Edinburgh 2020 digital changes everything v10
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The document discusses how digital technologies and data can help address sustainability issues like reducing CO2 emissions and increasing climate change resilience. It provides examples of how technologies like the Internet of Things, analytics, and cognitive computing can give insights into issues like energy usage, transportation flows, and infrastructure management. These insights can then be used to improve operational efficiency, better prepare for and respond to events, and foster new forms of collaboration. The document argues that, with the right approaches and by treating data as a new natural resource, digital technologies have the potential to significantly reduce CO2 emissions from various economic sectors and help build more climate resilient systems.
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Edinburgh 2020 digital changes everything v10
- 1. “Digital Changes Everything” Why , How and Some Examples
- 2. Digital Agenda Sustainability Agenda Data is the New Natural Resource Trend
- 3. The issues of sustainability , C02 emissions and climate change resilience are to significant extent information issues: Peter Williams CTO: IBM Big Green Innovations For CO2 & Climate Change: • What damage are we causing? • What might happen if we continue that damage? • How can we mitigate the damage ? • Is our mitigation working? For Climate Change Resilience : • What might happen? • What will break? • How can we mitigate the risk? • Are we on track with our plans? • What just broke? • Who/what needs help first?
- 4. Looking at Global CO2 Bad News Good News 2% 98% Use of IT accounts for 2% of C02 emissions Use of IT can significantly control and reduce the other 98% emissions caused by other sources
- 5. What is driving the change? It is estimate … 1 billion transistors per human Each costing 1/10 millionth of a cent.
- 6. http://research.ibm.com/cognitive-computing/neurosynaptic- chips.shtml#fbid=TlDdajeaAJZ IBM: SyNAPSE Chip: 5 Billion Transistor Inspired by the brain, very low power consumption
- 7. An opportunity to think and act in new ways : economically, socially and environmentally. + + = A Smarter Planet Something profound is happening…. Instrumented Interconnected Intelligent
- 8. As a New Natural Resource Data is changing quickly VolumeofData PercentageofUncertaindata
- 9. 9 Big Data This is then amplified by a network effect, combining technologies in new ways Source: Bill Chamberlin Cloud Virtualization Social Business Mobile Data Visualization Green IT Analytics Consumerization BYOD Digital Marketing Cognitive Computing IoT Gamification Security
- 10. 10 Look for the triangles for new interesting innovations … Source: Bill Chamberlin Mobile Data Visualization Analytics IoT
- 11. What makes a system smarter?
- 12. 3 Patterns that enable C02 reduction AND increased performance Operational Efficiency Incident/Event Management Planned Maintenance Asset Failure Extreme Weather GOAL:- Situational awareness to keep on track GOAL:- information & processes to better respond Incidents GOAL:- connecting BOTH people AND systems to drive collaboration Intelligent Operations Better Collaboration and Response
- 13. • Situational awareness • Analytics • Smarter response Foresight examples: • Cave Man • Agricultural Revolution • Industrial Revolution • Digital Revolution • Environmental Revolution What is it that drives the ability to change the system? Foresight
- 14. Some Examples of the Digital Agenda enabling improvements in CO2 reduction
- 15. Using Biomimicry Self Assembly Molecular Chemistry to Increase Energy Efficiency of Computer Chips By more precise insulation layers IBM POWER Processor“Air Gap” Chip Technology Designing like snowflakes and sea shells
- 16. IBM & Airlight Energy Solar Concentrator: IBM Zurich:
- 17. IBM & Airlight Energy Poly-generation: Electricity, Heat, Cooling
- 18. Solar Concentrator = Sun x 2000 • Simple Materials: concrete structure, Plastic foil film mirrors • High efficiency solar cells • Supercomputer Cooling Output = 50% overall system efficiency 12 kwatts of electrical power 20 kwatts of heat (on a sunny day) Hot Water at over 85 DegC http://www.ted.com/watch/ted-institute/ted-ibm/gianluca-ambrosetti-solving-the-energy- crisis-one-sunflower-at-a-time
