What Data Scientists Really Do by @hugobowne https://t.co/7ruK2RpBmQ
— Hilary Mason (@hmason) August 15, 2018
What Data Scientists Really Do by @hugobowne https://t.co/7ruK2RpBmQ
— Hilary Mason (@hmason) August 15, 2018
Where will our machines take us? My Scientific American article on the future of AI: https://t.co/jQ36Da8uHz
— Pedro Domingos (@pmddomingos) August 15, 2018
Playing with text-to-image generation is really interesting. I made a small app to generate images in real time while you type. It's like having a visual translator.
— Cris Valenzuela (@c_valenzuelab) August 15, 2018
Next step, port it to @runwayml
I'll add more videos in this thread. Some results are very surprising. pic.twitter.com/VO7Rk1t2OL
This 👇. Sadly, trying to reproduce machine learning results from a PDF is kinda like trying to reproduce an extravagant dish from its Instagram photo. Sharing code and data, and starting from that, are critical! https://t.co/Yv8KWsWoKp
— Ben Hamner (@benhamner) August 15, 2018
Reflections on their first year of running a new kind of scientific journal: “We have learned a lot of valuable lessons in our first year, but we still have a lot of room to grow.” @distillpub https://t.co/jEB84ldDwo
— hardmaru (@hardmaru) August 14, 2018
I generally suggest the opposite. Work from a high level view of the paper, and do lots of experiments as you go and use common sense. When you're all done, compare to their implementation. I often find I come up with better ways, which I wouldn't if I were copying closely
— Jeremy Howard (@jeremyphoward) August 14, 2018
.@tanyacash21 digs up the first known job listing for a Data Scientist, from 2008.
— David Robinson (@drob) August 14, 2018
“No specific technical skills are required.”
Times have changed! pic.twitter.com/zYDiHSH4xy
Good overview of the benefits and limitations of FPGAs (field-programmable gate arrays) compared to CPUs and GPUs for high-performance, low-latency computations https://t.co/o1VmeNiKfO (via HN)
— David Smith (@revodavid) August 14, 2018
Not so much in a "they're human after all" kind of way, more like "if you could do *anything*, how would you spend your time? Ranting on Twitter, apparently"
— François Chollet (@fchollet) August 14, 2018
“A PhD trains someone to solve difficult problems, but doesn’t train them to decide which problems are worth solving.”
— David Robinson (@drob) August 14, 2018
.@quaesita kicks off the Data Science Leaders East Coast Network pic.twitter.com/9NzIPsFlgQ
things you read about on ML-arxiv these days
— hardmaru (@hardmaru) August 14, 2018
For me, one of the most interesting insights to be gained from Twitter is how people who are fabulously rich and successful can apparently waste their time being angry at the news and making fools of themselves on Twitter, just like the rest of us
— François Chollet (@fchollet) August 14, 2018