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.

Smart Networks: Blockchain, Deep Learning, and Quantum Computing

102 views

Published on

Considering high-impact emerging technologies (AI machine learning and blockchain) together suggests the emergence of a new class of global computational infrastructure: smart networks. Smart networks are intelligent self-operating computation networks such as deep learning neural nets, blockchains, UAV fleets, industrial robotics cloudminds

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Smart Networks: Blockchain, Deep Learning, and Quantum Computing

  1. 1. Melanie Swan Purdue University Emerging Technologies shaping the future of Fraud Detection, Banking, and Finance Association of Certified Fraud Examiners Indianapolis IN, August 8, 2019 Slides: http://slideshare.net/LaBlogga
  2. 2. 8 Aug 2019 EmergingTech 1 Melanie Swan, Technology Theorist  Philosophy Department, Purdue University, Indiana, USA  Founder, Institute for Blockchain Studies  Singularity University Instructor; Institute for Ethics and Emerging Technology Affiliate Scholar; EDGE Essayist; FQXi Advisor Traditional Markets Background Economics and Financial Theory Leadership New Economies research group Source: http://www.melanieswan.com, http://blockchainstudies.org/NSNE.pdf, http://blockchainstudies.org/Metaphilosophy_CFP.pdf https://www.facebook.com/groups/NewEconomies
  3. 3. 8 Aug 2019 EmergingTech Smart Network Thesis 2 Considering high-impact emerging technologies (AI machine learning and blockchain) together suggests the emergence of a new class of global computational infrastructure: smart networks (Smart networks: intelligent self-operating computation networks such as deep learning neural nets, blockchains, UAV fleets, industrial robotics cloudminds)
  4. 4. 8 Aug 2019 EmergingTech Agenda  Digital Transformation  Deep Learning Neural Networks  Blockchain Technology  Implications for Fraud 3
  5. 5. 8 Aug 2019 EmergingTech Top Job Growth Areas  Top job machine learning and data analysis supplanted by blockchain in 2018  1,775 US blockchain-related job openings August 2018  300 percent annual increase  Median salary: $84,884/year ($52,461 US avg) 4 Source: Glassdoor’s August 2018 Local Pay Report. https://www.glassdoor.com/research/rise-in-bitcoin-jobs/
  6. 6. 8 Aug 2019 EmergingTech Digital Transformation Journey  Digital transformation: digitizing information and processes in all enterprise and government functions  $3.8 trillion global IT spend 2019 (Gartner)  $1.3 trillion Digital Transformation Technologies (IDC) 5 Source: https://www.gartner.com/en/newsroom/press-releases/2019-01-28-gartner-says-global-it-spending-to-reach--3-8-trillio, https://www.idc.com/getdoc.jsp?containerId=prUS43381817  Digital transformation  Technology used to make existing work more efficient, now technology is transforming the work itself  Convergence of blockchain, IoT, AI, Cloud technologies
  7. 7. 8 Aug 2019 EmergingTech  Exascale supercomputing 2021e  Exabyte global data volume 2020e: 40 EB  Scientific, governmental, corporate, and personal Big Data ≠ Smart Data Sources: http://www.oyster-ims.com/media/resources/dealing-information-growth-dark-data-six-practical-steps/, https://www.theverge.com/2019/3/18/18271328/supercomputer-build-date-exascale-intel-argonne-national-laboratory-energy 6 Only 6% data protected, only 42% companies say they know how to extract meaningful insights from the data available to them (Oxford Economics Workforce 2020)
  8. 8. 8 Aug 2019 EmergingTech Why do we need Learning Technologies? 7  Big data is not smart data (i.e. usable)  New data science methods needed for data growth, older learning algorithms under-performing Source: http://blog.algorithmia.com/introduction-to-deep-learning-2016
  9. 9. 8 Aug 2019 EmergingTech Artificial Intelligence (AI)  Artificial intelligence is using computers to preform cognitive work (physical or mental) that usually requires a human  Deep Learning/Machine Learning is the biggest area in AI 8 Source: Swan, M. Philosophy of Deep Learning Networks: Reality Automation Modules. Ke Jie vs. AlphaGo AI Go player, Future of Go Summit, Wuzhen China, May 2017
  10. 10. 8 Aug 2019 EmergingTech Progression in AI Learning Machines 9 Single-purpose AI: Hard-coded rules Multi-purpose AI: Algorithm detects rules, reusable template Question-answering AI: Natural-language processing Deep Learning prototypeHard-coded AI machine Deep Learning machine Deep Blue, 1997 Watson, 2011 AlphaGo, 2016
  11. 11. 8 Aug 2019 EmergingTech 10 Conceptual Definition: Deep learning is a computer program that can identify what something is (physical or digital) Technical Definition: Deep learning is a class of machine learning algorithms in the form of a neural network that uses a cascade of layers of processing units to extract features from data sets in order to make predictive guesses about new data Source: Extending Jann LeCun, http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/facebook-ai-director-yann-lecun- on-deep-learning What is Deep Learning?
