recentpopularlog in


« earlier   
Natural honey is key cupboard item in my home. Now thanks to I'll only be buying the real stuff. R…
MachineLearning  from twitter
just now by jhill5
How 20th Century Fox uses machine learning to predict a movie audience • Google Cloud blog
<p>Understanding the market segmentation of the movie-going public is a core function of movie studios. Over the years, studios have invested in high-level data processes to try to map out customer segments, and to make predictions for future films. However, to date, granular predictions at the segment level, not to mention at the customer level, have remained elusive because of technological and institutional barriers.  

Miguel and his team have been able to lift some of those barriers by working with partners like Google Cloud. Together, we’ve built privacy-robust data partnerships to better understand moviegoers, and have developed in-house deep learning models that train on granular customer data and movie scripts to identify the basic patterns in audiences’ preferences for different types of films. In the span of 18 months, these models have become routine considerations for important business decisions, and provide one of their most objective, data-driven, and effective barometers to evaluate the tone of a movie, its affinity with core and stretch audiences, and its potential financial performance.

When it comes to movies, analyzing text taken from a script is limiting because it only provides a skeleton of the story, without any of the additional dynamism that can entice an audience to see a movie. The team wondered if there was some way to use modern, advanced computer vision to study movie trailers, which remain the single most central element of a movie’s entire marketing campaign. The trailer release for a new movie is a highly anticipated event that can help predict future success, so it behoves the business to ensure the trailer is hitting the right notes with moviegoers. To achieve this goal, the 20th Century Fox data science team partnered with Google’s Advanced Solutions Lab to create Merlin Video, a computer vision tool that learns dense representations of movie trailers to help predict a specific trailer’s future moviegoing audience.</p>

This is entirely predictable, though also slightly weird. However, notice what happens: the right-hand column is what it forecast, the left-hand what happened. The ones to pay attention to are the unexpected, grey ones - particularly Deadpool. <br /><img src="" width="100%" />
machinelearning  trailer  google  audience  film 
7 hours ago by charlesarthur
Which face is real?
Jevin West and Carl Bergstrom at the University of Washington:
<p>while we’ve learned to distrust user names and text more generally, pictures are different. You can't synthesize a picture out of nothing, we assume; a picture had to be of someone. Sure a scammer could appropriate someone else’s picture, but doing so is a risky strategy in a world with google reverse search and so forth. So we tend to trust pictures. A business profile with a picture obviously belongs to someone. A match on a dating site may turn out to be 10 pounds heavier or 10 years older than when a picture was taken, but if there’s a picture, the person obviously exists.

No longer. New adverserial machine learning algorithms allow people to rapidly generate synthetic 'photographs' of people who have never existed.

Computers are good, but your visual processing systems are even better. If you know what to look for, you can spot these fakes at a single glance — at least for the time being. The hardware and software used to generate them will continue to improve, and it may be only a few years until humans fall behind in the arms race between forgery and detection.

Our aim is to make you aware of the ease with which digital identities can be faked, and to help you spot these fakes at a single glance.</p>

So now we're using humans as the adversarial network (which calls out the generative network).
Machinelearning  gan  adversarial  internet 
7 hours ago by charlesarthur
This Cat Does Not Exist • TCDNE
It's thispersondoesnotexist but for cats. Though the adversarial network (the yin to the yang of the generative network, which creates the non-existent cat) needs some work; it still lets too many obviously non-cats through.
Machinelearning  artificialintelligence  cats 
7 hours ago by charlesarthur
DataHaskell is an open-source organization devoted to enabling reliable and reproducible data science and machine learning by leveraging the Haskell programming language.
haskell  datascience  machinelearning 
11 hours ago by ianchanning
Gradient Boosting explained [demonstration]
Provides a visualization of gradient boosting algorithms.
machinelearning  visualization 
11 hours ago by peterb
[1902.04114] Using Embeddings to Correct for Unobserved Confounding
"We consider causal inference in the presence of unobserved confounding. In particular, we study the case where a proxy is available for the confounder but the proxy has non-iid structure. As one example, the link structure of a social network carries information about its members."
victor-veitch  arxiv  research-article  machinelearning  confounding  causal-learning  causality 
15 hours ago by arthegall

Copy this bookmark:

to read