lindaliukas + book4   263

The Winograd Schema Challenge
… The Winograd Schema Challenge!
book4 
january 2018 by lindaliukas
Twitter
"My son’s practice conversation with Siri is translating into more facility with actual humans. Yesterday I had the…
book4  from twitter_favs
january 2018 by lindaliukas
Twitter
I prefer seeing incredibly tiny neural nets do amazing things. Like a demoscene for deep learning.
book4  from twitter_favs
january 2018 by lindaliukas
Twitter
Xi Jinping reads “texts on understanding AI, AR, algorithms, and machine learning, including The Master Algorithm b…
book4  from twitter_favs
january 2018 by lindaliukas
Twitter
Well done summary by ! A central piece of technology is human, from human inspiration, to human inte…
AI  book4  from twitter_favs
december 2017 by lindaliukas
AI and Deep Learning in 2017 – A Year in Review – WildML
I wrote up a (not so brief) summary of AI developments that stood out to me in 2017
book4  from twitter_favs
december 2017 by lindaliukas
Twitter
What else could be improved with ML? Sorting? Compiler inlining? Garbage collection? Path finding? Fourier transfor…
book4  from twitter_favs
december 2017 by lindaliukas
What a Photobook Curated By a Computer Can Teach Us
“Their flaws are often of technical nature, show their political/racial/cultural biases, or are just the result of [people] using them wrong,” Schmitt told Hyperallergic. For instance, the aforementioned image of a big leaf, captioned as a Wii controller, is evidence of how software from Silicon Valley is engineered with biases.
book4 
december 2017 by lindaliukas
Twitter
Well, the neural network is learning… something…
book4  from twitter_favs
december 2017 by lindaliukas
Twitter
if this wasn't research it would be art: computer vision researchers learn about disappointment by watching "deal o…
book4  from twitter_favs
november 2017 by lindaliukas
Nature Machine – BLDGBLOG
“Just how deferential [autonomous vehicles] are toward wildlife will depend on human choices and ingenuity. For now,” she adds, “the heterogeneity and unpredictability of nature tends to confound the algorithms. In Australia, hopping kangaroos jumbled a self-driving Volvo’s ability to measure distance. In Boston, autonomous-vehicle sensors identified a flock of sea gulls as a single form rather than a collection of individual birds. Still, even the tiniest creatures could benefit. ‘The car could know: “O.K., this is a hot spot for frogs. It’s spring. It’s been raining. All the frogs will be moving across the road to find a mate,”’ Smith says. The vehicles could reroute to avoid flattening amphibians on that critical day.”
book4 
november 2017 by lindaliukas
« earlier      
per page:    204080120160

Copy this bookmark:



description:


tags: