I recently had the wonderful opportunity to moderate an event with Jaclyn Rice Nelson, co-founder of Tribe AI, a community of top machine learning engineers and data scientists who partner with companies to build impactful AI solutions.

Photo by Women in AI

This is a topic that’s near and dear to my heart. As the social contract between employees and businesses keeps changing, particularly in these dizzying pandemic times, I love that organizations like Tribe are starting to pop up. Given their focus on community, diversity, and life optimization, I felt it was a match made in heaven for our “Hacking your AI Tribe” event on May 19th, a play on our recent Hackathon. (it was June 4–6th!! Check it out here!)

Couldn’t make the talk? Here’s the TL;DR: Tribe is forging a new path for AI talent by combining the compensation…

A Discussion with Debi Stack, AI VIP

I’ve had the immense privilege, during the course of the last year, of organizing and moderating events with women in various capacities within AI and Data Science fields through my work with Women in AI and Women in Data. Shameless plug — check out BOTH these orgs if you are a student, career-transitioner, seasoned professional, professor, enthusiast, mentor or just a curious human.

Throughout my LinkedIn galavanting, I come across people that I think would really contribute to these events and talks and I have discovery calls with them to understand what they’re passionate about sharing within the world of…

Image from WP Engine

My first loves were international relations, economics and politics. I grew up around cigar smoking patriarchs that talked at length about the US, Russia, China and all the innovations, wars, conflicts and progress in between. I marveled at our global world and I was fascinated by where influence originated and what it could do. I was frightened by the scale of the world’s superpowers and the myriad ways they affect the lives of innocent people all over the world. Nothing much has changed since those early days.

When it was time for me to choose a major when I started…


Article from Gesture blog can be found here.

We recently welcomed data scientist Irene Bratsis at Gesture and we’re starting the new year with revamping our data analytics and data science processes. In her new role, Irene will help the team fine tune the data analytics, reporting and deployment of data products that help Gesture run, as well as assimilate data streams from our newer business units, namely Gesture for Business, Gesture for Vendors, Gesture + and our Gift Cards.

We took some time with Irene to get to know her a bit and understand what motivates and drives her data science work. Irene comes from a background…

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As I prepare for the Amplifying Her Voice event on International Women’s Day hosted by The State of Women Institute (its a three day event! Register here), I wish I could be more excited about celebrating the progress of women in my field, but given my field is AI, Machine Learning and Data Science, my outlook on gender parity in the world as it is today is disappointingly unsatisfied.

I can imagine a world where women’s participation in AI fields is so normal that discussing gender parity is an afterthought. A world where there are so many examples of women…

Between the proliferation of data science, analytics and data transformation, many businesses are wising up to the trends that stand to set them apart from their competitors.

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Companies are beginning the journey toward becoming data driven and by the end of the decade, every industry will be driven by some form of automation and adoption of artificial intelligence. But how does a company go from being data savvy to data driven to an industry leader that fully harnesses AI?

Enter the data maturity curve!

Before you can run you walk and before you walk you crawl. The data maturity curve allows you to make smarter decisions about how to get to the next level in being able to leverage all the fun AI and ML tools you read about. Whether you’re at the beginning of your digital transformation journey or you’re already using models in deployment, this can serve as a handy guide to get a handle on where you are along this data maturity curve and where you want to be to reach your business goals.

What are the main components of data maturity?

Phase 1:

Very little leverage of data and analytics…

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Creating and crafting data science projects that have staying power and impact business value is a central focus for our data science team at Gesture and we aim to refine our commitment to creating a data driven environment continuously.

With Valentines Day on the horizon, we thought we would share some love with our broader data community and share some of the best practices that have worked for us. This is a tricky road to navigate for a lot of data science and analytics teams: how do we create and use tools that work for us and are used by our business partners? Defining and framing the problem we’re trying to solve, creating a solution, approach or design and translating the insights from that design into actions that change the way the business works and improves is a challenge…

image by author

How to best harness the power of data science at your startup and scale up so you can do the fun stuff.

Startups can be amazing environments to learn quickly and apply what you learn in ways that provide your business a direct impact and value. My biggest challenge (and opportunity!) in my current role here at Gesture is to prioritize tasks that make the most sense for the phase Gesture is currently in along the data maturity curve and I thought this could serve as a good resource for others that are currently in this situation.

1. Embrace being a Generalist:

Life at a big mature company is different from life at a startup. In larger organizations, you have clearer delineation of roles and responsibilities and…

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I will be taking a look at Ether pricing data and doing discovery, exploration, cleaning and general analysis through visualization, pair plots and correlation matrices to get a sense of the data before going into the model selection.

I’ve chosen this data set because I have Ether and I’m a big fan of the decentralized information and infrastructure applications Ethereum & Consensys make possible.

A bit about the data gathered: Ethereum dataset is obtained from Etherscan

Etherscan is a Block Explorer and Analytics Platform for Ethereum, a decentralized smart contracts platform. Ethereum is an open source, public, blockchain-based distributed computing platform and operating system featuring smart contract (scripting) functionality.

It supports a modified version of Nakamoto consensus via transaction-based state transitions. Ether is a cryptocurrency generated by the Ethereum platform and used to compensate mining nodes for computations performed. Each Ethereum account has an ether balance and ether may be transferred from one account to another.

Original content can be found on Etherscan.io …

Interview with OSCE on the impact of Artificial Intelligence

I had the pleasure of giving a virtual talk today on the impact of AI, ML and deep learning models and how they’re impacting our society. The video will appear in a few weeks and this is a transcript of the discussion today.

What is AI, Machine Learning & Deep Learning?

Rule based algorithms have been around since the 50s and when we talk about AI today, what we’re talking about is mostly deployments of machine learning and deep learning models. When we use the term “artificial intelligence”, we’re talking about a set of algorithms and intelligence to try to mimic human intelligence.

Generally most people’s idea of…

Irene Bratsis

Women in Data NYC Chapter Co-Lead, Boston Lead; Women in AI speaker and partner outreach; Women in Trusted AI; Data Scientist Machine Learning Engineer; Artist

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