In this blog post, I share how data science is like the Model T or the Boeing 747https://t.co/saWOcyTPjM pic.twitter.com/AliMXMTyiE
— David Robinson (@drob) June 25, 2018
In this blog post, I share how data science is like the Model T or the Boeing 747https://t.co/saWOcyTPjM pic.twitter.com/AliMXMTyiE
— David Robinson (@drob) June 25, 2018
Researching voices in the #AI + #ethics space. Who should I know about? Starting, emerging, unknown, known, experts, not-an-expert-but-interested, not-an-expert-but-really-you-are. Specifically seeking geographic + field of study diversity. Please RT. DM's open.
— amcasari (@amcasari) June 25, 2018
Every course in #statistics #biostatistics #datascience #machinelearning#rstats should BEGIN with the Quartz Guide to Bad Data by Chris Groskopf @onyxfish at least if your doing nonfiction. Data analysis is like painting a house: 90% is in the preparation https://t.co/yErss4ITUk pic.twitter.com/Gc6e1Jq8Aa
— Edward Tufte (@EdwardTufte) June 25, 2018
My thoughts on facial recognition & law enforcement. (Spoiler alert: 🚨 They don’t mix well) https://t.co/xRVwB603L8
— Brian Brackeen (@BrianBrackeen) June 25, 2018
The cool thing about 2018 is that if your company uses a linear regression at any time you are now a hot AI company somehow
— Austen Allred (@AustenAllred) June 25, 2018
Glad people are thinking and talking more about the risks of face recognition lately, but important to remember that it's only one of many emerging computer vision tools. Others with similar implications include action recognition, emotion recognition, video summarization, etc.
— Miles Brundage (@Miles_Brundage) June 24, 2018
The more econometrics I learn, the less I trust any given estimate that's not a simple difference in means from an experiment.
— Tatyana Deryugina (@TDeryugina) June 23, 2018
Dissolving the Fermi Paradox: why taking uncertainties into account makes the absence of aliens less weird, and why an empty sky doesn't foretell our doom. https://t.co/7C6g14Spbd Popular FAQ: https://t.co/3a6piKhAmi
— Anders Sandberg (@anderssandberg) June 23, 2018
Adobe is using machine learning to make it easier to spot Photoshopped images https://t.co/npnp5FLel0
— Nando de Freitas (@NandoDF) June 22, 2018
Deep learning is useful because it enables us to create programs that we could not otherwise code by hand. But the space of programs you can learn via deep learning models is a minuscule slice of the space of programs that we may be interested in.
— François Chollet (@fchollet) June 22, 2018
Everyone makes mistakes during data analysis. Literally everyone. The question is not what errors you make, it's what systems you put into place to prevent them from happening. Here are mine. [a thread because I'm sad to miss #SIPS2018]https://t.co/pOLfExrZoc
— Michael C. Frank (@mcxfrank) June 22, 2018
Who made this? 👏🏻...👏🏻...👏🏻 pic.twitter.com/TgTiYBpMMy
— Drew Conway (@drewconway) June 22, 2018