Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Collapsing the IT Stack: Clearing a path for AI adoption

1,726 views

Published on

From a keynote presentation delivered at SEMANTiCS 2018 on 12 September 2018.

See also the video of this talk at https://lnkd.in/gXxhaMD (starts at 15 minutes + in)

Technology innovations aren't all created equal. Most are incremental improvements (Type I), layers of a new stack, for example, that accumulate over time. Stacks become towers of Babel, triggering struggles with complexity. Migrating to the cloud simply hands the complexity problem over to service providers.

Then there are new abstraction paradigms (Type II) that make the stack more broadly accessible and easily used. Abstractions don't necessarily reduce complexity, but they can help manage it. Data virtualization and agent management of data and logic, for example can help address configuration shortfalls, and if broadly adopted, could reduce the number of data breaches by closing configuration gaps.

And then there are occasionally reduction paradigms (Type III) that can simplify the whole nature of how services are created and delivered. These suggest we could dispense with layers of the stack and enable broad simplification through a model-driven approach. Semantic graph model-driven platforms seem to be on that path.

We've been through Type I technology evolution, and Type II provides a way to sustain the gains of Type I. But complexity still looms in the background and remains an inhibitor. Where we've ended up on the business model front is thousands of companies now trying to replicate what the much touted unicorns such as AirBnB, Netflix, Snapchat and Uber of the late 2000s and 2010s have done, in broad range of different business niches.

Type III innovation could be the enterprise key to tackling IT complexity and becoming fit for growth at long last. A byproduct would be an AI-ready data layer and a cross-enterprise analytics capability that hasn’t been feasible for most.

