iot, ai, ml mix or how to deal with new technologies (borys pratsiuk technology stream)

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

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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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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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1

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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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*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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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

action in smart house environment.

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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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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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Bots are coming!

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Medalebot demo