- 19. Visualising Sustainability Data Visualisation Insight and change happen here with people in the system Collaboration
- 20. 1. Moving from guessing to knowing 2. Connecting a network of people, systems and data to create collective insight 3. Making sense of large amounts of data – in space and time 4. Evidence based decisions at pace 5. Ability to apply the right tools: • Automate the simple • Make sense of the complicated through analytics • Make sense of the complex & unpredictable through visualisation 5 Smarter City Essentials for making cities more efficient :
- 21. City in motion: monitoring using existing data to design better solutions Istanbul: Movement Analysis using Vodafone network data • 2.4 million phones w. 156 million events/week • Origin Destination analysis • Accurate detection of meaningful locations • Used for new metro line planning Dubuque, USA: Bus Route Optimization Optimized routes will: • Reduce OPEX and CO2 by 40% • Generated 37% more demand • Reduce average commuter travel time by 60%
- 22. Seeing the Whole System: Dublin Bus Network Public Transport Awareness Tool used by citizens and planners ‘Open Data’ + ‘Big Data’
- 23. http://www.smartplanet.com/blog/business-brains/predictive-analytics-at-work-predicting-traffic-jams-before-they-occur/10543 Smarter transport: Singapore, San Francisco and Lyon One hour ahead, 85% accuracy
- 24. Peterborough BedfordManchester Out of city 90 000 Tonnes BedfordManchester In to city Tonnes Peterborough Flow of Regulated Commercial Waste
- 25. Waste flowing out of Peterborough
- 26. Waste flowing into Peterborough
- 27. Hydro Electric: Situational Awareness, Analytics & Smarter Response Creating alternatives to coal fired power generation
- 28. Understanding the impact on the river flow
- 29. Putting the data in context: 3D landscape
- 30. Analytics to optimise the use of Water and Power: • Environmental needs • Power generation, • Flood control, • Energy storage
- 31. Using open data about public building Jump to Google Earth
- 32. Jump to Live system
- 33. Understanding city energy metabolism in 30 seconds Visualising City Scale Energy Load: Jump to short video
- 34. IoT: Internet of Things
- 35. Why the IoT will be important: http://jackuldrich.com/blog/internet-information-tech/10-unexpected-ways-the-internet-of-things-will-open-up-a-future-of-opportunity/ Thanks to Jack Uldrich
- 36. Fine Scale Environmental Sensing High density data to create new insights (3D)
- 37. Fine Scale Thermal Management: IBM Data Centre Sites : All of our Strategic Data Centres use this (4M sq ft) Saving 300 MkWhours per year through data (10% Reduction) • Tecomms Cell Towers • Hospitals • Public Building
- 38. https://www.youtube.com/watch?v=VxtHHaMuVMg Jump to 3 minute video IoT is Cool
- 39. What is needed in the future? Data by itself doesn’t do anything without some creativity… this is where you come in.
- 40. Principles in Information Science will be of use: 1. Whole system design (not sub) 2. Caching (clever use of read ahead) 3. Pipelining (fine scale priority management) 4. Real-time awareness (always monitoring and optimising) 5. Distributed inputs (creating systemic resilience) 6. The importance of storage in the system 7. Keeping things pure / Design for disassembly A B CFrom Linear To Network We need to find the ‘Moore’s Law’ for CO2 relentless performance improvement ‘doing more with different’ A E B D C
- 41. Cognitive Computing: People Compute power AutomationPeople Compute power Programming • Natural Language • Unstructured data • Confidence levels • Feedback by design Computing is Changing: How could this be used to reduce CO2? Programming: Imagine: Doing 10,000 weeks of reading in 15 seconds and understanding all the relationships ! Ingest Learn Test Experience
- 42. Jump to One Example: • Watson Debater • Watson Analytics • Watson Discovery • Watson Engagement Advisor • Watson Healthcare • Watson API
- 43. Replace Energy, Resources and CO2 With Information and Design The grand challenge is to… In summary: “Digital Changes Everything” that’s when
