Dr. Michael Housman, Chief Data Scientist and Co-Founder of RapportBoost.AI, discusses what to look for when determining the value of data science sources in creating better enterprise artificial intelligence solutions at the JMP Securities Conference panel on AI at the San Francisco Ritz-Carlton in 2017.
There are companies that are using data science to sell a particular outcome. What makes me nervous is there is a big gap between what they say they can do and what they actually do. I think that is challenging for the data scientists and investors in this room because it’s really hard to separate the wheat from the chaff. When data scientists or experienced business people looking for better enterprise artificial intelligence solutions ask tough questions, the fluff usually evaporates.
One solution is to look at the academic credentials of the data science team. If they have the academics who have published in peer-reviewed journals, it is easier to assess credibility without doing the data science yourself. One can also look at the company's marketing material to see if it is data-driven or more smoke and mirrors and flow charts. For example: are the results correlated with a trend and P-value? I look for those sorts of specific things to determine how good the data is and the core infrastructure are.
For me as a Data Scientist, the advances and the speed of which things are changing is unbelievable. It is hard to keep tabs the rapidly changing advances in artificial intelligence with the latest and greatest companies out there. Deep learning is an important factor in many business conversations involving e-commerce. We think of ourselves trying to add EQ and rapport to agents and bots so they can pick up nuances in conversation.
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