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Connected Intelligence

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Enterprise AI transforms business, impacts performance, and increases efficiencies through insight generation, customer engagement, business acceleration, and enterprise transformation.

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Connected Intelligence

  1. 1. 1
  2. 2. Major conceptual advances that power economic growth seem to occur about 2-3 times a century. Previous waves, e.g., the steam engine and its descendants, mechanized muscle power. Connected intelligence (aka Industry 4.0) is the latest and most transformative because it augments and automates mental power—the ability to use our brains to understand and shape our environments — and is accelerating exponentially. 2This content included for educational purposes.
  3. 3. This content included for educational purposes. 3 • Lawrence Mills Davis is founder and managing director of Project10X, a research consultancy known for forward-looking industry studies; multi-company innovation and market development programs; and business solution strategy consulting. Mills brings 30 years experience as an industry analyst, business consultant, computer scientist, and entrepreneur. He is the author of more than 50 reports, whitepapers, articles, and industry studies. • Mills researches artificial intelligence technologies and their applications across industries, including cognitive computing, machine learning (ML), deep learning (DL), predictive analytics, symbolic AI reasoning, expert systems (ES), natural language processing (NLP), conversational UI, intelligent assistance (IA), and robotic process automation (RPA), and autonomous multi- agent systems. • For clients seeking to exploit transformative opportunities presented by the rapidly evolving capabilities of artificial intelligence, Mills brings a depth and breadth of expertise to help leaders realize their goals. More than narrow specialization, he brings perspective that combines understanding of business, technology, and creativity. Mills fills roles that include industry research, venture development, and solution envisioning. Lawrence Mills Davis Managing Director Project10X mdavis@project10x.com 202-667-6400
  4. 4. LONG WAVES OF INNOVATION
  5. 5. This content included for educational purposes. Long waves of innovation 5 Major conceptual advances that power economic growth seem to occur about 2-3 [mes a century. The chart shows six long waves. Inven[ons in coon- spinning, iron- making, and steam power propelled the first boom. It lasted from the 1780s to the 1840s. The second wave arrived with innova[ons in steelmaking and railways, las[ng for half a century before running out of steam around 1900. Electrifica[on and the internal- combus[on engine powered the third 50-year wave. The fourth industrial wave was launched in the early 1950s on the back of petrochemicals, electronics, compu[ng and aerospace. A fi`h long wave, connected intelligence, started in the 1970s with the precursors of the Internet. It con[nued with the adop[on of client-server corporate networking, and rapidly accelerated following the introduc[on of the World Wide web and mobile devices. Following the global recession, this wave has shi`ed into a new growth gear. Far from being over, the connected intelligence wave (also called Industry 4.0) has probably another 35 years to go. Meanwhile, a sixth wave is forming that will be powered by nanotechnology, bioscience, and clean energies as well as AI. Source: Joseph Schumpeter, Norman Poire Connected Intelligence is a long wave of innova-on that combines compu-ng, communica-ons, and distributed intelligence (AI). It brings fundamental shi=s in paradigm, economics, and technology.
  6. 6. We are at the beginning of the most transformative revolution ever Industry 4.0 Industry 4.0 Connected intelligence `1 Industry 1.0 Agriculture Industry 2.0 Industrial Industry 3.0 Information Age Source: Publicis•Sapient This content included for educational purposes. 6
  7. 7. Computers and other digital advances are doing for mental power – the ability to use our brains to understand and shape our environments – what the steam engine and its descendants did for muscle power. 7 Erik Brynjolfsson, Director, MIT Initiative on the Digital Economy, 
 Massachusetts Institute of Technology, USA This content included for educational purposes.
  8. 8. The speed of current breakthroughs has no historical precedent. When compared with previous industrial revolutions, the Fourth is evolving at an exponential rather than a linear pace. Moreover, it is disrupting almost every industry in every country. 8 Klaus Schwab, Founder & Executive Chairman, World Economic Forum This content included for educational purposes.
  9. 9. “As a society, we are entering uncharted territory.” - Marc Benioff,
 Salesforce CEO
 18 January 2016 Industry 1.0 Agriculture Industry 3.0 Information Age Industry 4.0 Connected Intelligence 150 Years 75 Years 40 Years ?? Years Industry 2.0 Industrial Source: Publicis•Sapient “As a society, we are entering uncharted territory.” - Marc Benioff,
 Salesforce CEO
 18 January 2016 9This content included for educational purposes.
  10. 10. This content included for educational purposes. Rise of the 4th Platform — Fabric of community, data, devices & intelligence 10 Source: Dion Hinchcliff
  11. 11. Mainframe Computers Desktop Computers User
 Friendly GUI Internet Business Value Created (NASDAQ*) Industry 3.0 | Information Age Industry 4.0 | The Connected Intelligence Age Big Data Platforms Machine Learning Ubiquitous User Interfaces TBD… Business Value Created (NASDAQ*) WE ARE HERE… 1970 1980 2010 20302025202020151990 2000 2010 YearsDecades During the Information Age, NASDAQ composite index (weighted market cap) increased 3.5 times during the 4 decades, from 1970 to 2010 . In the Connected Intelligence Age, NASDAQ composite index (weighted market cap) will increase more than 3 times in the 2 decades from 2010 to 2030 . 600 – 2150 – – 2150 – 6500 – – Sources: World Economic Forum, NASDAQ, Forbes Global, BAML, McKinsey, Forrester, Frost & Sullivan, Gartner, IDC, Analysis Group, 
 BCC Research, Rubin Worldwide, Tractica, Publcis.Sapient, CB-Insights, Statista, O’Reilly, TechEmergence, Venture Scanner, Wikibon, Mobile Connected Population 11This content included for educational purposes.
  12. 12. 12 ERA INFORMATION AGE CONNECTED INTELLIGENCE AGE Time span 1970 to 2010 2010 to 2030 Technology users Thousands to millions to hundreds of millions of users Billions of users, trillions of things, everywhere Products Designed for the enterprise only to designed for enterprise first and then “downsized" for the consumer Designed for the consumer and Internet of everything, enhanced for the enterprise Devices Mainframe, Minicomputer & Terminals to LAN/Internet, Client/ Server and personal computer Mobile Broadband, Cloud Services, Big Data/Analytics, Social Business, Mobile Devices Applications Hundreds of programs to tens of thousands of apps Millions of apps Focus Business recordkeeping to enterprise core applications to personal apps that digitized types of information and computerized routine tasks to user-friendly GUIs, Internet, and mobile connected population Big data platforms, machine learning, AI, robotics, 3D printing, Internet of Things, cognitive systems, next generation security, and ubiquitous natural user interfaces Data 10 Petabytes in 1985 to 1 zettabyte of data in 2010 40 zettabytes by 2020, to 1 Yottabyte by 2030 World population 3.7B to 6.9B people. From $5,000 to $10,000 per capita GDP. 6.9B to 8.5B people. From $10,000 to $14,000 per capita GDP. World GDP $18T to $68T $68T to $119T World ICT GDP $270B in 1970 to $4T in 2010 $4T in 2010 to $13T in 2030 ICT as % World GDP 1.5% in 1970 to 6% in 2010 6% in 2010 to 11% in 2030 Sources: World Economic Forum, NASDAQ, Forbes Global, BAML, McKinsey, Forrester, Frost & Sullivan, Gartner, IDC, Analysis Group, BCC Research, Rubin Worldwide, Tractica, CB-Insights, Publcis.Sapient, Statista, Wikibon, TechEmergence, Venture Scanner This content included for educational purposes.
