decrypting practical ai: empowering or enslaving … · o 20% of citizens in developed nations will...
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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.
2018 Dell EMC Proven Professional Knowledge Sharing 3
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
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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.
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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
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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.
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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)
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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
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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
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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
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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
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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
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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)
2018 Dell EMC Proven Professional Knowledge Sharing 21
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.
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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.
2018 Dell EMC Proven Professional Knowledge Sharing 23
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
2018 Dell EMC Proven Professional Knowledge Sharing 24
[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
2018 Dell EMC Proven Professional Knowledge Sharing 25
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
2018 Dell EMC Proven Professional Knowledge Sharing 26
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.
2018 Dell EMC Proven Professional Knowledge Sharing 27
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)
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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)
2018 Dell EMC Proven Professional Knowledge Sharing 29
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