Published in: Business

Collapsing the IT Stack: Clearing a path for AI adoption

  1. 1. Collapsing the IT stack Clearing a path for AI adoption Alan Morrison Senior Research Fellow Integrated Content | Emerging Tech
  2. 2. PwC | Collapsing the IT stack Outline for today’s talk 2 Early adopters--winning the data war Collapsing the IT stack Diagnosing the problem Progress on solutions Conclusion
  3. 3. PwC | Collapsing the IT Stack Early adopters--winning the data war 3PwC | Collapsing the IT stack
  4. 4. PwC | Collapsing the IT stack Largest change in market cap by company (2009 to 31 March 2018) 4 Company name Location Industry Change in market cap 2009-2018 ($bn) Market cap 2018 ($bn) 1 Apple United States Technology 757 851 2 Amazon.Com United States Consumer Services 670 701 3 Alphabet United States Technology 609 719 4 Microsoft Corp United States Technology 540 703 5 Tencent Holdings China Technology 483 496 6 Facebook United States Technology 383(1) 464 7 Berkshire Hathaway United States Financial 358 492 8 Alibaba China Consumer Services 302(2) 470 9 JPMorgan Chase United States Financials 275 375 10 Bank of America United States Financials 263 307 (1)Change in market cap from IPO date (2)Market cap at IPO date Source: Bloomberg and PwC analysis
  5. 5. PwC | Collapsing the IT stack Widening corporate inequality – Top versus bottom of Top 100 5 337 329 417 559 416 469 725 604 754 851 40 61 69 64 70 81 85 76 88 97 0 200 400 600 800 1000 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Market caps of top and bottom companies Number 1 Number 100 8,402 17,438 20,035 0 3,000 6,000 9,000 12,000 15,000 18,000 21,000 24,000 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Total market cap of Top 100 companies as at 31 March Apple Apple Apple Apple AppleApple AppleExxon Mobil PetroChina Exxon Mobil MarketCap($bn)MarketCap($bn)
  6. 6. PwC | Collapsing the IT stack Widening corporate inequality in the Top 100—by country/region to 31 March 6 3,805 5,170 5,538 6,202 6,739 8,052 9,322 9,636 10,928 12,187 1,061 1,260 1,459 1,226 1,145 1,131 2,012 1,517 1,801 2,8222,272 3,311 3,370 2,980 3,347 3,997 3,424 2,996 3,031 3,362 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 US China Europe 42 39 38 41 43 47 53 54 55 54 9 9 9 8 7 7 10 10 10 12 31 33 31 27 28 30 26 24 22 23 0 10 20 30 40 50 60 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 US China Europe Market cap change 2017 to 2018 • US up $1,259bn • Europe up $331bn • China up $1,021bn MarketCap($bn)MarketCap($bn) Source : Bloomberg and PwC analysis
  7. 7. PwC | Collapsing the IT stack Widening corporate inequality – by country to from 2009 to 31 March 2018 7 3 5 7 9 9 42 3 4 4 5 10 55 3 4 4 5 12 54 0 10 20 30 40 50 60 Switzerland Germany France United Kingdom China United States 2018 2017 2009* *2009 figures do not add to 100 due to seven companies in the 2009 Top 100 being in locations of domicile that are no longer in the Global Top 100 Source: Bloomberg and PwC analysis Number of companies in the Top 100
  8. 8. PwC | Collapsing the IT stack San Francisco Bay Area now in Top 20 economies worldwide…. but for how long? 8 “The Bay Area has the 19th-largest economy in the world, ranking above Switzerland and Saudi Arabia…. Startups, particularly those in the consumer-internet business, increasingly struggle to attract capital in the shadow of Alphabet, Apple, Facebook et al.” --The Economist, “Why startups are leaving Silicon Valley,” 30 Aug 2018
  9. 9. PwC | Collapsing the IT stack Largest change in market cap by company (2009 to 31 March 2018) 9 Known knowledge graph builders Known KG builders Operator of Taobao and KG builder (1)Change in market cap from IPO date (2)Market cap at IPO date Source: Bloomberg and PwC analysis Company name Location Industry Change in market cap 2009-2018 ($bn) Market cap 2018 ($bn) 1 Apple United States Technology 757 851 2 Amazon.Com United States Consumer Services 670 701 3 Alphabet United States Technology 609 719 4 Microsoft Corp United States Technology 540 703 5 Tencent Holdings China Technology 483 496 6 Facebook United States Technology 383(1) 464 7 Berkshire Hathaway United States Financial 358 492 8 Alibaba China Consumer Services 302(1) 470 9 JPMorgan Chase United States Financials 275 375 10 Bank of America United States Financials 263 307 v
  10. 10. PwC | Collapsing the IT Stack Collapsing the IT stack 10PwC | Collapsing the IT Stack Wikimedia Commons, 2007 PwC | Collapsing the IT stack
  11. 11. PwC | Collapsing the IT stack Most innovations are incremental, adding to the stack, with data as an afterthought (Type I) 11 Hardware DBMS OS Custom code Hardware Lots of OSes 1,000+ SQL/NoSQL DBs Custom code ERP+ suites Hardware A few more OSes More DBMSes Custom code ERP+ suites Hardware Lots more OSes 5,000+ databases Component -ized suites Custom code Cloud layer Hardware More types of OSes 10,000+ DBs + blockchains Multicloud layer Suites as services Various SaaSes Custom code Hardware A few DBMSes A few OSes ERP+ suites Custom code Threat of more application centric sprawl Early1990s Late 1990s 2000s 2010s1973-1990sPre 1970 2020s
  12. 12. PwC | Collapsing the IT stack Most of the IT workforce just adds to or keeps track of the sprawl 12 0.01 0.9 2 1.6 3.2 US IT workforce in 2016 (in mIllions) Semantic data Data related (less semantics) General Network/hardware Software Sources: US Bureau of Labor Statistics and PwC estimates, 2018 Total IT workforce = 7.7 million (= 5 percent of the US overall workforce in 2016)