  13. 13. This content included for educational purposes. What are the top 10 strategic technology trends of 2017 according to Gartner? 1. AI landscape 2. AI company research scope 3. AI company briefs Source: Gartner 13
  14. 14. This content included for educational purposes. Forrester TechRadar:
 Artificial Intelligence Technologies, 2017 Q1 Categories, relative business value, adoption trajectory and ecosystem phase 1. AI landscape 2. AI company research scope 3. AI company briefs Source: Forrester 14
  15. 15. TECHNOLOGIES FOR CONNECTED INTELLIGENCE
  16. 16. This content included for educational purposes. Huge drivers exist for transformation. • Technologies that will change the world 1000X. • Smart processes to power mul[-trillion dollar economic expansions. • 50X increases in knowledge worker produc[vity aainable by 2030. • Intelligent ci[es and enterprise ecosystems compe[ng to become vibrant cultural and economic centers. 16
  17. 17. Connected intelligence arises 
 at the intersection big data, 
 cloud, mobility, social computing, the internet of things, 
 and artificial intelligence. CLOUD BIG
 DATA MOBILITY IOT AI Connected
 intelligence 17This content included for educational purposes.
  18. 18. 18 This content included for educational purposes.
  19. 19. This content included for educational purposes. 19https://www.theclearinghouse.org/-/media/action%20line/documents/volume%20vii/20161201_tch_ey_fintech_paper.pdf?la=en Source: EY Comparative maturity & impact of technologies that enable cognitive transformation.
  20. 20. This content included for educational purposes. 20https://www.theclearinghouse.org/-/media/action%20line/documents/volume%20vii/20161201_tch_ey_fintech_paper.pdf?la=en Source: EY Ten example cognitive capabilities resulting from combining emerging technologies for connected intelligence
  21. 21. This content included for educational purposes. Source: PwC Distributed electronic ledger that uses software algorithms to record and confirm transactions with reliability and anonymity. The record of events is shared between many parties and information once entered cannot be altered, as the downstream chain reinforces upstream transactions. Blockchain Example Use Cases • Provenance / traceability • Asset registration / ownership • Trade finance • Record management • Identity management • Voting • Peer to peer transactions • Supply chain management • Smart contracting Air- or water-based devices and vehicles, for example, Unmanned Aerial Vehicles (UAV), that fly or move without an onboard human pilot. Drones can operate autonomously (via on-board computers) on a predefined flight plan or be controlled remotely. Drones Example Use Cases • Construction site management • Forestry management • Facility inspection (wind turbine, oil rig, etc) • Insurance claim validation • Precision farming • Infrastructure inspections • Railway safety • Cargo delivery Network of objects – devices, vehicles, etc. – embedded with sensors, software, network connectivity and compute capability, that can collect and exchange data over the Internet. IoT enables devices to be connected and remotely monitored or controlled. The term IoT has come to represent any device that is now “connected” and accessible via a network connection. The Industrial IoT is a subset of IoT and refers to its use in manufacturing and industrial sectors. Internet of Things (IoT) Example Use Cases • Data integration and analytics • Connected service parts management • Remote service • Real time market insights • Flexible billing and pricing models • Inventory and material tracking • Real-time asset monitoring • Connected operational intelligence • Customer self-service • Usage and performance benchmarking Electro-mechanical machines or virtual agents that automate, augment or assist human activities, autonomously or according to a set of instructions – often a computer program. Robots Example Use Cases • Service industry • Automation of predictable tasks • Data management • Manufacturing • Hazardous industries • Hotels and tourism Additive manufacturing techniques used to create three-dimensional objects based on digital models by layering or “printing” successive layers of materials. 3D printing relies on innovative “inks” including plastic, and more recently, glass and wood. 3D Printing Example Use Cases • Supply chain optimization • Customized products • Remote location production • Healthcare and smart medical devices • Tools and end use parts • Prototyping • Bridge manufacturing Computer-generated simulation of a three dimensional image or a complete environment, within a defined and contained space, that viewers can interact with in realistic ways. VR is intended to be an immersive experience and typically requires equipment, most commonly a helmet/headset. Virtual reality (VR) Example Use Cases • Big data management • Entertainment • Healthcare • Merchandising • Immersive journalism • Virtual workplaces • Manufacturing/product design • Architecture & construction • Education&training Addition of information or visuals to the physical world, via a graphics and/ or audio overlay, to improve the user experience for a task or a product. This “augmentation” of the real world is achieved via supplemental devices that render and display said information. Augmented Reality (AR) Example Use Cases • Printing and advertisers • Retail environments • Marketing • Virtual showrooms • Education • Travel and tourism • Gaming Software algorithms that are capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making and language translation. AI is an “umbrealla” concept that is made up of numerous subfields, such as machine learning, which focuses on the development of programs that can teach themselves to learn, understand, reason, plan, and act (i.e. become more intelligent) when exposed to new data in the right quantities. Artificial intelligence (AI) Example Use Cases • Customer support, transactions and helpdesks • Data analysis and advanced analytics • Managing personal finances • Trading systems • Real time fraud and risk management • Automated virtual assistants • Underwriting loans and insurance Plenty of use cases for connected intelligence. This chart identifies 60+ example use cases 21
  22. 22. Cloud computing, mobility, big data and analytics, robotic process automation, distributed ledger technology, and internet of things are paving the way for AI technology to deepen the understanding, connection, and creation of superior experiences for both consumers and employees. 22This content included for educational purposes.
