Stories are a fundamental human tool that we use to communicate thought. Creating a stories about a image is a difficult task that many struggle with. New machine-learning experiments are enabling us to generate stories based on the content of images. This experiment explores how to generate little romantic stories about images (incl. guest star Taylor Swift). neural-storytellerneural-storyteller
By Ross Goodwin In May 2015, Stanford PhD student Andrej Karpathy wrote a blog post entitled The Unreasonable Effectiveness of Recurrent Neural Networks and released a code repository called Char-RNN. Both received quite a lot of attention from the machine learning community in the months that followed, spurring commentary and a number of response posts from other researchers. I remember reading t
(definitely a hot take — let me know what you think!) A few hours ago I noticed that something was bugging me about the TensorFlow announcement: I mean, it’s yet another deep learning platform — and there are lots of them. Those in the know — such as very smart ML engineers at Google — point out that it’s particularly close in features and approach to theano. (FTR, I’m in that vast majority) In ot
Artificial intelligence is back. Whether in the dystopian portrayals of recent movies or the utopian singularities dreamed of in the tech world, the general agreement is that we are on the path to thinking machines. But as fun, twisted and thought-provoking as the dystopian show Black Mirror is, I don’t believe machines are going to think or achieve a human level of consciousness any time soon. I
I read technology articles quite often and see plenty of authors attempt to dissect or describe the teenage audience, especially in regards to social media. However, I have yet to see a teenager contribute their voice to this discussion. This is where I would like to provide my own humble opinion. For transparency, I am a 19-year-old male attending The University of Texas at Austin. I am extremely
As we’re on the cusp of using machine learning for rendering basically all kinds of consequential decisions about human beings in domains such as education, employment, advertising, health care and policing, it is important to understand why machine learning is not, by default, fair or just in any meaningful way. This runs counter to the widespread misbelief that algorithmic decisions tend to be f
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