  12. 12. 8 Aug 2019 EmergingTech How are AI and Deep Learning related? 11 Source: Machine Learning Guide, 9. Deep Learning  Artificial intelligence:  Using computers to do cognitive work that usually requires a human  Machine learning:  A statistical method in which computers perform tasks by relying on information patterns and inference as opposed to explicit instructions  Neural network:  A computer system modeled on the human brain and nervous system  Deep learning:  Program that can recognize objects Deep Learning Neural Nets Machine Learning Artificial Intelligence Computer Science Within the Computer Science discipline, in the field of Artificial Intelligence, Deep Learning is a class of Machine Learning algorithms, that are in the form of a Neural Network
  13. 13. 8 Aug 2019 EmergingTech What is a Neural Network? 12  Intuition: create an Artificial Neural Network to solve problems in the same way as the human brain
  14. 14. 8 Aug 2019 EmergingTech Agenda  Digital Transformation  Deep Learning Neural Networks  Blockchain Technology  Implications for Fraud 13
  15. 15. 8 Aug 2019 EmergingTech Why is it called “Deep” Learning?  Hidden layers of processing (2-20 intermediary layers)  “Deep” networks (3+ layers) versus “shallow” (1-2 layers)  Basic deep learning network: 5 layers; GoogleNet: 22 layers 14 Sandwich Architecture: visible Input and Output layers with hidden processing layers GoogleNet: 22 layers
  16. 16. 8 Aug 2019 EmergingTech Why Deep “Learning”?  System is “dumb” (i.e. mechanistic)  “Learns” by making trial-and-error guesses about the data it receives to log the relevant features in order to identifying similar examples  Usual AI argument: big enough data is what makes a difference (“simple” algorithms run over large data sets) 15 Input: Big Data (e.g.; many examples) Method: Trial-and-error guesses to adjust node weights Output: system identifies new examples
  17. 17. 8 Aug 2019 EmergingTech Sample task: is that a Car?  Create an image recognition system that determines which features are relevant (at increasingly higher levels of abstraction) and correctly identifies new examples 16 Source: Jann LeCun, http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
  18. 18. 8 Aug 2019 EmergingTech Two classes of Learning Systems Supervised and Unsupervised Learning  Supervised  Classify already- labeled data  Unsupervised  Find patterns in unlabeled data 17 Source: https://www.slideshare.net/ThomasDaSilvaPaula/an-introduction-to-machine-learning-and-a-little-bit-of-deep-learning
  19. 19. 8 Aug 2019 EmergingTech Early success in Supervised Learning (2011)  YouTube: user-classified data perfect for Supervised Learning 18 Source: Google Brain: Le, QV, Dean, Jeff, Ng, Andrew, et al. 2012. Building high-level features using large scale unsupervised learning. https://arxiv.org/abs/1112.6209
  20. 20. 8 Aug 2019 EmergingTech 2 main kinds of Deep Learning neural nets 19 Source: Yann LeCun, CVPR 2015 keynote (Computer Vision ), "What's wrong with Deep Learning" http://t.co/nPFlPZzMEJ  Convolutional Neural Nets  Image recognition  Convolve: roll up to higher levels of abstraction to identify feature sets  Recurrent Neural Nets  Speech, text, audio recognition  Recur: iterate over sequential inputs with a memory function  LSTM (Long Short-Term Memory) remembers sequences and avoids gradient vanishing
  21. 21. 8 Aug 2019 EmergingTech Image Recognition and Computer Vision 20 Source: Quoc Le, https://arxiv.org/abs/1112.6209; Yann LeCun, NIPS 2016, https://drive.google.com/file/d/0BxKBnD5y2M8NREZod0tVdW5FLTQ/view Marv Minsky, 1966 “summer project” Jeff Hawkins, 2004, Hierarchical Temporal Memory (HTM) Quoc Le, 2011, Google Brain cat recognition Convolutional net for autonomous driving, http://cs231n.github.io/convolutional-networks History Current state of the art - 2019
