3. This content included for educational purposes.
Direct competitors for Publicis.Sapient include digital agencies, consultants, IT services, which are providing AI
and cognitive platforms as a basis for custom solutions, products/services, and XaaS offerings to markets
addressed by Publicis.Sapient
AI encompasses multiple technologies that can be combined to
sense, think, and act as well as to learn from experience and
adapt over time.
SENSE
Computer vision, audio and
affective processing aim to
actively perceive the world
around them by acquiring and
processing images, sounds,
speech, biometrics, and other
sensory inputs. One example is
identity analytics for facial
recognition. Lie detection is
another.
THINK
Natural language processing
and inference engines enable
AI systems to analyze,
interpret, and understand
information. One example is
speech analytics and language
translation of search engine
results. Another is
interpretation of user intent by
virtual assistants..
ACT
AI systems take action in digital
or physical worlds using
machine learning, expert
systems and inference engines.
Recommendation systems are
one example. Another is auto-
pilot and assisted-braking
capabilities in cars. Cognitive
robotics is another.
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• Cybersecurity is the body of technologies, processes and practices designed
to protect networks, computers, programs and data from attack, damage
or unauthorized access.
• User security is shifting from reliance on usernames, passwords and
security questions to incorporate biometric factors including voice
recognition, facial recognition, iris recognition, fingerprints and other
biometric data.
• Biometric security incorporates AI techniques for pattern recognition and
anomaly detection.
• Facial recognition technology is already a big business; it’s being used to
measure the effectiveness of store displays, spot cheaters in casinos, and
tailor digital ads to those passing by.
• Cognitive security analytics provide capabilities for predicting and assessing
threats, recommending best practices for system configuration, automating
defenses, and orchestrating resilient response.
AI machine perception
for user security is
incorporating biometric
factors.
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Does My ai really
understand what
he feels and
what he is
saying to
me?
Affective computing
• Detecting emotions from videos, audio, text,
facial expressions and gestures is a growth
market and important part of future cognitive
systems.
• Audio and video analytics for interpreting
sentiment, emotion and veracity
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Selected vendors* by category of machine perception analytics
AI Platforms
with APIs for
Image & Text
• Apple (Emotient)
• Facebook
• Google
• IBM
• Microsoft
Facial
Analytics
• Affectiva
• Clarifai
• CrowdEmotion
• Eyeris/EmoVu
• Faciometrics
• Imotions
• Kairos
• Noldus
• nViso
• RealEyes
• Sightcorp/
Sonic
Analytics
• BeyondVerbal
• EMO Speech
• Nemesysco
• NICE
• Verint
• Vokaturi
Gesture
Analytics
• GRT—Gesture
Recognition
Toolkit
Text
Analytics
• Clarabridge
• Crimson
Hexagon
• IBM Alchemy API
• Indico
• Receptiviti
Document
Image Analytics
• Cvision
• Parascript
• Signotec
• Topaz Systems
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* Not included in this research deck.
35. This content included for educational purposes.
Machine learning:
• Supervised— Correct classes of the
training data are known.
• Unsupervised— Correct classes of the
training data are not known
• Reinforcement— Machine or software
agent learns behavior based on feedback
from the environment. This behavior can
be learned once and for all or continue to
adapt as time goes by.
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Text analytics
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Text mining is the discovery by computer of new, previously
unknown information, by automatically extracting it from
different written resources. A key element is the linking
together of the extracted information together to form new
facts or new hypotheses to be explored further by more
conventional means of experimentation.
Text analytics is the investigation of concepts, connections,
patterns, correlations, and trends discovered in written
sources. Text analytics examine linguistic structure and apply
statistical, semantic, and machine-learning techniques to
discern entities (names, dates, places, terms) and their
attributes as well as relationships, concepts, and even
sentiments. They extract these 'features' to databases or
semantic stores for further analysis, automate classification
and processing of source documents, and exploit visualization
for exploratory analysis.
IM messages, email, call center logs, customer service survey
results, claims forms, corporate documents, blogs, message
boards, and websites are providing companies with enormous
quantities of unstructured data — data that is information-rich
but typically difficult to get at in a usable way.
