DECRYPTING PRACTICAL AI: EMPOWERING OR ENSLAVING … · o 20% of citizens in developed nations will use AI assistants to help them with an array of everyday, operational tasks o AI

DECRYPTING PRACTICAL AI: EMPOWERING OR ENSLAVING HUMANS?

Mohammed HashimDigital Infrastructure – Automation & Tools [email protected]

Knowledge Sharing Article © 2018 Dell Inc. or its subsidiaries.

2018 Dell EMC Proven Professional Knowledge Sharing 2

Table of Contents

1. Primer ............................................................................................................................................. 3

2. Industry Trends and Market Landscape ......................................................................................... 5

3. Artificial Intelligence, Machine Learning and Deep Learning ......................................................... 7

4. Artificial Intelligence Continuum .................................................................................................... 9

5. Decentralized AI ............................................................................................................................ 10

6. DAI Architecture & Guidelines ...................................................................................................... 14

7. Practical AI in Action ..................................................................................................................... 16

8. Impact on Human Decision Making .............................................................................................. 20

9. Future of AI ................................................................................................................................... 22

Appendix A: Research References ........................................................................................................ 23

Appendix B: AI Platform Architecture .................................................................................................. 25

Appendix C: AI Functioning & Evolution ............................................................................................... 27

Appendix D: Alternate AI Continuum ................................................................................................... 28

Disclaimer: The views, processes or methodologies published in this article are those of the authors. They do not necessarily reflect Dell EMC’s views, processes or methodologies.


1. Primer

Artificial Intelligence (AI) technologies simply try to mimic human capabilities to Sense, Think

and Act. (Forrester Research1)

The borders of AI are vanishing as the digital world has started shifting from a 'Mobile first'

to an 'AI first' way of operations, equipped with advanced cognitive computing preceding

almost every Human Machine Interaction (HMI) as illustrated in Figure 1.

Figure 1: Artificial Intelligence Technologies (Source2: Project 10X AI-Externalization of Mind)

Today, AI exists pretty much everywhere from search engines, chat bots, smartphones, virtual

personal assistants to smart wearable making our lives easier. An alternate viewpoint is the

metamorphosis of AI bots to Killer bots can pose a lasting lethal threat to humanity, without

us even realizing it.

AI co-workers are collaborating in the daily workplaces primarily supporting enhanced

productivity and not replacing human labor as such. While the more intensive subjective and


creative work still needs humans, AI remains quite successful in the preliminary processing of

umpteen complex tasks across the spectrum.

So which line of work is being affected? Well, almost everything – from language translators,

customer support, banking agents, film editors, attorneys, doctors, scientists to astronomers

– all further getting equipped with continuous machine learning and deep learning. Again,

the million-dollar question is: Are we unleashing an uncontrolled virtual workforce which can

trigger the next digital apocalypse resulting in enslavement and ultimate decimation of

mankind?

The current narrowed focus of AI to realize economic goals vis-a-vis societal goals, further

exacerbates these apprehensions. The topics discussed in this article include: Understanding

AI, ML and Deep Learning, Industry trends and Market landscape, AI Continuum,

Decentralization of AI, Key Tenets and Reference DAI Architecture, Democratization of DAI

with common use cases across business verticals, Guidelines for adopting DAI, AI Analysis:

Human Empowerment vs. Enslavement and the Future of AI in the Digital world shifting

balance of powers.

This paper will help organizations, business consultants, technology partners, solution

architects, data scientists, futurists and the education community interested in

understanding AI and its complex ecosystem, its practical applications; discover newer AI-

based business opportunities; design DAI solutions; and adopt AI at large without fear or bias

of killer bots dominating mankind in the coming AI revolution.


2. Industry Trends and Market Landscape

AI is going to continuously redefine the very operating fundamentals of a digital society.

Gartner3 predicts that by 2020

o 20% of citizens in developed nations will use AI assistants to help them with

an array of everyday, operational tasks

o AI bots will power 85% customer service interactions and will drive up to USD

33 trillion of annual economic growth

AI BOT developed by Alibaba4 beats humans in Stanford Question Answering Dataset,

a large-scale reading comprehension test, paving the way for BOTs replacing people

in customer service. Alibaba’s machine learning models had scored 82.44 compared

to 82.304 by humans.