  22. 22. 8 Aug 2019 EmergingTech Image Classification 21 Source: https://cs.stanford.edu/people/karpathy/deepimagesent/?hn  Human-level image recognition and captioning (2018)
  23. 23. 8 Aug 2019 EmergingTech Machine “Understanding” of Concepts 22 Source: https://cs.stanford.edu/people/karpathy/deepimagesent/?hn  “Understanding” is the system’s three-step process  Image -> internal representation -> text  Labels “tennis racket” = concepts  Machine learning: Kantian-level object recognition, not Hegelian
  24. 24. 8 Aug 2019 EmergingTech Problem: correctly recognize “apple” 23 Source: Michael A. Nielsen, Neural Networks and Deep Learning
  25. 25. 8 Aug 2019 EmergingTech Modular Processing Units 24 Source: http://deeplearning.stanford.edu/tutorial 1. Input 2. Hidden layers 3. Output X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X  Unit: processing unit, logit (logistic regression unit), perceptron, artificial neuron
  26. 26. 8 Aug 2019 EmergingTech Image Recognition Digitize Input Data into Vectors 25 Source: Quoc V. Le, A Tutorial on Deep Learning, Part 1: Nonlinear Classifiers and The Backpropagation Algorithm, 2015, Google Brain, https://cs.stanford.edu/~quocle/tutorial1.pdf
  27. 27. 8 Aug 2019 EmergingTech Image Recognition Log features and trial-and-error test 26 1. Input 2. Hidden layers 3. Output X X X X X X X X X X X X X X X Source: http://deeplearning.stanford.edu/tutorial; MNIST dataset: http://yann.lecun.com/exdb/mnist  Mathematical methods used to update the weights  Linear algebra: matrix multiplications of input vectors  Statistics: logistic regression units (Y/N (0,1)), probability weighting and updating, inference for outcome prediction  Calculus: optimization (minimization), gradient descent in back- propagation to avoid local minima with saddle points Feed-forward pass (0,1) 1.5 Backward pass to update probabilities per correct guess .5.5 .5.5.5 1 10 .75 .25 Inference Guess Actual Feature 1 Feature 2 Feature 3
  28. 28. 8 Aug 2019 EmergingTech Learning process 27 Source: http://neuralnetworksanddeeplearning.com/chap2.html  Vary the weights and biases for improved outcome  Repeat until the net correctly classifies the data Edge Input value = 4 Edge Input value = 16 Edge Output value = 20 Node Operation = Add Input Values have Weights w Nodes have a Bias bw1* x1 w2*x2 N+b .25*4=1 .75*16=12 13+2 15 Input Processing Output Variable Weights and Biases Basic Node Structure (fixed) Basic Node with Weights and Bias (variable)
  29. 29. 8 Aug 2019 EmergingTech Image Recognition Levels of Abstraction Object Recognition 28 Source: Jann LeCun, http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf  Layer 1: Log all features (line, edge, unit of sound)  Layer 2: Identify more complicated features (jaw line, corner, combination of speech sounds)  Layer 3+: Push features to higher levels of abstraction until full objects can be recognized
  30. 30. 8 Aug 2019 EmergingTech Image Recognition Higher Abstractions of Feature Recognition 29 Source: https://adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html
  31. 31. 8 Aug 2019 EmergingTech Example: NVIDIA Facial Recognition 30 Source: NVIDIA  First hidden layer extracts all possible low-level features from data (lines, edges, contours); next layers abstract into more complex features of possible relevance
  32. 32. 8 Aug 2019 EmergingTech Deep Brain Face and Cat Recognition 31 Source: Quoc V. Le et al, Building high-level features using large scale unsupervised learning, 2011, https://arxiv.org/abs/1112.6209  Google image net