  23. 23. CLOUD
  24. 24. This content included for educational purposes. Cloud Computing • An internet-based model for delivering information technology services, which enables IT resources to be centrally pooled, rapidly provisioned, and quickly redeployed. It enables enterprises to scale infrastructure and access advanced technologies developed by other providers cost-effectively. 24
  25. 25. This content included for educational purposes. A new computing paradigm is emerging Tabulating Systems Era Programmable Systems Era Cognitive Systems Era Source:IBM Tabulation: • Punch cards • Time card reader Programmatic: • Search • Deterministic • Enterprise data • Machine language • Simple outputs Cognitive: • Discovery & recommendation • Probabilistic • Big data & knowledge bases • Natural language as interface • Intelligent options 25
  26. 26. This content included for educational purposes. Cloud direction: from information systems to connected intelligence Cognitive ComputingTraditional User interaction • Static Library • Taxonomy driven hierarchy • FAQ • Library of documents Applications • Static content • Hierarchical search • Ask and answer • Limited social connections • Document management • Relational database systems • Enterprise file management systems • Limited workflow automation Process & Store • Internet of Things • Any data / any device • Integration of sensors, conversational UI • Interactive interfaces for the KM lifecycle • From create to share and enhance of content • Continuous capture, analysis & publishing • Machine learning categorization • Advanced search –multidimensional • Contextual analysis proposes new questions • Shared social connections • Store any data, any time, any volume • Pattern recognition and machine learning analysis and recommendations • Integration workflow across communities • Knowledge models and reasoning Connected Intelligence AgeInformation Age 26
  27. 27. This content included for educational purposes. Connected intelligence: three IoT software stacks Source: Eclipse Foundation 27 3 software stacks are required for cognitive IoT solutions: 1) Constrained Devices, 2) IoT Gateways and Smart Devices, and 3) IoT Cloud Platforms.
  28. 28. 28 Source:BessemerVenturePartners This content included for educational purposes.
  29. 29. This content included for educational purposes. Source: Naveen Balani Enterprise cognitive 
 IOT stack Diagram depicts architecture for a connected world. Every object in the world has the potential to connect to the Internet and provide their data so as to derive actionable insights on its own or through other connected objects. 29
  30. 30. This content included for educational purposes. Example big data stack 
 for AI startups This diagram depicts an architecture for supporting a range of AI applications including deep learning, blockchain, big stack data, augmented reality, ambient intelligence, and context computing. Source: The Hive AI Application 30 This content included for educational purposes.
  31. 31. This content included for educational purposes. Machine learning and deep Learning in 
 the cloud will accelerate enterprise AI. Powerful partnerships are already in place. 31This content included for educational purposes.
  32. 32. This content included for educational purposes. Source: Aragon Research Major cloud platform providers Smart software that has built in AI capabilities is the new frontier. And it is a race. All major cloud platform providers offer machine learning services today. Application vendors are revamping their offerings to make them predictive and real-time, or being left behind. The era of analog software is over. 32
  33. 33. This content included for educational purposes. Cloud Services: Summary of major vendor emphasis 33
  34. 34. This content included for educational purposes. 34 AI-optimized hardware • The industry is actively pursuing alternatives to Von Neumann computing, which separates the modules for input/output, instruction-processing, and memory. • Deep neural networks is an alternative model of computing inspired by what is known about biological neural networks that aims of improving the hardware efficiency and robustness of computing systems. • Quantum computing is another model. It makes direct use of quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data. Quantum computers are different from binary digital electronic computers based on transistors. • DNA computing is another paradigm. It uses DNA, biochemistry, and molecular biology hardware, instead of the traditional silicon-based computer technologies. This content included for educational purposes.
  35. 35. This content included for educational purposes. 35 “From the hardware side of things, we will start seeing embedded devices with specialized architectures for running neural nets. Those things will pop out in self-driving cars, vacuum cleaners, maintenance robots, smart cameras, etc. Perhaps smart phones and tablets eventually.” Yann LeCunn Director of AI Research Facebook This content included for educational purposes.
  36. 36. This content included for educational purposes. 36 “We are endowing systems with human-centered qualities, including more natural, fluid conversation and the ability to address several topics or needs in one ongoing interaction, with deeper understanding of human values and intentions, such as recognizing and acting upon commitments we make to others in our email and text messaging. Human-aware AI seeks to give systems new abilities to understand human attention, memory, and choices so as to better understand when to remind people about things they will forget, how to help them to make better decisions, and how to orchestrate the best services to fulfill their intents.” Eric Horvitz
 Technical Fellow and Director,
 Microsoft Research
  37. 37. This content included for educational purposes. DRONE Application programming interfaces (APIs) An API formalizes access to software modules through standard inputs and outputs, and hides details of the operations of the module. A predictive API takes input in the form of data, and uses some form of AI to produce a predictive output. 37This content included for educational purposes.
  38. 38. This content included for educational purposes. 38 Cybersecurity technologies Source: CB Insights
  39. 39. This content included for educational purposes. 39 Cybersecurity 
 market map
  40. 40. This content included for educational purposes. Periodic table of cybersecurity (Q1 2017) 40This content included for educational purposes.
  41. 41. This content included for educational purposes. 41 Security challenges Security challenges are shi`ing from: (a) predictable, slowly- evolvable threat & risk models, interac[on scenarios and behavior paerns to unpredictable and highly-dynamic ones; (b) plazorm monopolies to massively distributed systems exhibi[ng unprecedented levels of sw/hw plazorm heterogeneity; (c) device- and infrastructure-centric security models towards user-context and informa[on-centric ones; (d) predefined to opportunis[c interac[ons with unknown par[es in open, inherently insecure environments; and (e) limited and fragmented data to unparalleled level of personal informa[on richness and precision collected/ processed/stored and communicated. Our expecta[on is for secure, trustworthy pervasive environments where: (1) users control which data is being collected and the manner in which it is aggregated, processed, stored and distributed; (2) informa[on is disclosed only to authorized par[es and used for authorized tasks only; (3) Individuals are always sure with whom they are interac[ng; (4) Users are surrounded by millions of invisible, data collec[ng nano-devices building a huge, complex and dynamic system an omnipresent life-recorder; and (5) Data are captured con[nuously with unprecedented precision and completeness...both inside and outside us. Autonomic security requires self-awareness at the lowest level of granularity as well as the capability to see into knowledge embedded inside objects. Source: Imrich Chlamtac Concept-level transparency is key to evolving fine grain, 
 autonomic, and effective security mechanisms. This content included for educational purposes.
  42. 42. BIG DATA & ANALYTICS
  43. 43. This content included for educational purposes. Big data & analytics • Enable the sourcing, aggregation, and analysis of large amounts of data, whether structured, multi- structured, or unstructured. Analytics enable discovery, interpretation, and communication of meaningful patterns within data using statistical and symbolic methods. 43
  44. 44. From real-world data, computers can learn to recognize patterns too complex, too massive, or too subtle for hand- crafted software. 44 This content included for educational purposes.