- 45. Augmenting Products with Data Products Data Codifying a distinctive service capability (Products become services) + industry Bindustry A New insight & value = Combining data within and across industries New insight & value trading Data Trading DataAssets Digitising assets Data replaces the use of product or service: • Less energy • Less Resources • Less cost (human intervention) Data Products Composable components (Products, assets, services) Finer scale products & services enabled by data: • Accuracy • Control • Feedback • Complexity Data DataData Homework: Using data to enable a new low carbon economy 1 2 3 4 7 6 5 8
- 46. Foresight creating a network of benefits Where projects in one domain create multiple benefits in other domains Energy Management Pipe Failure Prediction Distributed incident management Leakage management Customer Portal Situational Awareness Meter data analytics External Data Weather Impact and recovery Understanding the system dynamics using data Example from Water Utility
- 47. Watson is the culmination of several cognitive technologies © 2014 International Business Machines Corporation 47 Visualizes with Supporting Evidence Learns Through Expert Training Understands Scientific Entities & Relationships Integrates All Types of Big Data Ingest Learn Test Experience
- 48. Watson enables insights by connecting and analyzing hundreds of internal and external data sources in minutes rather than weeks © 2014 International Business Machines Corporation 48 Learn Test Experience Ingest 16M+ patents from US, Europe, WIPO 23M+ abstracts 100+ journals 50+ books 11,000+ drug labels 20,000+ genes 12M+ chemical structuresWatson Corpus Over 1TB of data Over 40m documents Over 100m entities and relationships Internal Data In vitro tests In vivo studies Compounds Toxicology reports Clinical trial data Lab notes Other Available External Data Chemical database Public genomics Medical textbooks Medline Other journals FDA drugs/labels Patents
- 49. Not just a search engine, Watson understands and interprets the language of science © 2014 International Business Machines Corporation 49 Learn Test Experience Ingest Diagram Formula Names (149) Chemical ID Valium, Dizapam Alboral, Aliseum,AlupramAmiprol, Asiolin, Ansiolisina Apaurin, Apoepam, etc. CAS# 439-14-5 C16H13CIN2ORich dictionaries enable Watson to link all entity representations H C3 O CI N N
- 50. More than mere text mining, Watson can identify relationships © 2014 International Business Machines Corporation 50 Learn Test Experience Ingest Symptoms Arthritis pain Chronic pain Fever Headache Drug class Antiplatelet NSAID Analgesic Adverse Effects GI pain Gastritis GI bleeding Nausea Indications Reduce MI Reduce stroke Reduce fever Reduce pain Anti- Inflammatory Aspirin Illustrative Example Ontologies: The relationship between any entity and other scientific domains
- 51. Annotators allow Watson to read and extract appropriate information © 2014 International Business Machines Corporation 51 Learn Test Experience Ingest …doxorubicin results in extracellular signal-regulated kinase (ERK)2 activation, which in turn phosphorylates p53 on a previously uncharacterized site, Thr55… Extracts Preposition Recognizes preposition location on Thr55 Extracts Entities ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid Extracts Verb Maps to domain of Post Translational Modification Recognizes subject / object relationships Extracts Entities ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid Extracts Entities ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid ERK2 phosphorylates p53 on Thr55
- 52. Machine learning enables Watson to teach itself over time © 2014 International Business Machines Corporation 52 Learn Test Experience Ingest Aspirin GI Pain Valium Depression Annotator Logic Watson Applies Annotators to Text Watson Creates Knowledge Graph • Aspirin is an antiplatelet indicated to reduce the risk of myocardial infarction • Known side effects include Gastrointestinal (GI) pain, GI upset, ulcers, GI bleeding, and nausea • Valium or Diazepam is a benzodiazepine derivative, indicated for the treatment of anxiety, muscle spasms • Valium may cause depression, suicidal ideation, hyperactivity, agitation, aggression, hostility… • Drug = entity • Side effect = entity association cause • Cause = relating verb • Rule = 1 drug to 1 side effect