  13. 13. PwC | Collapsing the IT stack The US as a whole has more opioid abusers than it does IT workers 13 US Census Bureau, Bureau of Labor Statistics, and Health and Department of Human Services, 2018 0 50 100 150 200 250 300 350 IT workers All opioid abusers Disabled and over 65 years old Entire US workforce Entire US population Number of IT workers in the US in 2016, in context (in millions)
  14. 14. PwC | Collapsing the IT stack Object virtualization (Type II) manages complexity, just so IT can get its arms around the sprawl 14 EnterpriseWeb and PwC, 2015
  15. 15. PwC | Collapsing the IT stack 15 Type III: data-centric architecture reduces both application and database sprawl Applications for execution only, models exposed with the dataApp code trapped in Database orphans and models Data lake or hub Semantic model/rules    Applets
  16. 16. PwC | Collapsing the IT stack Identify and declare the few hundred business rules you need as a model 16 “In every company I’ve ever studied, there are only a few hundred key concepts and relationships that the entire business runs on. Once you understand that, you realize all of these millions of distinctions are just slight variations of those few hundred important things.” --Dave McComb, author of Software Wasteland, quoted in Strategy + Business See “Are you Spending Way too Much on Software at https://www.strategy-business.com/article/Are-You-Spending-Way-Too- Much-on-Software?
  17. 17. PwC | Collapsing the IT stack Call the model to reuse those rules whenever necessary 17 “You discover that many of the slight variations aren’t variations at all. They’re really the same things with different names, different structures, or different labels. So it’s desirable to describe those few hundred concepts and relationships in the form of a declarative model that small amounts of code refer to again and again.” --Dave McComb (as previously cited) See “Are you Spending Way too Much on Software at https://www.strategy-business.com/article/Are-You-Spending-Way-Too- Much-on-Software?
  18. 18. PwC | Collapsing the IT Stack Diagnosing the bigger problem 18PwC | Collapsing the IT Stack PwC | Collapsing the IT Stack PwC | Collapsing the IT stack
  19. 19. PwC | Collapsing the IT stack What AI needs versus what it has 19 What it needs: Contextualized, disambiguated, highly relevant and specific integrated data, flowing to the point of need What it has: Single batch datasets cleaned up to be good enough by data scientists, who spend 80% of their time on cleanup What it needs: Knowledge engineers, and many bold Data Visionaries in addition to big D Data Scientists, data-centric architects, pipeline engineers, specialists in many new data niches What it has: A growing group of tool users versed only in probability theory, neural networks, python and R, including small D data scientists, engineers and architects, plus scads of entrenched application-centric developers Finance Operations Marketing Input Output Input layer Hidden layer 1 Hidden layer 2 Output layer
  20. 20. PwC | Collapsing the IT stack The real inhibitors to adoption aren’t technological – they’re rooted in tribal biases and resistance to change 20 Tribalism CollectivismIndividualism Anarchy TotalitarianismLocus of inertia Daniel Quinn, Beyond Civilization and Alice Linsley, “Daniel Quinn: A Return to Tribalism?”, college-ethics.blogspot.com, 2018
  21. 21. PwC | Collapsing the IT stack Tribalism – Machine learning edition 21 Source: Pedro Domingos, The Master Algorithm, 2015 More at “Machine learning evolution”: http://usblogs.pwc.com/emerging-technology/machine-learning-evolution-infographic/, PwC, 2017 Symbolists Bayesians Connectionists Evolutionaries Analogizers Use symbols, rules, and logic to represent knowledge and draw logical inference Assess the likelihood of occurrence for probabilistic inference Recognize and generalize patterns dynamically with matrices of probabilistic, weighted neurons Generate variations and then assess the fitness of each for a given purpose Optimize a function in light of constraints (“going as high as you can while staying on the road”) Favored algorithm Rules and decision trees Favored algorithm Naïve Bayes or Markov Favored algorithm Neural network Favored algorithm Genetic programs Favored algorithm Support vectors
  22. 22. PwC | Collapsing the IT stack Tribalism – Data integration edition 22 Trend toward more data centricity this way Application-centric RESTful developers Relational database linkers Data-centric knowledge graphers Application-centric ESB advocates Semantic Web Company, 2018 Computerscience wiki.org, 2018 TIBCO, 2014 Oracle DBA’s Guide, 2018 User Scott Select FROM emp Local database PUBLIC SYNONYM Emp - > emp@HQ.ACME>COM Database link (undirected) Remote database EMP table Portals Net Application B2B Interactions Enterprise data Business process management Web Services Mobile Applications DEE Application ERP CRM SFA Legacy System ESBCustom environment Common environment API Unstructured Data Semi- structured Data Structured Data Schema mapping based on ontologies Entity Extractor informs all incoming data streams about its semantics and links them Unified Views RDF Graph Database PoolParty Graph Search
