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4 Critical Requirements for Building Truly Intelligent AI Models


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Did you know 85% of AI projects will fail because of a lack of training data?

Before investing time and money in machine learning, discover 4 critical requirements every company needs to employ in order to build effective machine learning applications and bring intelligence to artificial intelligence.

Published in: Business
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4 Critical Requirements for Building Truly Intelligent AI Models

  1. 1. 4 Critical Requirements for Building Truly Intelligent AI Models #DATAFORAI
  2. 2. The market for AI is expected to reach $191Bby 2024* However… 85%AI projects will fail* *Gartner, 2019 #DATAFORAI
  3. 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. 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. 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. 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
  8. 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. 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. 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. 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