computer vision / AI / ML

In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.

Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.

Graphs as the Front End for Machine Learning

In this episode of the Data Show, I spoke with Guillaume Chaslot, an ex-YouTube engineer and founder of AlgoTransparency, an organization dedicated to helping the public understand the profound impact algorithms have on our lives. We live in an age when many of our interactions with companies and services are governed by algorithms. At a time when their impact continues to grow, there are many settings where these algorithms are far from transparent. There is growing awareness about the vast amounts of data companies are collecting on their users and customers, and people are starting to demand control over their data. A similar conversation is starting to happen about algorithms—users are wanting more control over what these models optimize for and an understanding of how they work.

I first came across Chaslot through a series of articles about the power and impact of YouTube on politics and society. Many of the articles I read relied on data and analysis supplied by Chaslot. We talked about his work trying to decipher how YouTube’s recommendation system works, filter bubbles, transparency in machine learning, and data privacy.

The Importance of Transparency and User Control in Machine Learning

Walmart & Microsoft have a new partnership. What is it, and can it ever hope to challenge Amazon's online retail and cloud dominance? 

Plus our Fast Five (Elon Musk's deep div,  IBM nice earnings, Google's Crazy Loon Balloons & EU's issues, and Microsoft's Windows IoT play). 

In our Tech Bites segment it's Zuckerberg's face-plant, and our Crystal Ball looks at the future of Uber.

This episode features: Daniel Newman (@danielnewmanuv), Olivier Blanchard (@oablanchard), Fred McClimans (@fredmcclimans). If you haven't already, please subscribe to our show on iTunes or SoundCloud. For inquiries or more information on the show you may email the team at info@futurumresearch.com or follow @FuturumXYZ on Twitter and feel free to direct inquires through that channel as well. To learn more about Future research please visit www.futurumresearch.com

As a reminder, the Futurum Tech Podcast is intended as an informational newsletter only. No investment advice is offered. While equities are frequently discussed, no investment advice is offered or implied.

computer vision / AI / ML

Meredith Patrick