  33. 33. 8 Aug 2019 EmergingTech Speech, Text, Audio Recognition Sequence-to-sequence Recognition + LSTM 32 Source: Andrew Ng  LSTM: Long Short Term Memory  Technophysics technique: each subsequent layer remembers data for twice as long (fractal-type model)  The “grocery store” not the “grocery church”
  34. 34. 8 Aug 2019 EmergingTech Actual: same structure, more complicated 33
  35. 35. 8 Aug 2019 EmergingTech 34 Source: https://medium.com/@karpathy/software-2-0-a64152b37c35 Same structure, more complicated values
  36. 36. 8 Aug 2019 EmergingTech Same Structure 35
  37. 37. 8 Aug 2019 EmergingTech Loss function optimization Backpropagation  Problem: Combinatorial complexity  Inefficient to test all possible parameter variations  Solution: Backpropagation (1986 Nature paper)  Optimization method used to calculate the error contribution of each neuron after a batch of data is processed 36 Source: http://neuralnetworksanddeeplearning.com/chap2.html
  38. 38. 8 Aug 2019 EmergingTech Gradient Descent  Gradient: derivative to find the minimum of a function  Gradient descent: optimization algorithm to find the biggest errors (minima) most quickly  Error = MSE, log loss, cross-entropy; e.g.; least correct predictions to correctly identify data  Technophysics methods: spin glass, simulated annealing 37 Source: http://briandolhansky.com/blog/2013/9/27/artificial-neural-networks-backpropagation-part-4
  39. 39. 8 Aug 2019 EmergingTech Agenda  Digital Transformation  Deep Learning Neural Networks  Applications  Blockchain Technology  Implications for Fraud 38
  40. 40. 8 Aug 2019 EmergingTech Applications: Cats to Cancer to Cognition 39 Source: Yann LeCun, CVPR 2015 keynote (Computer Vision ), "What's wrong with Deep Learning" http://t.co/nPFlPZzMEJ Computational imaging: Machine learning for 3D microscopy https://www.nature.com/nature/journal/v523/n7561/full/523416a.html
  41. 41. 8 Aug 2019 EmergingTech Radiology: Tumor Image Recognition 40 Source: https://www.nature.com/articles/srep24454  Computer-Aided Diagnosis with Deep Learning  Breast tissue lesions in images  Pulmonary nodules in CT Scans
  42. 42. 8 Aug 2019 EmergingTech Melanoma Image Recognition 41 Source: Nature volume542, pages115–118 (02 February 2017 http://www.nature.com/nature/journal/v542/n7639/full/nature21056.html 2017
  43. 43. 8 Aug 2019 EmergingTech Melanoma Classification 42 Source: https://www.techemergence.com/machine-learning-medical-diagnostics-4-current-applications/  Diagnose skin cancer using deep learning CNNs  Algorithm trained to detect skin cancer (melanoma) using 130,000 images of skin lesions representing over 2,000 different diseases
  44. 44. 8 Aug 2019 EmergingTech DIY Image Recognition: use Contrast 43 Source: https://developer.clarifai.com/modelshttps://developer.clarifai.com/models How many orange pixels? Apple or Orange? Melanoma risk or healthy skin? Degree of contrast in photo colors?
  45. 45. 8 Aug 2019 EmergingTech Deep Learning World Clinic  WHO estimates 400 million people without access to essential health services  Earlier stage diagnosis, personalized health clinic  Smartphone-based diagnostic tools with AI for optical detection and EVA (enhanced visual assessment) 44 Source: http://www.who.int/mediacentre/news/releases/2015/uhc-report/en/
  46. 46. 8 Aug 2019 EmergingTech Agenda  Digital Transformation  Deep Learning Neural Networks  Blockchain Technology  Implications for Fraud 45
  47. 47. 8 Aug 2019 EmergingTech Blockchain 46 Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491  Relocating payments and finance to digital networks
  48. 48. 8 Aug 2019 EmergingTech 47 Conceptual Definition: Blockchain is a software protocol; just as SMTP is a protocol for sending email, blockchain is a protocol for sending money Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 What is Blockchain/Distributed Ledger Tech?
