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4 Critical Requirements for
Building Truly Intelligent AI Models
#DATAFORAI
The market for AI is
expected to reach
$191Bby 2024*
However…
85%AI projects will fail*
*Gartner, 2019 #DATAFORAI
Common Challenges
Sinking Most AI Projects
• Scarcity of semantically enriched data -
structured or unstructured
• Lack of clean, accurate, quality data
• Inability to interpret the “right data” to
answer specific business questions
• Insufficient resources of subject matter
experts to interpret and annotate data
#DATAFORAI
Data-related challenges
are a top reason (our)
clients have halted or
canceled artificial-
intelligence projects.
Arvind Krishna
SVP, Cloud & Cognitive Software
Companies pursuing AI projects
generally lack an expert
understanding of what data is
needed for machine-learning
models and struggle with preparing
data in a way that’s beneficial to
those systems.
Michele Goetz
Principal Analyst
You can’t feed the
algorithms if you don’t have
data. Solid, clean data in
large volumes, well-tagged
and well organized is
crucial.
Michael Conlin
Chief Data Officer
CHALLENGES ECOHED ACROSS THE INDUSTRY
#DATAFORAI
R A H U L S I N G H A L
C H I E F P R O D U C T O F F I C E R
#DATAFORAI
“Bringing business rules into
ontologies and taxonomies are
critical to solve AI challenges.
The Key to AI Success:
Annotation and Labeling
• 80% of AI project time spent on data preparation*
• Companies spend 5X as much on internal data labeling than
with 3rd parties*
• Annotation and labeling is essential for training AI and machine
learning; it’s what makes them truly intelligent
• Even small errors could prove to be disastrous, therefore
human-annotated data is essential
• Humans are simply better than computers at managing
subjectivity, understanding intent, and coping with ambiguity
*Cognilytica, 2019
#DATAFORAI
4WAYS TO BRING
INTELLIGENCE TO AI
#DATAFORAI
Having access to the right raw data set has
proven to be a critical factor in piloting an AI
project. Raw data is information that has typically
not been processed or analyzed and is routinely
considered inoperable. But deeper analysis can
yield opportunities to turn raw data into useful
insight.
01
#DATAFORAI
RAW DATA
Ontologies play a critical role in machine learning.
According to the Wikipedia definition, ontologies are
"formal naming and definition of the types, properties,
and interrelationships of the entities that really or
fundamentally exist for a particular domain of discourse."
In other words, ontologies give meaning to things.
Think of this as teaching your AI to communicate using a
common language. It is critical to identify the problem
statement and understand how AI can interpret data to
semantically solve a certain use case. The need for out-of-
box ontologies or availability of client ontologies that can
be used as the basis to form the data labeling is critical.
02
#DATAFORAI
ONTOLOGIES
Annotation (also known as data labeling) is quite critical to
ensuring your AI and machine learning projects can scale. It
provides that initial setup for training a machine learning model
with what it needs to understand and how to discriminate
against various inputs to come up with accurate outputs. There
are many different types of data annotation, depending on what
kind of form the data is in.
It can range from image and video annotation, text
categorization, semantic annotation, and content
categorization. Humans are needed to identify and annotate
specific data so machines can learn to identify and classy
information. Without these labels, the machine learning
algorithm will have a difficult time computing the necessary
attributes. How data is annotated and labeled brings us to our
next and most crucial requirement: subject matter expertise.
03
#DATAFORAI
ANNOTATION
Our clients have learned how important it is to have subject
matter experts (SME) that understand their specific industry and
complex needs. This goes back to the need for annotated data. If
there are even slight errors in the data or in the training sets
used to create predictive models, the consequences can be
potentially catastrophic. That’s why the need for specific domain
expertise is so crucial, and why human knowledge still plays a
pivotal role in artificial intelligence.
For example, being able to interpret complex legal obligations
and agreements from ISDA contracts require legal specialists
that can identify and label the most appropriate information.
The same goes for other fields like science and medicine where
deep understanding and fluency of the content cannot be taken
for granted.
04
#DATAFORAI
SUBJECT MATTER
EXPERTS
#DATAFORAI
GRAB OUR GUIDE TO AI
TO LEARN MORE
www.innodata.com/machine-learning-tips/

