PowerPoint Presentation

Deep machine learning formaking sense of biotech dataFROM clean energy to smart farming

Wesley De NeveIDLab, Ghent University imec, BelgiumCenter for Biotech Data Science, Ghent University Global Campus, KoreaStephen DepuydtPlant Systems Biology, Ghent University VIB, BelgiumCenter for Plant Bioactive Compound Research, Ghent University Global Campus, KoreaNovember 2, 2016 Korea-Europe International Conference on the 4th Industry Revolution

lab of plant growth analysis

Background Wesley De NeveAcademic credentialsMasters degree in computer science (2002)at Ghent University, BelgiumPh.D. degree in computer science engineering (2007)at Ghent University, Belgium

EmploymentIDLab at Ghent University imec, BelgiumGhent University Global Campus (GUGC), Korea2

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Background Stephen DepuydtAcademic credentialsBio-engineer in cell and gene biotechnology (2004)at Ghent University, BelgiumPh.D. degree in plant biotechnology (2008)at Ghent University, Belgium

EmploymentPlant Systems Biology at Ghent University VIB, BelgiumGhent University Global Campus (GUGC), Korea3

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Ghent university global campus4

Bachelor

Master

Ph.D.

Environmental Technology

Molecular Biotechnology

Food Technologysuccessful collaboration between Korea and EU (Belgium)

Ghent university global campus5

Incheon Global CampusSUNY at Stony BrookGeorge Mason UniversityUniversity of UtahGhent University

Ghent university global campusFive research centersPlant Bioactive Compound ResearchFood Research Environmental and Energy ResearchBiomedical Research Biotech Data Science

Combination of wet lab and dry lab research and educationCenter for Biotech Data Science provides data analytics for wet labs6

context7

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A world of exponential change

Law of Moore has resulted in an exponential growth of datawill bring a whole new wave of societal and economic change

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cleaner energyhealthier societyimproved agriculture

A world of exponential change

Human brain power is not increasing at an exponential rateneed for advances in the field of big data science, facilitating automatic extraction of actionable knowledge from vast amounts of raw data

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artificial intelligence

Machines will soon extract and store most of the worlds knowledge, and may become as intelligent as people

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FacebookMAppleSiri

AmazonAlexa

MicrosoftCortana

IBMWatson

GoogleAssistant

Economic impact$57trillion dollar by 2025source: report McKinsey Global Institute, May 2013

Creation of the first $1 trillion company?source: Venture Beat, October 201611

Mobile InternetAutomation of knowledge workInternet of ThingsCloudAdvanced roboticsAutonomous and near-autonomous vehiclesNext-generation genomicsEnergy storage3-D printingAdvanced materials

Deep machine learningAlgorithms able to automatically construct expert knowledgetypically through the use of artificial neural networks

Alternative to manual construction of expert knowledgetime consuming and thus expensive

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InputFeedbackOutputSelf-learning,black-box systems

popularity of deep learning13

Geoff Hinton

Andrew Ng

Yann LeCunIntel is paying more than $400 million to buy deep-learning startup Nervana Systems (Aug 2016)Google Acquires Artificial Intelligence Startup DeepMind For More Than $500M(Dec 2014)Deep Learning Enterprise Software Spending to Surpass $40 Billion Worldwide by 2024 (May 2016)Twitter pays up to $150M for Magic Pony Technology, which uses neural networks to improve images (Jun 2016)Apple Acquires Machine Learning Startup Turi For $200 Million (Aug 2016)Yoshua Bengio

popularity of deep learning

Deep neural networks: not the new kid on the block

1943: McCulloch-Pitts network1958: Perceptron by Rosenblatt1960: Delta rule by Widrow and Hoff1975: Backpropagation by Werbos1975: Cognitron by Fukushima1980: Neocognitron by Fukushima1989: Convolutional networks by LeCun

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popularity of deep learningAvailability of cheap and massive computational powerGPU computingcloud computing

Availability of large data setssocial media applicationssensor output (Internet of Things / Internet of Services)

New algorithmic techniquesdropoutrectified linear units

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popularity of Deep learning16

Google DeepMind &Ghent University (S. Dieleman)

popularity of deep learning17Samsung applies deep learning to createbreast cancer ultrasound algorithmFierce Biotech 6/2016New company plans to revolutionize genomic medicine with deep learningNew Atlas 7/2015Why Genomic Medicine Needs Machine LearningMIT Technology Review 5/2016Google DeepMind pairs with NHSto use machine learning to fight blindnessThe Guardian 7/2016Google Translate Star Leaves Venter'sHuman Longevity For Illumina-Backed GrailForbes 9/2016

cleaner energy18

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wind turbine condition monitoringMulti-sensor monitoring of bearings to detect faults early oninfrared imaging, vibration data, and temperature dataClassificationwhite box models: random decision forests and support vector machinesblack box models: convolutional neural networks19

Healthy wind turbineBroken wind turbine

wind turbine condition monitoring20

healthier societies21

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Genome annotation22?What part of a genome corresponds to what functionality?Which anomalies in a genome correspond to diseases?Can we manipulate a genome to avoid or cure diseases?

Requires mapping the high-level structure of a genome

ACCAGGTAAGCGCATCCGACATCTCTCAACGAGTCGAC

Start of gene: yes or nodeep learning

Genome annotation23

Ongoing race to create the first database with >1 million genomesparticipants include AstraZeneca and Craig Venters Celera Genomics

Breast cancer detection24

MammogramNormalBenignMalignant

1) Classify an input image as either normal (no lesion), benign, or malignant2) Upon classification as either benignor malignant, segment the lesionUpon classification asnormal, no segmentationis used The red part of the heatmap below shows wherethe lesion is located

deep learning

Breast cancer detection25

Digital Mammography Challengeorganized by DREAM & Sage Networkspart of Vice President Joe Bidens$1 billion Cancer Moonshot initiativewill make 640,000 de-identifiedmammograms available

improved agriculture26

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Farm condition monitoring27?Where will we find enough food for 9 billion people by 2050?

Smart farming: use sensors to make farms more intelligent and connected, so to be able to collect data about crop yields, soil-mapping, fertilizer applications, weather, and plant growth

Collected data allow for improved decision-making

condition monitoring inautomated / programmablegreenhouses (food computers)condition monitoring ingreenhouses with aerial robots (drones)

condition monitoring in greenhouses28

condition monitoring in greenhouses29

Drone-based phenomicsmeasurement of the physical and bio-chemical traits of organisms as a function of genetic and environmental changeshypothesis: gives less stress to plants

Research questionswhat is (not) possible?what is the role of (deep) data analytics?how to scale to a real-world farm?

concluding remarks30

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Concluding remarksIndustry 4.0 is about the construction of condition monitoring systems for both industrial and biological machinerydeep machine learning will play a key role in detecting and recognizing patterns in the vast amounts of data generated31

open challengesDeep machine learning is an art, not a scienceneed for hard and fast rules to come up with an architecture that works

Deep machine learning is a black box approachneed for interpretable features that help explain certain decisions

Deep machine learning does not support reasoning and common senseneed for better insight into the way the human brain worksbest computer available

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open challengesMachine and human intelligence need to be combinedrace with the machines, not against the machines (Erik Brynjolfsson)33

Thank you for your attention{wesley.deneve, stephen.depuydt}@ghent.ac.kr