vehicles

•  Launched in Pittsburgh, US on September 2016

•  Fleet includes 15 vehicles

•  Cars still have human drivers behind the wheel

“Developing an autonomous vehicle is basically existential for us” Travis Kalanick, Uber CEO

Uber launched first US self-driving taxis


Logistics > Autonomous Vehicles

•  Civil Aviation Authority (CAA) has granted Amazon special permission to test its aerial vehicles in UK

•  Amazon is also implementing AI in warehouses spawning an ecosystem where supply chains link themselves with each other enabling seamless flow of products and information from one end to the other

•  According to Accenture, 85% of organisations have adopted or will adopt digital technologies in their supply chain within 1 year

Amazon moving toward a nimble supply chain


Travel > Personal Assistant

•  AI-based travel concierge

•  Inspiration-based - Lets customers ask natural language questions

•  Recommendation engine based on IBM Watson

•  Connects interests with places, offers, social, cultural and economic data to provide targeted travel suggestions and offers

•  White label, Widget or APIs

Wayblazer AI powered travel concierge


Finacial Services > Customer Assistant

•  Assistant for banking customers

•  Based on IBM Watson

•  Started as an internal support tool, now provides answers to end customer's questions

•  Performs simple banking tasks like money transfers

RBS Luvo now responds to customer queries


Financial Services > Customer Assistant

Spixii transforms insurance sales

•  Guides users through the process of purchasing insurance based on their needs

•  Helps compare options and features

•  Conversational interface

•  Context based notifications

•  Also promotes offers and pushes upselling opportunities


Sales and Marketing > Customer Assistant

•  Developed in collaboration with chatbot company Allia

•  The car does the talking, with a tone of voice and personality

•  It automates the initial conversations with a potential client

•  Direct, emotional connection with the product

AI brings Jeep Renegade to life


Sales and Marketing > Algorithms and Deep Learning •  Campaign was made up of 2 components:

•  A video ad with people sent on blind dates based on matching taste profiles

•  A ‘flavour profiler’ branded website

•  Based on the IBM Watson cognitive engine

•  The profiler uses flavour profiles to suggest tailor made recipes

•  7 million views on YouTube in three days

Unilever Knorr “Love at first taste” AI campaign


Sales and Marketing > Algorithms and Deep Learning

•  Mariana identifies new target customers on social media platforms based on a comprehensive segmentation methodology based on AI algorithms and machine learning

•  The AI engine uses 50K data points from social, web and proprietary sources to define personas and exact customer archetypes

•  It also supports conversion, automating contents and communication. The deep learning engine crafts suggested topic areas based on each persona’s interests

AI-enhanced social targeting


Sales and Marketing > Algorithms and Deep Learning

•  Predictive lead scoring representing the likelihood each lead will convert into an opportunity

•  Forecasting sales and spending

•  Create predictive journeys

•  Proactive recommendations about what communication or product each lead may want

•  Sales and marketing automation

Salesforce integrated an AI platform in its CRM


What next

Robots Emotions

Zero UI

Creativity

Autonomous Vehicles

Sense and Feel

Learn and Act

Smarter Assistants

Pepper the robot with Emotion Robotics

Demo #3