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AI and Blockchain 2017
This document discusses artificial intelligence and blockchain technology. It provides an overview of AI concepts like neural networks and deep learning. It then covers blockchain topics such as the definition of blockchain, how blockchain works, examples like Bitcoin and Ethereum, and organizations working in this space like IBM, Microsoft, and R3. Finally, it discusses opportunities for combining AI and blockchain, such as using AI to analyze data stored securely on blockchain networks.
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AI and Blockchain 2017
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- 5. 1st Steam Power 3rd Computing 2nd Electricity & Mass Production 19th Century 17th Century 21stCentury 4th Artificial Intelligence 20th Century
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- 18. Blockchain • Distributed digital ledger - an entirely decentralized system •Invented by Satoshi Nakamoto in 2008 (inventor of bitcoin) • A technology that is revolutionizing the financial services industry by empowering millions across the globe to authenticate and transact immediately without costly intermediaries • Design goal is to speed up and simplify how transactions are recorded • Any type of asset can be transacted using the blockchain • Makes possible a currency without a central bank 18© Ivy Data Science
- 19. Blockchain • Can be public or private • Bitcoinis an interesting application for blockchain but there are thousands of applications and wider use cases beyond that - every kind of digital record and transaction • Settlement and clearing time is reduced to seconds • The distributed ledger is replicated on thousands of computers around the world • Kept secure by encryption algorithms • May in time spur new changes in how companies and governments work 19© Ivy Data Science
- 20. Blockchain • Systems without centralized record-keeping that can be just as trustworthy as those that have them • Demanding a cross-industry, open-source collaboration in order to advance the technology for all •2017 as the year of the blockchain pilot, and 2018 as the year blockchain technology will be used in production in financial services • “The internet finally has a public data base” - Chris Dixon of Andreessen Horowitz • “Distributed ledgers are a significant innovation that could have far- reaching implications in the financial industry” - Bank of England 20© Ivy Data Science
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- 24. Opportunities • Disruptive new technology providingopportunities to innovate • Introduce new products and services • Reduce costs of existing services by • Introducing process efficiencies • Automating existing services • Significantly reduce transaction costs • According to White & Case, Blockchain could reduce banks’ infrastructure costs worldwide by $20 Billion a year by year 2022* • Significantly reduce transaction times (days to seconds) • The next Google or Facebook? *http://news.crowdvalley.com/news/state-of-blockchain-and-artificial-intelligence-ai-in-fintech 24© Ivy Data Science
- 25. Risks & Challenges • Security • Bitcoin & Ethereumcryptocurrencies have both been hacked • New safeguards put in place each time to close vulnerabilities • Environmental • Impact on environment due to energy required to run network • Expensive equipment • Cryptocurrency mining (data centers) • Interoperability • There will likely be many blockchains • The ability to work with other banks’ and partners’ blockchain technology • The idea of making trust a matter of coding, rather than of democratic politics, legitimacy and accountability, is not necessarily an appealing or empowering one 25© Ivy Data Science
- 26. Blockchain Applications • Legal agreements (contracts) • Finance •Cryptocurrency - peer-to-peer version of electronic cash • Other asset classes (bonds, commodities, etc.) • Credit Suisse has conducted 10 proofs of concept with blockchain startups to achieve cost reductions • Autonomous vehicles • Transportation infrastructure • Accommodation • Hotels, apartments, smart locks • Energy grid • Internet of Things • ”The Internet of Everything needs a Ledger of Everything” • Supply chain management • Things that haven’t been thought of yet • Blockchain as a Service 26© Ivy Data Science
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- 28. Examples - Bitcoin • Firstdecentralized digital currency • Invented by an unidentified programmer, or group of programmers, under the name of Satoshi Nakamoto, bitcoin was introduced on 31 October 2008 • Like paper money and gold before it, bitcoin is a currency that allows parties to exchange value • Platform for innovation with open source code • Bitcoin is mathematically limited to 21million bitcoins and that can never be changed • People store Bitcoin in software called a wallet, available for most devices • More and more businesses are accepting Bitcoin payments (>100,000) • Can be mined, or bought from one of the trusted exchanges – asset class • https://www.youtube.com/watch?v=Gc2en3nHxA4 28© Ivy Data Science
