Hey @spacy_io people (@honnibal, @_inesmontani), those speed comparisons on https://t.co/0s2zkJxrRj are not only outdated—as you note—but the speed for the Stanford Tokenizer is just way wrong. Time to take them down? Here are our measurements: https://t.co/XAoVAFUAAJ #NLProc
— Christopher Manning (@chrmanning) July 1, 2018
In the ML research field there have been efforts to follow OSS model and attract people outside of academic institutions to participate. For example, in "AI-ON" there's serious projects there proposed by Google Brain, MILA, etc., looking for collaboration: https://t.co/TxriH0nwXd
— hardmaru (@hardmaru) July 1, 2018
Automated essay grading that takes into account factual accuracy and content coherence is several years away. In the meantime, NLP and AI researchers not paid by Pearson should push back against school systems that rely on this deeply flawed technology for standardized testing. https://t.co/cJnUZRRGwR
— James Bradbury (@jekbradbury) July 1, 2018
Here’s what one #statistician at @datacamp loves about #statistics https://t.co/PABDct0F5x @drob pic.twitter.com/PRx7EJgTTA
— This is Statistics (@ThisisStats) July 1, 2018
This is not how AI works, or how writing works, or how education works. https://t.co/KBPnzkRTpU
— Ethan Plaut (@ethanplaut) July 1, 2018
Brush more often, you dirtbag.
— Cathy O'Neil (@mathbabedotorg) June 30, 2018
I Am the Algorithm https://t.co/R7lOet4d5n
Tweeting, therefore, has the potential to disseminate scientific information widely.
— Richard (@RichardSocher) June 30, 2018
[...] encourage scientists to invest in building a social media presence.
👍
Beyond a threshold of ∼1000 followers, the range of follower types became more diverse.
Fromhttps://t.co/33DvzMuRF4 pic.twitter.com/bdWXAq1iOC
Whenever I say (paraphrased) "Twitter still houses the best community for #MachineLearning even though that theoretically and practically makes no sense", this is exactly what I mean. Ever more direct connections between those working in, reporting on, or learning about a field. https://t.co/2E9wQhnYkX
— Smerity (@Smerity) June 29, 2018
Attention model (via @PHDcomics ) pic.twitter.com/lEdfJh7xEd
— Delip Rao (@deliprao) June 29, 2018
Great new study about science outreach via Twitter: Initially, scientists mostly tweet to each other. But after accumulating about 1000 followers, scientists reach an increasing number of journalists, policy makers, and other members of the public.https://t.co/35sZgGfkgv pic.twitter.com/S8ybUQcdiA
— Jason Sheltzer (@JSheltzer) June 29, 2018
Ablation studies are crucial for deep learning research -- can't stress this enough.
— François Chollet (@fchollet) June 29, 2018
Understanding causality in your system is the most straightforward way to generate reliable knowledge (the goal of any research). And ablation is a very low-effort way to look into causality.
All models are wrong but some scientists share the code for their models and that makes them useful (the scientists, I mean)
— David Nicholson (@nicholdav) June 29, 2018