  49. 49. 8 Aug 2019 EmergingTech 48 Technical Definition: A blockchain is a distributed data structure that is an immutable, cryptographic, consensus-driven ledger Blockchain is the tamper-resistant distributed ledger software underlying cryptocurrencies such as Bitcoin, for transferring money, financial property, and real estate titles via the internet without third-party intermediaries Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 What is Blockchain/Distributed Ledger Tech?
  50. 50. 8 Aug 2019 EmergingTech Blockchain Technology: What is it? 49  Blockchain technology is the secure distributed ledger software that underlies cryptocurrencies like Bitcoin  Skype is an app for phone calls via Internet without POTS; Bitcoin is an app for money transfer via Internet without banks Internet (decentralized network) Blockchain Bitcoin Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 Application Layer Protocol Layer Infrastructure Layer SMTP Email VoIP Phone calls OSI Protocol Stack:
  51. 51. 8 Aug 2019 EmergingTech change. 50 “…financial institutions…face the risk that payment processing and other services could be disrupted by technologies, such as cryptocurrencies, that require no intermediation” 10K, Mar 2018
  52. 52. 8 Aug 2019 EmergingTech Trustless multi-party exchange with software 51 Source: Santander  Institutional functions relocated to execution by software, not human-based organizations  Blockchain software replaces intermediaries
  53. 53. 8 Aug 2019 EmergingTech internet traffic. 52 information. email. voice. video. money. point cloud SLAM. SLAM: simultaneous localization and mapping, point cloud data captures 3D positioning information about entities (humans, robots, objects) in the context of their surroundingsc
  54. 54. 8 Aug 2019 EmergingTech How does Bitcoin work? Use eWallet app to submit transaction 53 Source: https://www.youtube.com/watch?v=t5JGQXCTe3c Scan recipient’s address and submit transaction $ appears in recipient’s eWallet Wallet has keys not money Creates PKI Signature address pairs A new PKI signature for each transaction
  55. 55. 8 Aug 2019 EmergingTech P2P network confirms & records transaction 54 Source: https://www.youtube.com/watch?v=t5JGQXCTe3c Transaction computationally confirmed Ledger account balances updated Peer nodes maintain distributed ledger Transactions submitted to a pool and miners assemble new batch (block) of transactions each 10 min Each block includes a cryptographic hash of the last block, chaining the blocks, hence “Blockchain”
  56. 56. 8 Aug 2019 EmergingTech How robust is the Bitcoin p2p network? 55 p2p: peer to peer; Source: https://bitnodes.21.co, https://github.com/bitcoin/bitcoin  9,501 global nodes run full Bitcoind (7/31/19); 160 gb Run the software yourself:
  57. 57. 8 Aug 2019 EmergingTech mining. Source: https://www.illumina.com/science/technology/next-generation-sequencing.html 56 Proof of Work: secure but expensive.
  58. 58. 8 Aug 2019 EmergingTech What is Bitcoin mining? 57  Mining is the accounting function to record transactions (automated and fee-based)  Mining software constantly makes nonce (number used once) guesses  Rate of 2^32 (4 billion) hashes (guesses)/second  One machine at random guesses a winning answer  Winning machine confirms and records the transactions, and collects the rewards  Other nodes confirm the result and append the new block to their copy of the distributed ledger  “Wasteful” effort deters malicious players Run the software yourself: Fast because ASICs represent the hashing algorithm as hardware
  59. 59. 8 Aug 2019 EmergingTech How does Bitcoin mining work? • Problem: Create an internet economic system with untrusted parties • Solution: Use software based on cryptography, game theory, and economic incentives to produce trustworthy behavior • Nodes running the mining software are called "miners" • Automatically validate and package outstanding transactions into blocks • Mining software guesses answers to a cryptographic puzzle per known parameters (part of the Bitcoin software) • The winning answer is a number that, when combined with the data in the block and passed through a hash function, produces a result that is within a certain range (for Bitcoin, an integer between 0 and 4,294,967,296) • The resulting hash has to start with a pre-established number of zeroes • Cannot predict a winning number, consecutive integers give different results  First miner to guess within the desired range announces victory  Other miners confirm the answer and add the new block to the chain 58 Source: https://www.coindesk.com/information/how-bitcoin-mining-works Run the software yourself:
  60. 60. 8 Aug 2019 EmergingTech 59 How does Bitcoin mining work? https://blockexplorer.com/block/0000000000000000002274a2b1f93c85a489c5d75895e9250ac40f06268fafc0 Difficulty – system set computational number involving floating point operations, exponents, integrals Bitcoin nonce: an integer between 0 and 4,294,967,296 The Bitcoin hash is created by running the SHA-256 algorithm on six pieces of data: 1. The Bitcoin version number. 2. The previous block hash. 3. The Merkle Root of all the transactions selected to be in that block. 4. The timestamp. 5. The difficulty target. 6. The Nonce. Winning nonce: 869666145
  61. 61. 8 Aug 2019 EmergingTech 60 public chains. private chains. trustless. mined. p2p software. trusted. not-mined. enterprise software.
