Data Science Conversations Damien Deighan and Philipp Diesinger

Modern Data Infrastructures and platforms store huge amounts of multidimensional data. But - data pipelines frequently break and a machine learning algorithm's performance is only as good as the quality and reliability of the data itself.
In this episode we are joined by Lior Gavish and Ryan Kearns of Monte Carlo, to talk about how the new concept of Data Observability is advancing Data Reliability and Data Quality at Scale.


Episode Summary


A overview of Data Reliability/Quality and why it is so critical for organisations
The limitations of traditional approaches in the area of Data Reliability
Data observability and why it is different to traditional approaches to Data Quality
The 5 Pillars of Data Observability
How to improve data reliability/quality at scale and generate trust in data with stakeholders.
How observability can lead to better outcomes for Data Science and engineering teams?
Examples of data observability use cases in industry
Overview of O’Reilly’s upcoming book, The Fundamentals of Data Quality.

In this episode we are joined by Julia Stoyanovich from NYU, to talk about her work into how AI is being used in the hiring process.
Whether you are responsible for hiring on behalf of a business or are a job seeker, you will find this podcast very interesting, but for very different reasons.
Episode Summary
Algorithmic decision making in the hiring process - what does that mean for businesses and job seekers?
The hiring process - the funnel effect.
Lack of public disclosure about the use of algorithmic tools as part of the talent acquisition pipeline.
Are job seekers being unfairly screened out of the hiring process?
How AI based implementations of psychometric instruments are used today.
Is it possible to measure a person’s personality based on data alone?
Do these systems remove bias and discrimination from the hiring process?
Testing the stability and consistency of these algorithmic systems.
Vendors of systems and their lack of testing / recognising the issues.
Are new laws needed so the hiring process is fairer and more transparent?
What does the future of hiring look like - fewer AI systems and more human intervention?

In this episode we are joined by Professor Maurizio Porfiri from NYU, to talk about his latest academic research which is using data science to uncover why sales of guns in the USA increase after a mass shooting event.
His interest and research was borne from a very personal experience 14 years ago when he experienced a mass shooting event at Virginia Tech where he was studying.
Researching Complex Systems
Virginia Tech Mass Shooting event and its impact on Maurizio
What is the relationship between mass shooting events and the purchase of guns?
Analysis of time series data - 70 mass shootings in around 20 years
Can media coverage on mass shootings shape public opinion, thereby influencing firearm acquisition?
Examining the correlation between three distinct datasets
What are the causes of increased gun sales in the aftermath of mass shooting events?
Differences in the data at State level V National level?
Researching the complex firearm ecosystem with all its pieces - prevalence, violence and regulation

In this episode we are joined by Perry Marshall to talk about his latest scientific paper entitled “Biology Transcends the Limits of Computation”. We also discuss his $10 million Evolution 2.0 Science Prize, which is the largest prize in the world in science currently.
His paper pushes the boundaries in the field of evolutionary biology and his science prize is driving some truly fascinating and thought provoking implications for the development of strong AI.

In this episode we are joined by Arnon Houri Yafin, an Israeli entrepreneur who is the founder of a company called Zzapp Malaria, which recently won the AI XPRIZE sponsored by IBM Watson.
Their work in using AI to eliminate malaria in Africa is both interesting and inspirational.
Episode Summary
Moving from malaria control to malaria elimination
How Zzapp Malaria started and how investors were attracted
The use of drones, satellite imagery, topography, rain/humidity data and a new mobile app
The development of small neural networks to identify the potential for small water bodies
How IBM Watson assisted with funding but also the machine learning models
The use of biology agents rather than chemicals to treat stagnant water bodies
The NGO project in Ghana - reduced mosquitos by 60% in 100 days
Latest and biggest operation to date in São Tomé and Príncipe
The direct impact on the GDP of a country’s economy as a result of eliminating malaria
How malaria and poverty are interconnected
Winning the $3 million XPrize and how the money will be used
How you can help by supporting Malaria No More and Only Nets

In this episode we are joined by the Director of AI and Data Operations at XPRIZE whose career path into the world of AI is fascinating. Neama Dadkhahnikoo shares his journey from his early days at Boeing back in 2005, through start up ventures, Techspert and Caregivers Direct, and re-training right through to the present day at XPRIZE.
He reveals how anyone has the potential to make a real difference in using AI to help solve real world problems.
The history of challenge prize competitions and how the British Monarchy were involved
Challenge prize is philanthropy with capitalism thrown in
How a clockmaker determined longitude to win the first ever prize
How industries are born out of successful challenge prize competitions
The impact of XPRIZE on the commercial Space industry
The ethos of XPRIZE- a global positive future movement
How the challenges are chosen
The IBM Watson AI XPRIZE, a $5 million challenge for teams to use AI for good
How to monitor the after prize impact
Three AI XPRIZE finalists - Aifred Health, Marinus Analytics and ZzappMalaria
How was AI defined for the challenge?
How to use and get involved with AI for good