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- 30. Examples - Ethereum • Ethereumis a decentralized platform that runs smart contracts • Applications that run exactly as programmed without any possibility of downtime, censorship, fraud or third party interference • Runs on a custom built blockchain shared global infrastructure - moves value around and represents the ownership of property • Enables developers to create markets and many other things that have not been invented yet, without a middle man or counterparty risk • Offers a range of services that are not possible using bitcoin • Initially proposed in late 2013 by Vitalik Buterin • Github site https://github.com/ethereum/ 30© Ivy Data Science
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- 33. Blockchain Organizations Hyperledger Project https://www.hyperledger.org • Open-sourceblockchain project that will allow businesses to build a cross-industry distributed ledger solution • Lead by IBM and the Linux Foundation, members include Cisco, Intel, Accenture, ANZ Bank, Digital Asset, Fujitsu, State Street, LSE, SWIFT, Vmware, JPMorgan and Wells Fargo • IBM is contributing tens of thousands of lines of existing code to the project, as well as dedicated developers and intellectual property • Github repo: https://github.com/hyperledger Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 33© Ivy Data Science
- 34. Blockchain Organizations R3 http://www.r3cev.com • Studying how trading of securities, derivatives and loans can be overhauled by using distributed ledger and blockchain technology • Includes more than 70 banks including Bank of America, Citigroup, Deutsche Bank, Morgan Stanley, SocGen, Barclays, Goldman Sachs and JPMorgan EEA http://entethalliance.org •Thirty member companies including JPMorgan, BP, Microsoft and Intel • Will work to enhance the privacy, security and scalability of Ethereum blockchain • Make it better suited to business applications Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 34© Ivy Data Science
- 35. IBM & Blockchain • IBM offers Blockchain as a Service (announced this Monday) https://www.ibm.com/blockchain/getting-started.html •Create private and secure digital assets in test applications that can be traded quickly and securely over a permissioned network • Uses hyperledger fabric https://hyperledger-fabric.readthedocs.io/en/latest/ • This is also on github https://github.com/hyperledger/fabric/blob/master/docs/source/index.rst • In June 2016, IBM opened an incubator in Singapore where 5,000 computer scientists work to build rapid prototypes using the company’s blockchain and Watson AI tools for businesses in the APAC region • "Watson and blockchain are two technologies that will rapidly change the way we live and work” – Randy Walker, IBM CEO APAC Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 35© Ivy Data Science
- 36. Microsoft & Blockchain • Blockchain as a Service https://azure.microsoft.com/en-us/solutions/blockchain/ • EthereumBlockchain as a Service on Azure https://www.oreilly.com/topics/data-fintech • Project Bletchley is Microsoft’s Blockchain Architectural approach https://github.com/Azure/azure-blockchain- projects/blob/master/bletchley/bletchley-whitepaper.md • Open source code is on github https://github.com/Azure/azure-blockchain-projects/tree/master/bletchley • They learned that Consortium blockchains, which are members-only, permissioned networks for consortium members to execute contracts, are ideal Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 36© Ivy Data Science
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- 38. AI & Blockchain • What happens when we start to merge artificial intelligence and the blockchain into a single, powerful prototype? • We have blockchain tech's promise of near-frictionless value exchange and artificial intelligence’s ability to accelerate the analysis of massive amounts of data •The joining of the two could mark the beginning of an entirely new paradigm • Maximize security while remaining immutable* by employing artificial intelligent agents that govern the chain * An object whose state cannot be modified after it is created Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 38© Ivy Data Science
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- 40. State Street • Blockchain-based indices • 64% of wealth and asset managers polled expected their firms to adopt blockchainin the next five years – State Street Report • Further, 50% of firms said they expect to employ artificial intelligence • Data is stored and made secure using Blockchain - AI can analyze the data while it remains secure • "You, in theory, can now put all that data on a blockchain," Maiuri said. "I can never take possession of it, but I can ask questions of the data.” • Turn unstructured data turned into a structured instrument Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 40© Ivy Data Science
