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Future of AI: Blockchain and Deep Learning

The document discusses the intersection of artificial intelligence (AI), blockchain technology, and deep learning, exploring their implications for the future of work and societal structures. It raises critical discussion questions about the potential risks of AI, including existential threats, technological unemployment, and the need for ethical frameworks. Additionally, it outlines the evolution of network computing towards decentralized systems that leverage AI for decision-making and automation.

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World Future Society Scottsdale AZ, November 9, 2017 Slides: http://slideshare.net/LaBlogga The Future of Artificial Intelligence Blockchain & Deep Learning Melanie Swan Philosophy, Purdue University melanie@BlockchainStudies.org
9 Nov 2017 Blockchain Discussion Questions 1. Probability humans will extinct ourselves by mistake? _____% 2. How much are automated algorithms changing your workplace or everyday life? _____% 3. Would you prefer a mortgage that corresponds to your specific needs, or is standard (for the same cost)? 4. Would you like to make a digital backup of your mind? 1 ? ??
9 Nov 2017 Blockchain 2 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 invited contributor; FQXi Advisor Traditional Markets Background Economics and Financial Theory Leadership New Economies research group Source: http://www.melanieswan.com, http://blockchainstudies.org https://www.facebook.com/groups/NewEconomies
9 Nov 2017 Blockchain Agenda  Artificial Intelligence  Blockchain Technology  Deep Learning Algorithms  Future of Artificial Intelligence 3
9 Nov 2017 Blockchain 4 Considering blockchain and deep learning together suggests the emergence of a new class of global network computing system. These systems are self-operating computation graphs that make probabilistic guesses about reality states of the world. Future of AI Smart Network thesis
9 Nov 2017 Blockchain What are we running on networks? 5 Value (Money) Intelligence (Brains) Information 2010s-2020s 2050s(e) 1980s Thought- tokening Value- tokening
9 Nov 2017 Blockchain Future of AI: Smart Networks 6 Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html Fundamental Eras of Network Computing
9 Nov 2017 Blockchain What is Artificial Intelligence?  Artificial intelligence (AI) is a computer performing tasks typically associated with intelligent beings -Encyclopedia Britannica 7 Source: https://www.britannica.com/technology/artificial-intelligence Ke Jie vs. AlphaGo AI Go player, Future of Go Summit, Wuzhen China, May 2017
9 Nov 2017 Blockchain “Creeping Frontier” of Technology 8 Source: https://www.britannica.com/technology/artificial-intelligence  Achievements are quickly forgotten  AI = “whatever we can’t do yet” Innovation Frontier
9 Nov 2017 Blockchain What is the AI problem?  Computer capabilities can grow faster than human capabilities  Therefore, one day computers might become vastly more capable than humans (i.e. superintelligent)  And willfully or inadvertently present a danger to humans 9 Source: https://www.cbsnews.com/news/cbsn-on-assignment-instagram-filtering-out-hate/, https://deepmind.com/applied/deepmind- ethics-society/research/AI-morality-values/ “Pessimistic” “Optimistic”
9 Nov 2017 Blockchain Global Existential Risk 10 Source: Sandberg, A. & Bostrom, N. (2008): “Global Catastrophic Risks Survey”, Technical Report #2008-1, Future of Humanity Institute, Oxford University: pp. 1-5. Percent chance of different types of disaster before 2100 Method: Informal survey of participants, Global Catastrophic Risk Conference, Oxford, July 2008
9 Nov 2017 Blockchain Standard AI Ethics Modules?  Roboethics (how the machine behaves)  Facebook AI bots create own language  OpenAI self-play bot defeats top Dota2 player  Instagram “nice” filter eliminates hate speech  Criminal justice algorithms discriminate  Robotiquette (how the machine interacts) 11 Facebook AI bots OpenAI Dota2 Victory Source: Swan. M. In review. Toward a Social Theory of Dignity: Hegel’s Master-Slave Dialectic and Essential Difference in the Human-Robot Relation. In Robots, Power, Relationships. Eds. J. Carpenter, F. Ferrando, A. Milligan.
9 Nov 2017 Blockchain Is our human future doomed? 12
9 Nov 2017 Blockchain Technological Unemployment  Challenge: facilitate an orderly transition to Automation Economy  Half (47%) of employment is at risk of automation in the next two decades – Carl Frey, Oxford, 2015  Why are there still so many jobs in a world that could be automating more quickly? – David Autor, MIT, 2015 13 Source: Swan, M. (2017). Is Technological Unemployment Real? Abundance Economics. In Surviving the Machine Age: Intelligent Technology and the Transformation of Human Work. Hughes & LaGrandeur, Eds. London: Palgrave Macmillan. 19-33.
