proud to have supported the first summer camp of Women in AI


What a great time meeting young amazing girls (14-18 years old) and sharing with them my story.

Great questions included:

  • Why did you started?
  • What made you believe that you could?
  • What are you the proudest about?
  • Any great tips for a young girl?


About woman in AI :

We are a group of passionate international women who are experts and self-trained in the field of Artificial Intelligence. We felt the lack of diversity of women in this field and we want to change it.

Artificial Intelligence and Bad Data


Facebook, Google, and twitter lawyers gave testimony to congress on how they missed the Russian influence campaign. Even though the ads were bought in Russian currency on platforms chalk full of analytics engines, the problematic nature of the influence campaign went undetected. “Rubles + US politics” did not trigger an alert, because the nature of off-the-shelf deep learning is that it only looks for what it knows to look for, and on a deeper level, it is learning from really messy (unstructured) or corrupted and biased data. Understanding the unstructured nature of public data (mixed with private data) is improving by leaps and bounds every day. That’s one of the main things I work on. Let’s focus instead on the data quality problem.

Full article here: on a pannel at the Paris ISG Automation Summit

CaptureLesson 1: Waiting is the worse strategy. If you are still in a moving environnement, you are actually moving back.

Lesson 2: AI has disrupted online shopping with huge savings and increase in sales and tigher delivery. There is no doubt, AI will transform the world we know.

Lesson 3: Ask yourself the right questions, capture the data that will enable you to answer to it. As even the most powerful algorithms are not able to explained what has never been written.


If Your Data Is Bad, Your Machine Learning Tools Are Useless


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.

Full article: