Slicing Artificial Intelligence — Summary

Photo by Jason Leung on Unsplash

This week we looked at how to segment A.I. We started with generally defining A.I. as

a non-natural, adaptable system that has the ability to solve problems, or to create products, that are valued within one or more cultural settings.

Then we continued to define value

as an improvement for the the customer (VOC), the employee (VOE) or the business (VOB).

After that we segmented artificial intelligence into three groups:

  • Machine learning
  • Natural language processing
  • Robotics

Machine Learning

Machine learning is based on algorithms that can learn from data. The big scientific breakthrough came 2007 with Fei-Fei Li and the start of the third A.I. wave 2011 with IBM’s Watson winning Jeopardy. Machine learning itself can be divided into two groups:

Predicting & Sensing

Prediction includes predicting loss for banks or insurances, predictive maintenance for industry application or also anomaly detection for preventing elevator shutdowns. Recommendation engines from Netflix or Amazon are another application we find in this field.

Sensing on the other hand includes everything related to hearing, smelling, touching or seeing. Most popular is image recognition or image analysis (e.g Google Photos or Facebook).

NLP (Natural language processing)

NLP, natural language processing, helps to make everything “text-based or voice-based” smarter. It includes applications like Alexa (voice recognition, language processing), but also handwriting detection or text classification algorithms to sort out spam. Very popular in the text classification is sentiment analysis which allows to detect if a text has positive or negative connotation. A progressive area in NLP is the text creation and generation, where algorithms create headings, documents, articles or books by themselves.


Robots, physical machines which move within an environment with a certain degree of freedom, began to form symbiosis with A.I. systems since the early 2000s. The successfully finished DARPA challenge from 2004 was the starting point of the robotic/AI development. The most prominent example for this collaboration is the self-driving car. Its development from a non-AI system to a full A.I. system can be described by the international 5-Level system of autonomy. Elon Musk claims that Tesla will reach level 5 by the end of 2021.

As you see, there are very different groups of applications within A.I.. And I hope that these short articles this week already helped a little bit to segment artificial intelligence in your head and thus help to find potential usage for A.I. in your company.

If you are interested in A.I. leadership education or want to start your A.I. journey, just contact me at

AI Evangelist — CEO of Goldblum Consulting