Just wondering if there are some typical optimization problems or specific challenges that are fundamental to machine learning, i.e. something many or most frameworks should be capable of solving? For example, I'd like to try optimizing the coefficients of an ANN, and would love to have some basic, fundamental input/output problems to solve.

Ever since God created man, the Almighty has assured us of our sovereignty over the planet and control over all of its creatures. It is for this reason we fear the idea of aliens who may be more intellectually advanced than us or the emergence of intelligent machines that might think independently. Our fear of the appearance of those who surpass our intelligence is evident from the science fiction and futuristic films that speak about this subject (such as Terminator or Star Wars films). However, this fear of the emergence of entities that may surpass our intelligence has not stopped people from continuing to make machines that think for themselves. In America and Japan in particular, research is rapidly advancing towards this goal.

LG has just revealed that it will be showcasing its artificial intelligence solution for its upcoming high-end TVs at CES 2018 next week. The South Korean electronics company is also showing off its advanced image processor at the Las Vegas exhibition. On Wednesday, LG took to its online newsroom to announce its ThinQ AI and its new image processor for its upgraded OLED and SUPER UHD TVs. The main goal behind the first invention is to make it easier for consumers to interact and remotely control their televisions. On the other hand, the latter is designed to ensure that the picture quality and viewing experience of the new television screens will be unrivalled in the industry.

Many, especially science fiction writers, play somewhere in the middle. They write about technologies that don't yet exist. Or they assume the possibilities of technologies far ahead of their time. Melbourne, AUSTRALIA: Kellie Shaw (R) inspects one of Leonardo da Vinci's most famous designs which is recognised as the ancestor to the modern helicopter and is part of an exhibition featuring 50 models of 15th century inventions by Da Vinci, in Melbourne 04 July 2006. This craft is made of linen, reeds and iron thread and would have been operated by four men rotating a shaft. The exhibition focuses on four themes: mechanical, military, hydraulic and flying machines with each model built according to Da Vinci's drawing and are crafted from materials available in 15th century Italy.

The woman in the photo seems familiar. She looks like Jennifer Aniston, the "Friends" actress, or Selena Gomez, the child star turned pop singer. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. She was created by a machine. The image is one of the faux celebrity photos generated by software under development at Nvidia, the big-name computer chip maker that is investing heavily in research involving artificial intelligence. At a lab in Finland, a small team of Nvidia researchers recently built a system that can analyze thousands of (real) celebrity snapshots, recognize common patterns, and create new images that look much the same -- but are still a little different.

If you missed that thread and there's something you want to mention, post it and I'll put it up. Lots of categories didn't have an entry. You can also make a category yourself. "and we all realized what a pain in the ass Tensorflow was and how it didn't need to be that way. In the academic community, it certainly to me feels like pytorch has become the dominant framework (probably not backed up by actual stats... But my school's CV research lab has certainly switched over)" I might have nominated distill.pub

Yes, to be fair a lot of the points are valid, but they come from a wishy-washy, non-technical angle (one could argue this is because of his intended audience, but I submit that part of this is because of his lack of ML knowledge). And they are already well-known.

The iconic line from Star Wars, in which Luke Skywalker discovers the real identity of Darth Vader, marks the point in the series when two polar opposites that had been cleanly divided--the Jedi and the Dark Side--are suddenly mixed together in the most personal of ways. This ambiguity of opposition, which is part of what makes the series so compelling, is well known. There is another example of this opposition, though, that is easier to overlook: Star Wars both humanizes machines, so that we can like them, and dehumanizes them, so we can accept their slaughter. By the conclusion of the series, we feel a genuine warmth for the droid characters, R2-D2, C-3PO, and BB-8 and a concern for their safety. But why, when we know they are just machines? Part of the answer is that R2-D2, BB-8, and C-3PO certainly act as though they have a wide range of feelings.

FiveThirtyEight pitted 50 movies against 12 new ways of measuring Hollywood's gender imbalance The Bechdel-Wallace Test -- more commonly abbreviated to the Bechdel Test -- asks two simple questions of a movie: Does it have at least two named female characters? And do those characters have at least one conversation that is not about a man? A surprising number of films fail the test. Although the test is punchy and has become pervasive, it doesn't address the core inequalities in Hollywood films. Alison Bechdel -- an acclaimed cartoonist who was awarded a MacArthur "genius" grant in 2014 and whose memoir was adapted into a Tony Award-winning musical -- in no way set out to solve Hollywood sexism when she wrote the test into a comic strip in the mid-1980s.