Artificial Intelligence Workshop
20th October
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Somo accelerates mobile transformation through rapid innovation to create
products and experiences your customers and employees will love.
Transforming Live
Transforming Engagement
Transforming Content
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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
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We are seeing exponential growth where we are used to linear growth
We are in a period of change
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We are seeing exponential growth where we are used to linear growth
We are in a period of change
"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
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We are in a period of change
"The development of full artificial intelligence could spell the end of the human race." Stephen Hawkings
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We are in a period of change
"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
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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
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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
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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
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Who are the main players? We have seen intense acquisition activity in AI market 2011-2016
Source: CB Insights
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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
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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%
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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
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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
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Alexa, do you use AI?
A look under the bonnet
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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
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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
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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
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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
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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.
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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
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Training a classifier (2)
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.
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Training a classifier (3)
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.
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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).
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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.
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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.
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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.
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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!
How your business can benefit from Artificial Intelligence
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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
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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
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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
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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
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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
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Image recognition with Cortexica
Demo #3
Applications of Artificial Intelligence by Industry
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
Q&A