Delitio> If you are only allowed to store data for x month that's it.
Exactly. I'm not aware of any laws saying "you must delete this data immediately". More like "within X days or months". The permanently delete thing presumably skips some cooling-off period in the online database but not the backup, which seems perfectly appropriate, provided your backup retention is compliant.
Google has a nice page describing out their deletion process. [1] It doesn't go into product-specific technical details/steps (like marked as deleted within the product, row deleted from Bigtable/Spanner, major compaction guaranteed to happen, backups guaranteed to be deleted or unusable) but it says this:
Google> We then begin a process designed to safely and completely delete the data from our storage systems. Safe deletion is important to protect our users and customers from accidental data loss. Complete deletion of data from our servers is equally important for users’ peace of mind. This process generally takes around 2 months from the time of deletion. This often includes up to a month-long recovery period in case the data was removed unintentionally.
This is a best practice.
Delitio> It's your job to use technics which allow you to do this like using encryption on your backup and deleting the keys for it, for example.
If they'd thrown away the encryption key immediately, this would have been much worse. Instead of "we're down for 2 weeks?!?" (already quite bad) it'd be "our data is gone forever?!?". You never want to delete anything too quickly for exactly this reason.
The analog pictures are reasonably contrasty, but they do preserve highlights in a very nice way.
If you look at the top picture in the post then I'd have more dynamic range with my DSLR for most of that picture, but the parts in direct sunlight would be completely clipped instead.
Is there any source which explains what billion of parameters actually are?
In my mind a parameter is: language, dialect, perhaps context parameters (food, dinner, lunch, travel) and if we than talk about language and audio perhaps sound waves, gender.
Or are context parameters which gives you insight? Like a billion of parameters are literally something like travel=false, travel-europe=true people speaking=e, age, height,
Parameters are just floating point numbers, at most they can be seen as degrees of freedom or kind of like the order of a polynomial used in curve fitting.
They're too abstract to assign much meaning to individual parameters, as our understanding of why their values are exactly the way they are is extremely limited.
A parameter is a "weight" in this case (the lines drawn from neuron to neuron). The neurons are effectively runtime values or "activations." Parameters (weights) are updated during training and then set as constant during "inference" (also called "prediction").
There's unfortunately a ton of jargon and different groups use different words almost exclusively.
A parameter is a scalar value, most of which are in the attention matrices and feedforward matrices, you also hear these called “weights”. Any intro to DL course will cover these in detail. I recommend started with Andrew Ng’s Coursera class on Intro to Machine Learning, although there may be better ones out there now.
