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THE EMERGENCE OF AI IN ENTERPRISE IT
The Emergence of AI
in Enterprise IT
K R Sanjiv
K.R.Sanjiv, Senior Vice
President and CTO, Wipro
Ramaprasad K R
Chief Technologist and
Distinguished Member of
Technical Staff, AI & Cognitive Computing, Wipro
For centuries, one of the more ambitious
goals of mankind has been the creation of
machines that rigidly “obey” commands.
Around 322 BC Greek philosopher Aristotle imagined robots when he wrote, “If
every tool, when ordered…could do the
work that befits it…then there would be no
need either of apprentices for the master
workers or of slaves for the lords.” Since
then, inventors, scientists and innovators
have refined the idea of robots – from
Leonardo Da Vinci’s clockwork knight to
the Stanford Research Center which developed Shakey, the first mobile robot, Sony’s
AIBO, Honda’s ASIMO and Google with its
driverless cars. The quest has slowly turned
from pre-programmed machines that did
repetitive tasks to those that can sense the
environment learn and respond to it. But
the future belongs to advanced information
processing or cognitive systems. These will
bring about an epic shift in society, business
and governance.
AI (Artificial Intelligence) falls into two broad
categories. The first is Natural General
Intelligence (strong AI). Here, the focus
is on building machines that think like
human beings. The second is Applied AI.
Here, the focus is on the use of advanced
machine learning and knowledge engineering techniques to build smart machines.
In other words, Applied AI works at developing machines that act like people. The
technologies that enable AI applications
can be classified as Cognitive Computing
technologies.
Cognitive computing is a branch of computing that involves imparting cognitive capabilities to computers, so as to enable them to
solve fluid problems. These problems are full
of ambiguity; require contextual processing
of a differing number of disparate sources
which may not be known beforehand. Just
like humans get better through practice
and their goals change with their level of
expertise in processing such problems, a
cognitive computing system also improves
itself through learning techniques. A traditional rules logic based computing approach
will not be able to solve these problems.
The central hypothesis of cognitive science
is that thinking can best be understood in
terms of representational structures in the
mind and computational procedures that
operate on those structures. Designing,
building and experimenting with computational–representational models is the
Like humans get better through practice and
their goals change with their level of expertise in
processing such problems, a cognitive computing
system also improves itself through learning
techniques
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central method of developing modern AI
applications (see Figure 1: ‘Cognitive Versus
Traditional Systems’).
Vectors that shape AI applications
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significant corpus of historical information
in a specific domain will enable an AI application to extract key concepts, entitiesrecognition, associations, and hierarchies
and generate what we call smart data by
merging domain knowledge with ontologies.
Among many aspects that differentiate
cognitive systems from traditional systems,
the major ones are the ability to continuously
reprogram themselves thus remaining flexible and the ability to interact in ways that
are natural to humans. Apple’s Siri is one
such example – Siri lets you do everyday
things by talking. We are witnessing the
arrival of television sets and mobile phones
that respond to gesture, a glance or even
the way we hold the device (example: face
down for a mobile = mute).
The central hypothesis
of cognitive science is
that thinking can best
be understood in terms
of representational
structures in the mind
and computational
procedures that operate
on those structures.
The biggest thrust to cognitive computing
has come from the availability of data. A
The emphasis has to be on access to a
significant volume of associated data. AI
THE EMERGENCE OF AI IN ENTERPRISE IT
systems can be developed only when we
have a significant corpus of data.
For example in the Application Management
and Infrastructure Management corpus of
problems, diagnosis and resolutions, ITSM
ontology will be essential to hyper automate
processes and auto remediate problems
without human intervention.
When we explore some of the key characteristics of an enterprise AI application
we find that there are six characteristics
that are actually computable and relevant.
These characteristics will shape the AI
applications:
• Naturally Interactivity: Improved humancomputer interaction, with mechanical
middle layers such as a mouse being eliminated; these systems are conversational and
have dialogue oriented natural language
interfaces
• Knowledge Representation and Meaning: Ability to ingest and represent knowledge; use automated knowledge models;
dynamically extend links to internal and
external knowledge sources
• Algorithmic Intelligence and Hypothesis: Perform computations and pattern
recognition leveraging historical data – statistics, machine learning, NLP, optimization,
ranking & scoring among others; generate
evidence based hypothesis based on confidence scores.
