Poor data quality is enemy number one to the widespread, profitable use of machine learning. While the caustic observation, “garbage-in, garbage-out” has plagued analytics and decision-making for generations, it carries a special warning for machine learning. The quality demands of machine learning are steep, and bad data can rear its ugly head twice — first in the historical data used to train the predictive model and second in the new data used by that model to make future decisions.
To properly train a predictive model, historical data must meet exceptionally broad and high quality standards. First, the data must be right: It must be correct, properly labeled, de-deduped, and so forth. But you must also have the right data — lots of unbiased data, over the entire range of inputs for which one aims to develop the predictive model. Most data quality work focuses on one criterion or the other, but for machine learning, you must work on both simultaneously.
“In the years ahead, AI will raise three big questions for bosses and governments. One is the effect on jobs. ”
“A second important question is how to protect privacy as AI spreads. ”
“The third question is about the effect of AI on competition in business. A technology company that achieves a major breakthrough in artificial intelligence could race ahead of rivals, put others out of business and lessen competition. ”
“Retailing is an illustration of how AI can help large firms win market share. Amazon, which uses AI extensively, controls around 40% of online commerce in America, helping it build moats that make it harder for rivals to compete. ”
“Just as the internet felled some bosses, those who do not invest in AI early to ensure they will keep their firm’s competitive edge will flounder.”
Very glad that Lili.ai is used as an example of how Ecole Polytechnique is supporting start-ups by fostering collaboration with their mathematical laboratories.
1/3 shoppers: There’s a good reason for this influx of investment – consumers like AI. A new study from PointSource found that when artificial intelligence is deployed tactically, one-third of shoppers (34 percent) will spend more money online. Nearly half (49 percent) said they are willing to shop more frequently when AI is present.
83 percent: Despite these erroneous outcomes, the early adopters are being rewarded. According to a recent Deloitte survey, 83 percent of the most aggressive adopters of AI and cognitive technologies said their companies have already achieved either moderate (53 percent) or substantial (30 percent) benefits.
And more on this article: https://enterprisersproject.com/article/2018/2/state-ai-10-eye-opening-statistics
Lili.ai will be presenting along Philippe Brun / Thalès at Entretiens du Management de Projet, the top-notch event organized by Cécile Létoffé / EDF university. This first edition will gather 200 professionals with 10+ years experience in Project Management and working on the most strategic projects at EDF, Framatome, Thalès, Renault, Nokia, Orange.