If they leave at all info about security patterns, you get an AI that knows how to code, but doesn't know what can make code insecure. That doesn't seem like a great idea.
"JPEG" is short for Joint Photographic Experts Group, an ISO/ITU group that creates a lot of imaging standards. The JPEG image format you're thinking of is only one of the formats they've created.
Does anyone know what assets Greenpeace USA has? I imagine Greenpeace international will set up Greenpeace USA 2.0, all the volunteers/employees will move over, and the original will just go bankrupt.
> Imagine being a vim or emacs user and have those replaced by something you have to type entire sentences for functionality.
As a former vim user who uses cursor, I've found that as the models get better I'm typing less and less. I appreciate the vim key bindings, but eventually I can imagine not missing them.
In theory you could generate a bunch of code that seems mostly correct and then gradually tweak it until it's closer and closer to compiling/working, but that seems ill-suited to how current AI agents work (or even how people work). AI agents are prone to make very local fixes without an understanding of wider context, where those local fixes break a lot of assumptions in other pieces of code.
It can be very hard to determine if an isolated patch that goes from one broken state to a different broken state is on net an improvement. Even if you were to count compile errors and attempt to minimize them, some compile errors can demonstrate fatal flaws in the design while others are minor syntax issues. It's much easier to say that broken tests are very bad and should be avoided completely, as then it's easier to ensure that no patch makes things worse than it was before.
That's a fair point. Normally if you injected the "dog" token, that would cause a set of values to be populated into the kv cache, and those would later be picked up by the attention layers. The question is what's fundamentally different if you inject something into the activations instead?
I guess to some extent, the model is designed to take input as tokens, so there are built-in pathways (from the training data) for interrogating that and creating output based on that, while there's no trained-in mechanism for converting activation changes to output reflecting those activation changes. But that's not a very satisfying answer.
But LLMs have been measured to have some theory of mind abilities at least as strong as humans: https://www.nature.com/articles/s41562-024-01882-z . At this point you either need to accept that either LLMs are already conscious, or that it's easy enough to fake being conscious that it's practically impossible to test for - philosophical zombies are possible. It doesn't seem to me that LLMs are conscious, so consciousness isn't really observable to others.
A lot of processes don't handle ENOMEM well. Your mail client asks for memory for a buffer to write an email and it gets ENOMEM, what's it going to do? Silently fail to do anything? Pop up an error message (which will probably take memory to display)? Exit?
Do they really? What does it cost them if they're wrong?