  45. 45. This content included for educational purposes. 45 Source: IDC, EMC, Deloitte 2020 2013 4.4 ZB 44 ZB 37% 27% 10% Up to 90% of this data is unstructured Total size of the digital universe Data useful if analyzed Data from mobile devices and people Data from embedded systems Sources: EMC Digital Universe with research and analysis by IDC, “The digital universe of opportunities: Rich data and the increasing value of the Internet of Things,” April 2014; International Data Corporation, “IDC iView: Extracting value from chaos,” 2011, www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf, accessed December 29, 2016. 22% 17% 2% Expanding digital universe, 2013–2020 Projected to reach 44 zettabytes in 2020. One zettabyte is equal to one billion terabytes. Data valuable for enterprises, especially unstructured data from the Internet of Things and nontraditional sources is increasing in both absolute and relative sizes.
  46. 46. 90%Percentage of 
 the world’s data 
 created in the 
 past two years Source: Retailer’s Guide to 
 Big Data Infographic 6 BILLION Mobile subscriptions worldwide 1.01 Billion Facebook users worldwide 400 Million Tweets per day 87% World’s population 604 MILLION Users log-in monthly from mobile 84 MILLION Users access Twitter via Mobile SUBSCRIBE 46 This content included for educational purposes.
  47. 47. <0.5% of data is ever analyzed or used !47 This content included for educational purposes.
  48. 48. This content included for educational purposes. 48 Data is the new “oil”! AI and cognitive computing bring the power of descriptive, predictive, and prescriptive analytics to the enterprise. PRICING GUEST PREFERENC ES CUSTOMERPROFILE & TRANSACTIONAL ONLINESEARCH OPERATIONAL W EB CLICKSTREAM DEMAND&OCCUPANCY DESTINATION SOCIALNETWORK & USERGENERATED LOW MEDIUM HIGH VELOCITY VARIETY VOLUM E Source: Microsoft Enterprises Harnessing the Value of Big Data This content included for educational purposes.
  49. 49. This content included for educational purposes. Source: D-Zone TRANSIENT ZONE Ingest, Tag, & Catalog Data Apply Metadata, Protect Sensitive Attributes Data quality & Validation Enrich Data & Automate Workflows Data Catalog Data Prep Tools Data Visualization External Connectors RAW ZONE TRUSTED ZONE DATA LAKE ZONES CONSUMER SYSTEMSDATA REFINED ZONESTREAMING FILE DATA RELATIONAL Data lakes for extracting value 
 from big data Within a Data Lake, zones allow the logical and/or physical separation of data that keeps the environment secure, organized, and agile. A 4-zone system might include: • Transient Zone – holds ephemeral data, such as temporary copies, streaming spools, or other short-lived data before being ingested. • Raw Zone – is where raw data will be maintained and where sensitive data is encrypted, tokenized, or otherwise secured. • Trusted Zone – follows data quality, validation, or other processing on data, and becomes the “source of truth” for downstream systems. • Refined Zone – is where manipulated and enriched data is kept. E.g., to store output from tools like Hive or external tools that write into to the Data Lake. 49
  50. 50. This content included for educational purposes. 50Source: DELL-EMC Data analytics 
 lifecycle
  51. 51. This content included for educational purposes. Analytics continuum and stages Source:Gartner,Publicis.Sapient Increasing knowledge & data intensity 51
  52. 52. This content included for educational purposes. α !52 Fundamentals Macro Trends Business Strategy Competitive Dynamics Quality of Management Relevant Possible Big Data Machine Learning Natural Language Processing Cloud Computing Computer Vision Text Internet Search Social Media Satellite Images Sensors Where insights come from
  53. 53. This content included for educational purposes. Big data landscape 2016 53 Source: Matt Turk, David Rigg, First Market capital
  54. 54. ROBOTIC 
 PROCESS AUTOMATION
  55. 55. This content included for educational purposes. Robotic process automation (RPA) Captures and interprets existing means for conducting a task, processing a transaction, manipulating data, triggering responses, and communicating with other systems. This may include manual, repetitive tasks, intelligent automation of processes, and augmentation of resources. 55
  56. 56. This content included for educational purposes. Robotics • A robot is a programmable mechanical or software device that can perform tasks and interact with its environment, without the aid of human interaction. • Robotics is embracing cognitive technologies to create robots that can work alongside, interact with, assist, or entertain people. Such robots can perform many different tasks in unpredictable environments, integrating cognitive technologies such as computer vision and automated planning with tiny, high- performance sensors, actuators, and hardware. Current development efforts focus how to train robots to interact with the world in generalizable and predictable ways. • Deep learning is being used in robotics. Advances in machine perception, including computer vision, force, and tactile perception are key enablers to advancing the capabilities of robotics. Reinforcement learning helps obviate the need for large labeled data sets. 56
  57. 57. This content included for educational purposes. 57 au·to·ma·tion /ˌôdəˈmāSH(ə)n/ The use of software and equipment in a system or production process so that it works largely by itself with little or no direct human control. Robotic process automation and intelligent automation are the combination of AI and automation.What is automation?
  58. 58. This content included for educational purposes. 58 • “Automation” today can be defined as including any functional activity that was previously performed manually and is now handled via technology platforms or process automation tools like robotic process automation (RPA) platforms. • With increasing computer processing power, technology has reached a point where its ability to perform human-like tasks has become possible. • There are various names for referring to robotics in service industries such as Rapid Automation (RA), Autonomics, Robotic Process Automation, software bots, Intelligent Process Automation or even plain Artificial Intelligence. • These terms refer to the same concept: letting organizations automate current tasks as if a real person was doing them across applications and systems. • A primary opportunity for robotic process automation in the enterprise is to augment the creative problem-solving capabilities and productivity of human beings and deliver superior business results. Automation: letting organizations automate current tasks as if a real person was doing them across applications and systems.
