If you are on the wait list for my upcoming talk at @intersect_to , but want to learn more about algorithmic colonialism, I've pulled together a short reading list that's accessible for a wide range of expertise: https://t.co/hlrkd5VIkI https://t.co/fONaSN22FK
— Rumman Chowdhury (@ruchowdh) August 20, 2019
Poor meeting AV has probably destroyed more shareholder value than actual poor decision making. No wonder Zoom is worth so much!
— Hilary Mason (@hmason) August 20, 2019
What is the value of science?
— Fermat's Library (@fermatslibrary) August 20, 2019
Richard Feynman struggled with this question after World War II. In this 1955 essay he presents what he believes to be the true value of science.
Learn more here: https://t.co/NA9CKJoOUY
A great paper for your summer ☀️reading list 📚 pic.twitter.com/R535eP377j
Documentation with examples you can play with live strikes me as a great idea. I’m a huge fan of learning through “toy models” and Binder is lowering the friction on toy models to nearly zero. https://t.co/3AeyxoyW6U
— JD Long (@CMastication) August 20, 2019
If it doesn't work for everyone, it doesn't actually work at all.@rajiinio pic.twitter.com/C0Ra0ZfvOe
— Rachel Thomas (@math_rachel) August 20, 2019
You don’t. You make money by being useful. AI is just an ingredient. People confuse the ingredient for the main course.
— Delip Rao (@deliprao) August 20, 2019
The results of machine learning algorithms are highly sensitive to:
— Rachel Thomas (@math_rachel) August 19, 2019
- the problem definition
- the choice of dataset used to train the model
- social context
so despite insinuations to the contrary, the model is not merely revealing some latent truth in the data. pic.twitter.com/eqnO6zyksu
In the USA, housing discrimination has a long and vile history. Vast housing discrimination continues today, albeit often in subtler form. A new HUD rule would effectively encourage discrimination by algorithm. @aselbst https://t.co/1AilO1y5AH pic.twitter.com/kbYHGVTyz4
— Rachel Thomas (@math_rachel) August 19, 2019
Biased algorithms often bypass current anti-discrimination law with impunity. HUD is trying to make it official for housing algorithms. https://t.co/2ZZ9ER4VQA
— Cathy O'Neil (@mathbabedotorg) August 19, 2019
Labeling, transforming, and structuring training data sets for machine learning ፨📻 a conversation with @ajratner of @HazyResearch @uwcse https://t.co/a6pMXIP8mx pic.twitter.com/m0Chqx55vj
— Ben Lorica 罗瑞卡 (@bigdata) August 18, 2019
If you’re at all concerned about the place of #rstats in the TIOBE index, you owe it to yourself to read their protocol: https://t.co/h6qtrHMDeW. You’ll find that they’re comparing the number of search hit for “R statistical programming” to (eg) “python programming” 🙄🙄🙄
— Hadley Wickham (@hadleywickham) August 17, 2019
Readings recommended by @tsunamino to understand the sociological phenomenon that is online harassment, what causes it, and tactics to dispel misinformation: https://t.co/YaCXQPbMHx
— Rachel Thomas (@math_rachel) August 16, 2019