  62. 62. 8 Aug 2019 EmergingTech Enterprise Blockchain comparison Blockchain is a middleware enterprise IT 61 Source: Hfs Research, 2018
  63. 63. 8 Aug 2019 EmergingTech Blockchain Applications Areas 62 Source: http://www.blockchaintechnologies.com Smart Property Cryptographic Asset Registries Smart Contracts IP Registration Money, Payments, Financial Clearing Identity Confirmation  Impacting all industries because allows secure value transfer in four application areas
  64. 64. 8 Aug 2019 EmergingTech Agenda  Digital Transformation  Deep Learning Neural Networks  Blockchain Technology  Applications  Implications for Fraud 63
  65. 65. 8 Aug 2019 EmergingTech Global Trade: Maersk 15-carrier blockchain  Digitization = BPR 2.0 for secure transfer of money and information  15.8% of the world's global shipping fleet traffic ($236bn value, 628 ships)  On average, 30 people/organizations involved in the shipment of a product using a shipping container  Over 200 separate interactions, each requiring a new set of documents  IBM-Maersk shipping blockchain with 15 carriers (Hyperledger)  Pilot project: Relocate empty containers to available nearby ships 64 Source: https://www.coindesk.com/worlds-largest-shipping-company-tracking-cargo-blockchain/, https://www.coindesk.com/ibm-maersk-shipping-blockchain-gains-steam-with-15-carriers-now-on-board
  66. 66. 8 Aug 2019 EmergingTech Supply chain custody and traceability  Traceability system for materials and products  Bring verified information from supply chain to point of sale  Used by 200 suppliers  Convergence of IoT, mobile, and blockchain  Example  Smart tags used to track fish caught by fishermen with verified social sustainability claims 65 Source: Provenance (NL)
  67. 67. 8 Aug 2019 EmergingTech 66  Concept: global inventories of high-value items: jewels, controlled substances  Mechanism: registered with digital certificate  Diamond supply chain projects:  Everledger (2015)  Records and tracks the immutable provenance of an asset with blockchain, IoT, smart contracts  TrustChain Initiative (2018)  IBM, precious metals refiner Asahi Refining, jewelry retailer Helzberg Diamonds, precious metals supplier LeachGarner, jewelry manufacturer The Richline Group, and independent verification service UL Source: https://diamonds.everledger.io/; https://cointelegraph.com/news/ibm-and-jewelry-industry-leaders-to-use-blockchain-to- trace-origin-of-diamonds High-value tracking
  68. 68. 8 Aug 2019 EmergingTech Enterprise Blockchains  Single shared business processes with private views across the industry value chain  Controlled-use credentials and read/write access 67 Source: Swan, M. (2017.) Anticipating the Economic Benefits of Blockchain. Technology Innovation Management Review. 7(10): 6-13. https://timreview.ca/article/1109
  69. 69. 8 Aug 2019 EmergingTech Enterprise Blockchains: trade finance  Transparency, immutability, auditability, safety  All parties using the same software infrastructure prevents fraudulent (duplicate) invoices 68 Source: Swan, M. 2018. Blockchain Economics: 'Ripple for ERP' integrated blockchain supply chain ledgers. European Financial Review. Feb-Mar: 24-7. http://www.europeanfinancialreview.com/?p=21755
  70. 70. 8 Aug 2019 EmergingTech Counterfeit Airbags  Business case  30% global airbags sold and installed are counterfeit  Estimated 3.3% goods sold in the EC are counterfeit 69 Source: https://www.oecd.org/newsroom/trade-in-fake-goods-is-now-33-of-world-trade-and-rising.htm  Solution  Single shared process for airbag registration and lookup  Used by manufacturers, vendors, repair shops, end users