- 41. State Street • Set sentiment data into a smart contract along with information such as pricing, valuation and risk characteristics to create new types of investment advice • As of January, State Street has10 blockchain POC’s in the works • Looking for other ways to increase revenues, and provide better value for clients • IBM Watson is also merging blockchain with AI • References • http://www.coindesk.com/street-street-is-betting-ai-can-help-monetize- blockchain-tech/ • http://www.coindesk.com/ibms-asia-watson-blockchain-ai/ Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 41© Ivy Data Science
- 42. Bloq • Deep Learning technologies are combined with behavioral, predictive, graph and descriptive-prescriptive analytics to learn from the past • Anticipate and predict illicit activities, suspicious user behavior, fraud and anomalies •With more companies and institutions adopting blockchain-based solutions, and more complex, potentially critical data stored in distributed ledgers, there's a growing need for sophisticated analysis methods, which AI technology can provide • www.bloq.com Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 42© Ivy Data Science
- 43. Blockchain & Watson • Integrate Watson with the IoT • Watson IoTGroup – “Intelligent IoT” • Let devices perform tasks like running self-diagnoses at set times • Risk management – how blockchains might be used to make devices safer for their users, in part by reducing human involvement in their operation • Using blockchain tech, artificially intelligent software solutions could be implemented autonomously • An artificially intelligent blockchain will let joint parties collectively agree on the state of the device and make decisions on what to do based on language coded into a smart contract Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 43© Ivy Data Science
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- 45. Conclusions • A host of economic, legal, regulatory, and technological hurdles must be scaled before we see widespread adoption of blockchain technology, but first movers are making incredible strides. Within the next handful of years, large swaths of your digital life may begin to run atop a blockchain foundation - and you may not even realize it, PC Mag, Feb 2017 •AI is being used with blockchain technologies to analyze data securely and to make predictions • Both startups and corporations are running POC’s at the moment • Blockchain with AI is already being offered as a service by cloud providers IBM, Microsoft • In the future almost every transaction could be running on blockchain technology 45© Ivy Data Science
- 46. References • Blockchain: The Invisible Technology That's Changing the World http://www.pcmag.com/article/351486/blockchain-the-invisible-technology- thats-changing-the-wor • Microsoft and IBM Set Sights on the Next Cloud Frontier: Blockchain-as-a- Service http://www.pcmag.com/article/345899/microsoft-and-ibm-set-sights-on- the-next-cloud-frontier-blo •IBM’s New Watson Centre Merges Blockchain With AI http://www.coindesk.com/ibms-asia-watson-blockchain-ai/ • The Truth About Blockchain - HBR https://hbr.org/2017/01/the-truth-about-blockchain • The Impact of the Blockchain Goes Beyond Financial Services - HBR https://hbr.org/2016/05/the-impact-of-the-blockchain-goes-beyond- financial-services © Ivy Data Science 4646© Ivy Data Science
- 47. References • Ethereum Blockchain as a Service now on Azurehttps://azure.microsoft.com/en- us/blog/ethereum-blockchain-as-a-service-now-on-azure/ • Blockchain A to Z http://www.pcmag.com/article/344969/blockchain-a-z- everything-you-need-to-know-about-the-game-c • Banks are sold on blockchain https://www.americanbanker.com/news/banks-are-sold-on-blockchain-concerned- about-collaboration • Banking on Blockchain – Accenture Report https://www.accenture.com/us-en/insight-banking-on-blockchain Books • Tapscott, Dan - Blockchain Revolution, Portfolio, 2016 • Tale, Steve – Blockchain Technology, CreateSpace, 2017 © Ivy Data Science 4747© Ivy Data Science
- 49. AI Fellowship Program Overview • Ivy Data Science Artificial Intelligence Fellows Program is an intensive, twelve-week professional training fellowship •Bridges the gap between academic research or professional software engineering and a career in artificial intelligence • Based out of New York City, Silicon Valley or Boston, the program enables scientists, researchers, and engineers to learn the industry-specific skills needed to work in the growing field of artificial intelligence at leading companies • Eight weeks classroom (theory and labs), plus four weeks project placement within a company © Ivy Data Science 49© Ivy Data Science 49
- 50. AI In Industry • “Just as 100 years ago electricity transformed industry after industry, AI will now do the same.” -Andrew Ng, Chief Scientist at Baidu • Deep learning produces real results and is at the root of a real industry that makes money today. The promises of it in the near future are very exciting... in areas like self-driving cars, medical imaging, personalized medicine, content filtering/ranking, etc-- Yann LeCun, Facebook AI Research • Some of the ways these techniques have now started to be deployed in industry include healthcare, transportation, robotics, AI Assistants, chatbots, education, satellite imaging, speech recognition and language generation • In addition to these various applications, deep learning and other techniques in AI will soon impact additional areas ripe with new challenges, including video understanding, security, public safety, and the Internet of Things © Ivy Data Science 50© Ivy Data Science 50
