Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneurs and startuppers. Annually it takes place at the beginning of October in Lviv at Arena Lviv stadium. In 2016 the conference gathered more than 1800 participants and over 100 speakers from companies like Microsoft, Philips, Twitter, UBER and IBM. More details about the conference at itarena.lviv.ua.

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IoT, AI, ML Mix or How to Deal with New
Technologies
Borys Pratsiuk, Ph.D
Head of R&D Engineering

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C developer
2004 2005
С/С++
developer
2006
2009 - 2011
Asm, С, Android
2007 -
2013
defend Ph.D.
assistant
Profesor in KPI
2012 - now
Join Ciklum
• Senior Android
• Team Lead
• Android
Architect
• Head of R&D
Who I am

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3
Chat bots
overview
Agenda
What is it IoT?
Existing types.
Examples
Science converted into
the product. How
algorithms began matter
for business?
What happening
in AI today?
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Types of IoT
Smart Homes Wearable devices Connected devices
Industrial automation Agricultural Smart Cities

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Top 5 Industries
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3
4
5
*Gartner research 2016 – Industry grow

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Smart homes

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Wearables / gadgets

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Connected devices

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Connected devices

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Case study: Digital transformation in IoT
PoC results
• Wireless channel of data transmission through Wifi
connection from device to mobile and tablet
• Video streaming from night vision to mobile
• Remote device management
WiFi
Challenge
• Transform optical device into smart
IoT solution
• Share video and photos
• Create social network

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Industrial

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Agricultural

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Smart Cities

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IoT world 2016 hackathon
The Solution – Empowering Responders
Smart AED
Predix
Cloud
App
Spark
Channel
Responder’s
Android App
Predix
Time
Series
• Public Safety Images
• Traffic Speed
Reverse
Geocode

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Ciklum team solution
Technology stack:
• Java VM (Predix) for Intel
Edison
• Node.js for embedded
• Ruby on Rails
• Android Java
• Native C
• API’s integration

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Cloud providers for IoT

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Communication protocols
Frequency: 2.4GHz (ISM)
Range: 50-150m (Smart/BLE)
Data Rates: 1Mbps
(Smart/BLE)
Frequencies: 2.4GHz /
5GHz
Range: ~ 50m
Data Rates: 150-200Mbps
(latest 802.11-ac
standard should offer
500Mbps
to 1Gbps)
Frequency: 2.4GHz (ISM)
Standard: Thread, based
on IEEE802.15.4 and
6LowPAN
Frequency: 900MHz (ISM)
Range: 30m
Data Rates:
9.6/40/100kbit/s
Frequency: 2.4GHz
Range: 10-100m
Data Rates: 250kbps

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AI and human brain

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History of AI
McCulloch & Pitts Rosenblatt Ivakhnenko & Lapa
Group Method of Data Handling (GMDH)
Perceptron
A Logical Calculus of the
Ideas Immanent in Nervous
Activity
1943 1957 1965 …

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Why Deep Neural Network now?

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THE BIG BANG

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NVIDIA is a leader in AI hardware

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What is Neural Networks?
An artificial neural network is composed of many artificial neurons that are linked
together according to a specific network architecture. The objective of the neural
network is to transform the inputs into meaningful outputs.
Tasks:
• recognizing a visual object;
• anomaly detection;
• event prediction;
• voice recognition;
• deciding a category of potential object;
• natural language processing.
http://videolectures.net/deeplearning2015_vincent_machine_learning/

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Artificial Neuron

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ANN Example – not good http://playground.tensorflow.org/

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ANN Example – OK

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Things we want to do with data
Images
Audio
Text
Image labeling
Speech
recognition
Web Search and
Natural Language
Processing

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Network layers for Image recognition

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Case Study: Pedestrian Tracking
The smart IP camera with pedestrian tracking
technology could be used within shopping
malls for traffic counting, crowd monitoring
and business intelligence with the back-end
servers being freed up to perform data mining
and data collection. The technology could
sooner or later become integrated into
consumer video monitoring solutions.

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Case Study: Face Recognition
• Deep convolutional neural networks applied for the face recognition purposes could be used for
door smart locks and security systems.
• Face recognition technique combining with e.g. fingerprint scanner could increase security level and
permissions given.

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What is it ?
1. Machine understands what you speak
2. What you don’t speak
3. Other sounds too
What is it not . .
Does not deal with ultrasonic wavelength
Only human audible sounds are under study now
Problem?
Understand what language is in record
Speech processing and recognition

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• Cloud Speech API + AI = fast and furious Google Now
• Wolfram Alpha + AI = making jokes Apple Siri
• AWS + AI = customizable Amazon Alexa
• Bing Speech API + LUIS = stupid Microsoft Cortana
• SoundHound + AI = Hound
• api.ai Engine + AI = api.ai Assistant (Google!!!)
• wit.ai Engine + AI = Facebook wit.ai M
• other
What if to add Artificial Intelligence?

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• Messaging-as-OS: Messaging can be a
platform
• The app problem: People are reluctant
to install apps
• The “conversational interface”: A new
model for interacting with online
services
Major chatbot trends

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How to make your own chatbot? If you are
not a programmer
Select the Engine Provide scenarios and make bot
train
Talk with your bot

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Chatbot Engines
Engine Platforms Pricing
https://api.ai/ Facebook messenger,
Slack
0-899$/month
https://www.itsabot.org/ Slack, Twitter, email,
SMS
free
https://chatfuel.com/ Facebook messenger,
telegram
free
https://smooch.io/ Facebook messenger,
Telegram, Line,
WeChat, Shopify,
Twillo, Email, etc
0-100$
https://meya.ai/ Twitter, Facebook
Messenger, Telegram,
Slack and Kik
0-200$
https://wit.ai/ Facebook messenger free

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Conversational commerce
Bots monetization:
● Bots + sponsored & native content. After
subscription the client will receive a
context advertisements similar with bot’s
tematic
● Bots as a Services. B2B Bots that help
people and teams be more productive,
manage tasks or tackle communications
challenges will replicate business models
being used by existing B2B software
● Retail sales bots;
● Payment simplification bots

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AI betchat

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Case Study: bot and voice controlled lamp
Amazon Echo is a hands-free speaker
you control with your voice. Echo
connects to the Alexa Voice Service
to play music, provide information,
news, sports scores, weather, and
more—instantly. Its ready-to-use
technology but still quite raw all
you need to do is to write a custom
skill to do any action you want,
e.g. you are saying: “Alexa, ask
lamp switcher to set lamp color to
green!”

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Case Study: bot and voice controlled lamp
wit.ai service allows us to develop
bots for various applications.
Users can operate with the
different inputs of information
(voice, text messages, gestures,
etc.). After acquisition of command
the information could be send
directly to the bot (in case with
text input) or transmit to the
preprocessing service (as speech
recognition service in case with
voice control). The bot will answer
user’s request with a text, voice,
content or perform a command or an

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Case study: Fino app – AI in finance
https://www.facebook.com/finoapp
http://www.finomon.com

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Messengers with bot support
Native

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Cloud speech to text providers and bot
engines

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Case Study: Voice recognition and
natural language processing
Voice recognition and natural
language processing is one of the
most important things for IoT
purposes.
Voice recognition technique was
implemented to prepare your
favorite cocktail, e.g. you’re
saying: “Scoofy, make my favorite
drink!” and device makes your
favorite drink based on your
preference and previous history.

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Frameworks and tools for Deep Learning
https://github.com/aymericdamien/TensorFlow-Examples

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Bots are coming!

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Medalebot
demo