I have a problem with having to prove every point with data while ignoring countless stories. Qualitative data is data too, and picks up nuances that quantitative data misses. https://t.co/gPH35FQphd
— Rumman Chowdhury (@ruchowdh) May 4, 2019
I have a problem with having to prove every point with data while ignoring countless stories. Qualitative data is data too, and picks up nuances that quantitative data misses. https://t.co/gPH35FQphd
— Rumman Chowdhury (@ruchowdh) May 4, 2019
Collecting data is easy??? Am I missing something? Maybe the data that you already have coming to you, but getting the right data to solve your DS problem is perhaps the hardest part of the process. https://t.co/4g8UUBmXd9
— Bojan Tunguz (@tunguz) April 30, 2019
So much of being a successful employee revolves around effective communication. If you're rude to someone you're trying to get to hire you, how are you going to treat colleagues who you may have power over?
— jacobian (@jacobian) April 29, 2019
Most common lies before ML or analysis projects:
— Christoph Molnar (@ChristophMolnar) April 27, 2019
“We have perfect data and you will get access soon.”
“soon” = years
“perfect” = huge struggles to understand, clean, reshape the data that were collected for a completely different purpose
I'm going mostly off the video, where you type in e.g. "javascript" and then it generates an on-brand acceptable-language sentence you can use to express your desire for javascript.
— Joel Grus ☕ (@joelgrus) April 26, 2019
As described it's not so evil, but it's easy to see how it could be used for evil.
Facebook makes $15 billion per quarter in revenue. We keep fining Facebook on the order of $2 billion for their mistakes. It's the wrong scale. https://t.co/ckqfR1giq0
— Cathy O'Neil (@mathbabedotorg) April 26, 2019
This is such an extreme outlier I'm ready to believe that a nation state actor may have an expert AI team* reverse engineering and gaming social graphs and recommendation algorithms at scale silently.
— Smerity (@Smerity) April 26, 2019
* Asymmetric black hat research (see below) and/or StuxNet level competent
1/N https://t.co/M8SCXgtYbS
New blog post: A lot of people are saying that the answer to the Facebook problem is to quit Facebook. But in today’s world, opting out of digital life is truly a luxury. https://t.co/sOQhYlaeEr
— Vicki Boykis (@vboykis) April 25, 2019
This. Even in data science so many interviews are biased towards CS undergrad level knowledge. https://t.co/osgtLq6mBp
— Chris Albon (@chrisalbon) April 24, 2019
The longer history of technology suggests that whatever is today thought of as the big "race" which everyone should try and "win" has a decent chance of seeming, in retrospect, a major waste of time and resources
— Tim Wu (@superwuster) April 24, 2019
This story, along with a lot of the comments, show the same thing that has plagued the software industry for over two decades: programmers thinking they're smarter than everyone else and can learn entire industries or bring "insights" overnight.
— Anthony DeStefano (@adx) April 19, 2019
I haven't looked at the report, but I'm fairly confident the people doing the redacting would have been careful to do it in such a way that the redacted info furthermore is not predictable.
— Emily M. Bender (@emilymbender) April 19, 2019
(And, ahem, that the black-out can't just be deleted...) /7