VeriHelp5 is bringing AI out of the realm of science-fiction and into everyday lives. Its

retail acceleration technologies help in increasing retail revenue using AI by giving

predictive recommendations like personalized product bundles with a high probability

of sales conversion.

The Global Artificial Intelligence Market6 is poised to grow at a CAGR of around 41.7%

over the next decade to reach approximately $24.2 billion by 2025, based on the

report from RESEARCHANDMARKETS. The type of market segments includes

embedded system, artificial neural network, digital assistance system, expert system

and automated robotic system.

According to Tractica research7, the annual revenue generated from the direct and

indirect application of AI software will grow from $1.4 billion in 2016 to $59.8 billion

by 2025.

According to IDC8, by 2019, 40 percent of digital transformation initiatives will use AI

services; by 2021, 75 percent of commercial enterprise apps will use AI, over 90

percent of consumers interact with customer support bots, and over 50 percent of

new industrial robots will leverage AI.

The present AI industry is primarily based on the centralized processing paradigm wherein

machine learning (ML) solutions are delivered as a part of cloud-based APIs and software

packages deployed on remote nodes of AI value-based platform vendors. The industry is


rapidly moving towards the next frontier of Decentralized AI (DAI) that can run and learn

on local devices or make decisions in decentralized networks, like blockchain.

The main drawback of the otherwise successful Centralized AI solutions is high latency,

since advanced AI features accessed over the network involve complex ML algorithms

requiring extensive calculations. Moreover, the deep learning loop gets unreasonably

elongated while operating centrally.

In contrast, DAI functions locally on individual devices with access to user endpoint data

independent of network connections leading to both energy savings and faster processing

without latency; all while ensuring privacy. This is enabled with on-device optimization of

ML, newer technologies like Google’s Federated Learning, advancements in embedded AI

that runs and learns ML models on mobile devices and the use of AI in Decentralized

autonomous organizations (DAOs) on block-chain networks.

Eventually, AI will become more democratic and widespread than ever before, coupled

with embedded ML and DAOs.

Nevertheless, there is always a sense of skepticism on the future being dominated by

unmanned AI systems from the sort of inexplicable scientific experiments going on with

uncontrolled access to unlimited data using unregulated methods by unknown teams.


3. Artificial Intelligence, Machine Learning and Deep Learning

AI is nothing but a structured method to teach computers to make informed decisions and/or

execute processes. It includes Machine Learning algorithms, Natural language processing,

Composite knowledge representation and Automated reasoning.

Deep Learning (DL) is a sub-set of ML that analyzes data at various abstraction layers of the

neural network. It is a continuous iterative process to teach machines how to identify things

as humans do and subsequently learn to automate processes and make decisions. Over time,

the machine will self-learn to recognize the vital layers in the neural network required to

make decisions more quickly and cleverly.

Figure 2 represents the relationship of AI, ML and DL based on their continuous evolution and

market impact.

In simple terms:

1. Artificial Intelligence: AI is any technique that enables computing systems to mimic

human behavior. It primarily follows a workflow-driven approach from almost pre-

programmed knowledge based on past execution trends. AI can be a complex

statistical model or heap of nested conditional workflows and sub-workflows or

knowledge graphs. Essentially, it consists of rule engine-based systems programed by

humans to simplify otherwise cumbersome and/or mundane tasks. A simple example

Figure 2: AI-ML-DL Relationship (Source9: NVIDIA Blogs)


would be of a Virtual Tax Consultant that processes the financial information received,

runs it through a set of static rules and calculates the outstanding tax amount as

outcome.

2. Machine Learning: ML is a sub-set of AI that uses advanced statistics to enable

machines to self-learn by extracting patterns and co-relations from each execution

and improve experiences continuously. The key difference of ML from AI is its ability

to dynamically adjust itself when exposed to more data without human intervention.

The learning is realized when ML algorithms optimize continuously, like trying to

minimize error or maximize the likelihood of their predictions being true. It is like a

self-optimization algorithm wherein it repeatedly measures the error/difference from

its different guesses/outputs and then modifies its parameters until no

errors/differences are detected. A common example is on-demand music streaming

services like Pandora or Spotify that continually learns about users’ music

preferences, and then applies it to predict what other music the user might enjoy.