  59. 59. This content included for educational purposes. Source: HfS - 2016 Evolving landscape of service agents and intelligent automation: • From desktop automation to RPA, to chatbot, to assistant, to virtual agent. • From enhancement of data, to augmentation of human agents, to substitution of digital labor for the human agent. Example vendors: 59
  60. 60. This content included for educational purposes. 60Source: Deloitte Manual process vs robotic process automation
  61. 61. This content included for educational purposes. 61 Robotic Desktop Automation (RDA) • Personal robots for every employee • Call center, retail, branches, back office • 20-50% improvement across large workforce groups • RDA also provides dashboards and UI enhancements Robotic Process Automation (RPA) • Unattended robots replicating 100% of work • Back office, operations, repetitive • 100% improvement across smaller sub-groups • Runs on a virtual server farm (or under your desk) Comparing robotic desktop automation (RDA) and robotic process automation (RPA)
  62. 62. This content included for educational purposes. 62 • Robotic process automation gives humans the potential of attaining new levels of process efficiency, such as improved operational cost, speed, accuracy and throughput volume, and leaving behind the repetitive and time consuming low added-value tasks. • Top drivers for implementing robotic automation beyond cost savings include: - High quality by a reduction of error rates - Time savings via better management of repeatable tasks - Scalability by improving standardization of process workflow - Integration by reducing the reliance on multiple systems/screens to complete a process - Reducing friction (increasing straight-through processing) • For example, back-office tasks do not require direct interaction with customers and can be performed more efficiently and effectively off-site or by robots. It is feasible to re-engineer hundreds of business processes with software robots that are configured to capture and interpret information from systems, recognize patterns, and run business processes across multiple applications to execute activities including data entry and validation, automated formatting, multi-format message creation, text mining, workflow acceleration, reconciliations and currency exchange rate processing among others. Robotic process automation (RPA)
  63. 63. This content included for educational purposes. 63 Intelligent process automation is smart software with machine-learning capabilities: • Unlike RPA, which must be programmed to perform a task, AI can train itself or be trained to automate more complex and subjective work through pattern recognition • Unlike RPA, which requires a human expert to hard code a script or workflow into a system, AI can process natural language and unstructured data • Unlike RPA, AI responds to a change in the environment, adapts and learns the new way Intelligent process automation (IPA)
  64. 64. This content included for educational purposes. Intelligent automation stages Source: Shahim Ahmed, CA Technologies 64
  65. 65. This content included for educational purposes. 65 Trigger based Rules-based dynamic language Rules-based standardized language Structured CHARACTERISTIC OF DATA / INFORMATION Unstructured without patternsUnstructured patterned Data Center Automation: Runbook Scripting Scheduling Job control Workload automation Process orchestration SOA Virtualization Cloud services RPA Cognitive Computing Artificial Intelligence BPM Workflow ERP Autonomics PROCESS CHARACTERISTICS Source: HfS - 2016 Intelligent automation continuum The spectrum of intelligent process automation spans robotic process automation, cognitive computing, autonomics, and artificial intelligence. The direction of travel is 
 along three dimensions. Stages overlap.
  66. 66. DISTRIBUTED LEDGER TECHNOLOGY
  67. 67. This content included for educational purposes. 67 Distributed ledger technology (aka blockchain) Shared database distributed across a network (of individuals, organizations or devices) that maintains a growing list of transactions between participants. Transaction records are synchronized. Each copy is identical, automatically updated, and immutable.
  68. 68. This content included for educational purposes. 68 block·chain /ˈbläkˌCHān/ Blockchain is a new class of resilient information technology that provides distributed digital ledgers in which transactions, documents, and smart contracts are recorded chronologically and publicly. Blockchain provides a comprehensive history of all transactions since its inception and which are recorded in a large ledger. What is blockchain?
  69. 69. This content included for educational purposes. 69 Conceptually, blockchain technology is: • A decentralized database, a transaction ledger • A new form of information technology • A globally-distributed always-on database system for secure, permanently-recorded independently-validated transactions • A universal organization and coordination system • A registry, listing, and management system for all of the world’s assets, smart property, and itemizable quanta • Asset registry, inventory, tracking, and exchange • A society’s public records repository, a representative and participatory legal and governance system • A tool for large-scale science, health, and business applications Blockchain 
 technology concept Source: Melanie Swan, Institute for Blockchain Studies
  70. 70. This content included for educational purposes. 70 Literally, today’s blockchain technology is: • Open-source software upon which Bitcoin and other cryptocurrencies run - A technology protocol layer like TCP/IP • A decentralized database/ledger - Giant ‘interactive Google doc spreadsheet’ that anyone can view and administrators (miners) continually verify and update to confirm that each transaction is valid - Secure network where any transaction can be independently confirmed as unique and valid without a centralized intermediary • Blocks (batches) of transactions posted sequentially to a ledger (chain)Blockchain 
 technology today Source: Melanie Swan, Institute for Blockchain Studies
  71. 71. This content included for educational purposes. 71 Source: CB Insights AI Application Bitcoin and blockchain startups market map: A lot is happening
  72. 72. This content included for educational purposes. 72 Source: Deloitte Three levels of Blockchain: • Storing digital records • Exchanging digital assets • Executing smart contracts Blockchain allows unprecedented control of information through secure, auditable, and immutable records of not only transactions but digital representations of physical assets. Storing digital records Users can issue new assets and transfer ownership in real time without banks, stock exchanges, or payment processors. Exchanging digital assets 1 2 Executing smart contracts3 Self-governing contracts simplify and automate lengthy and inefficient business processes. Ground rules Terms and conditions are recorded in the contract’s code. Implementation The shared network automatically executes the contract and monitors compliance. Verification Outcomes are validated instantaneously without a third party This content included for educational purposes.
  73. 73. This content included for educational purposes. What are smart contracts? • Agreements between parties posted to the blockchain for automated execution. • Following slides illustrate 12 enterprise smart contract use cases: - Digital identity - Records - Securities - Trade finance - Derivatives - Financial data recording - Mortgages - Land title recording - Supply chain - Insurance - Clinical trials - Medical research Source: Melanie Swan, Institute for Blockchain Studies Smart contracts 73
  74. 74. This content included for educational purposes. Digital identitySource: Bitcoin News Smart contracts can allow individuals to own and control their digital identity containing data, reputation and digital assets. Individuals decide what data to disclose to counterparties, providing enterprises the opportunity to seamlessly know their customers.
 Counterparties will not have to hold sensitive data to verify transactions. This reduces liability while facilitating frictionless know-your-customer requirements. It also increases compliance, resiliency and interoperability. 74 This content included for educational purposes.
  75. 75. This content included for educational purposes. RecordsSource: Bitcoin News Smart contracts can digitize the Uniform Commercial Code (UCC) filing and automate their renewal and release processes. They can also atomically perfect a lender’s security interest loan creation. They can automate compliance with rules that require destroying records at a future date. They also make possible UCC liens that auto-release, auto-renew or automatically request collateral. In performing such functions, smart contracts reduce legal costs. 75 This content included for educational purposes.
  76. 76. This content included for educational purposes. SecuritiesSource: Bitcoin News Smart contracts can simplify capitalization table management. They also circumvent intermediaries in the chain of securities custody and facilitate the automatic payment of dividends, stock splits and liability management, while reducing operational risks. With securities on a distributed ledger, smart contracts digitize work flows. There are considerations with securities. For example, the cryptographic signature of the State of Delaware can require enabling legislation to clarify that Delaware corporate law permits registration on a distributed ledger. While issuers will welcome visibility into who owns their securities, some buy-side firms protect this information. 76 This content included for educational purposes.