- 53. Machine learning also enables Watson to learn from experts © 2014 International Business Machines Corporation 53 Learn Test Experience Ingest Aspirin GI Pain Valium Depression Watson Creates Knowledge Graph Drugs can have more than one side effect Expert Interventio n Watson Applies Annotators & Refines Knowledge Graph Aspirin GI Pain GI Upset Nausea Ulcers GI Bleed Depression Valium Agitation Aggression Hostility Hyperactivity
- 54. Beyond mere algorithms, Watson evaluates supporting evidence © 2014 International Business Machines Corporation 54 Learn Test Experience Ingest • Quantity • Proximity • Relationship • Domain Truths/ Business Rules What genes contribute to developing colon cancer? Search Corpus Extract Evidence Score & Weigh Question • Side Effects • Lab Notes • Genes • Publications • Drugs • Animal Models • Clinical Trial Data
- 55. The Result: Watson enables breakthrough insights after analyzing thousands of articles and other corpus data in minutes © 2014 International Business Machines Corporation 55 Learn Test Experience Ingest Gene Network csnk1d ros1 pdlim7 prkcg aurka nrgn cdc20 ugcg hist1h1c ca2 dach1 prb3 ccnb11 ppm1d tp53inp1 mms tpt1 csnk2a1 mapk1 plk1 csnk1g2 ppp2r4 cdk7 gfm1 mapk14 mdm2hipk4 arl2 mapkapk2 cdk1 dyrk2 mapk8 chek1 tceal1 h2afx brca1 jun card16 atm atr stat3 cdk5 plk3 cdk9 mapk10 chek2 ep300 mapk9 nuak1 mgst1 pdik1lptch1 tgm2 cdc25c ccne1 dnm1l krt20 kat2b bbc3 stk11 nr1h2 cdk2 chmp1a aldh1l1 slco6a1 e2f1 prrt2 csnk1a1 tmprss11d ephb2 bard1 ptk2b agt cdkn2a ccn2a ptgs2 hdac6 vhl tbp pin1 sgsm3 dyrk1aprkdc des dusp26 tp53 csnk1d ros1 pdlim7 prkcg aurka nrgn cdc20 ugcg hist1h1c ca2 dach1 prb3 ccnb11 ppm1d tp53inp1 mms tpt1 csnk2a1 mapk1 plk1 csnk1g2 ppp2r4 cdk7 gfm1 mapk14 mdm2hipk4 arl2 mapkapk2 cdk1 dyrk2 mapk8 chek1 tceal1 h2afx brca1 jun card16 atm atr stat3 cdk5 plk3 cdk9 mapk10 chek2 ep300 mapk9 nuak1 mgst1 pdik1lptch1 tgm2 cdc25c ccne1 dnm1l krt20 kat2b bbc3 stk11 nr1h2 cdk2 chmp1a aldh1l1 slco6a1 e2f1 prrt2 csnk1a1 tmprss11d ephb2 bard1 ptk2b agt cdkn2a ccn2a ptgs2 hdac6 vhl tbp pin1 sgsm3 dyrk1aprkdc des dusp26 tp53 60534927591476718347480Proto-Oncogene Proteins 141062882603331334542169757Phosphorylation 19045070423056756308401Cell Cycle 75224756202167488821588076Cell Line 4106135439013003642528911125571728Humans 8943133032023216123305620060Apoptosis 137131016023957713092515820178Mice 27206382254401022216311004235507Animals 439241028910736465036465Tumor Suppressor Protein p53 2230471239721062969612327Aged 3942162138117609900130313252Mutation 268225416411321262984714728Middle Aged 623391893366485163922215559Signal Transduction 750 0 0 0 chek1 1745 255 0 198 cdk2MeSH Name Total pik3ca p53 braf chek2 epha2 Adult 12112 763 10291 928 144 47 Phosphatidylinositol 3-Kinases 11066 10726 0 271 0 0 Immunohistochemistry 10127 710 8930 309 38 57 Protein-Serine-Threonine Kinases 7413 2076 2600 162 1287 0 60534927591476718347480Proto-Oncogene Proteins 141062882603331334542169757Phosphorylation 19045070423056756308401Cell Cycle 75224756202167488821588076Cell Line 4106135439013003642528911125571728Humans 8943133032023216123305620060Apoptosis 137131016023957713092515820178Mice 27206382254401022216311004235507Animals 439241028910736465036465Tumor Suppressor Protein p53 2230471239721062969612327Aged 3942162138117609900130313252Mutation 268225416411321262984714728Middle Aged 623391893366485163922215559Signal Transduction 750 0 0 0 chek1 1745 255 0 198 cdk2MeSH Name Total pik3ca p53 braf chek2 epha2 Adult 12112 763 10291 928 144 47 Phosphatidylinositol 3-Kinases 11066 10726 0 271 0 0 Immunohistochemistry 10127 710 8930 309 38 57 Protein-Serine-Threonine Kinases 7413 2076 2600 162 1287 0 High Affinity Moderate Affinity Some Affinity no Affinity • Select entities from two different ontologies (i.e. disease/gene) • Visualize co-occurrence • Use statistics to spot the intersections • Drill down to see the evidence • Select two or more genes of interest • See network of relationships • Show strength, nature & proximity of the relationship • Colored vectors indicate the nature of the interaction • Hover over connections to see the evidence Co-occurrence Table