3. Deep Learning: DL is a sub-set of ML that facilitates computation of multi-layer neural

networks. It builds complex learning representations from ML outputs considering

many variation factors and applies the continuously derived knowledge dynamically

based on situations. It majorly involves deep artificial neural networks and deep

reinforcement learning to an extent. Deep artificial neural networks are present in

advanced image recognition, sound recognition, recommender systems, etc. A classic

example of DL is Google’s AlphaGo10 that learned how to play the abstract board game

Go and enhanced its ability to play against professional Go players. It was a

phenomenon that a machine could not only grasp such a complex game, but also use

its neural network to beat the world-renowned champions of Go. Another – Zendesk’s

Answer Bot11 – incorporates a DL model to better understand the meaning and

context of a customer support ticket with natural language processing and

sentimental analytics.


4. Artificial Intelligence Continuum

The AI Continuum depicts the scale of human involvement in decision making or the control

independency of AI BOTs or AI-enabled computing systems with the advancement of AI

technologies. The continuum defines the various types of AI from Assisted to Augmented to

Autonomous as illustrated in Figure 3.

Figure 3: Artificial Intelligence Continuum

Assisted AI: Here, humans and AI systems learn from each other and share the

execution of tasks. Typically, AI BOTs replicate user actions at various stages in a

process based on pre-defined workflows where a majority decision-making is

controlled by humans.

Augmented AI: Here, AI systems learn from both humans and data to understand

context adaptively. AI applies ML on data to improve and enhance future results by

executing on behalf of humans. The decision making control is shared by both humans

and AI BOTs.

Autonomous AI: Here, humans start trusting AI systems and assign the majority of

decision-making control to AI BOTs, based on its efficiency and accuracy in

successfully executing real and simulated tasks from different scenarios over a period

of time.

An alternate depiction of AI Continuum based on applying machine reasoning and

decision-making software algorithms is depicted in Appendix D.


5. Decentralized AI

Currently, AI is mostly used for business use cases involving Predictive Analytics, Data Mining,

Task automations and Proactive anomaly detections or scenario identifications. The most

widely used ML algorithms/methods include neural network, decision trees and linear

regression. A Decentralized AI (DAI) platform is the combination of Blockchain, On-device AI,

Edge computing and Internet of Things. Among this On-device AI and Blockchain would be

transforming enterprise data strategies and information architecture as they struggle to wield

the massive amounts of data flowing to and from various devices, applications, infrastructure,

business process workflows and users.

Blockchain leverages distributed database architecture, wherein the record and verification

of a transaction or event relies on multiple parties agreeing on the validity of that transaction,

rather than relying on a single centralized authority. The basic tenets of Blockchain and AI to

work in synchronization are:

1. Data Sharing – Various nodes on the network should agree and access information

from multiple parties. AI applications benefit from and often rely on Big Data

platforms for complex and decentralized AI computing.

2. Data Security – Secured data management should be in place across the stack to

mitigate risks and prevent misuse especially when transferring high value, volume and

velocity transactions to blockchain infrastructure. Decentralized ledgers in the case of

blockchain, and automated analysis and decision-making in the case of AI facilitates

necessary safeguard levels.

Decentralized AI

•Blockchain

•On-device AI

•Edge Computing

•Internet of Things


3. Digital Trust – Trust across the process transactions lifecycle, platform architecture,

analytics and/or autonomous decision-making is crucial to eliminating data

uncertainty and increase wider adoption. The unprecedented adoption of a

distributed ledger mechanism unlike a centralized one, introduces huge strategic and

cultural barriers for which trust will be essential to overcome. In addition, businesses

must be accountable to all their stakeholders and the law of the land. Hence, both

blockchain and AI systems must be auditable, adhere to compliance and regulation,

uphold secure identity access protocol and data protections, and respect privacy

ethics to the highest possible extent.

AI and Blockchains are complementary and synergistic when it comes to realizing DAI. While

AI helps in assessing, understanding, recognizing and decision making, Blockchains help

verify, execute, record and track actions. Hence, ML and DL methods of AI continuously

identify the right opportunity and improvises decision making. Blockchain Smart contracts

can automate verification of the different process transactions involved in the use case

lifecycle.