9 Nov 2017 Blockchain Future of “Work”? 14 http://www.robotandhwang.com/attorneys  “Work” = meaningful engagement of human capacities
9 Nov 2017 Blockchain What is important for our Future? 15 Maslow’s hierarchy of needs Survive Flourish & Thrive Source: Swan, M. (2017). Cognitive Easing: Human Identity Crisis in a World of Technology, http://ieet.org/index.php/IEET/more/Swan20170107.  Enable human potential, Maslow’s self-actualization  Freed from obligatory work, who will we be? Aspirational Needs Material Needs
9 Nov 2017 Blockchain Privacy Pendulum: Swinging back to more privacy 16  Historically: lots of privacy; Surveillance era: strange logic of few bad apples so insecure surveillance of all; centralized (Equifax) cybersecurity does not work  Future era: swing back to privacy; restore checks & balances Institutionally- specified Reality Self-determined Reality More Privacy
9 Nov 2017 Blockchain Our AI Future: high-impact emerging tech 17 Big Data & Deep Learning Blockchain CRISPR & Bioprinting
9 Nov 2017 Blockchain 18 Top disruptors: Deep Learning & Blockchain Source: https://www.ipe.com/reports/special-reports/securities-services/securities-services-blockchain-a-beginners- guide/10014058.article
9 Nov 2017 Blockchain Job Growth Skills in Demand 1. Robotics/automation/data science/deep learning 2. Blockchain/Bitcoin 19 Source: https://www.computerworld.com/article/3235972/financial-it/blockchains-explosive-growth-pushes-job- skills-demand-to-no-2-spot.html
9 Nov 2017 Blockchain Future of AI: Smart Networks 20 Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html Fundamental Eras of Network Computing  Future of AI: intelligence “baked in” to smart networks  Blockchains to confirm authenticity and transfer value  Deep Learning algorithms for predictive identification
9 Nov 2017 Blockchain Species of Networks 21 Source: https://www.cbsnews.com/news/cbsn-on-assignment-instagram-filtering-out-hate/, https://deepmind.com/applied/deepmind- ethics-society/research/AI-morality-values/  Social Networks  Transportation  Communications  Information  Biological  Superorganisms  Ecosystems  Organisms  Plants  Finance, credit, payment  Deep Learning Superorganisms: Trans-individual, Trans-national
9 Nov 2017 Blockchain Agenda  Artificial Intelligence  Blockchain Technology  Deep Learning Algorithms  Future of Artificial Intelligence 22
9 Nov 2017 Blockchain Blockchain 23 Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491  To inspire us to build this world
9 Nov 2017 Blockchain 24 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?
9 Nov 2017 Blockchain 25 Technical Definition: Blockchain is the tamper-resistant distributed ledger software underlying cryptocurrencies such as Bitcoin, for recording and transferring data and assets such as financial transactions and real estate titles, via the Internet without needing a third-party intermediary Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 What is Blockchain/Distributed Ledger Tech?
9 Nov 2017 Blockchain How does Bitcoin work? Use eWallet app to submit transaction 26 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
9 Nov 2017 Blockchain P2P network confirms & records transaction 27 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”
9 Nov 2017 Blockchain How robust is the Bitcoin p2p network? 28 p2p: peer to peer; Source: https://bitnodes.21.co, https://github.com/bitcoin/bitcoin  11,690 global nodes run full Bitcoind (11/17); 160 gb Run the software yourself:
9 Nov 2017 Blockchain What is Bitcoin mining? 29  Mining is the accounting function to record transactions, fee-based  Mining ASICs “find new blocks” (proof of work)  Network regularly issues random 32-bit nonces (numbers) per specified cryptographic parameters  Mining software constantly makes nonce guesses  At the rate of 2^32 (4 billion) hashes (guesses)/second  One machine at random guesses the 32-bit nonce  Winning machine confirms and records the transactions, and collects the rewards  All nodes confirm the transactions and append the new block to their copy of the distributed ledger  “Wasteful” effort deters malicious players Sample code: Run the software yourself: Fast because ASICs represent the hashing algorithm as hardware
9 Nov 2017 Blockchain Distributed Networks 30 Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 Decentralized (based on hubs) Centralized Distributed (based on peers)  Radical implication: every node is a peer who can provide services to other peers
9 Nov 2017 Blockchain P2P Network Nodes provide services 31 Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 Centralized bank tracks payments between clients “Classic” Banking Peer Banking  Nodes deliver services to others, for a small fee  Transaction ledger hosting (~11,960 Bitcoind nodes)  Transaction confirmation and logging (mining)  News services (“decentralized Reddit”: Steemit, Yours)  Banking services (payment channels (netting offsets)) Network nodes store transaction record settled by many individuals
9 Nov 2017 Blockchain Public and Private Distributed Ledgers 32 Source: Adapted from https://www.linkedin.com/pulse/making-blockchain-safe-government-merged-mining-chains-tori-adams  Private: approved users (“permissioned”)  Identity known, for enterprise  Approved credentials  Controlled access  Public: open to anyone (“permissionless”)  Identity unknown, for individuals  Ex: Zcash zero-knowledge proofs  Open access Transactions logged on public Blockchains Transactions logged on private Blockchains Any user Financial Inst, Industry Consortia, Gov’t Agency Examples: Bitcoin Ethereum Examples: R3 Hyperledger
9 Nov 2017 Blockchain Blockchain Applications Areas 33 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
9 Nov 2017 Blockchain Agenda  Artificial Intelligence  Blockchain Technology  Deep Learning Algorithms  Future of Artificial Intelligence 34
9 Nov 2017 Blockchain  Global Data Volume: 40 EB 2020e  Scientific, governmental, corporate, and personal Big Data…is not Smart Data Source: http://www.oyster-ims.com/media/resources/dealing-information-growth-dark-data-six-practical-steps/ 35 35
9 Nov 2017 Blockchain Big Data requires Deep Learning 36  Older algorithms cannot keep up with the growth in data, need new data science methods Source: http://blog.algorithmia.com/introduction-to-deep-learning-2016
9 Nov 2017 Blockchain Broader Computer Science Context 37 Source: Machine Learning Guide, 9. Deep Learning  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
9 Nov 2017 Blockchain 38 Conceptual Definition: Deep learning is a computer program that can identify what something is Technical Definition: Deep learning is a class of machine learning algorithms in the form of a neural network that uses a cascade of layers (tiers) of processing units to extract features from data and make predictive guesses about new data Source: Swan, M., (2017)., Philosophy of Deep Learning, https://www.slideshare.net/lablogga/deep-learning-explained What is Deep Learning?
9 Nov 2017 Blockchain Deep Learning & AI  System is “dumb” (i.e. mechanical)  “Learns” with big data (lots of input examples) and trial-and-error guesses to adjust weights and bias to identify key features  Creates a predictive system to identity new examples  AI argument: big enough data is what makes a difference (“simple” algorithms run over large data sets) 39 Input: Big Data (e.g.; many examples) Method: Trial-and-error guesses to adjust node weights Output: system identifies new examples
9 Nov 2017 Blockchain 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 40 Source: Jann LeCun, http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
9 Nov 2017 Blockchain Supervised and Unsupervised Learning  Supervised (classify labeled data)  Unsupervised (find patterns in unlabeled data) 41 Source: https://www.slideshare.net/ThomasDaSilvaPaula/an-introduction-to-machine-learning-and-a-little-bit-of-deep-learning
9 Nov 2017 Blockchain Early success in Supervised Learning (2011)  YouTube: user-classified data perfect for Supervised Learning 42 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
9 Nov 2017 Blockchain Machine learning: human threshold 43 Source: Mary Meeker, Internet Trends, 2017, http://www.kpcb.com/internet-trends  All apps voice-activated and conversational?
9 Nov 2017 Blockchain 2 main kinds of Deep Learning neural nets 44 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 in 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
9 Nov 2017 Blockchain 3 Key Technical Principles of Deep Learning 45 Reduce combinatoric dimensionality Core computational unit (input-processing-output) Levers: weights and bias Squash values into Sigmoidal S-curve -Binary values (Y/N, 0/1) -Probability values (0 to 1) -Tanh values 9(-1) to 1) Loss FunctionPerceptron StructureSigmoid Function “Dumb” system learns by adjusting parameters and checking against outcome Loss function optimizes efficiency of solution Non-linear formulation as a logistic regression problem means greater mathematical manipulation What Why
9 Nov 2017 Blockchain How does the neural net actually learn?  System varies the weights and biases to see if a better outcome is obtained  Repeat until the net correctly classifies the data 46 Source: http://neuralnetworksanddeeplearning.com/chap2.html  Structural system based on cascading layers of neurons with variable parameters: weight and bias
9 Nov 2017 Blockchain Backpropagation  Problem: Inefficient to test the combinatorial explosion of all possible parameter variations  Solution: Backpropagation (1986 Nature paper)  Backpropagation of errors and gradient descent are an optimization method used to calculate the error contribution of each neuron after a batch of data is processed 47 Source: http://neuralnetworksanddeeplearning.com/chap2.html
