Data Driven

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

The Data Driven Podcast was joined by Ben Emson, the Chief Technical Officer of Topolytics.  Topolytics is a data analytics business that is making the world's waste Visible, Verifiable, and Valuable.  Topolytics' customers use its WasteMap to help them gain visibility and control of environmental data immediately or over time, turning complex and varied metrics into useful information for management teams, regulators, customers, and communities making it engaging, sensible and actionable. Some of the topics that Ben and I discussed: Ben's introduction to computers was a Commodore PET His first owned computer was a Sinclair ZX Spectrum How learning how to program a computer changes your mind-set How using data is different than writing code Data value increases through layers How data can empower the building and strengthening of the circular economy The necessity to 'Innovate Out of Problems' Data can be malleable

The Data Driven Podcast had an insightful conversation with Krishna Kumar Ramanujam, the Chief Architect, EVP, and India Country Head for Abzooba.  KK is a Solutions Architect with more than 20 years of experience in architecture, design, and development of high-performance software products across domains. He is a specialist in the design of frameworks to enable non-technical end-users to create business applications.   He earned a Bachelors's and a Master's degree from the prestigious India Institute of Technology, Bombay in Electrical Engineering. Some of the topics we discussed on the podcast include: Working at IBM and KK's introduction to Data Mining Early Data Engineering and Data Science in the NBA and the 1996 Olympics in Atlanta A typical company's AI journey The accelerating emergence of Data Science and MLOps How choosing the right tools can really make a difference Transfer Learning and Data Augmentation How small and medium-sized companies can take advantage of MLOps platforms xpresso.ai