Attempt of a classification of AI use cases by problem class

There are many attempts to classify and structure the various AI techniques in the internet produced by a variety of sources with specific interests in this emerging market and the fact that some new technologies make use of multiple techniques does not make the task easier to provide an easy, top-down access and guideline through AI for business decision makers. Most sources structure the AI techniques by their core ability (e.g. supervised vs. unsupervised learning) but even this sometimes controversial (e.g. genetic algorithms). The approach taken here is to find groups of use cases that represent similar problem-solving strategies (just like distinguishing "search" from "sort" without reference to a particular technique like "Huffman search" or "qsort"). Of course, most AI techniques are combinations but with a different focus.

There are many different sorting criteria to cluster use cases and these criteria determine how well and if at all the above objectives may be achieved. The target is to find “natural” classes of problems that in an abstract way can be applied to all the corresponding use cases. Since the clustering is used to determine which AI techniques are applicable, the classes should correspond to the typical characteristics of AI technique.

problem class

core problem description

sample use cases

key measure

AI techniques

Normalization

Pre-process and convert unstructured data into structured data (patterns)

  • Big data pre-processing
  • Sample normalization (sound, face images, …)
  • Triggered time sequences
  • Feature extraction
  • Conversion quality

Clustering

detect pattern accumulations in a data set

  • customer segment analysis
  • optical skin cancel analysis
  • music popularity analysis
  • inter- and intracluster resolution

Feature Extraction

Detect features within patterns and samples

  • Facial expression analysis (eyes and mouth)
  • Scene analysis & surveillance (people ident.)
  • Accuracy
  • completeness

Recognition

detect a pattern in a large set of samples

  • image/face recognition
  • speaker recognition
  • natural language recognition
  • associative memory

  • accuracy
  • recognition speed
  • learning or storage speed
  • capacity

Generalization

Interpolation and extrapolation of feature patterns in a pattern space

  • adaptive linear feature interpolation
  • fuzzy robot control/navigation in unknown terrain
  • accuracy
  • prediction pattern range
  • Kohonen maps (SOM, SOFM)
  • any backpropagation NN
  • Fuzzy logic systems

Prediction

predict future patterns (e.g. based on past experience, i.e. observed sequences of patterns)

  • stock quote analysis
  • heart attack prevention
  • next best action machines
  • weather/storm forecast
  • pre-fetching in CPU's
  • accuracy
  • prediction time range

Optimization

optimize a given structure (pattern) according to a fitness- or energy function

  • (bionic) plane or ship construction
  • agricultural fertilization optimization
  • genetic programming
  • convergence
  • detection of local /global optimum
  • (heaviness) cost of optimization

Conclusion

detect or apply a (correlative) rule in a data set

  • QM correlation analysis
  • next best action machines
  • consistency
  • completeness
  • rule-based systems
  • Expert systems

 
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Outline/Structure of the Talk

  • AI technique
  • AI technology
  • AI use case
  • AI Use case class

Learning Outcome

Benefits

Clustering in AI use cases according to underlying (abstract) core problems has the following benefits:

  • development of class specific and reusable solutions that are applicable for each use case of that class
  • ability to apply the right AI techniques and solutions to a given use case by classifying it
  • determine the pre-requisites and limitations of AI techniques and solutions for a given use case
  • early understanding of realistic objectives (and risk to overrate AI capabilities)
  • apply the AI results from similar use cases (and even classes) using transfer learning
  • develop a deeper understanding of the use case per se and it’s differentiators from others
  • create a greater economy of scale effects and more cost-efficient use of AI solutions
  • speed up the application of AI for new use cases

Target Audience

Data Scientists, NLP , Deep Learning, Machine Learning domain

Prerequisites for Attendees

Participants are expected to know what is AI, Machine Learning and Deep Learning. Some basics around the Data Science lifecycle including data, features, modeling and evaluation.

schedule Submitted 2 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  1 month ago
    reply Reply

    Dear Suvro: The learning objective from the talk is not clear to me. Also, could you please considering include a video of your talk or a recorded introduction to the topic?

    Warm Regards

    Vikas


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    No longer limited to providing basic phone and Internet service, the telecom industry is at the epicentre of technological growth, led by its mobile and broadband services in the Internet of Things (IoT) era.This growth is expected to continue,The driver for this growth? Artificial intelligence (AI).

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    Robotic process automation (RPA)

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    Summary

    Artificial intelligence applications in the telecommunications industry is increasingly helping CSPs manage, optimize and maintain not only their infrastructure, but their customer support operations as well. Network optimization, predictive maintenance, virtual assistants and RPA are examples of use cases where AI has impacted the telecom industry, delivering an enhanced CX and added value for the enterprise overall.