  77. 77. This content included for educational purposes. Trade financeSource: Bitcoin News Smart contracts can streamline international transfers of goods via fast Letter of Credit and trade payment initiation, while enabling a greater liquidity of financial assets. They can also improve financing efficiencies for buyers, suppliers and institutions. There are trade finance considerations. Industry standards for smart contract procedures are needed for wider acceptability. Legal implications in case of an execution fall-out has to be determined, particularly in the cases of disputes and defaults. The integration of settlement systems, technology requirements and off-chain ecosystems are important to success. 77 This content included for educational purposes.
  78. 78. This content included for educational purposes. DerivativesSource: Bitcoin News Smart contracts can streamline post-trade processes, removing duplicative processes executed by each counterparty for verifying trades and conducting appropriate trade events. They enable a standard set of contract conditions and optimize post- trade processing of over-the-counter derivatives. They also enable real-time valuation of positions for monitoring and reducing errors. When considering derivative smart contracts, it is important to address protocol changes related to regulatory reform. 78 This content included for educational purposes.
  79. 79. This content included for educational purposes. Financial data recordingSource: Bitcoin News Financial organizations can utilize smart contracts for accurate, transparent financial data recording. Smart contracts allow for uniform financial data across organizations, improving financial reporting and reducing auditing costs. By improving data integrity, smart contracts support increased market stability. Also, they reduce accounting costs by allowing cost sharing among organizations. Interoperability among the distributed ledger network and legacy systems is important in financial reporting. 79 This content included for educational purposes.
  80. 80. This content included for educational purposes. MortgagesSource: Bitcoin News Smart contracts can automate mortgage contracts by automatically connecting the parties, providing for a frictionless and less error-prone process. The smart contract can automatically process payment and release liens from land records when the loan is paid. They can also improve record visibility for all parties and facilitate payment tracking and verification. They reduce errors and costs associated with manual processes. Digital identity is a key requirement. 80 This content included for educational purposes.
  81. 81. This content included for educational purposes. Land title recordingSource: Bitcoin News Smart contracts that facilitate property transfers can deter fraud, improve transaction transparency and efficiency, and strengthen confidence in identity. They also reduce auditing costs. Common protocols need to be developed for electronic record filing. 81 This content included for educational purposes.
  82. 82. This content included for educational purposes. Supply chainSource: Bitcoin News Smart contracts can provide real-time visibility for every step in a supply chain. Internet of Things devices can record each step as a product moves from a factory floor to the store shelves. They facilitate granular-level inventory tracking, benefitting supply chain financing, insurance and risk. Enhanced tracing and verification reduce the risk of theft and fraud. The identities of supply chain players have to be attested over time, including companies, institutions, individuals, sensors, facilities and products. 82 This content included for educational purposes.
  83. 83. This content included for educational purposes. InsuranceSource: Bitcoin News Smart contracts can improve insurance. For example, the disjointed car insurance process. A smart contract can record the policy, driving record and driver reports, allowing Internet of Things-equipped vehicles to execute claims shortly after an accident. They automate claims processing, verification and payment. Each policyolder’s repository includes driving record, vehicle and accident report history. Eliminating duplicated reporting will yield savings. Cross-industry collaboration is needed to address technological, regulatory and financial challenges. 83 This content included for educational purposes.
  84. 84. This content included for educational purposes. Clinical trialsSource: Bitcoin News Smart contracts can improve clinical trials through increased cross-institutional visibility. Privacy-preserving computation improves data sharing between institutions while automating patient data. They can streamline processes for trials, improve access to cross-institution data, and can increase confidence in patient privacy. Authentication, authorization and identity remain open issues for smart contracts executed on blockchain-enabled networks. 84 This content included for educational purposes.
  85. 85. This content included for educational purposes. Medical researchSource: Bitcoin News Smart contracts can facilitate the sharing of medical research, for example, cancer data. They can facilitate the patient consent management process and aggregate data contribution and data sharing while protecting patient privacy. New forms of blockchain technologies may be needed to provide real-time access and protection of data confidentiality. 85 This content included for educational purposes.
  86. 86. INTERNET OF THINGS
  87. 87. This content included for educational purposes. Internet of things The network of sensors embedded into physical devices/things, which collect data and share it across the web with people, applications, and other devices. AI can process and use the resulting huge amounts of data for intelligent and useful purposes. 87
  88. 88. This content included for educational purposes. 88 Internet of things noun • The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. • IoT has evolved from the convergence of wireless technologies, micro- electromechanical systems (MEMS), microservices, and the internet. The convergence has helped tear down the silo walls between operational technology (OT) and information technology (IT), allowing unstructured machine-generated data to be analyzed for insights that improve decisions and drive improvements. • AI and IoT are shaping up to be a symbiotic pairing. AI doesn’t just depend upon large data inputs; it thrives upon them. Given new data and scenarios, cognitive systems evolve and improve over time, inferring new knowledge without being explicitly programmed to do so. What is the 
 internet of things?
  89. 89. To be successful, the IoT needs to be
 intelligent, interoperable, and intuitive. Source: NUANCE This content included for educational purposes.
  90. 90. 90 The more intelligent and interoperable things are, the more intuitive they become… …and the more we use them, the more they learn, the more intelligent they become. Source: NUANCE This content included for educational purposes.
  91. 91. This content included for educational purposes. Source: Deloitte IoT to 2020 Source: CISCO 91
  92. 92. 92 IoT arrived faster than anticipated. new things connected every day this year 26 billion connected devices by 2020 2003 2016 0.08connected devices per person 3.64connected devices per person Source: CISCO IBSG, globalwebindex, and Gartner This content included for educational purposes.
  93. 93. This content included for educational purposes. 93Source: Deloitte IoT
 reference architecture
 functional view
  94. 94. This content included for educational purposes. 94Source: Deloitte IoT
 reference architecture
 implementation view
  95. 95. This content included for educational purposes. Internet of things landscape 2016 95 Source: Matt Turk, David Rigg, First Market capital
  96. 96. ARTIFICIAL INTELLIGENCE
  97. 97. This content included for educational purposes. Artificial intelligence and machine learning Enable computers to learn from data to make predictions and decisions beyond human scale. AI seeks to emulate human traits like learning, understanding content, developing conclusions, engaging in natural dialog, and communicating in both human and machine interpretable ways. 97
  98. 98. AI is the new electricity! – Andrew Ng, Baidu 98 This content included for educational purposes.
  99. 99. - Sundar Pichai Google CEO, 28 April 2016 “WE WILL MOVE FROM A MOBILE FIRST TO AN AI FIRST WORLD.” 99 This content included for educational purposes.
