The Conversational Intelligence Challenge

NIPS 2017 Live Competition

Recent advances in machine learning have sparked a renewed interest for dialogue systems in the research community. In addition to the growing real-world applications, the ability to converse is closely related to the overall goal of AI. This NIPS Live Competition aims to unify the community around the challenging task: building systems capable of intelligent conversations. Teams are expected to submit dialogue systems able to carry out intelligent and natural conversations about specific news articles with humans. At the final stage of the competition participants, as well as volunteers, will be randomly matched with a bot or a human to chat and evaluate answers of a peer. We expect the competition to have two major outcomes: (1) a measure of quality of state-of-the-art dialogue systems, and (2) an open-source dataset collected from evaluated dialogues.

Organizers

Mikhail Burtsev, Valentin Malykh, MIPT, Moscow
Ryan Lowe, McGill University, Montreal
Iulian Serban, Yoshua Bengio, University of Montreal, Montreal
Alexander Rudnicky, Alan W. Black, Carnegie Mellon University, Pittsburgh

http://convai.io

#nlp #qa #nips

Outdated, but still valuable blog to dive into deep learning:

http://deeplearning.net/tutorial/

#tutorial #wheretostart

Nothing special, just siberian mathematicians went down to streets on national Labour Day holiday.

Spotify + Emoji = ❤️

https://insights.spotify.com/es/2017/05/02/spotify-emoji-music/

#insights #emoji #spotify

Deep Learning Papers by task

https://github.com/sbrugman/deep-learning-papers

#deeplearning #list #github

Learning Deep Learning with Keras

http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

#keras

A Neural Representation of Sketch Drawings

https://arxiv.org/abs/1704.03477

#deeplearning #sketch

Using Deep Learning at Scale in Twitter’s Timelines



https://blog.twitter.com/2017/using-deep-learning-at-scale-in-twitter-s-timelines

Picasso: A free open-source visualizer for Convolutional Neural Networks:

https://medium.com/merantix/picasso-a-free-open-source-visualizer-for-cnns-d8ed3a35cfc5

#vizualization #deeplearning

Notes on effectively Using Matplotlib:

http://pbpython.com/effective-matplotlib.html

#python #vizualization

Data Science:
“Deep adversarial learning is finally ready 🚀 and will radically change the game”

https://medium.com/intuitionmachine/deep-adversarial-learning-is-finally-ready-and-will-radically-change-the-game-f0cfda7b91d3

Pix2code: Generating Code from a Graphical User Interface Screenshot. Front-end developer​s might start getting scared.

https://arxiv.org/abs/1705.07962

Microsoft released new version of High-Performance, Open-Source, Deep Learning Toolkit

https://news.developer.nvidia.com/microsoft-releases-new-version-of-high-performance-open-source-deep-learning-toolkit/

#microsoft #nvidia #news

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https://affinelayer.com/pixsrv/ stunning demo for turning your hand drawing pictures into actual photoes.

#CV #pix2pix #tensorflow

Russian Search Engine has opened sources of the CatBoost — library which claims to be replacement of Yandex’s famous MatrixNet. Researches claim that CatBoost results are comparable with XGboost.

https://techcrunch.com/2017/07/18/yandex-open-sources-catboost-a-gradient-boosting-machine-learning-librar/

#opensource #yandex #xgboost #catboost

How semantic changes over time