%0 Journal Article %1 Earley17itpro %A Earley, Seth %D 2017 %J IT Professional %K 01841 ieee paper ai data science enterprise knowledge processing learn optimize requirements %N 4 %P 63--67 %R 10.1109/MITP.2017.3051331 %T The Problem With AI %V 19 %X Data science can be messy, with data scientists often starting with poor-quality or inconsistent data that must be cleaned up before it can be utilized to an organization's best advantage. Here, the author discusses artificial intelligence---in particular machine learning-technologies and how they can help or hinder organizations in curating the vast amounts of data such organizations have access to.

@article{Earley17itpro, abstract = {Data science can be messy, with data scientists often starting with poor-quality or inconsistent data that must be cleaned up before it can be utilized to an organization's best advantage. Here, the author discusses artificial intelligence---in particular machine learning-technologies and how they can help or hinder organizations in curating the vast amounts of data such organizations have access to.}, added-at = {2017-10-30T16:12:59.000+0100}, author = {Earley, Seth}, biburl = {https://www.bibsonomy.org/bibtex/27dc5baa22a4ff3eae482358fceb2c02b/flint63}, doi = {10.1109/MITP.2017.3051331}, file = {IEEE Digital Library:2017/Earley17itpro.pdf:PDF}, groups = {public}, interhash = {b9c489ce13163c7300a965b377dfcc76}, intrahash = {7dc5baa22a4ff3eae482358fceb2c02b}, issn = {1520-9202}, journal = {IT Professional}, keywords = {01841 ieee paper ai data science enterprise knowledge processing learn optimize requirements}, number = 4, pages = {63--67}, timestamp = {2018-04-16T11:31:48.000+0200}, title = {The Problem With {AI}}, username = {flint63}, volume = 19, year = 2017 }