I know nobody reads the articles but what about reading the title?
130 karma · joined September 12, 2012
It is still just connecting blocks though as pierrec says below so it can take a lot of connections to get something done
So when you request the large file, the CDN (cache) doesn’t have it, so it immediately passes through to the original source. The details of how this is implemented don’t really matter in the sense that they are not going to host the large file(s) in the CDN, so they will always fall through.
Their decline and fall were the direct result of their human problems. And, briefly, their human problems stemmed from their human problems.
Culture and management matter.
~~ Charles Ponzi
If the financial motivation is there, companies will build custom hardware. Companies like Apple and Google (TPUs) are building custom processors for various purposes.
For example, an x-ray computed tomography (CT) image volume in 3D may be projected into various 2D synthetic planar x-ray projections (digitally reconstructed radiographs).
There are countless situations where projections are very useful.
Given how many iOS and Mac devices (many Macs have an ARM processor for things like the touchbar) Apple ships, they’re already one of the larger custom high-end ARM licensees. It will be interesting to see how it goes for them.
Right now it is mostly laws that protect categories of people that keep insurance companies from charging people more.
What’s the plan here, use machine learning in a “hands off” way with a black box algorithm to apply pricing discrimination in a way that a human could not because of regulation?
Of course, we still have people working on our stuff. If it were some abandoned hardware product I could see this being a disaster.
If you’d like to have a smooth looking censored image you can then blur the mosaic result to have a smooth transition between the censored and original image.
If you simply blur or simply downsample there’s a significant ability to recover data or iterate over data to recover likely inputs. Other posts have discussed deconvolution, but think of a downsample as a hash - you can build a rainbow table of inputs, easily for numbers, with more difficulty for faces. If you have a limited pool of “suspects” this technique can work well. Just as with hashing, you should add a salt to the image before downsampling or blurring to make recovery of the original input more difficult. In this case the “salt” is random noise.
DICOM has its issues, but it’s much better than HL7.
Unfortunately it is likely too late to really adopt the metric system for financial transactions. But the K has crept in for thousands.