9 Nov 2017 Blockchain Agenda  Artificial Intelligence  Blockchain Technology  Deep Learning Algorithms  Future of Artificial Intelligence 48
9 Nov 2017 Blockchain Future of Artificial Intelligence 49 Source: https://www.slideshare.net/lablogga/deep-learning-explained  Blockchain & Deep Learning  Next-gen global computing network technology  Computation graphs  Self-operating state engines  Make probabilistic guesses about reality states of the world
9 Nov 2017 Blockchain Future of AI: Smart Networks 50 Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html Fundamental Eras of Network Computing  Future of AI: intelligence “baked in” to smart networks  Blockchains to confirm authenticity and transfer value  Deep Learning algorithms for predictive identification
9 Nov 2017 Blockchain Deep Learning Chains: cross-functionality  Deep Learning Applications for Blockchain  TensorFlow for Fee Estimation  Predictive pattern recognition for security  Fraud, privacy, money-laundering  Deep Learning techniques (backpropagations of errors, gradient descent, loss curves) to optimize financial graphs  Formulate debt-credit-payment problems as sigmoidal optimizations to solve with machine learning  Blockchain Applications for Deep Learning  Secure automation, registry, logging, tracking + remuneration functionality for deep learning systems as they go online  BaaS for network operations (LSTM is like a payment channel)  Blockchain P2P nodes provide deep learning network services: security (facial recognition), identification, authorization 51
9 Nov 2017 Blockchain Deep Learning Chains: App #1  Autonomous Driving & Drone Delivery, Social Robotics  Deep Learning (CNNs): identify what things are  Blockchain: secure automation technology  Track arbitrarily-many units, audit, upgrade  Legal liability, accountability, remuneration 52
9 Nov 2017 Blockchain Deep Learning Chains: App #2 53 Source: https://www.illumina.com/science/technology/next-generation-sequencing.html  Big Health Data  Large-scale secure predictive analysis of big health data to understand disease prevention Population 7.5 bn people worldwide
9 Nov 2017 Blockchain Deep Learning Chains: App #3  Leapfrog technology for human potential  Financial Inclusion  2 bn under-banked, 1.1 bn without ID  70% lack access to land registries  Health Inclusion  400 mn no access to health services  Does not make sense to build out brick- and-mortar bank branches and medical clinics to every last mile in a world of digital services  eWallet banking and deep learning medical diagnostic apps 54 Source: Pricewaterhouse Coopers. 2016. The un(der)banked is FinTech's largest opportunity. DeNovo Q2 2016 FinTech ReCap and Funding ReView., Heider, Caroline, and Connelly, April. 2016. Why Land Administration Matters for Development. World Bank. http://www.who.int/mediacentre/news/releases/2015/uhc-report/en/ Digital health wallet
9 Nov 2017 Blockchain Deep Learning Chains: App #4 55  Enact Friendly AI  Digital intelligences running on consensus-managed smart networks (not in isolation)  Good reputational standing required to conduct operations  Transactions to access resources (like fund-raising), provide services, enter into contracts, retire  Smart network consensus only validates and records bonafide transactions from ‘good’ agents Sources: http://cointelegraph.com/news/113368/blockchain-ai-5-top-reasons-the-blockchain-will-deliver-friendly-ai, http://ieet.org/index.php/IEET/more/swan20141117
9 Nov 2017 Blockchain  Deep-thinkers Registry  Register deep learners with blockchains and monitor with deep learning algorithms  Secure tracking  Remuneration  Examples  Autonomous lab robots  On-chain IP discovery tracking  Roving agriculture bots  Manufacturing bots  Intelligent gaming  Go-playing algorithms 56 Source: Swan, M. Future of AI Thinking: The Brain as a DAC. Neural Turing Machines: https://arxiv.org/abs/1410.5401. IPFS (Benet): https://medium.com/@ConsenSys/an-introduction-to-ipfs-9bba4860abd0#.bgig18cgp Deep Learning Chains: App #5
9 Nov 2017 Blockchain Conclusion  Deep learning chains: needed for next-generation challenges  Financial inclusion, big health data, global energy markets, and space  Smart networks: a new form of automated global infrastructure  Identify (deep learning)  Validate, confirm, and route transactions (blockchain)  Future of AI is smart networks  Running value  Running intelligence  Possible answer to AI worries 57
World Future Society Scottsdale AZ, November 9, 2017 Slides: http://slideshare.net/LaBlogga The Future of Artificial Intelligence Blockchain & Deep Learning Melanie Swan Philosophy, Purdue University melanie@BlockchainStudies.org

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Future of AI: Blockchain and Deep Learning

  • 1.
    World Future Society ScottsdaleAZ, November 9, 2017 Slides: http://slideshare.net/LaBlogga The Future of Artificial Intelligence Blockchain & Deep Learning Melanie Swan Philosophy, Purdue University [email protected]
  • 2.