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4 Critical Requirements for Building Truly Intelligent AI Models

  • 1. 4 Critical Requirements for Building Truly Intelligent AI Models #DATAFORAI
  • 2. The market for AI is expected to reach $191Bby 2024* However… 85%AI projects will fail* *Gartner, 2019 #DATAFORAI
  • 3. Common Challenges Sinking Most AI Projects • Scarcity of semantically enriched data - structured or unstructured • Lack of clean, accurate, quality data • Inability to interpret the “right data” to answer specific business questions • Insufficient resources of subject matter experts to interpret and annotate data #DATAFORAI
  • 4. Data-related challenges are a top reason (our) clients have halted or canceled artificial- intelligence projects. Arvind Krishna SVP, Cloud & Cognitive Software Companies pursuing AI projects generally lack an expert understanding of what data is needed for machine-learning models and struggle with preparing data in a way that’s beneficial to those systems. Michele Goetz Principal Analyst You can’t feed the algorithms if you don’t have data. Solid, clean data in large volumes, well-tagged and well organized is crucial. Michael Conlin Chief Data Officer CHALLENGES ECOHED ACROSS THE INDUSTRY #DATAFORAI
  • 5. R A H U L S I N G H A L C H I E F P R O D U C T O F F I C E R #DATAFORAI “Bringing business rules into ontologies and taxonomies are critical to solve AI challenges.
  • 6. The Key to AI Success: Annotation and Labeling • 80% of AI project time spent on data preparation* • Companies spend 5X as much on internal data labeling than with 3rd parties* • Annotation and labeling is essential for training AI and machine learning; it’s what makes them truly intelligent • Even small errors could prove to be disastrous, therefore human-annotated data is essential • Humans are simply better than computers at managing subjectivity, understanding intent, and coping with ambiguity *Cognilytica, 2019 #DATAFORAI
  • 7. 4WAYS TO BRING INTELLIGENCE TO AI #DATAFORAI
  • 8. Having access to the right raw data set has proven to be a critical factor in piloting an AI project. Raw data is information that has typically not been processed or analyzed and is routinely considered inoperable. But deeper analysis can yield opportunities to turn raw data into useful insight. 01 #DATAFORAI RAW DATA
  • 9. Ontologies play a critical role in machine learning. According to the Wikipedia definition, ontologies are "formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain of discourse." In other words, ontologies give meaning to things. Think of this as teaching your AI to communicate using a common language. It is critical to identify the problem statement and understand how AI can interpret data to semantically solve a certain use case. The need for out-of- box ontologies or availability of client ontologies that can be used as the basis to form the data labeling is critical. 02 #DATAFORAI ONTOLOGIES
  • 10. Annotation (also known as data labeling) is quite critical to ensuring your AI and machine learning projects can scale. It provides that initial setup for training a machine learning model with what it needs to understand and how to discriminate against various inputs to come up with accurate outputs. There are many different types of data annotation, depending on what kind of form the data is in. It can range from image and video annotation, text categorization, semantic annotation, and content categorization. Humans are needed to identify and annotate specific data so machines can learn to identify and classy information. Without these labels, the machine learning algorithm will have a difficult time computing the necessary attributes. How data is annotated and labeled brings us to our next and most crucial requirement: subject matter expertise. 03 #DATAFORAI ANNOTATION
  • 11. Our clients have learned how important it is to have subject matter experts (SME) that understand their specific industry and complex needs. This goes back to the need for annotated data. If there are even slight errors in the data or in the training sets used to create predictive models, the consequences can be potentially catastrophic. That’s why the need for specific domain expertise is so crucial, and why human knowledge still plays a pivotal role in artificial intelligence. For example, being able to interpret complex legal obligations and agreements from ISDA contracts require legal specialists that can identify and label the most appropriate information. The same goes for other fields like science and medicine where deep understanding and fluency of the content cannot be taken for granted. 04 #DATAFORAI SUBJECT MATTER EXPERTS
  • 12. #DATAFORAI GRAB OUR GUIDE TO AI TO LEARN MORE www.innodata.com/machine-learning-tips/

Editor's Notes

  1. Intro, what I do at Dun & Bradstreet