5) what (about present data-point and/or historical data) caused this failure mode?, 6) what else can I do to avoid (such) errors?, 7) how would the decision change if model had this additional piece of info, 8) how confident is the model in its decision?
— Mert Sabuncu (@mertrory) June 14, 2018
The different goals ppl have in mind when they say "to interpret a model". 1)What can I change to alter outcome?, 2)what part of this data-point influenced this decision (most)?, 3)which historical (training) data-points influenced this decision?, 4)show me typical failure modes
— Mert Sabuncu (@mertrory) June 14, 2018
Dog: Data scientist
— Randy Olson (@randal_olson) June 13, 2018
Food: Solution to #DataScience problem
Closed door: Exciting new #MachineLearning algorithm
Open door: New data source pic.twitter.com/aemSIlb0uO
What will Justin Timberlake look like in 20 years?
— News from Science (@NewsfromScience) June 13, 2018
Artificial intelligence is making artificial aging more accurate than ever: https://t.co/xXKbZqkX4V
I have the same exact conclusion. I saw again and again (at least at Airbnb), massive upfront investment in data engineering enables development of new ML applications down the line. Sometimes you only appreciate this after the fact. https://t.co/OWdtKXnh5X
— Robert Chang (@_rchang) June 13, 2018
.@Kubota_Yoko on the latest tool in China's surveillance arsenal: RFID chips installed in cars to help authorities track movements. Adding them will be mandatory by next year for new cars https://t.co/2w9dVlZtPc
— Te-Ping Chen (@tepingchen) June 13, 2018
Clickagy, a data harvesting firm that claims to track "behavioral data on 91% of online devices in the US", sells data to target people who "may have recently gotten into a car accident" or are suffering from other injuries.https://t.co/koMH6U1tdm /cc @BobbyAllyn pic.twitter.com/4wdAsdG5Tp
— Wolfie Christl (@WolfieChristl) June 12, 2018
a new breed of deep learning tools are so easy to use, even an incompetent like me can train his own AI https://t.co/9GZelFxjOP pic.twitter.com/7peberHsi4
— James Vincent (@jjvincent) June 12, 2018
Why Most Research Findings Are False, by J.Ioannidis will tell you why
— Fermat's Library (@fermatslibrary) June 12, 2018
false positive findings, non-reproducible research and biased research plague academia and represent the majority of published research.
PAPER: https://t.co/4Usa1KpmjM
Here are Dr. Ioannidis's 6 corollaries pic.twitter.com/U89rHcB4CS
Nice thread debating between academic concise style of writing, or a longer, reader-friendly scientific writing style. https://t.co/uflBCPmosP
— hardmaru (@hardmaru) June 12, 2018
my thoughts on the trouble with #d3js https://t.co/1JfLFHl1Lt
— Ian Johnson (@enjalot) June 12, 2018
to me it's about setting our expectations for learning, and guiding folks on different paths in different ways pic.twitter.com/pZ67Krs0CX
People often ask me how they can create some open source, and the only answer–as far as I’m concerned–is to make something you *need*. You make software every day, there is something missing, some library, some tool, make it encapsulated, make it generally useful and release it.
— Max Howell (@mxcl) June 12, 2018