• Continuous Learning: Learn and evolve
with common sense logic, new information/
inputs, new analysis, new users, new interactions, scenario modeling and simulation
• Reasoning: Leverage language structure,
probability, fuzzy logic, semantics and
relationships to draw inferences
• Hybrid Data Handling: Capable of integrating multiple heterogeneous data sources
(structured and unstructured, static and
streaming) and facilitate synthesizing ideas
or answers from them
We have already witnessed the power of
predictive systems in reducing down time
in manufacturing and transport, improv-
Cognitive Systems function differently, coming closer to ways that humans think and work
Figure 1: Cognitive Versus Traditional Systems
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ing healthcare and boosting efficiencies
in industries as diverse as utilities, mining and retail. These predictive systems
are ensuring that inventory is trimmed,
maintenance is just-in-time and the right
skill sets are available at the right place to
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minimize loss. AI applications will raise the
bar by creating intelligent virtual assistants,
rapid software releases, straight through
processing, diagnostics and resolutions,
process dissipation, digital experience
sense-and-respond, etc.
THE EMERGENCE OF AI IN ENTERPRISE IT
The Coming Wave of Autonomous
Computational Systems
Not all of these systems will look the way
we imagine them to be – part human, part
machine in design, closer to a vision straight
from a sci-fi movie. Rather, when applied to
enterprise, these would largely be systems
that use vast amounts of data, but apply
algorithms that are shaped by a changing
environment.
Simplistically put, cognitive systems can
sense using a variety of inputs ranging from
sensor data to machine scanning of emails;
they can learn through algorithms, statistical
models, logic and probability; they can infer
using analytics/computational intelligence/
artificial intelligence to mimic thinking and
they can interact using natural language or
gestures. “If companies take full advantage
of intelligent automation,” says one Deloitte
report, “the overall impact on business
could rival that of the enterprise resource
planning wave of the 1990.”
The future is now
The development and deployment of such
systems requires enterprises to become
conversant with new disciplines and methodologies. They will need to create a deep
understanding and competencies centered
on the following 6 application categories
for AI systems:
• Anticipatory and Predictive Systems:
These would allow organizations to be proactive rather than reactive systems
• Intelligent Virtual Agents: Graphical
bots that can interact with humans and
respond to words and gestures
• Phantom Robotic Process Automation:
This allows process automation without
human intervention
• Visual Computing & Human Computer
Interface: These would include advanced
models for data ) (images, video and text)
representation (3D could be one example)
and processing it using with new methods
of interacting with machines such as language, gestures and glance to make the
interaction more natural
• Knowledge Processing Systems: These
would include logic and decision trees that
enable agents to work more accurately
by acquiring, retrieving and processing
knowledge on their behalf
• Autonomous Robots & Drones: These
would be intelligent machines that operate
independently in environments that humans
may find hazardous or impossible to access
within limited budgets.
Major productivity gains will be
unlocked by the wave of autonomous
computational systems that can
sense, learn, infer and interact.
Enterprises are now plugging in cognitive
computing technologies to develop AI applications. The vast data that they have in
their data warehouse and AI engine which
learn from the data and helps them do
predictive analytics, automated hypothesis,
verification and generation are enabling
them to deploy such systems. Enterprises
can create bots for process and task automation, virtualize knowledge, build mobile
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virtual agents for the digital customer that
can enable enhanced interactions and
user experience with the customers. The
big shift will be at the intersection of business process that will be mapped to an AI
engine to enable business efficiencies and
productivity.
AI applications developed using Cognitive
computing technologies are among the
most interesting recent developments in
computational science. Several popular
open source stacks are available using
which IT service providers are creating
loosely coupled services for a wide application across IT and business processes.
Open source stacks, corpus of data and
related domain specific ontologies can
create killer applications.
They impact practically every business discipline and replace human beings in several
tasks and enhance abilities in key process
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areas. The benefits of productivity, speed,
quality and scalability are immense. They
take practically every function and business
process to a higher level of performance.
Integrating these systems at every level
within organizations will also call for changes
in people and process practices. For the
moment, organizations must ask themselves how deep they want to plunge into
leveraging cognitive systems. Enterprises
should start experimenting through pilots
using innovation offerings from IT services
and only after proven pilots decide on
vendor specific propositions. Do they have
relationships in place with the ecosystem
of AI labs and service providers who are
already in the data, analytics, machine
learning and natural language processing
space? If not, it is time to do so.