Essentially, Blockchain will make Artificial Intelligence applications accountable. Technology

giants like AWS, Dell EMC, IBM, Microsoft, Google and startups like Numerai, Blockchain AI,

Colony.io, and Yojee are already experimenting with blockchain architectures with “smart

agents” at different layers of the architecture. The Pandora Boxchain12 project-recommended

six-tier architecture is gaining momentum towards a standardized model that can help

developers and industry experts correlate the various entities involved. Figure 4 depicts the

various abstraction layers of a Blockchain architecture based on the Pandora Boxchain model.


Figure 4: Blockchain Architecture Abstraction Layers (Source13: Pandora Boxchain)

The various layers and their roles are:

1. P2P Network: This layer is responsible for inter-node communications that involves

discovery and data transfer such as transactions and block propagation. An example

is the DEVP2P library used by Ethereum project.

2. Consensus: This is a critical layer present in all standalone blockchains. It is needed to

generate the order of blocks creation and validate blocks created by other nodes in

the network. Common examples are the PoW (Proof of Work) consensus used by

Bitcoin, PoS (Proof of Stake), present in NXT and Graphene-related blockchains (e.g.

Bitshares, Steem) in the form of dPoS.

3. Virtual Machine: This is a transactional engine responsible for changes in a

blockchain’s world state. Although present in all blockchains, it's frequently not being

distinguished separately from the consensus layer. Common examples include

WebAssembly (WASM) integrated into Ethereum, RChain virtual machine with

concurrency, the TrueBit virtual machine, etc.

4. API Layer and Language Level: This is a twin layer consisting of

a. APIs are used by on-chain applications in runtime. Ethereum JSON RPC

protocol and Web3.js are common examples.


b. Programming languages are used in development time and compiled for

runtime into a binary code that is embedded in blockchain and understood by

the virtual machines. Common examples are EVM languages like LLL, Solidity;

WebAssembly-compiled languages like C/C++, Swift, Python, Ruby, Rust;

Common Language Runtime that includes all .NET-based languages from

Microsoft (C#, F#, VisualBasic.NET etc.); Functional smart contract language

variants from Haskell and others.

5. App Business Logic: This is a vertical solution-specific layer consisting of

a. On-chain, consisting of the smart contract code in any language (level 4) which

after compilation becomes part of the blockchain state (level 2) and can be

executed by an appropriate virtual machine (level 3)

b. Off-chain is the code required to link the smart contract business logic to the

outside world or to create inter-blockchain business logic.

The classic example of App Business Logic is the growing ecosystem of Ethereum-

based dApps and smart contracts.

6. Application UI: This is the actual UI of the dApp presented to user. Typically, it is

created with Javascript/HTML interacting with the App business logic (level-5) linked.

The fundamentals of an AI platform architecture with a sample is discussed in Appendix B.


6. DAI Architecture & Guidelines

Decentralized AI (DAI) is a composite model based on the existing models of:

a. Multi-agent systems like the BOINC that serve as Distributed AI

b. Blockchain systems that serve as examples of Swarm Intelligence (SI)

DAI combines the two systems compensating for their individual disadvantages and

increasing their collective benefits. DAI will use BOINC parts for distribution and integration

of the tasks, as well as Ethereum smart contracts and P2P technology for decentralization.

The basic tenet is that these smart contracts will eventually engage in end-to-end automated

operations, including auto-configuration, auto-optimization, auto-resolution, auto-

regulation, auto-management and auto-retirement. These checks and balances of blockchain

make AI more accountable; while software and hardware intelligence enhance blockchain

development, application and process automation. The DAI Architecture concepts discussed

above are based on the paper14 ’Multi-agent Systems and Decentralized Artificial

Superintelligence’ by Ponomarev S., Voronkov A. E.

A representation of the three AI DAO models is primarily based on the placement of AI in the

ecosystem according to the study15 of AI DAOs by Trent McConaghy.

1. AI at the Center

Here, a complex AI entity would be the core

of the smart contract. This entity would be a

feedback control system; wherein its

feedback loop would continue by itself, taking

inputs, updating its state, and actuating

outputs, with the resources to do so

continually. This framework is how most

modern AGIs (Artificial General Intelligences)

are set up, as well as DeepMind’s AlphaGo.


2. AI at the Edges

AI is decentralized with intelligent decision

making at the edges leveraging on-device AI.

This facilitates Autonomous Decision making.

The impending tidal wave of AI will see a

growth in — and shift of — computing

power from the cloud back to the edge of the

networks.

3. Swarm Intelligence

Swarm intelligence (SI) is made up of simple

reactive agents that perform basic functions

and forms a smart network. SI as a system,

even though it is decentralized, can solve a

very narrow range of tasks because all its

agent perform the same operation in one

moment of time (the principle of redundant

paralleling of the tasks).

The key enablers which would shift the balance over time and shape the future of AI are:

•Dynamically transforming User Interactions and User Experiences

Insights & Foresights with ML/DL/NN

•Building Digital Trust and extending DAI

Smart Contracts powered by Blockchains

•Leading Digital Enterprises adoption of AI

AI Talent

•Processing Advanced AI Algorithms

Data Lakes

•Promoting Edge Computing to realize SI

End Device Networks


7. Practical AI in Action

AI can continually create value across industries by:

a. Augmenting R&D, Smart Insights and Predictive Foresights

b. Executing Operations with higher productivity, efficiency and lower costs

c. Delivering Products and Services at the right price, with right messages to right

targets

d. Enhancing customized user experience and improving satisfaction

The classic market segment of DAI involves decentralized autonomous organizations (DAOs)

deployed on Ethereum blockchain. DAOs are algorithmic firms run by AI-based managerial

BOTs. This operating model has tremendous potential and can be effectively leveraged in the

distribution of royalties, subscription payments and more.

The other prime segment is represented by device-centric AI powered by mobile ML like

Apple’s CoreML. On-device AI relates to AI-based edge computing that allows complex ML/DL

algorithms to run on IoT devices like smart sensors, POS endpoints, security cameras, drones

or autonomous vehicles. The prominent use cases across various industry segments where

DAI is in action are mapped in Table 1.