Text analytics goes beyond search to turn documents and
messages into data. It extends Business Intelligence (BI) and
data mining and brings analytical power to content
management. Together, these complementary technologies
have the potential to turn knowledge management into
knowledge analytics.
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Symbolic methods
• Declarative languages (Logic)
• Imperative languages
C, C++, Java, etc.
• Hybrid languages (Prolog)
• Rules — theorem provers,
expert systems
• Frames — case-based
reasoning, model-based
reasoning
• Semantic networks, ontologies
• Facts, propositions
Symbolic methods can find
information by inference, can
explain answer
Non-Symbolic methods
• Neural networks — knowledge
encoded in the weights of the
neural network, for
embeddings, thought vectors
• Genetic algorithms
• graphical models — baysean
reasoning
• Support vectors
Neural KR is mainly about
perception, issue is lack of
common sense (there is a lot of
inference involved in everyday
human reasoning
Knowledge Representation
and Reasoning
Knowledge representation
and reasoning is:
• What any agent—human,
animal, electronic,
mechanical—needs to
know to behave
intelligently
• What computational
mechanisms allow this
knowledge to be
manipulated?
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Knowledge encoding
Natural language Documents, speech, stories
Visual language Tables, graphics, charts, maps,
illustrations, images
Formal language Models, schema, logic,
mathematics, professional and
scientific notations
Behavior language Software code, declarative
specifications, functions,
algorithms
Sensory language User experience, human-computer
interface, haptic, gestic.
Humans encode thoughts, represent knowledge, and share meanings using
paberns and language.
PaNerns are knowledge units. A pabern is a compact and rich in seman_cs
representa_on of raw data. Seman_c richness is the knowledge a pabern
reveals that is hidden in the huge quan_ty of data it represents. Compactness
is the correla_ons among data and the synthe_c, high level descrip_on of data
characteris_cs. For example, an image.
Language is a system of signs, symbols, gestures, and rules used in
communica_ng. Meaning is something that is conveyed or signified.
Humans have plenty of experience encoding thoughts and meanings using
language in one form or another… Our proficiency varies. We tend to be
beber at some kinds of language, and not so good at others.
Project teams osen combine different skills and exper_se, e.g. to make a
movie; design and construct a building; or coordinate response to an
emergency.
The table to the right gives examples of five forms of human language:
natural, visual, formal, behavioral, and sensory language.
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Ontology
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An ontology is a formal explicit specification of a shared conceptualization.
An ontology defines the terms and axioms used to describe, represent, and
reason about an area of knowledge (subject matter). It is the model (set of
concepts) for the meaning of those terms. It defines the vocabulary and the
meaning of that vocabulary as well as the assertions, rules, and constraints
used in reasoning about this subject matter. An ontology is used by people,
databases, and applications that need to share domain information.
A domain is a specific subject area or area of knowledge, like medicine, tool
manufacturing, real estate, automobile repair, financial management, etc.
Ontologies include computer-usable definitions of basic concepts in the domain
and the relationships among them. They encode domain knowledge (modular).
Knowledge that spans domains (composable). They make knowledge available
(reusable).
Ontologies are usually expressed in a logic-based language that enables
detailed, sound, meaningful distinctions to be made among the classes,
properties, & relations as well as inferencing across the knowledge model.
Source: Leo Obrst
Source: Tom Gruber
Source: Andreas Schmidt
The diagram above shows that shared ideas and knowledge can
be expressed with different degrees of formality.
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▪ Statistics is the study of the collection, analysis, interpretation,
presentation, and organization of data.
▪ Two main statistical methodologies are used in data analysis: descriptive
statistics, which summarizes data from a sample using indexes such as
the mean or standard deviation, and inferential statistics, which draws
conclusions from data that are subject to random variation (e.g.,
observational errors, sampling variation).
▪ Descriptive statistics are most often concerned with two sets of properties
of a distribution (sample or population): central tendency (or location)
seeks to characterize the distribution's central or typical value, while
dispersion (or variability) characterizes the extent to which members of the
distribution depart from its center and each other.
▪ Inferences on mathematical statistics are made under the framework of
probability theory, which deals with the analysis of random phenomena.
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Statistical inference
“He told me I was average.
I told him he was mean.”