  23. 23. PwC | Collapsing the IT stack Crossing the chasm between the tribes 23 Reducing the amount of unfamiliarity developers confront--familiar document means to achieve comparable ends to graph: • Semantic suites that use the JSON format and familiar hierarchies: SWC’s PoolParty is an example • GraphQL: A popular document shape language that talks to APIs using SELECT-like statements and tree shapes; backend-agnostic; just uses a mental model for graph; addresses the API endpoint proliferation problem • Accessible web as database methods: JSON-LD and Schema.org, etc. vocabularies • Document “schemas” via data objects: JavaScript objects to developers = documents to NoSQL DB types; Object Data Modeling instead of database semantics • Mongoose or MongoDB JSON schema features + GraphQL: MongoDB object modeling and querying that can be used for subdocument filtering within a GraphQL context • HyperGraphQL: A GraphQL UI for Linked Data, restricted to certain tree-shaped queries • Universal Schema Language: Mike Bowers’ document/graph query and modeling language still in development • COMN: Ted Hills’ well-defined NoSQL + SQL data modeling notation
  24. 24. Progress on solutions PwC | Collapsing the IT stack 24
  25. 25. PwC | Collapsing the IT stack Types of logic most used in AI-enabled systems Rule-based systems (includes KR) “Handcrafted knowledge” is the term DARPA uses; rule-based programming + procedure replication in process automation, + some knowledge representation (KR) • Strong on logical reasoning in specific concrete contexts - Procedural + declarative programming + set theory, etc. - Deterministic • Can’t learn or abstract • Still exceptionally common and useful Statistical machine learning • Probabilistic • From Bayesian algorithms to neural nets (yes, deep learning also) • Strong on perceiving and learning (classifying, predicting) • Weak on abstracting and reasoning • Quite powerful in the aggregate but individually (instance by instance) unreliable • Can require lots of data Contextualized, model-driven approach • Contextualized modeling approach—allows efficiency, precision and certainty • Combines power of deterministic, probabilistic and description logic • Allows explanations to be added to decisions • Accelerates the training process with the help of specific, contextual human input • Takes less data Example: Consumer tax software Perceiving Learning Abstracting Reasoning Perceiving Learning Abstracting Reasoning Perceiving Learning Abstracting Reasoning Example: Facial recognition using deep learning/neural nets Example: Explains first how handwritten letters are formed so machines can decide based on these individual models—less data needed, more transparency. John Launchbury of DARPA (https://www.youtube.com/watch?v=N2L8AqkEDLs), Estes Park Group and PwC research, 2017 Previously dominant On the rise and rapidly improving Nascent, just beginning 1
  26. 26. PwC | Collapsing the IT stack Most automated knowledge graph – Diffbot? 26 “Diffbot’s crawler regularly refreshes the DKG with new information and its machine learning algorithms are smart enough to pass over sites with histories of producing ‘logically inconsistent’ facts. “‘That’s one of the reasons why we fuse information together from different sources,’ Tung said. ‘Our scale is such that there’s minimal potential for errors. We’d bet the business on it.’ “Diffbot launched in 2008 and counts 28 employees among its core staff of engineers and data scientists.” --Mike Tung of Diffbot, quoted in VentureBeat Diffbot claims an automated knowledge graph of 1 trillion + facts, designed to grow without humans in the loop. That compares with 1.6 billion crowdsourced facts in Google’s knowledge graph, according to VentureBeat. Kyle Wiggers, “Diffbot launches AI-powered knowledge graph of 1 trillion facts about people, places, and things,” VentureBeat, 30 August 2018
  27. 27. PwC | Collapsing the IT stack Versus more explicit, precise, contextualized meaning with a triadic, Peircean knowledge graph and less than 1M concepts? 27 “There are many different approaches for distinguishing a logical basis for ontologies, but Peirce basically says to base everything around 3s, explains [Mike Bergman of Cognonto]. That is, 1. the object itself; 2. what a particular agent perceives about the object; 3. and the way that agent needs to try to communicate what that is. ‘Without that triad it’s hard to ever get at differences of interpretation, context or meaning,’ he says, whether that be between something like events and activities or individuals and classes. Once you adopt that mindset, a lot of things that seemingly were irreconcilable differences begin to fall away, and the categorization of information becomes really very easy and smooth....” --Mike Bergman of Cognonto, quoted in Dataversity Jennifer Zaino, “Cognonto Takes On Knowledge-Based Artificial Intelligence,” Dataversity, 23 November 2016
  28. 28. PwC | Collapsing the IT stack Contextual AI via a large knowledge graph at Fairhair.ai 28 Meltwater, 2018 Media Intelligence Apps New Apps Enterprise Solutions 3rd party Apps 100M documents ingested daily 150 NLP/IR pipelines 100’s Billions of Searches Global Monitoring Analyze & Report Distribute Influence & Engage Employee App Freemium At-Powered Reporting Outside Insight Custom Solutions PaaS Service Layer Context Building Enriching & Analysis Outside Data Streaming, Search, Analytics, APIs Building block to leverage the platform Knowledge Graph Enable cognitive applications on top of our data by connecting the dots Data Enrichment Platform Enrich, analyze & build by interoperating with all major players AI-driven data Acquisition Bring high quality outside to our repository with minimal human effort
