Amplifying Human ngenuity - WKO.at

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A mplifying Human I ngenuity with Intelligent Technology DI. Harald Leitenmüller Chief Technology Officer Microsoft Österreich GmbH. www.Microsoft.com/AI

Transcript of Amplifying Human ngenuity - WKO.at

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Amplifying Human Ingenuitywith Intelligent Technology

DI. Harald LeitenmüllerChief Technology OfficerMicrosoft Österreich GmbH.

www.Microsoft.com/AI

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Breakthrough intelligence capabilities, in the cloud and at the edge

Digitalization - “Survival of the most Scalable”

t

2020

Harnessing signals from sensors and devices, managed centrally by the cloud

Intelligence offloaded from the cloud to IoT devices

Cloud

IoT (Industry 4.0)

Edge

AI

Globally available, unlimited compute resources

2022$1.9T $2.6T $3.3T $3.9T*

* “Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017-2025”, Gartner, April 2018.

Stan

dard

izat

ion

Aut

omat

ion

Self

Serv

ice

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Promise

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85%of Enterprises using AI

by 20201

25%Have started or are

planning to start their AI initiatives in the near

term2

1.Source: Gartner, Smarter with Gartner, 2017.2. Source: Gartner, CIO Report, 2017.3. Source: Gartner, Top challenges to the adoption of AI within your organization

Identifying use cases for AI

Defining AI strategy

Lack of skills

Top challenges for Enterprise3

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Machine LearningDeep Learning

Neural Networks

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1950 1960 1970 1980 1990 2000 2010

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2 + 3 = 5DataProgram/Rules

Output

TRADITIONAL PROGRAMMING

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Classic Programming vs. ML

2 + 3 = 5Easy

Not Easy

DataProgram/Rules

Output

TRADITIONAL PROGRAMMING

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Classic Programming vs. ML

2 + 3 = 5Easy

Not Easy

DataProgram/Rules

Output

TRADITIONAL PROGRAMMING

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Classic Programming vs. ML

For each photo: Cat ? Yes/No

MACHINE LEARNING

DataOutput/Labels

Program

DataProgram/Rules

Output

TRADITIONAL PROGRAMMING

Training Data

Engine

Model(z.B. ResNet 50)

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Standard Deep Neural Networks

http://scs.ryerson.ca/~aharley/vis/conv/

Data Input OutputDigit

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Deeper learning = representation learningDeep learning network

Inputlayer

Hiddenlayer 1

Hidden layer 2

Hidden layer 3

Output layer

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Source: Graphcore, ResNet-50 conv2

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Vision Speech Language

Microsoft AI breakthroughs

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Vision Analysis Decisions

Analysis DecisionsSpeech

Language

Learning

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Microsoft’s AI Capabilitieswww.Microsoft.com/AI

AI Platform

Businesssolutions

Infusing AI

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Microsoft Azure Cognitive Services AI Platform

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The Microsoft Intelligence Platform

InfrastructureIntelligence ON DATA

Cosmos DB

Intelligence COMPUTE

SQL DB

SQL DW Data Lake Spark DSVM Batch AI ACS

CPU, GPU, FPGA+

Edge

Services

Bot Framework

CUSTOM SERVICES

Azure Machine Learning

CONVERSATIONAL TRAINED SERVICES

Cognitive Services

Tools

VS Tools for AI

Azure ML Studio

CODING & MANAGEMENT TOOLS

Azure ML Workbench

DEEP LEARNING FRAMEWORKS

Cognitive Toolkit TensorFlow Caffe

Others (Scikit-learn, MXNet, Keras, Chainer, Gluon…)

3rd Party

Others (PyCharm, Jupyter Notebooks…)

AI Platform

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Ethical AI Decision Framework

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Let’s create a People First World with Intelligent Technology and without barriers!

Thank You!

https://www.microsoft.com/en-us/ai/ai-for-good