- 51. Who are the best AI Professionals? • “I think people need to understand that deep learning is making a lot of things, behind-the-scenes, much better.” -Geoff Hinton, Google • The best AI professionals have a passion for solving complex machine learning challenges or engineering robust machine learning systems • Scientists: Research experience at a Postdoc, PhD or Masters level, have used machine learning to further research and now want to pursue AI as a full-time career • Software Engineers: Have worked in machine learning in current professional roles. Have a degree in computer engineering or computer science and wants to upskill and cross train into deep learning © Ivy Data Science 51© Ivy Data Science 51
- 52. Mentorship • Company Mentors - AI teams at leading companies from diverse sectors and sizes who will share the problems they are currently solving •Industry and Open Source Leaders - Pioneers at the forefront of Artificial Intelligence that help you learn best practices & disruptive trends in the industry • Your Fellows – Experienced, ambitious scientists, researchers, and engineers with common goals and a diverse set of skills that complement yours. Learn the way you do in the industry, by collaborating and working through challenges with your peers • Ivy Data Science Alumni – Fellowship Alumni from our Artificial Intelligence program have transitioned into AI roles at top companies. They provide individualized guidance and practice with interviews • Ivy Data Science Team - Ivy Data Science offers continued guidance throughout the entire process. We point you to the right resources to help troubleshoot tough issues © Ivy Data Science 52© Ivy Data Science 52
- 53. AI Trainers © Ivy Data Science 53© Ivy Data Science 53 Peter Morgan Chuck Pace V.T. RajanMark Meloon Ryan Somers Dmytro LituievMikhail Gorelkin Vishal Goklani Rahul Kumar
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- 55. AI Techniques • Computer Vision: Convolutional Neural Networks (CNNs) are deep neural networks specifically designed to take advantage of the structure of image input • Natural Language Processing: Neural networks to understand language •Recurrent Neural Network: RNN architectures redefined “state of the art” in the tasks of document summarization, language generation, and language to language translation (machine translation) • Reinforcement Learning: Algorithms that learn action choices to maximize an objective • Unsupervised Learning: Systems that learn the underlying structure of the data • Scalable Learning: Distributed models and training of deep networks and AI agents © Ivy Data Science 55© Ivy Data Science 55
- 56. AI Emerging Trends • Combining probabilistic programming with deep learning for uncertainty estimates • Regularizing weights with new priors, and creating hybrid networks •Leveraging GAN and variational approaches for semi-supervised learning • Using memory networks to combine attention, memory, and reasoning for language • Understanding and developing Q&A systems © Ivy Data Science 56© Ivy Data Science 56
- 57. Applied AI • Finance • Healthcare •Transportation • Robotics • Personalized AI Assistants • Video Understanding • Security • Public Safety • Education • Etc. © Ivy Data Science 57© Ivy Data Science 57
- 58. Ivy Data Science Benefits • Full tuition scholarship paid for by the hiring companies, so Fellows pay nothing to participate in the program • Paid consulting remote project on our partner marketplace platform (experfy.com) •Mentorship from alumni Ivy Data Science Fellows whose experience at Ivy Data Science and at their current roles, make them a great resource for guidance and feedback • Personalized company matching. We help you figure out which companies are the best fit for you based on our experience and in-depth conversations with the hiring managers. We then help you arrange interviews during the final weeks of the program • Help navigating the negotiation of final employment terms once companies have made their employment offers to you • Guidance and mentorship from industry professionals at every stage of the program and as you prepare for interviews © Ivy Data Science 58© Ivy Data Science 58
- 59. Training with Job Placement • Led by Experts from Harvard, Columbia, UMass, UCL • Opportunity for paid projects in the last four weeks of bootcamp •Practical hands-on training that will prepare you for a career as a Machine Learning or AI Practitioner • Bootcamp cost is $12k (financing available) • Evenings AI course specializing in AI for FinTech or AI for Healthcare - $6k Ivy Data Science AI - State of Play v0.10 Peter Morgan Dec 2016 59© Ivy Data Science
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