Data Science Conversations Damien Deighan and Philipp Diesinger

In this episode we discuss the rapidly developing field of Satellite Imaging.
Our guests on this show are Heidi Hurst and 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=shareandutm_medium=ios_appandutm_name=iossmf (https://www.reddit.com/r/dataisbeautiful/comments/j7pj62/oc_one_hour_of_active_satellites_orbiting_earth/?utm_source=shareandutm_medium=ios_appandutm_name=iossmf)

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

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

xView - http://xviewdataset.org/ (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 and 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 and 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 (https://deepmind.com/research/open-source/alphastar-resources)




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


MaNa Profile: - https://liquipedia.net/starcraft2/MaNa (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 and 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 (https://deepmind.com/research/open-source/alphastar-resources)


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


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

This is Part Two of our conversation about Deep Fakes with two experts in their respective fields.
We talk to Dr Eileen Culloty of the Institute for Future Media and Journalism at Dublin City University and Dr Stephane Lathuiliere of Telecom Paris.
EPISODE SUMMARY:
The implications of disinformation on the media
The role of fact checking
How to deal with Deep fakes
What Adobe and Microsoft are doing about Deep Fakes
The future role of GAN’s in detecting Deep Fakes
Resources:
Video of First Order Motion Model For Video Animation:
https://www.youtube.com/watch?v=u-0cQ-grXBQandab_channel=AliaksandrSiarohin
PROVENANCE program: https://fujomedia.eu/provenance/ (https://fujomedia.eu/provenance/)

This is Part one of our conversation about Deep Fakes with two experts in their respective fields.
We talk to Dr Eileen Culloty of the Institute for Future Media and Journalism at Dublin City University and Dr Stephane Lathuiliere of Telecom Paris.
Stephane reveals what is possible and what is not possible technically with current Deep Fakes Technology.
Eileen helps us cut through the hype about Deep Fakes and tells us about their real world social and political impact.
EPISODE SUMMARY:
Short History of Media Manipulation
The breakthroughs in Deep Learning enabling current Deep Fake Technology.
The role of increased data availability in generating Deep Fakes.
Why Cheap fakes are still a bigger problem than Deep Fakes.
How the First Order Motion Model has advanced the field of Image Animation.
Positive use cases of Deep Fake Technology.
The Future of Image Animation/Deep Fake Technology.
Challenges for media and Journalism in the age of Deep Fake Technology.
Societal impact of Disinformation and fake news content.
Deep Fakes V Cheap Fakes during COVID Pandemic.


RESOURCES:
Video of First Order Motion Model For Video Animation:
https://www.youtube.com/watch?v=u-0cQ-grXBQandab_channel=AliaksandrSiarohin

PROVENANCE program: https://fujomedia.eu/provenance/ (https://fujomedia.eu/provenance/)

This is Part 2 of our conversation with Professor Philipp Koehn of Johns Hopkins University. Professor Koehn is one of the world’s leading experts in the field of Machine Translation and NLP.
In this episode we delve into commercial applications of machine translation, open source tools available and also take a look into what to expect in the field in the future.
Episode Summary:

Typical datasets used for training models
The role of infrastructure and technology in Machine Translation
How the academic research in Machine Translation has manifested into industry applications


Overview of what’s available in Open source tools for Machine Translation

The Future of Machine Translation and can it pass a Turing test



Resources:

Philipp Koehn latest book - Neural Machine Translation - Amazon link:

https://www.amazon.com/Neural-Machine-Translation-Philipp-Koehn/dp/1108497322 (https://www.amazon.com/Neural-Machine-Translation-Philipp-Koehn/dp/1108497322)

Omniscien Technologies - Leading Enterprise Provider of machine translation services:

https://omniscien.com/ (https://omniscien.com/)

Open Source tools:

- Fairseq https://fairseq.readthedocs.io/en/latest/ (https://fairseq.readthedocs.io/en/latest/)
- Marian https://marian-nmt.github.io/ (https://marian-nmt.github.io/)
- OpenNMT https://opennmt.net/ (https://opennmt.net/)
- Sockeye https://awslabs.github.io/sockeye/ (https://awslabs.github.io/sockeye/)

Translated texts (parallel data) for training:

- OPUS http://opus.nlpl.eu/ (http://opus.nlpl.eu/)
- Paracrawl https://paracrawl.eu/ (https://paracrawl.eu/)

Two papers mentioned about excessive use of computing power to train NLP models:

- GPT-3 https://arxiv.org/abs/2005.14165 (https://arxiv.org/abs/2005.14165)
- Roberta https://arxiv.org/abs/1907.11692 (https://arxiv.org/abs/1907.11692)