    9 Nov 2017 Blockchain DiscussionQuestions 1. Probability humans will extinct ourselves by mistake? _____% 2. How much are automated algorithms changing your workplace or everyday life? _____% 3. Would you prefer a mortgage that corresponds to your specific needs, or is standard (for the same cost)? 4. Would you like to make a digital backup of your mind? 1 ? ??
  • 3.
    9 Nov 2017 Blockchain2 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 invited contributor; FQXi Advisor Traditional Markets Background Economics and Financial Theory Leadership New Economies research group Source: http://www.melanieswan.com, http://blockchainstudies.org https://www.facebook.com/groups/NewEconomies
  • 4.
    9 Nov 2017 Blockchain Agenda Artificial Intelligence  Blockchain Technology  Deep Learning Algorithms  Future of Artificial Intelligence 3
  • 5.
    9 Nov 2017 Blockchain4 Considering blockchain and deep learning together suggests the emergence of a new class of global network computing system. These systems are self-operating computation graphs that make probabilistic guesses about reality states of the world. Future of AI Smart Network thesis
  • 6.
    9 Nov 2017 Blockchain Whatare we running on networks? 5 Value (Money) Intelligence (Brains) Information 2010s-2020s 2050s(e) 1980s Thought- tokening Value- tokening
  • 7.
    9 Nov 2017 Blockchain Futureof AI: Smart Networks 6 Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html Fundamental Eras of Network Computing
  • 8.
    9 Nov 2017 Blockchain Whatis Artificial Intelligence?  Artificial intelligence (AI) is a computer performing tasks typically associated with intelligent beings -Encyclopedia Britannica 7 Source: https://www.britannica.com/technology/artificial-intelligence Ke Jie vs. AlphaGo AI Go player, Future of Go Summit, Wuzhen China, May 2017
  • 9.
    9 Nov 2017 Blockchain “CreepingFrontier” of Technology 8 Source: https://www.britannica.com/technology/artificial-intelligence  Achievements are quickly forgotten  AI = “whatever we can’t do yet” Innovation Frontier
  • 10.
    9 Nov 2017 Blockchain Whatis the AI problem?  Computer capabilities can grow faster than human capabilities  Therefore, one day computers might become vastly more capable than humans (i.e. superintelligent)  And willfully or inadvertently present a danger to humans 9 Source: https://www.cbsnews.com/news/cbsn-on-assignment-instagram-filtering-out-hate/, https://deepmind.com/applied/deepmind- ethics-society/research/AI-morality-values/ “Pessimistic” “Optimistic”
  • 11.
    9 Nov 2017 Blockchain GlobalExistential Risk 10 Source: Sandberg, A. & Bostrom, N. (2008): “Global Catastrophic Risks Survey”, Technical Report #2008-1, Future of Humanity Institute, Oxford University: pp. 1-5. Percent chance of different types of disaster before 2100 Method: Informal survey of participants, Global Catastrophic Risk Conference, Oxford, July 2008
  • 12.
    9 Nov 2017 Blockchain StandardAI Ethics Modules?  Roboethics (how the machine behaves)  Facebook AI bots create own language  OpenAI self-play bot defeats top Dota2 player  Instagram “nice” filter eliminates hate speech  Criminal justice algorithms discriminate  Robotiquette (how the machine interacts) 11 Facebook AI bots OpenAI Dota2 Victory Source: Swan. M. In review. Toward a Social Theory of Dignity: Hegel’s Master-Slave Dialectic and Essential Difference in the Human-Robot Relation. In Robots, Power, Relationships. Eds. J. Carpenter, F. Ferrando, A. Milligan.
  • 13.
    9 Nov 2017 Blockchain Isour human future doomed? 12
  • 14.
    9 Nov 2017 Blockchain TechnologicalUnemployment  Challenge: facilitate an orderly transition to Automation Economy  Half (47%) of employment is at risk of automation in the next two decades – Carl Frey, Oxford, 2015  Why are there still so many jobs in a world that could be automating more quickly? – David Autor, MIT, 2015 13 Source: Swan, M. (2017). Is Technological Unemployment Real? Abundance Economics. In Surviving the Machine Age: Intelligent Technology and the Transformation of Human Work. Hughes & LaGrandeur, Eds. London: Palgrave Macmillan. 19-33.
  • 15.
    9 Nov 2017 Blockchain Futureof “Work”? 14 http://www.robotandhwang.com/attorneys  “Work” = meaningful engagement of human capacities
  • 16.
    9 Nov 2017 Blockchain Whatis important for our Future? 15 Maslow’s hierarchy of needs Survive Flourish & Thrive Source: Swan, M. (2017). Cognitive Easing: Human Identity Crisis in a World of Technology, http://ieet.org/index.php/IEET/more/Swan20170107.  Enable human potential, Maslow’s self-actualization  Freed from obligatory work, who will we be? Aspirational Needs Material Needs
  • 17.