  100. 100. 100 AI market forecasts vary significantly, but... All predict growth of substantial direct markets for AI, analytics, machine learning, cognitive systems, and natural UIs. All predict net economic impact in the $ trillions. This content included for educational purposes.
  101. 101. This content included for educational purposes. 101 ar·ti·fi·cial in·tel·li·gence /ˌärdəˈfiSHəl inˈteləjəns/ AI is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, translation between languages, and decision-making. Some attributes of intelligent behavior include: • Think and reason • Use reason to solve problems • Learn or understand from experience • Acquire and apply knowledge • Express creativity and imagination • Deal with complex situations • Respond quickly and successfully to new situations • Recognize the relative importance of elements in a situation • Handle ambiguous and incomplete information. What is 
 artificial intelligence?
  102. 102. This content included for educational purposes. Six attributes of AI 102
  103. 103. This content included for educational purposes. 103 Artificial Intelligence (AI) capabilities: 1. Capture information, which can be done through: • Vision recognition (e.g., recognizing a face or photo), • Sound recognition (e.g., transcribing spoken words), • Search (e.g., extracting data from unstructured or semi-structured documents), • Data analysis (e.g., identifying clusters of behaviors in customer data). • Each of these turns data into information and are the most mature application of AI in business today. 2. Turn that information into something useful through: • Natural language processing (e.g., extracting meaningful data from an email), • Reasoning (e.g., should I act based on the information given), • Prediction (e.g., predicting buying behavior based on past purchases) 3. Understand why something is happening: • This capability feeds off the first two categories described above. • This is the least advanced area of AI, is a focus for business AI applications, and will have a huge impact as it matures. Artificial intelligence capabilities
  104. 104. This content included for educational purposes. Artificial intelligence — definitions • AI — Intelligent machines and software that can sense, learn, plan, act, understand and reason. • Machine Learning—is one area or sub-field of AI. It is the science and engineering of making machines “learn.” There are multiple approaches to machine learning, including Bayesian learning, evolutionary learning and symbolic learning. • Deep Learning— is a type of machine learning that uses multi-layered neural networks to learn. • Cognitive Computing— is a subset of AI, not an independent area of study, that focuses on simulating human thought process based on how the brain works. It is also viewed as a “category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition. • AI and Data Science—Data science refers to the interdisciplinary field that incorporates statistics, mathematics, computer science and business analysis to collect, organize and analyze large amounts of data to generate actionable insights. The types of data (e.g., text, audio, video) and the analytic techniques (e.g., decision trees, neural networks) that both data science and AI use are very similar. Source: Roger C Shank 104
  105. 105. This content included for educational purposes. Artificial intelligence — domains * Source: Forrester Expert assistance. (e.g., Apple’s Siri, Google Now, and Microsoft’s Cortana). Underlying AI capabilities: Machine learning algorithms, voice processing, reasoning and knowledge representation, and natural language processing. Predictive customer engagement. Software agents that gather the information (and answers) to predict customer needs before the customer makes contact or while a customer is calling in and talking to the operator. Underlying AI capabilities: Machine learning, predictive analytics, interactive voice response (IVR), data modeling, real-time decisioning, text understanding, and reasoning. Intuitive communication. Beyond simple voice recognition and natural language processing (expert assistants all provide that!), to resolve ambiguity and tolerate unpredictability. Underlying AI capabilities: Machine learning, voice processing, text understanding, sentiment analysis, and semantic technologies, and vision. Intelligent narratives. Automatically generate an intelligent and intuitive story out of comprehensive, complex, and curated data. Underlying AI capabilities: Machine learning and analytics (time, cohort, comparison series analysis), knowledge representation and reasoning, and story architecture with NLP generation. 105
  106. 106. This content included for educational purposes. ARTIFICIAL INTELLIGENCE TECHNOLOGIES Expert Systems Inference Engines Machine Learning Robotic Process Automation Deep Learning Sensor Processing Knowledge Representation Virtual Agents Robotics Biometrics Cognitive Analytics Facial/
 Gesture Recognition AI-optimized Hardware Audio/Video
 Analytics NLP/NLU NLG/TTS/
 Visualization 106This content included for educational purposes.
  107. 107. This content included for educational purposes. VISUAL Algocian Captricity Clarifai Cortica Deepomatic DeepVision Netra Orbital Insight* Planet Spaceknow AUDIO Capio Clover Intelligence Expect Labs Gridspace* Mobvoi Nexidia Pop Up Archive* Quirious TalkIQ Twilio SENSOR Alluvium C3 IoT GE Predix Imubit KONUX Maana Planet OS Preferred Networks Sentenai ThingWorx Uptake INTERNAL DATA Alation* Arimo* Cycorp Digital Reasoning IBM Watson Kyndi Outlier Palantir Primer Sapho* MARKET Bottlenose CB Insights DataFox Enigma Mattermark Predata Premise Quid Tracxn ENTERPRISE INTELLIGENCE Automat Facebook CommAI Howdy* Kasisto KITT.AI Maluuba Octane AI OpenAI Gym Semantic Machines AGENTS AND CONVERSATIONAL INTERFACES (AGENT ENABLERS) Ayasdi BigML Dataiku DataRobot Domino Data Lab* Kaggle* RapidMiner Seldon SparkBeyond Yhat Yseop Bonsai CognitiveScale Context Relevant* Cycorp Datacratic deepsense.io Geometric Intelligence H2O.ai HyperScience Loop AI Labs minds.ai Nara Logics Reactive Scaled Inference Skymind SparkCognition MACHINE LEARNING DATA SCIENCE Agolo AYLIEN Cortical.io Lexalytics Loop AI Labs Luminoso MonkeyLearn Narrative Science spaCy NATURAL LANGUAGE AnOdot Bonsai Fuzzy.ai Hyperopt Kite Layer 6 AI Lobe.ai Rainforest SigOpt SignifAI DEVELOPMENT DATA CAPTURE AND ENRICHMENT OPEN SOURCE LIBRARIES Amazon Mechanical Turk CrowdAI CrowdFlower Datalogue DataSift Diffbot* Enigma Import.io Paxata Trifacta WorkFusion Amazon DSSTNE Apache Spark MLlib Baidu PaddlePaddle Caffe Chainer DeepLearning4j H2O.ai Keras Microsoft Azure ML Microsoft CNTK Microsoft DMTK MXNet Nervana Neon scikit-learn TensorFlow Theano Torch7 Weka 1026 Labs Cadence Tensilica Cirrascale Google TPU KNUPATH Intel (Nervana) Isocline NVIDIA DGX-1/Titan X Qualcomm Tenstorrent HARDWARE Cogitai Kimera Knoggin NNAISENSE Numenta OpenAI Vicarious RESEARCH MACHINE LEARNING TECHNOLOGY STACK CUSTOMER SUPPORT ActionIQ Clarabridge DigitalGenius* Eloquent Labs Kasisto Preact Wise.io Zendesk SALES/FINANCE 6sense AppZen Aviso* Clari Collective[i] Fusemachines InsideSales Salesforce Einstein Zensight* MARKETING AirPR BrightFunnel* CogniCor Lattice LiftIgniter Mintigo msg.ai Persado Radius Retention Science SECURITY Cylance Darktrace Deep Instinct Demisto Drawbridge Networks* Graphistry* LeapYear SentinelOne SignalSense Zimperium RECRUITING Entelo Gigster* HiQ HireVue SpringRole Textio* Unitive Wade & Wendy ENTERPRISE FUNCTIONS AGRICULTURE Abundant Robotics AgriData Blue River Technology Descartes Labs Mavrx* Pivot Bio TerrAvion Trace Genomics Tule* UDIO EDUCATION AltSchool Content Technologies (CTI) Coursera Gradescope* Knewton Volley MATERIALS/ MANUFACTURING Calculario Citrine Informatics Eigen Innovations Ginkgo Bioworks Sight Machine Zymergen RETAIL FINANCE Affirm Betterment Earnest Lendo Mirador Tala (fka InVenture) Wealthfront ZestFinance INVESTMENT FINANCE AlphaSense Bloomberg Cerebellum Capital Dataminr iSentium Kensho Quandl Sentient LEGAL Beagle Blue J Legal Everlaw Legal Robot Ravel Law ROSS Intelligence Seal TRANSPORTATION/ LOGISTICS Acerta ClearMetal Marble NAUTO PitStop Preteckt Routific INDUSTRIES PATIENT DATA Atomwise CareSkore Deep6 Analytics IBM Watson Health Numerate Oncora Medical pulseData Sentrian Zephyr Health IMAGE DATA 3Scan Arterys Bay Labs Butterfly Network Enlitic Google DeepMind Imagia BIOLOGICAL DATA Atomwise Color Genomics Deep Genomics* Grail iCarbonX Luminist Numerate Recursion Pharmaceuticals Verily Whole Biome HEALTH CARE GROUND AdasWorks Auro Robotics comma.ai Drive.ai Google Mobileye nuTonomy Tesla Uber Zoox AERIAL Airware DJI DroneDeploy Lily Pilot AI Labs Shield AI* Skycatch Skydio INDUSTRIAL Clearpath Robotics Fetch Robotics Harvest Automation Jaybridge Robotics Kindred* Osaro Rethink Robotics AUTONOMOUS SYSTEMS PERSONAL Amazon Alexa Apple Siri Google Now/ Allo Facebook M Microsoft Cortana Replika PROFESSIONAL Alien Labs Butter.ai Clara SkipFlag Slack Sudo Talla x.ai Zoom.ai AGENTS *COMPANIES IN WHICH SHIVON ZILIS AND JAMES CHAM HAVE INVESTMENTS The State of Machine Intelligence, 2016 © HBR.ORGSOURCE SHIVON ZILIS AND JAMES CHAM What is the state of Machine Intelligence 
 and AI in 2016? Over the past year, machine intelligence has exploded, with $5 billion in venture investment, and a few big acquisitions. If this year’s landscape shows anything, it’s that the impact of machine intelligence is already here. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses. The table to the right charts 250+ vendors of AI, cognitive, and robotics related products, services, platforms, and solutions grouped into 34 categories. — Shivon Zilis & James Cham, Bloomberg Beta 107
  108. 108. This content included for educational purposes. Artificial intelligence landscape 108 Source:VentureScanner This AI landscape maps 957 companies developing technologies for machine learning, computer vision, smart robots, virtual personal assistants, natural language processing, speech translation, context aware computing, gesture control, recommendation engines, and video content recognition
  109. 109. This content included for educational purposes. 100 startups using artificial intelligence to transform industries 109 Source: CB Insights
  110. 110. This content included for educational purposes. AI and cognitive capability company briefs and case examples* • Company briefs and case examples* highlight connected intelligence capability development in areas such as: - Speech, and image processing, natural language understanding and genera[on — AI2, Arria, Expert Systems, Google, Kasisto, Ki, Kira, Luminoso, Microso`, Narra[ve Science, Nuance, Vicarious - Knowledge graphs — Cyc, Di†ot, Google, IBM, Luminoso, Viv - Intelligent assistance — Amazon, Apple, Baidu, Equals3Media, Google, Facebook, inBenta, IPso`, Kensho, Kore, Microso`, Samsung, X.ai - Cogni[ve analy[cs, deep learning, machine learning — Baidu, Amazon, Digital Reasoning, Facebook, Google, H2O, Intel, Microso` * Not part of this research deck. Source:JeffHuntington,SilkRoad 110
  111. 111. This content included for educational purposes. AI and cognitive capabilities • Computer vision: The ability of computers to iden[fy objects, scenes, and ac[vi[es in unconstrained (that is, naturalis[c) visual environments • Machine learning: The ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instruc[ons • Natural language processing (NLP): The ability of computers to work with text the way humans do— for instance, extrac[ng meaning from text or even genera[ng text that is readable, stylis[cally natural, and gramma[cally correct • Speech recognikon: The ability to automa[cally and accurately transcribe human speech • Opkmizakon: The ability to automate complex decisions and trade-offs about limited resources
 • Planning and scheduling: The ability to automa[cally devise a sequence of ac[ons to meet goals and observe constraints • Rules-based systems: The ability to use databases of knowledge and rules to automate the process of making P.S.ences about informa[on • Robokcs: The broader field of robo[cs is embracing cogni[ve technologies to create robots that can work alongside, interact with, assist, or entertain people. Such robots can perform many different tasks in unpredictable environments, integra[ng cogni[ve technologies such as computer vision and automated planning with [ny, high- performance sensors, actuators, and hardware. AI2 Amazon Apple Arria Baidu Connotate Diffbot Digital Reasoning Equals3Media Expert Systems EY Facebook GNIP Google Houndify H2O.ai IBM Watson inBenta Intel IPsoft Kasisto Kenosha Kira Kitt.ai Lavastorm LinkedIn Luminoso Manthan Microsoft Narrative Science Nuance Prognoz Ross Intelligence Samsung SemanticMachines Skytree SparkBeyond Twilio Twitter Vicarious WebHose.io X.ai Yahoo AI and cognitive capability development vendors* 111 This content included for educational purposes. * Not part of this research deck.
  112. 112. © Copyright Project10x | Confidential

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