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This diagram maps
cognitive technologies by
how autonomously they
work, and the tasks they
perform.
It shows the current state
of smart machines—and
anticipates how future
technologies might
unfold.
SPRING 2016 MIT SLOAN MANAGEMENT REVIEW
WHAT TODAY’S COGNITIVE TECHNOLOGIES CAN — AND CAN’T — DO
Mapping cognitive technologies by how autonomously they work and the tasks they perform shows the current
state of smart machines — and anticipates how future technologies might unfold.
LEVELS OF INTELLIGENCE
TASK
TYPE
SUPPORT FOR
HUMANS
REPETITIVE TASK
AUTOMATION
CONTEXT
AWARENESS
AND LEARNING SELF-AWARENESS
T
G
C
Analyze
Numbers
Business intelligence,
data visualization,
hypothesis-driven
analytics
Operational analytics,
scoring, model
management
Machine learning,
neural networks
Not yet
Analyze
Words
and
Images
Character and
speech recognition
Image recognition,
machine vision
IBM Watson, natural
language processing
Not yet
Perform
Digital
Tasks
Business process
management
Rules engines, robotic
process automation
Not yet Not yet
Perform
Physical
Tasks
Remote operation
of equipment
Industrial robotics,
collaborative robotics
Autonomous robots,
vehicles
Not yet
Source: MIT Sloan Management Review, Spring 2016
What today’s cognitive technologies can and cannot do
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Recommender system
• Recommend — to put forward (someone or
something) with approval as being suitable for a
par_cular purpose or role.
• RecommendaHon engines automate the process of
making real-_me recommenda_ons to customers.
• A simple example: an online customer who is browsing
a store for one item (e.g. a power drill), places the
item in their shopping cart, and is then recommended
to buy a complementary item (e.g., a set of drill bits).
This example is trivial. Machine learning can go
further, osen uncovering unexpected buying paberns,
based on unforeseen rela_onships between different
customers and between different products.
• Recommender systems take into account where on
the site the customer had visited, their history of
purchases at the site and even their social network
history. It may be that the customer browsed for
mortar on the last visit to the site. Perhaps the user
also asked friends about selec_ng bathroom _les on
Facebook. In this case it might make sense to
recommend a mortar mixing abachment – since it is
clear the customer is doing a _ling project. For a
machine learning algorithm, iden_fying non-explicit
rela_onships like this is typical.
• A machine learning recommender system improves
with _me. It learns from successful, and unsuccessful
recommenda_ons. The same underlying technology
can be used to provide customers with many other
kinds of personalized experiences, based on data of
many kinds.
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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:
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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)
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• 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)
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Four aspects of self-management as they are now
and as they become with autonomic computing
Concept Current computing Autonomic computing
Self-configuration Corporate data centers have multiple
vendors and platforms. Installing,
configuring, and integrating systems is
time consuming and error prone.
Automated configuration of components
and systems follows high-level policies.
Rest of system adjusts automatically and
seamlessly.
Self-optimization Systems have hundreds of manually set,
nonlinear tuning parameters, and their
number increases with each release.
Components and systems continually seek
opportunities to improve their own
performance and efficiency.
Self-healing Problem determination in large, complex
systems can take a team of programmers
weeks.
System automatically detects, diagnoses,
and repairs localized software and
hardware problems.
Self-protection Detection of and recovery from attacks and
cascading failures is manual.
System automatically defends against
malicious attacks or cascading failures. It
uses predictive analytics and early
warning to anticipate and prevent
systemwide failures.
Autonomic computing
Autonomic computing
refers to the self-managing
characteristics of AI-based
distributed computing
resources, adapting to
unpredictable changes
while hiding intrinsic
complexity to operators
and users.
113. This content included for educational purposes.
AI encompasses multiple technologies that can be combined to sense, think,
and act as well as to learn from experience and adapt over time. Sense refers to
pattern recognition, machine perception, speech recognition, computer vision
and affective computing. Think refers to natural language processing, knowledge
representation and reasoning, machine learning and deep learning, and
cognitive computing. Act refers to search engines and question answering, rules
engines, expert systems, recommender systems, automated planning and
scheduling, autonomic computing, and autonomous systems.
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