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Cognitive Computing pp 31–65Cite as

Artificial Intelligence – The Big Picture

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Abstract

This article presents an introduction to key concepts in the field of artificial intelligence and how they relate to each other. It offers a scheme to assess the maturity levels of current and future technologies or applications and lays out why today’s solutions cannot be reasonably considered intelligent. At its heart, the article includes an overview of fundamental solution approaches, the resulting capabilities, required enablers and the critical aspects of robustness, transparency and trust of produced results. Using novel ways to organize existing knowledge, it aims to enable the reader to accurately separate different notions and to gain a broad understanding of the key concepts under consideration. Based on a more comprehensive framework, it is deliberately limited in scope to provide a well-rounded overview to help the reader identify topics on which more detailed information should be sought out.

Vollständig neuer Beitrag.

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Notes

  1. 1.

    Throughout this text the term artificial intelligence and its popular abbreviation AI will be used interchangeably.

  2. 2.

    https://www.ibm.com/watson

  3. 3.

    The ability to deal with complexity is implicitly taken into account by this model. More complex environments tend to require more mature capabilities on both axis of the model.

  4. 4.

    The model is applicable for both, biological and artificial systems.

  5. 5.

    “True” was purposely placed in quotation marks, as it has not been clearly defined what exactly an artificial intelligence is.

  6. 6.

    Note that, in practice, several different approaches may be used in combination to accomplish the best results. This deliberate categorization is based on a thorough literature analysis that reflects the understanding of the authors. It is not a final state, but rather a dynamic wheel that must be continuously adjusted to incorporate future developments.

  7. 7.

    Note that, in today’s machine learning systems, the learning part usually takes place in a distinct training phase and not during normal operation (depending on the chosen learning strategy). It is, thus, not equivalent to the way humans learn on the fly.

  8. 8.

    While pre-trained models are increasingly more prevalent, deploying organizations will mostly still have to train models for a particular task or context.

  9. 9.

    Similar difficulties can also routinely be observed in large IT organizations running traditional information technology, where it is not uncommon to have a number of legacy systems in operation that are nearly impossible to decommission, mainly for the reason that no one knows precisely what services they support and for whom.

  10. 10.

    The process of combining and rendering explicit valuable information contained in various datasets, interpreting it and enhancing/enriching it with additional information.

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Acknowledgements

The authors would like to thank their colleagues of Swiss Post and reviewers at the Human-IST Institute of the University of Fribourg for their constructive feedback and their valuable inputs.

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  1. Swiss Post, Bern, Switzerland

    David Abele

  2. Human-IST Institute, University of Fribourg, Fribourg, Switzerland

    Sara D’Onofrio

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    Edy Portmann

  2. Human-IST Institut, Universität Fribourg, Fribourg, Switzerland

    Sara D'Onofrio

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Abele, D., D’Onofrio, S. (2020). Artificial Intelligence – The Big Picture. In: Portmann, E., D'Onofrio, S. (eds) Cognitive Computing. Edition Informatik Spektrum. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-27941-7_2

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