Abstract
The promise of AI with its breathtaking range of applications seems to be without limits. To elaborate on limitations of AI may therefore be perceived by some of our readers as a spin in opposite directions. AI is so much associated with accelerating innovation, insight, and decision making that we see its opportunities as immeasurable. And yet, even for AI, there are limits and challenges, as we learn about in this chapter.
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Notes
- 1.
See [1] for more information on comparing the human brain with a computer and AI.
- 2.
See [2] and [3] for more information on neural design, neural information theory, and the mathematical background on DL.
- 3.
See [4] for more information on comparing biological with ANNs.
- 4.
See [5] for more information on the structure of the human brain.
- 5.
See [6] for more information on how our brain works.
- 6.
See the section on additional research topics on novel approaches for ANNs to forget.
- 7.
See [7] and [8] for more information on data labeling for medical applications.
- 8.
See [9] for more information on autonomous DL.
- 9.
See [10] for more information on autonomous RL.
- 10.
See [11] for more information on AlphaGo and AlphaGo Zero.
- 11.
See [12] for more information on multitask learning.
- 12.
See [13] for more information on IBM Research’s strategy to enable AI solutions to inspire confidence.
- 13.
See [14] for more challenges regarding self-driving cars in weird situations.
- 14.
See [15] for more information on human-like generalization of learning.
- 15.
See [16] for more AI research areas.
- 16.
Adversarial networks are ANN network architectures, where two ANNs compete and work with each other to improve the overall accuracy of the resulting ANN.
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© 2020 Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
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Hechler, E., Oberhofer, M., Schaeck, T. (2020). Limitations of AI. In: Deploying AI in the Enterprise. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-6206-1_13
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DOI: https://doi.org/10.1007/978-1-4842-6206-1_13
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