    9 Nov 2017 Blockchain PrivacyPendulum: Swinging back to more privacy 16  Historically: lots of privacy; Surveillance era: strange logic of few bad apples so insecure surveillance of all; centralized (Equifax) cybersecurity does not work  Future era: swing back to privacy; restore checks & balances Institutionally- specified Reality Self-determined Reality More Privacy
  • 18.
    9 Nov 2017 Blockchain OurAI Future: high-impact emerging tech 17 Big Data & Deep Learning Blockchain CRISPR & Bioprinting
  • 19.
    9 Nov 2017 Blockchain18 Top disruptors: Deep Learning & Blockchain Source: https://www.ipe.com/reports/special-reports/securities-services/securities-services-blockchain-a-beginners- guide/10014058.article
  • 20.
    9 Nov 2017 Blockchain JobGrowth Skills in Demand 1. Robotics/automation/data science/deep learning 2. Blockchain/Bitcoin 19 Source: https://www.computerworld.com/article/3235972/financial-it/blockchains-explosive-growth-pushes-job- skills-demand-to-no-2-spot.html
  • 21.
    9 Nov 2017 Blockchain Futureof AI: Smart Networks 20 Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html Fundamental Eras of Network Computing  Future of AI: intelligence “baked in” to smart networks  Blockchains to confirm authenticity and transfer value  Deep Learning algorithms for predictive identification
  • 22.
    9 Nov 2017 Blockchain Speciesof Networks 21 Source: https://www.cbsnews.com/news/cbsn-on-assignment-instagram-filtering-out-hate/, https://deepmind.com/applied/deepmind- ethics-society/research/AI-morality-values/  Social Networks  Transportation  Communications  Information  Biological  Superorganisms  Ecosystems  Organisms  Plants  Finance, credit, payment  Deep Learning Superorganisms: Trans-individual, Trans-national
  • 23.
    9 Nov 2017 Blockchain Agenda Artificial Intelligence  Blockchain Technology  Deep Learning Algorithms  Future of Artificial Intelligence 22
  • 24.
    9 Nov 2017 Blockchain Blockchain 23 Source:http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491  To inspire us to build this world
  • 25.
    9 Nov 2017 Blockchain24 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?
  • 26.
    9 Nov 2017 Blockchain25 Technical Definition: Blockchain is the tamper-resistant distributed ledger software underlying cryptocurrencies such as Bitcoin, for recording and transferring data and assets such as financial transactions and real estate titles, via the Internet without needing a third-party intermediary Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 What is Blockchain/Distributed Ledger Tech?
  • 27.
    9 Nov 2017 Blockchain Howdoes Bitcoin work? Use eWallet app to submit transaction 26 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
  • 28.
    9 Nov 2017 Blockchain P2Pnetwork confirms & records transaction 27 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”
  • 29.
    9 Nov 2017 Blockchain Howrobust is the Bitcoin p2p network? 28 p2p: peer to peer; Source: https://bitnodes.21.co, https://github.com/bitcoin/bitcoin  11,690 global nodes run full Bitcoind (11/17); 160 gb Run the software yourself:
  • 30.
    9 Nov 2017 Blockchain Whatis Bitcoin mining? 29  Mining is the accounting function to record transactions, fee-based  Mining ASICs “find new blocks” (proof of work)  Network regularly issues random 32-bit nonces (numbers) per specified cryptographic parameters  Mining software constantly makes nonce guesses  At the rate of 2^32 (4 billion) hashes (guesses)/second  One machine at random guesses the 32-bit nonce  Winning machine confirms and records the transactions, and collects the rewards  All nodes confirm the transactions and append the new block to their copy of the distributed ledger  “Wasteful” effort deters malicious players Sample code: Run the software yourself: Fast because ASICs represent the hashing algorithm as hardware
  • 31.
    9 Nov 2017 Blockchain DistributedNetworks 30 Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 Decentralized (based on hubs) Centralized Distributed (based on peers)  Radical implication: every node is a peer who can provide services to other peers
  • 32.
    9 Nov 2017 Blockchain P2PNetwork Nodes provide services 31 Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491 Centralized bank tracks payments between clients “Classic” Banking Peer Banking  Nodes deliver services to others, for a small fee  Transaction ledger hosting (~11,960 Bitcoind nodes)  Transaction confirmation and logging (mining)  News services (“decentralized Reddit”: Steemit, Yours)  Banking services (payment channels (netting offsets)) Network nodes store transaction record settled by many individuals
  • 33.
