So is it "on me?" Or is it just that the game just is high variance?
1,503 karma · joined April 2, 2010
So is it "on me?" Or is it just that the game just is high variance?
Just play blue, that's not a problem then.
Usually, the problem with developing a driver isn't "writing the code," it's "finding documentation for what the code should do."
Ctrl-S may or may not end up stopping the program, depending on how much it's printing, and how much output buffering there is before it blocks on writing more.
The width of the models is typically wide enough to "explore" many possible actions, score them, and let the sampler pick the next action based on the weights. (Whether a given trained parameter set will be any good at it, is a different question.)
The number of attention heads for the context is similarly quite high.
And, as a matter of mechanics, the core neuron formulation (dot product input and a non-linearity) excels at working with ranges.
This is similar to how different credit risks get assigned different interest rates.
Companies failing (or close enough to failing that they restructure and wipe out common) is not uncommon in start-ups. If you want a more or less sure thing, you'll have to work at a more or less sure employer, and the risk/reward will be different.
Now, whether those who exercised their Philz options and paid for the shares, were really aware in what they were doing -- I don't know! But there doesn't seem to be anything explicitly sinister about the way this was set up, or went down -- simply, the business didn't do well enough. Which is too bad, because I think their coffee is actually good.
That's a good thing, right?
"good enough" is, indeed, just good enough to make it not worthwhile to rip out all the upstreams and roll your own everything from scratch, because the cost of the occasional outages is much lower than the cost of reinventing every single wheel, nut, bolt, axle, bearing, and grease formulation.
Roblox not only runs entirely on Lua, but they've been working on their own type inference version of Lua named Luau, and open sourced it, and it's still in very active development.
Then again, some data centers just use re-generation from giant flywheels, where the grid powers the flywheel to build up inertia, and thus load can be smoothed that way. The flywheels need to keep running at 60 Hz for at least 30 seconds to give the Diesel generators time to start, should the grid fail.
Run a flywheel at the main transformer, run batteries at your PDC and add supercaps at each point of load, and you may very well be able to show a much smoother load to the grid.
Now, if someone makes an infringing use of the thing I put up on the internet. Then I have some kind of recourse, at least through the courts, if I have a lot of money to pay lawyers.
But if someone makes a fair use of the thing I put up on the internet, then I don't have any recourse, because that's the way the law works.
As far as I understand it, using data as input data to a machine learning model that substantially transforms and does not duplicate the input data is currently believed to be fair use.
So, the training use of freely available data seems pretty straightforward that authors can't control when they make it freely available.
It seems like Facebook made use of data that wasn't freely available, though -- ebook rip library type stuff. That's the bit I think they could be in trouble for. But that's just a plain-old "Napster" style copyright question, as far as I understand it.
The lawyer's argument that Llama "obliterates the market" for written works seems weak. I, and anyone I know, put down AI slop fiction before the first paragraph is done, because it's not the same thing as real fiction.
This is why Result (or Maybe, or runExceptT, and so on in other languages) is a perfectly safe way of handling unexpected or invalid data. As long as you enforce your invariants in pure code (code without side effects) then failure is safe.
This is also why effects should ideally be restricted and traceable by the compiler, which, unfortunately, Rust, ML, and that chain of the evolution tree didn't quite stretch to encompass.
From tools like "spin" and "tla+" to proof assistants like Coq to full languages like Idris and Agda.
Some of the stronger-typed languages already give us some of those benefits (Haskell, OCaml) and with restricted effects (like Haskell) we can even make the compiler do much of this work without it leaking into other parts of the program if we don't want it to.