WIT.Connect Preview: Machine Learning & Humanizing Big Data

It won’t be long before machine learning becomes a kind of “new normal,” with people expecting this type of Artificial Intelligence (AI) as a component in every form of technology.

However, as machines make more and more decisions about the healthcare, security, and consumer products we get exposed to, humanizing big data will also see advancements as companies seek more qualitative knowledge from their data and consumers seek more empathetic approaches to decision making.

What should we be mindful of as these issues and challenges arise? 

On June 21,2018 Women In Technology will welcome Dalila Benachenhou, CEO, Femvestor, Inc. and Swathi Young, Founder, TechNotch Solutions, for a discussion on Machine Learning & Humanizing Big Data.  In advance of the June 21 event, we asked Dalila and Swathi to share their thoughts on the topic with the following Q & A.   

WIT Q: What is one main trend in Machine Learning/Big Data that women need to know about? 

Swathi: There is an increasing need for women in machine learning and big data in order to build viable business applications that can cater to various types of customers.  This would eliminate unconscious bias while designing big data and machine learning app (e.g., Apple HealthKit with iOS9 ignored women’s health).  Bringing diversity to machine learning is important to bring in gender data sets and gender interaction principles that help build a successful AI application.

Dalila: One of the most important trends is the move to Cloud computing.  Cloud computing reduces the need for hardware management, which can be huge when one has a large amount of data (e.g. AWS).  In machine learning in business: while businesses already know the value of structured data, many are looking for a way to mine unstructured data (text, PDF documents).  An interesting new trend in government: thanks to the Open Data act, many new businesses have sprouted.

WIT Q: Why is Machine Learning/Big Data relevant to women in leadership positions?

Swathi: Since machine learning is at the adoption curve of the hype cycle, businesses who are early adopters have a tremendous competitive advantage.  Hence womenleaders who advocate using machine learning/big data in their industries make a transformational impact and get ahead of the curve.  Since machine learning is the most important AI technology of our era, women leaders who embrace it will thrive and get ahead.

Dalila: Unbiased decision making.  With data you remove the human bias from the equation and you come with more informed decisions.  I’ll be sharing lots of relevant examples during the session.

WIT Q: What are the key resources to be aware of when learning about Machine Learning/Big Data?

Swathi: There are three main key resources that you should be aware of:

  • Technology – Understand the fundamental types and algorithms and the major platforms (e.g. IBM Watson, Google tensor flow, AWS and Microsoft Azure platforms.  Statistics and math form the foundation of big data and machine learning;
  • Business Use cases – Understanding the business use cases and the risk involved is even more important, especially as more and more companies are experimenting with machine learning and big data.  Figuring out what is the best use case that will maximize the return on investment (ROI) is important;
  • Ethics and diversity – Women can bring a lot of value to the table through the diversity of thought in the algorithms that are designed along with discussion about the ethics in AI.


  • Understand statistics. 
  • Learn calculus. 
  • Most of the hard lifting is done by software, but you need to understand your data (industry, type etc.) to build any model, or else you get what we call in Computer Science ‘garbage in, garbage out’.”
  • More variables doesn’t mean a better model.
  • Always build the less complex machine learning model before trying the most complex one.  In many cases, the simplest is the most appropriate.
  • What no one tells you: you spend a lot of time cleaning the data, even if the data are clean.

To learn more about Machine Learning & Humanizing Big Date please join us on Thursday June 21, 2018 from 6:00 PM – 8:00 PM EDT at Valo Park (formerly the Gannett Building), 7950 Jones Branch Drive, McLean VA 22102

Click here to register!


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