SE G ME

NT KEY USE CASES

Re

tail

Ind

ust

ry

a. Fashions Trending & Seasonal Predictions

b. Users Sentiments & Behavioral Analytics

c. Sales Promotions & Loyalty Offers

d. Smart Clothes Designing and E-Commerce

e. Intelligent Customer Relationship Management

f. Visual Search based Sizing and Fitting

g. Warehouse and Supermarket Shelf Analytics

h. Total Customer Satisfaction -Pre/During/Post

Tran

spo

rtat

ion

a. Vehicular Object Detection/Identification/Avoidance

b. Predicting Traffic Density

c. Traffic Lights & Signs Management

d. Vehicular Sensors Data Management

e. Navigation Localization and Mapping

f. Vehicle Network and Data Security

g. Emergency Response Management


h. Weather Forecasting and Guidance

Man

ufa

ctu

rin

g

a. 3D Printing Arm Control

b. Machine Object Detection/Identification/Avoidance

c. Predictive Maintenance

d. Real-Time Video Analytics

e. Factory Floor Localization and Mapping

f. Sensor Data Fusion in Machinery

g. Voice and Speech Recognition

Me

dic

al H

eal

thca

re

a. Automated Medical Diagnosis, Recommendations & Reporting

b. Discovery of Bio-Markers, Clustering and Phenotypes

c. Computational Drug Discovery

d. Genomic Data Mapping for Personalized & Precision Medicines

e. Digital Patient Management Systems

f. Market Intelligence for Life Sciences

g. Medical Image Analysis

h. Medication Compliance for Clinical Trials and General Usage

i. Mining, Processing and Making Sense of Clinical Notes

j. Portable and Low-Cost Ultrasound Device

k. Predicting Illness and Patient Outcomes

l. Virtual Assistants for Doctors and Patients

Edu

cati

on

a. Personalized Tutoring and Adaptive Learning

b. Automated Class Notes and Quiz Generators

c. Automated Grading of Tests and Progress Tracking

d. Education for Autistic and Differently abled Children

e. Foreign Language Tutoring and Spoken Fluency Evaluation

f. Career Counselling and Doctoral Research Guidance

g. Simulative Practical and Advanced Problem-Solving Labs

Info

rmat

ion

Tec

hn

olo

gy a. Automated Code Development, Testing & Bug Fixing

b. Rapid Application Development

c. User centric Computer aided Design

d. Predictive Business Analytics

e. Cognitive IT Operations, Business Continuity and Recovery

f. Dynamic Environment Management

g. Robotic Process Automation

h. Self-Healing Platforms

Go

vern

me

nt

Serv

ices

a. Virtual Agents for Decision-making

b. End User Sentimental & Social Media Analytics

c. Public Distribution of Assets & Services

d. Biometric Systems and Facial Recognition

e. Real-Time Video Analytics & Surveillance


f. Natural Disaster and Relief Management

g. Crowd/Mob Analytics & Social Unrests Predictions

h. Geopolitical Events & Demographics Management

i. Emergency Services & Security Enforcement BOTs

j. Resource Allocations and Wastes Management Fi

nan

cial

Ser

vice

s a. Automated Credit Scoring

b. Predictive Financial Modeling

c. Automated Reporting & Forecasts

d. Digital Assets & Blockchain Security

e. Claims Scrutiny and Processing

f. Employees Expense Management

g. Loan Analysis- Initiation/Repayment/Closure

h. Personal Financial Advisor

i. Risk Assessment and Compliance

j. Tax Filing and Processing

k. Transactions Fraud Detection & Remediations

Agr

icu

ltu

re

a. Food Safety & Smart Food chains

b. Livestock Management

c. Machines Detection/Identification/Avoidance

d. Satellite Imagery for Geo-Analytics

e. Farm Sensor Data Analytics

f. Smart Cultivations- Plant, Grow and Harvest

g. Farmland Localization and Mapping

h. Weather Forecasting and Prepare

i. Weed Identification and Cure

Co

nsu

me

r Se

rvic

es

a. Augmented Reality Modeling

b. Consumer Behavioral Analytics

c. Virtual Personal Assistants

d. Facial Recognition & Language Translation

e. Computer-Aided Arts and Design

f. Contextual Intelligence for Mobile and Social Media

g. Local Search and Discovery Preferences

h. Movies and Music Recommendations

i. Automated Tour Guide and Itinerary Service

j. Travel Concierge and Booking Service

k. Personalized Health, Fitness and Wellness Improvement

l. Business Relationships and Matchmaking

m. Retail & Consumer Products Recommendations

n. Smart Devices Management

o. User Recognition, Classification, and Tagging


p. Customized Chef and Food/Diet Recognition

De

fen

ce &

Mil

itar

y

a. Agent-Based Simulations for Decision-Making

b. Localization and Mapping in Aircraft and Drones

c. Weaponry Objects Detection/Identification/Avoidance

d. Military Vehicles Detection/Identification/Avoidance

e. Predictive Maintenance of Defence Aircraft, Drones, Satellites

f. Prevention against Cybersecurity Threats

g. Satellite Imagery for Geo-Analytics & Security

h. Border Surveillance and Reconnaissance

i. Smart Defence Personnel Training

j. Sensor Data Fusion in Aircraft, Drones, Satellites

k. Network and Data Security in Spy Aircrafts, Drones, Satellites

Table 1: AI Application Use cases across Segments


8. Impact on Human Decision Making

The basic idea of AI involves simulation of human behavior and thought processes across

industries; where AI BOTs and machines take autonomous decisions using dynamic policies,

continuously monitoring and optimizing its performances and automatically adjusting itself

to changing conditions and evolving business rules and user dynamics. It involves self-learning

systems that use data mining, pattern recognition, natural language processing, advanced

neural networking and others, to mimic the human brain functioning and minimize human

intervention.

Real fears that advancement of AI exceeding human intelligence can have negative

implications for the future of humanity have been voiced by respected scientists like Stephen

Hawking16. So even if robots don’t eliminate mankind, a more realistic and concerning

scenario is the intelligent automation of human labor, leading to profound societal change –

maybe for the better, or maybe for the worse.

Perhaps, this fathomable concern led to establishment of the foundation17 'Partnership on

AI' by Google, Facebook, Amazon, IBM and Microsoft in 2016. This technology industry

consortium focuses primarily on establishing best practices for AI systems, advocating ethical

implementations of AI, setting guidelines for future research and deployment of robots and

AI for the overall benefit of people and society. In the end, practical AI is indeed empowering

Humans.

