Data-driven decision making in 2019: In-depth Guide

Most successful organizations treat data-driven decision as a primary objective and pursue it with religious zeal. However, data-driven decision making, the steps leading to it and how AI is changing it are not well-defined.

In an AI-first company, data-driven decision making means

  • Strategic decisions are made by a diverse group including executives that rely on a sufficiently comprehensive set of information. By definition, a bad strategic decision should be important enough to lead to failure of the company.
  • Most operational decisions are handled by continuously learning machine learning models which produce explainable decisions. Operational decisions are frequent (once a week or more frequent) and not critical (a single mistake is unlikely to lead to failure of the company).
  • Operational decisions which can not be automated with good accuracy are delegated to humans.
    • If data is lacking, opinion based decisions are made.
    • If data exists and has been analyzed, decision maker relies on analysis.
    • If data exists but is not analyzed yet, cost of analysis determines whether an opinion or data based decision will be made.

    Before settling on this framework for decision making for modern corporations, we need to identify how we can evaluate different decision making models. However, if you like you can directly skip to the sections that interest you: Read more

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Affective Computing: In-Depth Guide to Emotion AI [2019]

A digital heart

Affective computing systems automatically identify emotions. Affective computing is also called emotion detection, emotion AI, artificial emotional intelligence or affective AI. Understand affective computing in detail:

What is affective computing?

Affective computing systems auto-recognize emotions. See a longer definition below:

Affective computing  is the development of systems that can recognize, interpret, process, and simulate human feelings and emotions.

Wikipedia

It may seem strange that machines can do something that is so inherently human. However, there is growing research supporting the point that human emotions are recognizable using facial and verbal clues. Read more

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Machine Learning Accuracy: Learn the Metric to Assess ML Models [’19]

Demonstrates the 4 possible categories of results of a model: True positive, false positive, true negative, false negative

There are various theoretical approaches to measuring accuracy* of competing machine learning models however, in most commercial applications, you simply need to assign a business value to 4 types of results: true positives, true negatives, false positives and false negatives. By multiplying number of results in each bucket with the associated business values, you will ensure that you use the best model available.

Further complicating this situation is the confidence vales provided by the model. Almost all machine learning models can be built to provide a level of confidence for their answer. A high level approach to using this value in accuracy* measurement is to multiply it with the results, essentially rewarding the model for providing high confidence values for its correct assessments. Read more

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Top RPA platforms for Python developers in 2019: AIMultiple Guide

It is practically impossible to teach good programming to students that have had a prior exposure to BASIC: as potential programmers they are mentally mutilated beyond hope of regeneration.

Edsger W. Dijkstra

In Dikstra’s days, computing was an emerging niche, like the RPA of today. From the perspective of computer scientists, BASIC or its latest incarnation, Visual Basic are mutilating young minds in both cases. However, there are emerging RPA platforms emerging on Python so Python developers no longer need to use .Net to develop RPA solutions to benefit from this fast growing market. Read more

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RPA Workflows & Reusable Bots: Faster RPA Implementation in 2019

Reusable RPA plugins can be added to your RPA bot to take care of specific tasks like checking and sending emails, manipulating spreadsheets, translating text etc. Therefore, they reduce development efforts, error rates and implementation time.

Reusable RPA plugins have numerous names. This is because RPA marketplaces that sell these tools have only been recently launched and industry has not yet converged on a common terminology. Different names for reusable RPA plugins include app, bot, solution, component, dashboard, workflow, skill, connector, asset, snippet, component, activity or plugin. Read more

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