Data Driven Michael Waitze Media

The Data Driven Podcast welcomed Iva Gumnishka, the Founder and CEO of Humans In the Loop, a social enterprise that provides ethical humans-in-the-loop workforce solutions to power the AI industry.

Some of the topics that Iva and I discussed:


Training artificial intelligence models
Where humans come into play in object identification
The use of neural networks
The importance of taxonomies and classes
Model iterations and data drift
Data collection in controlled environments versus out in the wild
Identifying an object versus understanding the meaning of that object
Data annotation is frequently undervalued but is extremely important

The Data Driven Podcast was joined by Arthur Wandzel, a co-Founder and the Head of AI at Jamm.  Arthur grew up in Detroit in a house where both of his parents were psychologists.  In his words, his parents combined both an emotional and logical outlook.  The combination of the robotics (logic) and the human psychology side of his upbringing really inspired his direction.

Some of the topics we covered included:


Jamm is fundamentally an IoT dashboard camera that gives you behavioral insights into how you drive that you are able to leverage for car insurance savings
Through the understanding of data and the resulting behavioral changes, Jamm may be able to save you money
How humans use the sum-total of their experiences to understand the world
Standardization is hard to create unless you have some kind of ’Truth’
The three pillars Jamm measures are alertness (attention patterns on the road), style (telematic information), and composure
Smart cities that adjust to you based on data
IoT as the next wave of computers

The restaurant whose name I could not remember (Although I was kind of close...) is El Bulli.

The Data Driven Podcast was joined by Tristan Rouillard, a co-Founder of Hasty.ai. Hasty.ai supports vision AI practitioners and their evolving needs by developing best-in-class vision AI tools that are supported by a community of machine learning engineers, data scientists, and software developers. (A shout out to the incredible Head of Operations at Hasty.ai, Lisa Wantig, for helping to make this possible.)

Some of the topics that Tristan and I discussed:


The genesis of the idea for Hasty came from the founders' own experiences
Manual image labeling and just how time-consuming that is
The idea was NOT to change the technology but to change the process
The necessity to bring the neural networks into the process earlier on
The importance of having a clean data set
With Hasty, one is not looking for mistakes in the dataset, but fixing the ones the neural network has found
There is no substitute for a ground-truth data set
When you are building a product, you are looking for ‘love’ and ‘wow’
Hasty is changing a process and that is the hardest thing to change
Bringing agile methodology to machine learning
How sometimes the biggest competition is the status quo
Taking domain-specific assets and leveraging them in a good way
Data ownership and one of Hasty’s USPs
It is likely that the training data is the differentiator, not the neural network
The nuance and complexity of image recognition is yet to be understood
The need for data documentation
Hasty is teaching machines how to identify and see the world the same way we do
Everybody that joins Hasty has to label a data asset



The quote from Alan Nichol, a co-Founder and the CTO of Rasa, came from this LinkedIn post.

The Data Driven Podcast had an in-depth conversation with Dr. Elias Willemse, a co-Founder and the Chief Technology Officer at Waste Labs.  Waste Labs helps smart cities and companies to design and operate sustainable waste collections and recycling.

Some of the topics we covered included:


Using AI, Data Analytics, and Technology to have a positive impact on cities and companies
Routing optimization and improving logistics operations
How you dispose of your waste will determine where it ends up
What happens to waste after we are done using it
Using data to extrapolate what type of waste comes from what kind of home or business
What is the optimal way of collecting waste?
What is the optimal way of treating the waste?
The importance of getting data into the right format
Geographical Visualizations and Digital Twins

The Data Driven Podcast had a killer conversation with Tanja Magas, the Chief Data and Analytics Officer at Democrance.  Democrance enables insurers to access new market segments, particularly in emerging and mobile-first markets. With its technology, insurers can create and enhance digital sales mechanisms, while increasing operational efficiency and profitability.

Some of the topics that Tanja and I discussed:


Born in Bosnia, Sarajevo and immigrated to Austria and Germany as part of a refugee program
Living, going to school, and working in New York City
Working at Lehman Brothers just before the collapse
The importance Data Availability and Quality
The joy of building things from scratch
Innovative, Differentiated Non-traditional Data Sets
Regulation and Data Privacy
Democrance's approach to and philosophy on data science
Five layers of complexity
The importance of building a single source of data that is accurate and real-time
A simple, but very powerful, dashboard structure
Going to fun as opposed to going to work

The Data Driven Podcast had a really enjoyable and inspiring conversation with Karen Nelson-Field, Ph.D., the Founder, and CEO of Amplified Intelligence.  Karen is a globally acclaimed researcher in media science and her work has been noted in the New York Times, Bloomberg Business, CNBC, Forbes, and the Australian Financial Review, among others.  Karen is also the author of, "The Attention Economy and How Media Works" - a must-read for anyone in the advertising and media space.

On top of all of that, it was simply a joy to talk to Karen...we laughed a lot.

Some of the topics that Karen and I discussed included:


Accidental entrepreneurialism and how there are not enough hours in the day
Generalizability Is the Building Blocks for Meaningful Results - Ehrenberg-Bass Institute for Marketing Science
What is the Attention Economy
The technology stack that Amplified Intelligence has built to measure 'Attention'`
The early days of Facebook and social media and how 'Likes' do not lead to more brand loyalty
Reach is not free and simply building Likes, Followers, or Communities does not necessarily mean it will nudge people to buy
The concept of 'negative diffusion' and the misconceptions around content virality
Are there ways to predict across platforms if a human is paying attention
There are functional factors of a platform that foster inattention versus attention and that is predictable
Based on her extensive research and findings, the top-tree measure had to be 'Attention'
How anything that measures 'Reach' can have an 'Attention' adjustment applied
Yachtsmen, Architects, and Serendipity