Figure 5: Augmenting Humanity through AI (Source18: Ray Wang / Constellation Research)


According to Constellation Research, the rush to AI will enable Augmented Humanity. As

depicted in Figure 5, while the market leaders and fast followers have yet to achieve mass

personalization, they are already focused on investments in AI. Further, to attain a

competitive advantage and avoid getting disrupted, CXO’s have rushed to AI as the next big

thing. The continuing investments made, and research done in AI subsets like machine

learning, deep learning, natural language processing and cognitive computing have moved

from science projects to new digital business models powered by smart services.

A glimpse of how AI functions, key enterprise focus areas and evolution is explained in

Appendix C.


9. Future of AI

The field of AI has awed scientists and users equally over the years. In fact, AI is gradually

turning out to be an alternate intelligence to Human Intelligence. New HCI would be realized

with advancements in edge computing on mobile devices. According to IDC Future Scape

2018 Predictions:

By 2019, 40% of Digital Transformation initiatives will use AI Services19

By 2020, 25% of enterprise mobile applications will utilize on-board artificial

intelligence/machine learning capabilities on smart devices for a variety of

applications, including computer vision, depth perception, augmented reality and

edge intelligence20

By 2021, 75% of Commercial Enterprise Apps will use AI, over 90% of consumers will

interact with customer support BOTs and over 50% of new industrial robots will

leverage AI19

The key to all this is the continuous evolution of AI with enhanced collaboration between

academia, public sector and private sector. This will also preclude any apprehensions of AI

innovations being at odds with human interest. Besides, the AI continuum would come to the

forefront to determine which AI applications should be falling under augmented, assisted and

autonomous intelligence to serve users across the spectrum, making their lives easier. At the

same time, the continuum can also be used to understand economic ramifications,

complexity of use and decision-making implications of AI adoption.

A collaborative innovation environment with regular dialog between academia, private and

public sectors will help identify newer fields and operations among the masses. This is crucial

to find newer avenues for increasing the efficiency and effectiveness of various products

being developed and services delivered.

The prospects of AI are boundless with staggering business results. It continues to unveil

bigger and broader transformations beyond just intelligent machine/man interactions,

thereby empowering humans.


Appendix A: Research References

Footnotes

[1] Forrester TechRadar: Artificial Intelligence Technologies, Q1 2017

[2] https://www.slideshare.net/Mills/ai-externalization-ofmind

[3] https://www.forbes.com/sites/blakemorgan/2017/03/21/how-chatbots-will-transform-

customer-experience-an-infographic/#6bb403bd7fb4

[4] http://www.scmp.com/tech/china-tech/article/2128243/alibabas-artificial-intelligence-bot-

beats-humans-reading-first

[5] http://www.indiaretailing.com/2018/01/15/retail/increasing-retail-revenue-using-artificial-

intelligence/

[6] https://www.researchandmarkets.com/research/hgbzgp/global_artificial

[7] https://www.tractica.com/newsroom/press-releases/artificial-intelligence-software-

revenue-to-reach-59-8-billion-worldwide-by-2025/

[8] https://www.idc.com/getdoc.jsp?containerId=IDC_P37531

[9] https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-

learning-deep-learning-ai/

[10] https://en.wikipedia.org/wiki/AlphaGo

[11] https://www.zendesk.com/answer-bot/

[12] https://medium.com/pandoraboxchain/towards-common-blockchain-architecture-

d8a5f0e2541e

[13] Ibid.

[14] https://arxiv.org/ftp/arxiv/papers/1702/1702.08529.pdf

[15] https://blog.bigchaindb.com/ai-daos-and-three-paths-to-get-there-cfa0a4cc37b8

[16] https://www.cnbc.com/2017/11/06/stephen-hawking-ai-could-be-worst-event-in-

civilization.html

[17] https://www.partnershiponai.org/

[18] https://www.enterpriseirregulars.com/109415/seven-factors-precision-decisions-artificial-

intelligence/

[19] IDC FutureScape: Worldwide IT Industry 2018 Predictions

[20] IDC FutureScape: Worldwide Mobility 2018 Predictions

[21] http://www.comfiz.com/en/solutions/2/ai-business-processes-and-technology

[22] https://www.pwc.in/assets/pdfs/publications/2017/artificial-intelligence-and-robotics-

