mplementing AI into robotics can truly make the world a better place beyond our imagination. In this session we’ll dive into the basics of how AI works and explore the possibilities using this technology combined with robots in real-life scenarios.

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USE ARTIFICIAL INTELLIGENCE TO
MAKE SMART DECISIONS
Pieter Zuliani
Technology Evangelist
pieter.zuliani@pegus-apps.com
TRACK 3 - ROBOTICS FOR A BETTER WORLD

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CONTENT
What is Blockchain?
Decentralized Apps
Blockchain Decision Tree
Business Use Cases
Platforms
Crypto Currencies
Challenges

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CONTENT
Definition of a robot
Artificial intelligence
AI use cases
Deep dive into deep learning
Mixing AI and robotics
Robotics using AI today
The future of robotics using AI
Deep learning ethics

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DEFINITION OF A ROBOT

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DEFINITION
“A machine capable of carrying out a complex
series of actions automatically”
“A physically embodied artificially intelligent agent
that can take actions that have effects on the physical world”
“A machine that resembles a living creature in being capable of moving independently
and performing complex actions such as grasping and moving objects”

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• Industrial robots
• Domestic or household robots
• Medical robots
• Service robots
• Military robots
• Entertainment robots
• Space robots
• Hobby and competition robots
DIFFERENT KINDS OF ROBOTS

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• Industrial robots expected to grow from USD 44.02 billion in 2018 to USD
69.14 billion by 2023 (+9.45%)
• Service robots expected to grow from USD 11.27 billion in 2018 to USD
29.76 billion by 2023 (+21.44%)
ROBOTICS MARKET

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ARTIFICIAL INTELLIGENCE

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• Intelligence exhibited by machines or software,
rather than humans or other animals
• Weak AI (narrow) vs Strong AI (AGI)
• Huge boost in AI due to better
processors (TPU’s)
ARTIFICIAL INTELLIGENCE

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• Intelligence exhibited by machines or software,
rather than humans or other animals
• Weak AI (narrow) vs Strong AI
• Huge boost in AI due to better
processors (TPU’s)
AI OVER THE YEARS

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• Global revenues from AI for enterprise applications is projected to grow from $1.62B
in 2018 to $31.2B in 2025 attaining a 52.59% CAGR in the forecast period.
• Image recognition and tagging
• Patient data processing
• Localization and mapping
• Predictive maintenance
• Predict and thwart security threats
• Intelligent recruitment
• ...
AI - BOOMING BUSSINES

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• Garbage in = Garbage out
• You can’t buy intelligence
• Successful outcome is not guaranteed
AI - GROUND RULES

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AI - USE CASES

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• IDx-DR
• AI-based diagnostic system
• Early treatment can prevent blindness in more than 90 percent of cases
• The algorithm takes 20 seconds
AI USE CASES: DIABETIC RETINOPATHY DIAGNOSIS

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• Death risk assessment
• 95% accurate
• Analyzed 216.211 hospitalizations
• Analyzed 114.003 patients
• 46 billion data points
USE CASES - GOOGLE DEATH PREDICTION

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• Efficient cooling of data centers
• DeepMind Machine learning
• 40% reduction of energy usage
USE CASES - GOOGLE DATA CENTER COOLING

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DEEP DIVE IN DEEP LEARNING

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NEURAL NETWORKS

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• Modeled after connections in human brain
• Connections may "fire” signals
NEURAL NETWORKS
Signal
Inputs Outputs

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NEURAL NETWORKS
• Network of many neural nodes and links
(like, a LOT)
OutputInput

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NEURAL NETWORKS
• Network of many neural nodes and links
• Some connections are stronger than others
OutputInput

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NEURAL NETWORKS
• Network of many neural nodes and links
• Some connections are stronger than others
OutputInput

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NEURAL NETWORKS
• Network of many neural nodes and links
• Some connections are stronger than others
• Some nodes fire faster than others
Output
Configuration of
weights}
Input

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NEURAL NETWORKS - EXAMPLE
0.75 0.85 … 0.24
0.68 0.59 ⋯ 0.60
⋮ ⋮ ⋱ ⋮
0.43 0.17 ⋯ 0.97
0.91
0.76
0.43
0.33
0.99
0.81
0.12
0.91
0.11
0.94
“Six”

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NEURAL NETWORKS - TRAINING
• “Training” a Neural network
= adjust weights so output is correct
“Six”
0.91
0.76
0.11
0.94
0.43
0.33
0.99
0.81
0.12
0.91

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NEURAL NETWORKS
• “Training” a Neural network
= adjust weights so output is correct
0.63
0.13
0.79
0.61
0.22
0.11
0.91
0.76
0.97
0.04
“Five”

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NEURAL NETWORKS
• “Training” a Neural network
= adjust weights so output is correct
• Repeat for other examples
like… a LOT
Output

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DEEP LEARNING

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DEEP LEARNING
• Uses layered networks
• Each layer: abstraction built on top of previous layers
=> Hierarchy of concepts
Output
Input layer Output layerLayer 1 Layer 2

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• Recurrent Neural Networks (RNNs)
• Allows backlinks => state / memory
• Language modelling, music analysis
• Convolutional Neural Networks (CNNs)
• Computer Vision, Speech Recognition
• …
DEEP LEARNING – DIFFERENT NETWORKS

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DEEP LEARNING - PROPERTIES
• Good at the trained task
• Not applicable outside of task it was trained for
(Nowadays may be high-level tasks)
“Six”

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DEEP LEARNING - PROPERTIES
• Good at the trained task
• Not applicable outside of task it was trained for
(Nowadays may be high-level tasks)
• In reality, learned concepts not human-readable
Output???
???
??? ???
???
???

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DEEP LEARNING - TAKE-AWAY
• Uses Neural Networks
• Layered structure => learn a hierarchy of concepts
• Train the network to get correct weights for the tasks at hand
• Not human-readable
OI

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• Unsupervised learning
• Supervised learning
• Reinforcement learning
DEEP LEARNING IN ROBOTICS TODAY

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MIXING AI AND ROBOTICS

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• Computer vision
• Natural Language Processing
• Cognitive computing
• Robotic Process Automation (RPA)
AI COMBINED WITH ROBOTICS

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ROBOTICS USING AI TODAY

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• Launched 2002 : No AI
• Now : iAdapt 2 using VSLAM
• Advanced decision making capabilities powered by AI.
• Identifying obstacles
• Remembering the most efficient routes
CONSUMER GRADE - IROBOT ROOMBA

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• Multi-purpose Robot (Mowing, Security, Entertainment)
• Mapping of surroundings
• Object recognition
• Autonomous decision making
CONSUMER GRADE - TOADI

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TOADI IN ACTION

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• Dedicated 14 robots to the task.
• Train a deep convolutional neural network
• 3,000 hours of practice (and 800,000 grasp attempts) before
it saw “the beginnings of intelligent reactive behaviors.”
GOOGLE TEACHES ROBOTS TO GRASP RANDOM OBJECTS

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• Collaborative robots
• Trained by demonstration
• Collecting data through sensors
• Taking appropriate action on data gathered
• Learning from their work
COBOTS - BAXTER

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BOSTON DYNAMICS - SPOT MINI

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BOSTON DYNAMICS - SPOT MINI COLLABORATION

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BOSTON DYNAMICS - ATLAS

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THE FUTURE OF ROBOTICS USING AI

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• Robots will keep us safer, especially from disasters
• AI is to become really an assistant for humans
• AI will improve medical care
• Work will shift from repetitive tasks to more creative tasks.
THE FUTURE OF AI IN ROBOTICS

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AI ETHICS

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• Unemployment: What happens after the end of jobs?
• Inequality: How do we distribute the wealth created by machines?
• Humanity: How do machines affect our behavior and interaction?
• Artificial stupidity: How can we guard against mistakes?
• Racist robots: How do we eliminate AI bias?
• Security: How do we keep AI safe from adversaries?
• Evil genies: How do we protect against unintended consequences?
• Singularity: How do we stay in control of a complex intelligent system?
• Robot rights: How do we define the humane treatment of AI?
AI ETHICAL ISSUES

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A driverless car is carrying a passenger. A truck is speeding towards the car from the back
and the algorithm knows there is going to be an accident.
• Option 1: Stay in the current lane, allow the truck to ram it.
• The passenger will die.
• Option 2: Change lanes to the right
• Will result in a fatal accident for the car on the right which is carrying a 65-year-old
• The passenger in the driverless car will survive with minor injuries.
• Option 3: Change lanes to the left
• Will result in a fatal accident for the car on the left which is carrying a 10-year-old girl to school
• The passenger in the driverless car will survive with minor injuries.
ETHICS EXAMPLE : DRIVERLESS CAR

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• AI is already all around us and fast evolving
• AI and robotics will evolve beyond our imagination
• AI can hugely improve and automate your business processes
• The rise of Cobots will enhance the way people work
ROUND UP

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One group, five brands
Our services are marketed through 5 brands each
addressing specific missions in product development.
INTEGRATED PRODUCT DEVELOPMENT
ON-SITE
PRODUCT
DEVELOPMENT
DIGITAL
PRODUCTS
DEVELOPMENT
OPTICAL
PRODUCTS
DEVELOPMENT
VENTURING