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Soccer On Your Tabletop
We present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device. At the heart of our paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games. We compare with state of the art body pose and depth estimation techniques, and show results on both synthetic ground truth benchmarks, and real YouTube soccer footage.
ar  machinelearning  computervision  mixedreality 
55 minutes ago by endquote
Ways to think about machine learning — Benedict Evans
I don't think, though, that we yet have a settled sense of quite what machine learning means - what it will mean for tech companies or for companies in the broader economy, how to think structurally about what new things it could enable, or what machine learning means for all the rest of us, and what important problems it might actually be able to solve. 
trend  machinelearning  ml 
1 hour ago by euler
Ways to think about machine learning — Benedict Evans
I think one could propose a whole list of unhelpful ways of talking about current developments in machine learning. For example:

Data is the new oil
Google and China (or Facebook, or Amazon, or BAT) have all the data
AI will take all the jobs
And, of course, saying AI itself.

More useful things to talk about, perhaps, might be:

Enabling technology layers
Relational databases.

Five years ago, if you gave a computer a pile of photos, it couldn’t do much more than sort them by size. A ten-year-old could sort them into men and women, a fifteen-year-old into cool and uncool and an intern could say ‘this one’s really interesting’. Today, with ML, the computer will match the ten-year-old and perhaps the fifteen-year-old. It might never get to the intern. But what would you do if you had a million fifteen-year-olds to look at your data? What calls would you listen to, what payments would you inspect?


No-one looked at SuccessFactors or Salesforce and said "that will never work because Oracle has all the database" - rather, this technology became an enabling layer that was part of everything.

So, this is a good grounding way to think about ML today - it’s a step change in what we can do with computers, and that will be part of many different products for many different companies. Eventually, pretty much everything will have ML somewhere inside and no-one will care.

Talking about ML does tend to be a hunt for metaphors, but I prefer the metaphor that this gives you infinite interns, or, perhaps, infinite ten year olds.
AI  machine_learning  machinelearning  thefuture  insights_analytics  data 
4 hours ago by JohnDrake
Amazon’s Clever Machines Are Moving From the Warehouse to Headquarters - Bloomberg
Amazon began automating retail team jobs several years ago. Under an initiative called “hands off the wheel,” the company shifted tasks like forecasting demand, ordering inventory and negotiating prices to algorithms, people familiar with the matter say. At first, humans could easily override the machine’s decisions. For instance, if a brand notified Amazon about an upcoming marketing blitz for a product, an Amazon manager could increase the order in anticipation of demand the algorithm didn’t expect. But such tinkering was increasingly discouraged as the machines proved their precision, the people say. Anyone overriding the machines had to justify their decision, and the push to automate made them reluctant.
machinelearning  statistics 
7 hours ago by miaridge
Can we open the black box of AI? : Nature News & Comment
“At some point, it’s like explaining Shakespeare to a dog.”
Great piece on the decades of work around understanding how the black box of AI learns to increase accountability & transparency:
AI  Article  DataScience  Inspiration  Reference  introduction  machinelearning 
7 hours ago by rasagy
Ways to think about machine learning — Benedict Evans
I spend quite a lot of time meeting big companies and talking about their technology needs, and they generally have some pretty clear low hanging fruit for machine learning. There are lots of obvious analysis and optimisation problems, and plenty of things that are clearly image recognition problems or audio analysis questions. Equally, the only reason we’re talking about autonomous cars and mixed reality is because machine learning (probably) enables them - ML offers a path for cars to work out what’s around them and what human drivers might be going to do, and offers mixed reality a way to work out what I should be seeing, if I’m looking though a pair of glasses that could show anything. But after we’ve talked about wrinkles in fabric or sentiment analysis in the call center, these companies tend to sit back and ask, ‘well, what else?’ What are the other things that this will enable, and what are the unknown unknowns that it will find? We’ve probably got ten to fifteen years before that starts getting boring. 
machinelearning  opportunity  innovation  automation  imagerecognition  relationaldatabases  review  BenedictEvans  2018 
yesterday by inspiral
Orange – Data Mining Fruitful & Fun
Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox.
machinelearning  visualization  tools 
yesterday by alssanro

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