2017.pdf


[23] https://www.slideshare.net/Mills/ai-externalization-ofmind

Bibliography

[24] Society of Mind by Marvin Minsky

[25] Machine Learning by Tom M Mitchell

[26] Programming Collective Intelligence by Segaran

[27] Artificial Intelligence 3e: A Modern Approach by Russell

[28] Artificial Intelligence and the Future of Computing by Azeem Azhar

[29] Machine Learning for Dummies by John Paul Mueller, Luca Massaron

[30] Practical Artificial Intelligence for Dummies by Kristian Hammond, PhD

[31] Digital Genesis: The Future of Computing, Robots and AI by Christopher Barnatt

[32] Digital vs Human: how we'll live, love, and think in the future by Richard Watson

[33] The Art of Digital Jujutsu by Mohammed Hashim from EMC Knowledge Sharing 2016

[34] Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms by Jeff Heaton

[35] Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms by Jeff Heaton

[36] Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks by Jeff

Heaton

[37] The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the

Human Mind by Marvin Minsky

[38] The Fourth Transformation: How Augmented Reality & Artificial Intelligence Will Change

Everything by Robert Scoble, Shel Israel

Web links

[39] aitrends.com

[40] www.aiia.net

[41] www.comfiz.com

[42] pwcartificialintelligence.com

[43] www.microsoft.com/en-us/ai

[44] www.mckinsey.com/global-themes/artificial-intelligence

[45] www.delltechnologies.com/en-us/perspectives/tags/artificial-intelligence


Appendix B: AI Platform Architecture

An ideal platform is crucial in realizing business-specific use cases in AI. A reference

architecture is depicted in Figure 6 based on Comfiz21 platform. It is designed to integrate

itself with all the components required to build a full-fledged AI solution across the

enterprise. The nature of AI use cases is diverse and would require different technologies and

infrastructures according to the type and volume of data to analyze.

The main processes that need to be carefully aligned to leverage the full value of AI are:

a. Data integration and data consolidation

b. Data preprocessing and data enrichment

c. Models design and training

d. Models deployment

e. Models maintenance

Figure 6: AI Platform Architecture21

The platform is designed to help organizations bridge any gaps between business, data

science and IT teams. The holistic architecture helps all the teams collaborate on a unified

platform and reap the benefits of synergy in teams:

1. Business Users: The immense domain knowledge of these users can bring valuable

insights and intuitions to AI problems. Apart from collaboration between data


scientists and business users, the platform architecture here helps non-technical

managers to better understand and trust ML and DL algorithms.

2. Data Scientists: Raw data such as that stored in data lakes and legacy application

systems are not of much use for design modelling of analytical insights and predictive

foresights. Further, accessing huge volumes of raw data is often disadvantageous

when it’s not correlated and not in context to help data scientists understand how

they can address the business problems they are trying to solve. The platform here

offers dynamic capacity to handle AI-optimized data stores, which are designed to

facilitate the extraction of high value features for analytical and predictive models.

3. IT Teams: Tech support teams are under constant pressure to cope with the pace of

deployment of new AI-driven services and fulfill the requests of data scientists and

business users. The platform here helps by integrating itself within the existing data

infrastructure to ease deployment of data environments dedicated to AI services

implementation. Further, it also integrates DevOps features for the complex

migration of models across non-production to production environments.


Appendix C: AI Functioning & Evolution

AI touches almost every aspect of our lives ranging from the virtual personal assistants in our

cellphones, to the demographic profiling, customization, automated intelligence and

cyber protection that lie behind more and more of our commercial interactions.

Yet, this is just the beginning with various forms of AI in use like:

a. Automated AI: This involves automation of manual/cognitive and

routine/nonroutine tasks.

b. Assisted AI: This helps people perform tasks faster and better.

c. Augmented AI: This helps people make wiser decisions.

d. Autonomous AI: This automates decision-making processes without human

intervention.

As humans and machines collaborate more closely, and AI innovations come out of the

research lab and enter the mainstream, the transformational possibilities are astounding. The

working of AI, key focus areas and evolution of AI is depicted in Figure 7 from the PwC report

on AI and Robotics.

Figure 7: AI Functioning and Evolution (Source22: PwC)


Appendix D: Alternate AI Continuum

An alternate depiction of AI Continuum based on machine reasoning and decision making is

shown in Figure 8. It describes, from left to right, the various programming approach

dynamically chosen with apt combination of methods for problem solving often on very short

notice.

Figure 8: AI Continuum (Source23: Project10X)


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