  71. 71. 8 Aug 2019 EmergingTech Health and Pharmaceutical  Electronic Medical Records (EMRs)  Smart contract-based consent  Digital health wallet  Identity credentials + EMR + health insurance + payment information  Health insurance claims  Automated claims billing  Multi-party value chain  Genomic research  Files too large (20-40 Gb) for centralized research repositories  Require secure validated access 70 Digital health wallet Use Case: Factom health insurance claims billing • Automated claims billing, validation, payment, and settlement • Multi-party value chain: patient, service provider, billing agent, insurance company, payor, government, collections
  72. 72. 8 Aug 2019 EmergingTech Agenda  Digital Transformation  Deep Learning Neural Networks  Blockchain Technology  Implications for Fraud 71
  73. 73. 8 Aug 2019 EmergingTech 72 the farther future: better horse is a car. new technology. better horse “horseless carriage” => car
  74. 74. 8 Aug 2019 EmergingTech risks. tech: scalability. political: regulation. social: adoption. Rapid Adoption Unfavorable Regulation Favorable Regulation Slow Adoption Future Scenarios 73 Status Quo Tech Cold War Trustful Privacy Regulatory Arbitrage
  75. 75. 8 Aug 2019 EmergingTech Quantum Computing  When will it be possible to break existing RSA cryptography standards with quantum computers?  Estimated unlikely within 10 years however methods are constantly improving  US National Academies of Sciences 2019 report: “highly unexpected that a quantum computer can compromise RSA 2048 within the next decade” 74 Source: Quantum Computing: Progress and Prospects (2019), The National Academies Press, https://www.nap.edu/catalog/25196/quantum-computing-progress-and-prospects  Status: quantum computers commercially available from IBM, D-WAVE Systems, Rigetti
  76. 76. 8 Aug 2019 EmergingTech Fraud  Law enforcement argument: criminality deploys in new technologies and so too must law enforcement  Example: Silk road 75
  77. 77. 8 Aug 2019 EmergingTech Fraud Detection  Corruption  Transparent process, private data  Cross-border trade error and malfeasance  Single-shared ledger, business processes  Counterfeiting and product traceability  Anomaly detection with statistical distributions  Machine learning  Quantum computing 76
  78. 78. 8 Aug 2019 EmergingTech Fraud detection leading the path ahead 77  Expertise in organizational, computational, behavioral, and psychological cues  Global reach, sophisticated business, technologically- intense solutions, real-time detection methods  Challenge is to envision and continue modernizing the way forward with fraud detection strategies
  79. 79. 8 Aug 2019 EmergingTech Conclusion • Deep learning is not merely an AI technique or a software program, but a new class of smart network information technology that is changing the concept of the modern technology project by offering real-time engagement with reality • Deep learning is a data automation method that replaces hard-coded software with a capacity, in the form of a learning network that is trained to perform a task 78 Conclusion  Deep learning is a class of machine learning algorithms in the form of a neural network that uses a cascade of layers of processing units to extract features from data sets in order to make predictive guesses about new data  A blockchain is a distributed data structure that is an immutable, cryptographic, consensus-driven ledger
  80. 80. 8 Aug 2019 EmergingTech Smart Network Thesis 79 Considering high-impact emerging technologies (AI machine learning and blockchain) together suggests the emergence of a new class of global computational infrastructure: smart networks (Smart networks: intelligent self-operating computation networks such as deep learning neural nets, blockchains, UAV fleets, industrial robotics cloudminds)
  81. 81. Melanie Swan Purdue University Emerging Technologies shaping the future of Fraud Detection, Banking, and Finance Association of Certified Fraud Examiners Indianapolis IN, August 8, 2019 Slides: http://slideshare.net/LaBlogga Thank you! Questions?

×