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Artificial Intelligence

is here

Jarkko Ylipaavalniemi

D.Sc. (Tech), IEEE CBP

Technology Architect, Accenture

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What is artificial intelligence?

1: a branch of computer science dealing with the simulation of intelligent behavior in computers

2: the capability of a machine to imitate intelligent human behavior

the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages

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Human intelligence

• The human brain connectome

– It contains around 100 billion neurons

– Each neuron can have up to 10 000 connections to other neurons

– Connections are mediated through an estimated 1 000 trillion synapses

– Perhaps a memory of 1 000 terabytes

– And tons of other stuff too…

The human brain has a peakpower consumption of 20 W

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AI in popular culture

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AI in practice – a typical day?

7 am Gesture Alarm Clock: Alarm clock wakes you up 15 minutes early because it detects traffic delays

8 am Tesla Autopilot: Model S automates steer, turn, speed, park, and data is transmitted to the fleet to help system learn and improve

10 am Charles Schwab’s Robo-Advisor: Allocates stocks and investments based on a completed questionnaire

2 pm Enlitic: Turns large amounts of medical diagnostic data into deep insights and finds subtle patterns

4 pm Cubic: Suggests a movie when you ask “What should I do tonight?”

5 pm Automated Insights: WordSmith finishes writing a news article to be published on Associated Press news in just two minutes

8 pm X.ai: Ask virtual assistant, Amy, to schedule a meeting with CEO at 2pm tomorrow

2 am Keeker: While you are sleeping, smart robot keeps your home secure

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AI in practice - commerce

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Where are we today?

Apple’s Siri serves over a billion requests a week with 5% word error rate

Facebook’s DeepFace uses a 9-layer deep neural network having over 120 million parameters trained with 4 million facial images belonging to 4.000 identities, reaching an accuracy of 97.35% on a benchmark dataset, beating previous state of the art by more than 27% and closely matching human-level performance

Google DeepMind’s Atari game player outperforms a professional human player in 29 of 49 games tested, trained with over 10 million frames of gameplay

Facebook’s Collaborative Filtering recommends items based on 100 billion ratings, more than a billion users, and millions of items

Google DeepMind’s AlphaGo just won 4-1 against the World Go champion.

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Why now? – Computing power

• Google DeepMind’s AlphaGo runs on 1 920 CPUs and 280 GPUs

– Roughly equivalent to the fastest supercomputer in the world around 2008

– Assuming humans would be able to do 10 games every day, AlphaGo first studied game records for 44 human-years, followed by playing against itself for 8 200 human-years

– Compared to Deep Blue (the famous Chess supercomputer from 1997), uses about 100 000 times more computing power, but still evaluates thousands of times fewer positions meaning that position evaluation in AlphaGo takes hundreds of millions times more computation

– Actually a single GPU wins best amateur professionals!

• What about chess then?– Using the same approaches and only a few GPUs a system can learn to play

chess on a master level in 3 days

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Why now? - Accuracy

• ImageNet Large Scale Visual Recognition Challenge– The standard algorithm benchmark– 1 000 object classes– 1 200 000 training images

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TECHNOLOGY LANDSCAPE

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Rapid technology advancements

• Unprecedented data volumes: By 2020, there will be more than 44 zettabytes of data, 35% of which will be considered useful for analysis in 2020

• Decreasing cost of storage: Over the past 30 years, the cost per gigabyte of hard disk data storage has halved every 14 months, from $3.5M in 1980 to $0.03 in 2015

• Virtually unlimited computing power: Public cloud computing will reach almost $70 billion in 2015 worldwide

• Advances in artificial intelligence technologies: AI startups in the US alone have increased 20-fold in the past four years

• Broadening IT scope: 88% of executives agree the IT organization needs to broaden its scope and keep pace with evolving IT needs

Sources: 1“EMC Digital Universe Study,” with data and analysis by IDC, April 2014; 2“Disk Drive Prices (1955-2015)”, John C. McCallum, 2015; 3“ Public Cloud Computing to Reach Nearly $70 billion in 2015 Worldwide, According to IDC,” IDC, Press Release, July 21, 2015; 4”Artificial Intelligence Startups See 302% Funding Jump in 2014,” CB Insights, February 10, 2015; 5 “Calibrating Multi-Speed IT for the Varied Demands of a Multi-Speed Business,” Accenture, 2015.

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Growing number AI vendors

Venture Scanner is tracking 957 AI companies across 13 categories, with a combined funding of $4.8B.

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Virtual Agents or Digital Assistants

Virtual Agents

Virtual Assistants for Personal Computing

Natural Language Question Answering

Virtual Service-Desk Assistant

A new UI: the“new keyboard”

Expert help: a new way to retrieve information

Replaces/helps back-office employees

Siri

Cortana

Silvia

Watson

Wolfram Alpha

IPsoft

NextIT

Nina

Artificial Solutions

Uses Uses

Personal Assistant that uses Google’s Knowledge Graph to answer questions.

EasyAsk’s Quirianswers CRM, ERP & Business Intelligence questions asked in natural language.

Interactive characters with human-like traits and communication styles. Virtual Agents can answer questions and perform business processes.

