Understanding the Artificial Intelligence Business Ecosystem

@article{Quan2018UnderstandingTA,
  title={Understanding the Artificial Intelligence Business Ecosystem},
  author={X. Quan and J. Sanderson},
  journal={IEEE Engineering Management Review},
  year={2018},
  volume={46},
  pages={22-25}
}
This technology manager's note piece identifies the major components in the artificial intelligence (AI) business ecosystem and discusses several implications for managers. Specifically, it emphasizes on the designing of AI user scenarios, data acquisition for AI, and building the AI ecosystem. 
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References

SHOWING 1-7 OF 7 REFERENCES
Automated Writing Evaluation System: Tapping its Potential for Learner Engagement
TLDR
Teaching and learning in today's knowledge based society has seen enormous transformation with the emergence of artificial intelligence (Al), so how to facilitate teaching and learning of a second language (L2) with cutting edging Al technologies has drawn increasing attention from both researchers and practitioners. Expand
Developing Machine Learning Products Better and Faster at Startups
  • Rushdi Shams
  • Computer Science
  • IEEE Engineering Management Review
  • 2018
TLDR
This paper demonstrates a three-phase ML product development workflow at OneClass that considers the pivotal idea generation for products that involves data reliability assessment, idea prioritization, expectation setting, and building trust among users. Expand
Artificial Intelligence, the Missing Piece of Online Education?
TLDR
The teaching and learning of economics is used as a case study to illustrate the application of artificial intelligence (AI) based robotic players to help engage students in online, asynchronous environments, potentially improving student learning outcomes. Expand
Qingfan Technology Assists Personal Growth of Students via Affective Computing Technology
  • Z. Wenzhu
  • Economics
  • IEEE Engineering Management Review
  • 2018
Qingfan Technology offers Al and data analysis services to empower schools and educational product manufacturers: it helps schools to integrate all data throughout the school for comprehensive dataExpand
A digital capitalism Marx might enjoy
  • MIT Technology Review
  • 2018
Artificial intelligence: the next digital frontier?