The Text Analytics Forum took place earlier this month in Washington DC. What impressed me this year was the extent to which Artificial Intelligence (AI) and Machine Learning techniques are becoming practical for projects that don’t have 7- or 8-figure budgets. I was particularly impressed by Dan Segal’s presentation, describing a pilot to deliver marketing content at IBM which was developed by his team over just a few months using mostly off-the-shelf cloud-based Watson tools.
What I found most interesting was a side-by-side comparison of his results using rules-based analysis vs. machine learning showing that even with the relatively small learning set, he was able to get better results with Machine Learning than with the rules-based approach. I think the tools I was seeing are opening the way to moving AI and Machine Learning into more mainstream application.
Mark Gross, President, Data Conversion Laboratory, is a recognized authority on XML implementation and document conversion. Mark also serves as Project Executive, with overall responsibility for resource management and planning. Prior to joining DCL in 1981, Mark was with the consulting practice of Arthur Young & Co. Mark has a BS in Engineering from Columbia University and an MBA from New York University. He has also taught at the New York University Graduate School of Business, the New School, and Pace University. He is a frequent speaker on the topic of automated conversions to XML and SGML.