    9 Nov 2017 Blockchain Publicand Private Distributed Ledgers 32 Source: Adapted from https://www.linkedin.com/pulse/making-blockchain-safe-government-merged-mining-chains-tori-adams  Private: approved users (“permissioned”)  Identity known, for enterprise  Approved credentials  Controlled access  Public: open to anyone (“permissionless”)  Identity unknown, for individuals  Ex: Zcash zero-knowledge proofs  Open access Transactions logged on public Blockchains Transactions logged on private Blockchains Any user Financial Inst, Industry Consortia, Gov’t Agency Examples: Bitcoin Ethereum Examples: R3 Hyperledger
  • 34.
    9 Nov 2017 Blockchain BlockchainApplications Areas 33 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
  • 35.
    9 Nov 2017 Blockchain Agenda Artificial Intelligence  Blockchain Technology  Deep Learning Algorithms  Future of Artificial Intelligence 34
  • 36.
    9 Nov 2017 Blockchain Global Data Volume: 40 EB 2020e  Scientific, governmental, corporate, and personal Big Data…is not Smart Data Source: http://www.oyster-ims.com/media/resources/dealing-information-growth-dark-data-six-practical-steps/ 35 35
  • 37.
    9 Nov 2017 Blockchain BigData requires Deep Learning 36  Older algorithms cannot keep up with the growth in data, need new data science methods Source: http://blog.algorithmia.com/introduction-to-deep-learning-2016
  • 38.
    9 Nov 2017 Blockchain BroaderComputer Science Context 37 Source: Machine Learning Guide, 9. Deep Learning  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
  • 39.
    9 Nov 2017 Blockchain38 Conceptual Definition: Deep learning is a computer program that can identify what something is Technical Definition: Deep learning is a class of machine learning algorithms in the form of a neural network that uses a cascade of layers (tiers) of processing units to extract features from data and make predictive guesses about new data Source: Swan, M., (2017)., Philosophy of Deep Learning, https://www.slideshare.net/lablogga/deep-learning-explained What is Deep Learning?
  • 40.
    9 Nov 2017 Blockchain DeepLearning & AI  System is “dumb” (i.e. mechanical)  “Learns” with big data (lots of input examples) and trial-and-error guesses to adjust weights and bias to identify key features  Creates a predictive system to identity new examples  AI argument: big enough data is what makes a difference (“simple” algorithms run over large data sets) 39 Input: Big Data (e.g.; many examples) Method: Trial-and-error guesses to adjust node weights Output: system identifies new examples
  • 41.
    9 Nov 2017 Blockchain Sampletask: 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 40 Source: Jann LeCun, http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
  • 42.
    9 Nov 2017 Blockchain Supervisedand Unsupervised Learning  Supervised (classify labeled data)  Unsupervised (find patterns in unlabeled data) 41 Source: https://www.slideshare.net/ThomasDaSilvaPaula/an-introduction-to-machine-learning-and-a-little-bit-of-deep-learning
  • 43.
    9 Nov 2017 Blockchain Earlysuccess in Supervised Learning (2011)  YouTube: user-classified data perfect for Supervised Learning 42 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
  • 44.
    9 Nov 2017 Blockchain Machinelearning: human threshold 43 Source: Mary Meeker, Internet Trends, 2017, http://www.kpcb.com/internet-trends  All apps voice-activated and conversational?
  • 45.
    9 Nov 2017 Blockchain 2main kinds of Deep Learning neural nets 44 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 in 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
  • 46.
    9 Nov 2017 Blockchain 3Key Technical Principles of Deep Learning 45 Reduce combinatoric dimensionality Core computational unit (input-processing-output) Levers: weights and bias Squash values into Sigmoidal S-curve -Binary values (Y/N, 0/1) -Probability values (0 to 1) -Tanh values 9(-1) to 1) Loss FunctionPerceptron StructureSigmoid Function “Dumb” system learns by adjusting parameters and checking against outcome Loss function optimizes efficiency of solution Non-linear formulation as a logistic regression problem means greater mathematical manipulation What Why
  • 47.
    9 Nov 2017 Blockchain Howdoes the neural net actually learn?  System varies the weights and biases to see if a better outcome is obtained  Repeat until the net correctly classifies the data 46 Source: http://neuralnetworksanddeeplearning.com/chap2.html  Structural system based on cascading layers of neurons with variable parameters: weight and bias
  • 48.
    9 Nov 2017 Blockchain Backpropagation Problem: Inefficient to test the combinatorial explosion of all possible parameter variations  Solution: Backpropagation (1986 Nature paper)  Backpropagation of errors and gradient descent are an optimization method used to calculate the error contribution of each neuron after a batch of data is processed 47 Source: http://neuralnetworksanddeeplearning.com/chap2.html
  • 49.