IPsoft

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Open platforms

• Everyone is in and shares very openly– Apple, Google, Facebook, Microsoft, Baidu, Amazon, IBM,

NVIDIA, Qualcomm, Samsung, Cloudera, Nervana, …

• Data analytics– Storm, Spark, Kafka, Hadoop, HPCC, …

• Machine learning– Scikit-learn, Shogun, Mahout, Samoa, OpenCV, Accord,

Oryx, Weka, Golearn, ConvNetJS, CUDA, …

• Deep learning– TensorFlow, Torch, Caffe, Theano, Chainer, CNTK, mxnet, …

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Machine learning ecosystems

Training as a Service

Vision API

Speech API

Translate API

Pre-trained Services ML-platform as a Service

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VIDEO ANALYTICS

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Accenture Video Analytics Service Platform

Command & Control Client TerminalsVisualization Operations External Systems

Operational and Predictive Analytics

Core Analytics

Video AnalyticsSocialMediaListening

SensorMonitoring

OperationalSystemsMonitoring

Data Sources

Face Matching License PlateMatching

Crowd Detection

Traffic Monitoring Tracking Flow Management

Anomaly Detection People Counting Vehicle Classification

CCTV Cameras People 3rd Party Sensors3rd Party OperationalSystems

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Video Analytics Applications in Customer Facing Facilities

Retail stores Large scale shopping malls

Facilities operators

Banks / Insurance companies

Public service agencies

Telco agencies

Petrol stations Entertainment parks

Video analytics enable real-time monitoring of facilities and activation of safety and security incident alerts and responses. In addition,

analytical and predictive modeling help optimize facilities operations, and gather marketing insights for commercial tenants

Real-time monitoring

•Visual tracking of people and vehicles, federated solution to monitor multiple buildings

Operational performance measurement

•Service time, wait time, queue monitoring

Commercial space layout optimization

•Identification of hot and cold zones, heat map

Personalized customer experience;

•Dynamic marketing displays, recognition of special customers

Customer analytics

•Demographics analysis, footfall analysis, identification of behavioral trends and anomalies

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Video Analytics Applications in Transportation

Ports Airports

Trains Public Transportation

Video analytics enable real-time monitoring of travel environments and activation of safety and security incident alerts and

responses. In addition, analytical and predictive modeling help optimize operations, and gather marketing insights on

passenger behaviors

Situational awareness

•Integrated central control centers, integration with other sensors and wearables

Onboard and in station real-time monitoring

•Visual tracking of people and vehicles; detection of security and safety incidents

Operational performance measurement

•Service time, wait time, route path analysis

Customer analytics

•Demographics analysis, travelers count, identification of behavioral trends and anomalies

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CASE(TOY) CAR INSURANCE

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Classification of car damage level

Given only an image, classify a car as:• Undamaged• Damaged• Totaled

Problem details:

•An Insurance Company wanted to automate claims processing using advanced machine learning technology, namely Deep Learning

•When customers sent a picture of their damaged car, the Company would like to have the ability of automatically detect the level of damage and use it to, for example, order spare parts and possibly detect fraud, if any

•Accenture developed a Convolutional Neural Network algorithm (which belongs to the family of Deep Learning techniques) using a data set of toy images

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About deep learning

Gradient-Based Learning Applied to Document RecognitionLeCun, Bottou, Bengio and Haffner, Proc. of the IEEE 1998

Imagenet Classification with Deep Convolutional Neural NetworksKrizhevsky, Sutskever and Hinton, NIPS 2012

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Training Data – 500 images per setUndamaged cars Damaged cars Totaled cars

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Damage classification results

90% accuracy achieved

Value delivered:

•By automatically detecting level of damage, an Insurance Company saves on sending a human to assess the damage

•Apply the same technique for more use cases and other lines of businesses like Home Insurance with enhanced complexity and accuracy

•For Auto Insurance, spare parts could be ordered automatically

•For Home Insurance, evaluate building resistance, check if customers are telling the truth about additions to houses, identify multiple damages

•Similarities/differences in damage patterns could be used to detect fraud

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CASEPUBLIC TRANSIT

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Use case – People counting

• Manual count is based on manual observations of the videos

• Estimation is the number of travellers estimated by the system

• Coloured bounding boxes computed by the different algorithms in place

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Use case – Abandoned objects

• Left object threshold set to 6 seconds

• The passengers nearby are not taken into consideration by the system for this assessment

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Accuracy and benefits

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BUSINESS POTENTIAL

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AI in enterprise

Text Analytics

Research Assistants

Image Analysis Multimedia Search Cognitive

RoboticsVirtual Agents

Expert Systems

Video Analytics

Identity Analytics

Data Visualization

Domain-specific Calculations

RecommendationSystems

Self-Adjusting IT Systems

Speech Analytics

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Technology vision survey

• 70% of corporate executives say they are making significantly more investments in AI than in 2013

• 55% say they plan to use machine learning and embedded AI solutions extensively

• 78% report the impact will be a significant change or complete transformation over the next three years for their industry

• 84% agree organizations will realize significant competitive advantages with AI

Source: “Accenture Technology Vision Survey,” Accenture, 2016.

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THE (NEAR) FUTURE

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Exponential growth

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Popular culture revisited

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