Artificial neural networks have been the model for "machine learning" for quite a while now. It is essentially just computer code inspired by the way the central nervous system works in animals (brains). For more information on artificial neural networks, you can go to: (https://en.wikipedia.org/wiki/Artificial_neural_network)
Well, Google recently created a "Deep Learning" algorithm based off research done with Artificial Neural Networks and "trained" it with huge databases of pictures of mammals, people, buildings, cars, etc. For example, the network is shown a thousand pictures of a duck and told repeatedly that this is a duck. When it learns the million things that make a duck distinctly a duck, they move on to other objects. Rinse, repeat and rinse, repeat. When a large database of information is established they can move on with the network and ask it to carry out different tasks with its newly-acquired information. To quote Google's blog here: (http://googleresearch.blogspot.ie/2015/06/inceptionism-going-deeper-into-neural.html)
"Instead of exactly prescribing which feature we want the network to amplify, we can also let the network make that decision. In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to enhance whatever it detected. Each layer of the network deals with features at a different level of abstraction, so the complexity of features we generate depends on which layer we choose to enhance. For example, lower layers tend to produce strokes or simple ornament-like patterns, because those layers are sensitive to basic features such as edges and their orientations. If we choose higher-level layers, which identify more sophisticated features in images, complex features or even whole objects tend to emerge. Again, we just start with an existing image and give it to our neural net. We ask the network: “Whatever you see there, I want more of it!” This creates a feedback loop: if a cloud looks a little bit like a bird, the network will make it look more like a bird. This in turn will make the network recognize the bird even more strongly on the next pass and so forth, until a highly detailed bird appears, seemingly out of nowhere."
Now recently, they decided to release the code, (http://googleresearch.blogspot.ie/2015/07/deepdream-code-example-for-visualizing.html) so people could see what the trained neural networks were seeing on any image that they wanted.
Inevitably, the internet has done awesome things with it so far, and this is what this post is about. This app (https://dreamscopeapp.com/create) created by a user on reddit allows users to upload any image they want, and run it through the "Deep Dream" algorithm. The results are creepy, awesome, trippy, and eerily resemble what MANY LSD users report when on Acid (seriously, they seem amazed by the similarity). I took the liberty to run a few pictures through the algorithm and posted the results. Feel free to do the same for your own pictures, or simply just discuss the topic at hand.
Are our brains on LSD essentially treating visual input exactly how this neural network perceives still images? Is this computer taking a picture and letting its imagination go wild? Similar to how a child looks at clouds in the sky? Are our brains actually just an advanced artificial intelligence?
I'm sure it will be a good time. Enjoy. Happy Off-Season.
Apparently the interwebs did not take kindly to Google trying to replicate Disney's clean-up of Times Square and they have relented on the no-sexybits rule.
MGoOverlords - any chance of a reprieve when celebration is called for?
- OR -Did I misunderstand Google's change?
(I'm not really this hard up for female content, but the board does seem to come to life for some beefcake / Kate Upton)
I figure, good that we have an online presence, broad of a representation of things as a "search" may be.
Tired of thinking about the inadequate leadership in our Athletic Department.
Quite OT, but I figured this would be of interest to a lot of readers since there seem to be a large number of EECS majors. Also, according to the latest surveys, everyone here is an Internet user.
Google and Verizon are reportedly close to having a deal to ensure Google traffic receives priority on Verizon networks. This is particularly disappointing to me since Google has been a key promoter of net neutrality but has apparently abandoned that approach and is now leading the way on taking advantage of a non-neutral future.
A lot of this is a result of a court ruling that the FCC, who tried to impose net neutrality, does not have jurisdiction over broadband Internet. So it wouldn't be surprising if this development pushed Congress to give the FCC that power, but if not it might move us in a direction where the idea of net neutrality is a thing of the past.
Google, of all companies, should be trustworthy to make U of M look great, right? Well, I'm sure this wasn't intentional, but they better get it cleaned up, quick!
I decided to check Google Maps to see if they had updated their satellite photos of Ann Arbor yet to include the stadium additions. They did, but the photos were taken when the field was stripped off! It's bare concrete. it looks terrible!
C'mon, Google! Wait for a new photo on the next satellite flyby. Edit the old field back in there. Something! Now Michigan stadium is gonna look like a construction zone for the next 5 years! They did it the last time, too! The last photo was up forever, when they had a section of seats all torn out to get a crane in there.
Just randomly discovered that if you do a Google search for "U", the University of Michigan homepage is the first result.
Eat it, 'Canes