As finding 10x engineers is hard, my policy would be to hire two 24/7 engineers. https://t.co/kcDnBtPrxj
— Ferenc Huszár🇪🇺 (@fhuszar) July 13, 2019
As finding 10x engineers is hard, my policy would be to hire two 24/7 engineers. https://t.co/kcDnBtPrxj
— Ferenc Huszár🇪🇺 (@fhuszar) July 13, 2019
Also this really good summary of what you can and can't do with logistic: https://t.co/iGCWxDlT5B
— Brendan Halpin (@BrendanTHalpin) July 13, 2019
Relevant? From @CookieSci https://t.co/HG43Liw82c
— Oscar Olvera (@oscar_olvera100) July 13, 2019
Many companies outside the software industry are still in the early phases of adopting AI. Landing AI’s @DongyanWang8 talks about selecting the right initial projects, and avoiding an initial fumble that saps momentum. @venturebeat https://t.co/SU6wIvTsgf
— Andrew Ng (@AndrewYNg) July 12, 2019
“The non-ML way to do it is to build up an understanding of the material from the ground up that is solid enough to solve any problem thrown at you. The ML way to do it would be to gather up old tests that the professor gave in previous years and use those to study for the test.” https://t.co/nMpexu6hs7
— hardmaru (@hardmaru) July 12, 2019
14% = Undesirably high response rate #GDPR “He contacted around 150 companies, requesting her data via a fake email account in her name. 83 of the firms had her data, and roughly a quarter of those provided it to him, no questions asked.” @JoeUchill @Axios https://t.co/WVLevwJM9J
— Ben Lorica 罗瑞卡 (@bigdata) July 12, 2019
9. 10x engineers are poor mentors as they can't teach others on what to do OR parcel the work. They always think "It takes too long to teach or discuss with others, I would rather do it myself." They are also poor interviewers.
— Shekhar Kirani (@skirani) July 11, 2019
Almost forgot to post this, but here are the slides from our closing talk at #spaCyIRL. Featuring the history of @spacy_io, the roadmap for v3, how we make money and a bonus pic of @honnibal with a cat on a leash 🐈 https://t.co/bfwbjDluLI
— Ines Montani 〰️ (@_inesmontani) July 11, 2019
Our newest policy research analyzes the game theory of industry cooperation on AI safety. We found 4 strategies the AI community can use today to enable long-term cooperation: https://t.co/AxMBQwORec pic.twitter.com/VO8NDVeJKJ
— OpenAI (@OpenAI) July 10, 2019
An AI program developed by Alibaba has notched up a record-high score on a reading comprehension test. It is not, however, “better at reading comprehension than humans.” At least not yet. 😬 https://t.co/NUm1abdVW3
— MIT Technology Review (@techreview) July 9, 2019
Drop Python wheels... @rapidsai did for this exact reason https://t.co/lh0SNGO0VB I wish others would join in... while having a serious conversation about how to improve wheels.
— Joshua Patterson (@datametrician) July 8, 2019
‘Social signals are in fact a factor when reviewing a pull request, even when programmers don’t think they are.’
— Mara Averick (@dataandme) July 8, 2019
"How Programmers REALLY Look at Pull Requests" by @denaefordrobin https://t.co/5wyPzqzsvI pic.twitter.com/VZ5F1CQrmm