Paris Machine Learning #8 Season 5, Chat with Self Driving Cars Engineers at Voyage, Funding AI at Zeroth, NVIDIA GTC, Quality inspection at Scortex and Low Rank Matrices

This meetup is going to be pretty exciting !

• Thanks to LightOn (http://lighton.io ) for hosting this meetup and
• thanks to DotAI (https://www.dotai.io/ ) for sponsoring the networking event.

We will have a chat with four Self Driving Car engineers from Voyage, then we will get to learn what happened at NVIDIA's GTC event, then will talk about quality inspection with Scortex, then will hear about funding with zeroth.AI and finally we'll hear about finding the low rank of matrices without factorization !

Here is the schedule:
6:30 PM doors open ; 6:45 PM : talks begin ; 9:00 PM : talks end 10:00 PM : end

Presentation of the meetup: Franck Bardol, Jacqueline Forien, Igor Carron

Short announcement: Sami Moustachir, Data for Good - Annonce du projet d'un serment d'hippocrate pour les travailleurs de la donnée

Dans le cadre d'un projet de l'association Data For Good, nous portons un projet de code de conduite ou "check-list" pour data scientists ou toute personne travaillant avec la donnée. Pour cela, nous avons crée un premier formulaire pour le data scientist ou toute personne travaillant la donnée pour nous aider à bâtir une première proposition.

Streaming is here:

Roundtable start at 7:10PM Paris time.

---Chat with Self Driving Car engineers  Tarin Ziyaee,, Emrah Adamey, Nishanth Alapati , Tarek El-Gaaly of Voyage (https://voyage.auto/). If you want to ask questions and you are not on site, send your question with the #MLParis on Twitter

The Villages, San Jose from Voyage on Vimeo.

We're bringing self-driving cars to a retirement community (and city) like no other: The Villages, Florida. With 125,000 residents, 750 miles of road and 3 distinct downtowns, The Villages is a truly special place to live.

Lien article: New York Times (https://www.nytimes.com/2017/10/04/technology/driverless-cars-testing.html)

Le blog de Voyage.

Talks :

--- Guillaume Barat, NVIDIA, NVIDIA updates (https://www.nvidia.com/en-us/gtc/topics/deep-learning-and-ai/) - How to accelerate AI ?

NVIDIA will come back on GTC annoucements (GPU Technology Conference) and how to accelerate AI workload.

--- Pierre Gutierrez, Scortex.io (http://scortex.io), Automating quality visual inspection using deep learning

Driven by Industry 4.0, Scortex deploys artificial intelligence at the heart of factories.

We offer a smart visual inspection solution for quality control. Scortex turnkey platform enables manufacturing companies to automate their most complex inspection tasks.

In this talk, we’ll share Scortex experience on computer vision for visual inspection in factory environment. We will explain what are our current challenges and how we plan to solve them.

Then, on a real use case example, we will discuss how we generate data through our own acquisition system and what are the advantages and drawbacks of this from the machine learning point of view. We will also discuss our labelling process as well as the leads we have to reduce the labelling efforts on our side.

--- Rodolfo Rosini , zeroth.ai (http://zeroth.ai/), we fund the stuff that nobody else will

Talk about the AI investments we do at Zeroth

-- Wenjie Zheng, Learning Low-rank Matrices Distributedly without Factorization

Learning low-rank matrices is a problem of great importance in statistics, machine learning, computer vision and recommender systems.

Because of its NP-hard nature, a principled approach is to solve its tightest convex relaxation: trace norm minimization.

Among various algorithms capable of solving this optimization is the Frank-Wolfe method, which is particularly suitable for high-dimensional matrices.

In preparation for the usage of distributed infrastructures to further accelerate the computation, this study aims at exploring the possibility of executing the Frank- Wolfe algorithm in a star network with the Bulk Synchronous Parallel (BSP) model and investigating its efficiency both theoretically and empirically.

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