Wednesday, September 14, 2016

Paris Machine Learning #1 Season 4: AlphaGo, Deep Learning & Global Biodiversity, DataScience Game

Tonight, we will have the first meetup of season 4. We just passed 4000 members, woohoo ! 
We should have three presentations, if you are interested to do one:  it is here.

 Speakers and slides:

Brief presentations

Longer presentations 
David Klein'Deep Learning for Global Biodiversity Monitoring'
Abstract: Healthy ecosystems with intact biodiversity provide human societies with valuable services such as clean air and water, storm protection, tourism, medicine, food, and cultural resources. Protecting this natural capital is one of the great challenges of our era. Species extinction and ecological degradation steadily continues despite conservation funding of roughly U.S. $20 billion per year worldwide. Measurements of conservation outcomes are often uninformative, hindering iterative improvements and innovation in the field. There is cause for optimism, however, as recent technological advances in sensor networks, data management, and machine intelligence can provide affordable and effective measures of conservation outcomes. I will present several working case studies using our system, which employs deep learning to empower biologists to analyze petabytes of sensor data from a network of remote microphones and cameras. This system, which is being used to monitor endangered species and ecosystems around the globe, has enabled an order of magnitude improvement in the cost effectiveness of such projects. This approach can be expanded to encompass a greater variety of sensor sources, such as drones, to monitor animal populations, habitat quality, and to actively deter wildlife from hazardous structures. I present a strategic vision for how data-driven approaches to conservation can drive iterative improvements through better information and outcomes-based funding mechanisms, ultimately enabling increasing returns on biodiversity investments. 

Stephane SenecalLes techniques de Machine Learning au cœur du succès d’AlphaGo

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and of selecting moves. After a brief review of the game of Go itself, we will focus on how researchers at Google DeepMind introduced a new approach to computer Go that uses “value networks” to evaluate board positions for predicting game issue and “policy networks” to select moves.
These models are in fact deep neural networks and are trained or learnt by a novel combination of supervised learning from human expert games, and reinforcement learning techniques from games of self-play. Basic concepts of deep neural networks models and of reinforcement learning framework will be explained.
Google DeepMind researchers also introduce a new search algorithm that combines Monte Carlo simulation techniques with value and policy networks. Using this search algorithm, and without any lookahead search (as for classical computer Chess), their program AlphaGo defeated the human European Go champion by 5 games to 0 and a world-class human Go champion by 4 games to 1. This is the first time that a computer program has defeated human professional players in the full-sized game of Go, a feat previously thought to be at least a decade away.
Antoine Ly + équipes UPMC & Polytechnique, DataScience Game
Presentation slides. 
Presentation slides UPMC
Le championnat du monde de data science étudiant 
L'histoire du Data Science Game, sa spécificité, son organisation, l'explication de ce succès.
Présence des équipes UPMC et X : questions réponses avec audience sur méthodes / algorithmes / stratégies etc

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