  29. 29. PwC | Collapsing the IT stack Montefiore’s semantic data lake 29 Montefiore Health, Franz, Intel and PwC research, 2017 Various data sources, some structured, some not, now all part of a knowledge graph with a simple patient care-centric ontology Hadoop cluster with high- performance processors and memory Scalable graph database supporting open W3C semantic standards Standard open source querying, ML and analytics frameworks, API accessibility HL7 feed Web services EMR LIMS Legacy OMICs CTMS Claims Annotation engine HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop AllegrographAllegrographAllegrographAllegrograph Allegrograph SDL loader ML-LIB/R SPARQL Prolog Spark Java API Doctors can query the graph or harness ML + analytics and receive answers from the system at the point of care via their handhelds. The system also acts as a giant feedback-response or learning loop which learns from the data collected via user/system interactions.
  30. 30. PwC | Collapsing the IT stack Siemens’ industrial knowledge graph “Deep learning fails when it comes to context. Knowledge graphs can handle context and enable us to address things that deep learning cannot address on its own.” --Michael May, Head of Company Core Technology, Data Analysis and AI, Siemens AI Algorithms 1 09:00 – Analyze Turbine data hub 2 11:00 – Configure Configure turbine 3 12:00 – Maintain Master data Mgmt. 4 13:00 – Mitigate Financial Risk Analysis 5 15:00 – Contact Expert & Communities 6 18:00 – Guide Rules & Regulations 3 4 5 4 2 1 6 Industrial Knowledge Graph
  31. 31. PwC | Collapsing the IT stack Pharma knowledge graphs for patient safety 31 Graph integration Natural language processing Data cleaning during analysis In-memory query engine Drug safety Heightened focus on safety Evolving regulatory demands Increasing public scrutiny Focus on analytics Increased sharing & transparency Doing more with the same or less PwC and Cambridge Semantics, 2018 SolutionsChallenges
  32. 32. PwC | Collapsing the IT stack NuMedii’s precision therapeutics knowledge graph 32 Ontotext and NuMedii, 2018 goTerm Calcium ion binding 2201 Protein binding Extracellular region ENSG 00000138829 Extracellular matrix disassembly Extracellular matrix Organization proteinaceous extracellular matrix positive regulation of bone mineralization Fibrillin - 2 Extracellular matrix Structural constituent Extracellular matrix micro fibril Camera-type eye development CHEMBL_TC_ 10038 Go Function Reference_ gosubset_prok 100001650 100001532 100001739 100000687 100002060
  33. 33. PwC | Collapsing the IT stack Thomson Reuters’ financial knowledge graph as a service Thomson Reuters, 2018
  34. 34. PwC | Collapsing the IT stack Colryut Group’s graph master data federation (Type II transformation) 34 This graph visualization + data editing/filtering environment allows scalable and articulated governance at the data layer, as well as communications between groups in different parts of the organization, including IT and executive management. DINTTA_GOEDKODE TA_GOEDKODE DINT GC21GL5Q DINT Product service center New Project DINT Product service center Source: Colruyt Group and Tom Sawyer Software, 2018 In order to minimize dependencies between transformation projects, Belgian supermarket chain Colruyt Group used a master data structuring, editing and visualization environment created by Tom Sawyer Software.
  35. 35. Conclusion and some suggestions PwC | Collapsing the IT stack 35
  36. 36. PwC | Collapsing the IT stack Tell your C-suite: The Third Wave of AI is missing half the data it needs 36 • Relationship data has long been overlooked, but specifying relationships is how you build context • Connected, relationship-rich data will be seen as the most important asset for companies • Can’t have governance without connected data • Can’t have connected, meaningful data without a semantic model • Can’t compete in the digital ecosystem and cross boundaries without meaningful data connectivity • When it comes to enabling the AI your company needs, think semantic graph:
  37. 37. PwC | Collapsing the IT stack The innovation graph must be semantic to scale 37 Once digitized (with the help of AI + blockchain, etc.), organizations play different roles than they've been accustomed to in the business ecosystem. Some because of their data collection heritage can become data providers. Others take up roles in the data supply chain, or position themselves as industry platforms or marketplaces.
  38. 38. PwC | Collapsing the IT stack Document models can be a stepping stone to graphs 38
  39. 39. PwC | Collapsing the IT stack Contextual graphs + statistics methods = innovation at scale 39
  40. 40. Questions or comments? pwc.com © 2018 PwC. All rights reserved. PwC refers to the US member firm or one of its subsidiaries or affiliates, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details. Alan Morrison Sr. Research Fellow PwC | Integrated Content | Emerging Tech Mobile: +1(408) 205 5109 Email: alan.s.morrison@pwc.com PricewaterhouseCoopers LLP 488 Almaden Blvd., Suite 1800 San Jose, CA 95110 USA http://usblogs.pwc.com/emerging- technology/author/amorrison009/ https://www.linkedin.com/in/alanmorrison @AlanMorrison quora.com/profile/Alan-Morrison

×