Day 46: A Whole New Level of Production Quality

Post Edited: On this 4th of July, let’s declare independence from “95%” https://t.co/S4i4WkphiP

— Andrew Gelman (@StatModeling) July 5, 2018

Welp, using adversarial examples to force neural networks to do free computation for you is definitely the most cyberpunk thing I’ve read this week so far. https://t.co/ziP4ZdKxjw pic.twitter.com/kcTZV6eKdl

— Will Wilson 🇭🇰🗿 (@WAWilsonIV) July 3, 2018

ALL M4 COMPETITION DATA HAS BEEN RELEASED

The link to download ALL M4 data (both test and forecasts) from the M4 site ishttps://t.co/knrV5t8sNk

The link to download ALL data for those using R ishttps://t.co/G2wRu275nk

Have fun researching/playing
(@robjhyndman)

— Spyros Makridakis (@spyrosmakrid) July 4, 2018

Our latest work demonstrates how agents achieve human-level performance in a complex first-person multiplayer game, and can even collaborate with human teammates! https://t.co/0FEKQIVIi9 pic.twitter.com/UujmnrBXUz

— Demis Hassabis (@demishassabis) July 3, 2018

The one thing I found most fascinating reading this blog post is this: there were 6 people working on visualizations. Six, designers and web developers, on visualizations - at least this is my understanding. They are arguably extremely nicely done visuals indeed. https://t.co/3bJwwrhlVB

— Ferenc Huszár (@fhuszar) July 4, 2018

Just had time to catch up on the DeepMind CTF (https://t.co/OBGtibIBRP) post/paper. I don’t know what to say. This is a whole new level of production quality with these visualizations and videos. When do we start getting full-length theater style movies with research papers?

— Denny Britz (@dennybritz) July 5, 2018

Adversarial examples for the GRE https://t.co/Sx8h7xTPtr

— Ian Goodfellow (@goodfellow_ian) July 4, 2018

Here's a good way to visualize factorial growth: imagine a cell that divides in 2, each child cell will then divide in 3, each grandchild cell will divide in 4 and and so on. pic.twitter.com/HQ0L1SrNWc

— Fermat's Library (@fermatslibrary) July 4, 2018

New blog post: Does batting order matter in @MLB? A simulation approach.#Python #DataScience #baseballhttps://t.co/mK9V6Cyh8B pic.twitter.com/YkAIoDbUjM

— Randy Olson (@randal_olson) July 4, 2018

I think election polling is for now the ideal application case for a value-suppressing uncertainty palette. @lenkiefer @jeffrey_heer pic.twitter.com/vyEZT19aHO

— Claus Wilke (@ClausWilke) July 4, 2018

@ClausWilke @lenkiefer free time on the 4th so I added some interactivity to the vsup election poll example#rstats #d3jshttps://t.co/vTM2DPn7Yu pic.twitter.com/buu2WEyELC

— timelyportfolio (@timelyportfolio) July 4, 2018

Check out @stephaniejyee and @tonyhschu's stunning visual guide explaining the tradeoff between bias and variance in model tuning https://t.co/fHGdY9k6Id

— Kaggle (@kaggle) July 4, 2018

✨ viz *and* interactive:
"An animated guide to Frequency Trails (aka Joyplots)" 👨‍🎤 @luiscarlihttps://t.co/YhRGbolwG2 #dataviz #infovis pic.twitter.com/cxnt7CZuUn

— Mara Averick (@dataandme) July 4, 2018

Second #blogdown post in my #BayesianBabySteps series - intro to using Laplace approximation to model #NFL score differentials, determining the value of passing efficiency relative to rushing with EPA from #nflscrapR and the effect of strong priors #rstats https://t.co/98cTHnKH9z pic.twitter.com/33ZulJdC4o

— Ron Yurko (@Stat_Ron) July 4, 2018

Interested in Biomedical Image Analysis? Check out this article introducing DLTK (The Deep Learning Toolkit for Medical Imaging), built on top of @TensorFlow by @m_rajchl and team.

Learn more here → https://t.co/x9ZgbFK7LH pic.twitter.com/DP0Esf9Jhg

— TensorFlow (@TensorFlow) July 4, 2018

#XGBoost is faster than ever, with better scaling, on #GPU thanks to the hard work of @nvidia & @h2oai! Check out the latest paper https://t.co/P2m31idljB, and more is coming very soon! #lightgbm #catboost #GBDT

— Joshua Patterson (@datametrician) July 3, 2018

ggplot2 3.0.0 now on CRAN — https://t.co/m8fSDaaSbu 🎉 Headline features are tidy eval, sf support, position_dodge2(), & viridis. Huge thanks to all 300+ contributors to this release, and particularly to @ClausWilke, @kara_woo, @_lionelhenry and @thomasp85 pic.twitter.com/igb9k0Z89C

— Hadley Wickham (@hadleywickham) July 4, 2018

Stop spilling our secrets!! Who got to you, Hilary! https://t.co/8TRPyVR1h6

— Chris Albon (@chrisalbon) July 5, 2018

Successfully training neural networks requires almost no math skills, but does require knowing a large number of otherwise useless tricks. https://t.co/3iQGYBHYCB

— David Sussillo ☝️🤓 (@SussilloDavid) July 3, 2018

‘China’s Google’ releases its first AI chip https://t.co/qqFso6f6vG

— MIT Tech Review (@techreview) July 4, 2018

i feel like all the recent papers on image-to-image translation are just polishing pix2pix

— helena sarin (@glagolista) July 5, 2018

China's Top 100 AI companies (pre-IPO), ranked by estimated operating income in 2018, per Yiou Intelligence report:
1st tier: 1-2b RMB (Face++, Cambricon, etc.)
2nd tier: 500-800m (出门问问, Terminus, etc.)
3rd tier: 300-400m (Mininglamp, Linkdoc, etc.)
< 200m RMB: ~70 others pic.twitter.com/5Lm3VyrC18

— Jeffrey Ding (@jjding99) July 4, 2018

They would have gone to the show but they had a prior engagement?

— Jeff Dean (@JeffDean) July 5, 2018

Sharing my national AI policy and strategies database: https://t.co/L3bNUfKZdS comes with a drop down of individual countries and a general mapping of the ecosystem (not exhaustive). #AIPolicy

— Charlotte Stix (@charlotte_stix) July 4, 2018

Algorithm matches human cardiologists in detecting heart attacks https://t.co/RMJzZAba6m

— MIT Tech Review (@techreview) July 2, 2018

Interesting Failures of SOTA Object Detectors, by @AmirRosenfeld et al. pic.twitter.com/17ZMSq7YNP

— hardmaru (@hardmaru) July 4, 2018