Data Science Conversations

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

1. A overview of Data Reliability/Quality and why it is so critical for organisations
2. The limitations of traditional approaches in the area of Data Reliability
3. Data observability and why it is different to traditional approaches to Data Quality
4. The 5 Pillars of Data Observability
5. How to improve data reliability/quality at scale and generate trust in data with stakeholders.
6. How observability can lead to better outcomes for Data Science and engineering teams?
7. Examples of data observability use cases in industry
8. 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

In this episode we are joined by an industry veteran who has worked for some of the biggest names in the enterprise Data world.  Tarush Aggarwal shares his journey from his early days at Salesforce and then WeWork, right through to the present day.

He reveals how to set Data Science & Engineering up for success in both small and large organisations.

Episode Summary

• How Salesforce leveraged data to grow their company fast
• How Mark Benioff ensured his vision was executed effectively at Salesaforce.com
• What it was like to join WeWork at the start of their data function
• The differences between how WeWork and Salesforce.com leveraged data
• How to structure a Data function - centralised V decentralised V hybrid model
• How Spotify structured their data team to scale the business
• Can a Fortune 500 business make the hybrid model work?
• The fundamentals for a new start up - how to get building a data function right
• Product company versus service delivery company - how does that affect the data function structure?
• What’s next for data privacy?
• The 5x Company - entry-level training program, what it is and who its for?
• Data Mastermind groups - are they the way forward?

In this episode we discuss the rapidly developing field of Satellite Imaging.

Our guests on this show are Heidi Hurst & Jerry He.  

They are two remarkable industry Data Scientists with a strong academic pedigree and experience in the field of Satellite Image Processing.  Heidi is based in Washington DC and Jerry is based in New York.

Join us as they discuss their journey into Satellite Imaging and share with us the latest developments in this fascinating and evolving area of Data Science.

Episode Summary

• Why is satellite image processing such an exciting field?
• What data sources is satellite image data based on?
• What are the challenges in using satellite image data?
• Sensors used in satellite imaging
• Methods used in satellite imaging - Image Processing, Deep Learning, CNN
• The Socio-economic applications
• Industry Applications for Satellite Imaging  - Agriculture, Supply Chain monitoring, Sales Prediction, Insurance
• The Future of Satellite Imaging

RESOURCES:

Cool Visual - One Hour of active Satellites orbiting Earth:

https://www.reddit.com/r/dataisbeautiful/comments/j7pj62/oc_one_hour_of_active_satellites_orbiting_earth/?utm_source=share&utm_medium=ios_app&utm_name=iossmf

DOTA - https://captain-whu.github.io/DOTA/ - Open dataset for object detection in overhead imagery

COwC - https://gdo152.llnl.gov/cowc/ - Cars Overhead with Context - specific detection dataset for car counting algorithms

xView - http://xviewdataset.org/ - dataset put together by the National Geospatial Intelligence Agency for an object detection challenge, including some particularly rare classes

Every so often on the podcast we will bring you something a little bit different.

This episode is part two of our conversation with Esports Legends TLO & MaNa.  They are professional Starcraft II players and they tell us the story of what it was like to compete against Google DeepMinds AlphaStar AI agent.

This is a fascinating discussion about the technical capability of AI agents and about the psychology involved when Humans take on the machines.

Episode Summary

• The live event Rematch against AlphaStar
• The game plan for the rematch
• Trying to match AlphaStar
• The importance of the human aspect to the future of Starcraft II
• What TLO & MaNa learned from AlphaStar
• The importance of human intervention to prevent mistakes from the AI
• What impact could AlphaStar have on improving Esports players?
• Did AlphaStar Show any signs of being able to improvise?
• Mistakes AlphaStar made
• Why limiting the abilities of an AI might make it smarter
• Can AI develop intuition in the future?

Resources:

Deepmind Alphastar Videos:

https://deepmind.com/research/open-source/alphastar-resources

TLO Profile: - https://liquipedia.net/starcraft2/TLO

MaNa Profile:  - https://liquipedia.net/starcraft2/MaNa

Every so often on the podcast we will bring you something that is a little bit different.

This episode is part one of a conversation with Esports Legends TLO & MaNa. They are professional Starcraft II players and they tell us the story of what it was like to compete against Google DeepMinds AlphaStar AI agent.

This is a fascinating discussion about the technical capability of AI agents and about the psychology involved when Humans take on the machines.

Episode Summary

• A typical day for an esports athlete
• The similarities and differences between Esports and normal sports
• The importance of actions and screens per minutes for high performance
• The role of gaming in driving development of AI
• Evolution of gaming AI agents
• The challenges in competing against a blackbox
• Exploiting the game playing tendencies of the AlphaStar
• The psychological pressure of playing against AlphaStar
• Underestimating the ability of AlphaStar

Resources:

Deepmind Alphastar Videos:

https://deepmind.com/research/open-source/alphastar-resources

TLO Profile: - https://liquipedia.net/starcraft2/TLO

MaNa Profile: - https://liquipedia.net/starcraft2/MaNa