    9 Nov 2017 Blockchain Agenda Artificial Intelligence  Blockchain Technology  Deep Learning Algorithms  Future of Artificial Intelligence 48
  • 50.
    9 Nov 2017 Blockchain Futureof Artificial Intelligence 49 Source: https://www.slideshare.net/lablogga/deep-learning-explained  Blockchain & Deep Learning  Next-gen global computing network technology  Computation graphs  Self-operating state engines  Make probabilistic guesses about reality states of the world
  • 51.
    9 Nov 2017 Blockchain Futureof AI: Smart Networks 50 Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html Fundamental Eras of Network Computing  Future of AI: intelligence “baked in” to smart networks  Blockchains to confirm authenticity and transfer value  Deep Learning algorithms for predictive identification
  • 52.
    9 Nov 2017 Blockchain DeepLearning Chains: cross-functionality  Deep Learning Applications for Blockchain  TensorFlow for Fee Estimation  Predictive pattern recognition for security  Fraud, privacy, money-laundering  Deep Learning techniques (backpropagations of errors, gradient descent, loss curves) to optimize financial graphs  Formulate debt-credit-payment problems as sigmoidal optimizations to solve with machine learning  Blockchain Applications for Deep Learning  Secure automation, registry, logging, tracking + remuneration functionality for deep learning systems as they go online  BaaS for network operations (LSTM is like a payment channel)  Blockchain P2P nodes provide deep learning network services: security (facial recognition), identification, authorization 51
  • 53.
    9 Nov 2017 Blockchain DeepLearning Chains: App #1  Autonomous Driving & Drone Delivery, Social Robotics  Deep Learning (CNNs): identify what things are  Blockchain: secure automation technology  Track arbitrarily-many units, audit, upgrade  Legal liability, accountability, remuneration 52
  • 54.
    9 Nov 2017 Blockchain DeepLearning Chains: App #2 53 Source: https://www.illumina.com/science/technology/next-generation-sequencing.html  Big Health Data  Large-scale secure predictive analysis of big health data to understand disease prevention Population 7.5 bn people worldwide
  • 55.
    9 Nov 2017 Blockchain DeepLearning Chains: App #3  Leapfrog technology for human potential  Financial Inclusion  2 bn under-banked, 1.1 bn without ID  70% lack access to land registries  Health Inclusion  400 mn no access to health services  Does not make sense to build out brick- and-mortar bank branches and medical clinics to every last mile in a world of digital services  eWallet banking and deep learning medical diagnostic apps 54 Source: Pricewaterhouse Coopers. 2016. The un(der)banked is FinTech's largest opportunity. DeNovo Q2 2016 FinTech ReCap and Funding ReView., Heider, Caroline, and Connelly, April. 2016. Why Land Administration Matters for Development. World Bank. http://www.who.int/mediacentre/news/releases/2015/uhc-report/en/ Digital health wallet
  • 56.
    9 Nov 2017 Blockchain DeepLearning Chains: App #4 55  Enact Friendly AI  Digital intelligences running on consensus-managed smart networks (not in isolation)  Good reputational standing required to conduct operations  Transactions to access resources (like fund-raising), provide services, enter into contracts, retire  Smart network consensus only validates and records bonafide transactions from ‘good’ agents Sources: http://cointelegraph.com/news/113368/blockchain-ai-5-top-reasons-the-blockchain-will-deliver-friendly-ai, http://ieet.org/index.php/IEET/more/swan20141117
  • 57.
    9 Nov 2017 Blockchain Deep-thinkers Registry  Register deep learners with blockchains and monitor with deep learning algorithms  Secure tracking  Remuneration  Examples  Autonomous lab robots  On-chain IP discovery tracking  Roving agriculture bots  Manufacturing bots  Intelligent gaming  Go-playing algorithms 56 Source: Swan, M. Future of AI Thinking: The Brain as a DAC. Neural Turing Machines: https://arxiv.org/abs/1410.5401. IPFS (Benet): https://medium.com/@ConsenSys/an-introduction-to-ipfs-9bba4860abd0#.bgig18cgp Deep Learning Chains: App #5
  • 58.
    9 Nov 2017 Blockchain Conclusion Deep learning chains: needed for next-generation challenges  Financial inclusion, big health data, global energy markets, and space  Smart networks: a new form of automated global infrastructure  Identify (deep learning)  Validate, confirm, and route transactions (blockchain)  Future of AI is smart networks  Running value  Running intelligence  Possible answer to AI worries 57
  • 59.
    World Future Society ScottsdaleAZ, November 9, 2017 Slides: http://slideshare.net/LaBlogga The Future of Artificial Intelligence Blockchain & Deep Learning Melanie Swan Philosophy, Purdue University [email protected]