Speculating: local ssds aren't as valuable in the cloud since they're effectively ephemeral. If the instance restarts, it would lose its storage. Trying to keep a workload affinitized to an SSD or to migrate data to a different SSD when an instance moves increases cost prohibitively.
For a lot of use cases such as caching (e.g., the ephemeral caching layer in Snowflake), ephemeral storage is good enough. If you really want to, you could also achieve persistence by replicating to multiple instances (afaik this is what DynamoDB does)
Thats difficult for most people to implement in their applciations, and it increases latency to be closer to networked SSD anyway. So it remains fairly niche.
If you want recognize all the common patterns, the code can get very verbose. But it's all still just one analysis or transformation, so it would be artificial to split into multiple files. I haven't worked much in llvm, but I'd guess that the external interface to these packages is pretty reasonable and hides a large amount of the complexity that took 16kloc to implement
From the previous article in the series, it looks like the biggest impediment to just using full llvm to compile the query is that they didn't find a good way to cache the results across invocations.
Sql server hekaton punted this problem in a seemingly effective way by requiring the client to use stored procedures to get full native compilation. Not sure though if they recompile if the table statistics indicate a different query plan is needed.
Yes, they did have the bug with the lock prefix. IOT people at Microsoft got NT booting on the Quark and we ran into that problem. I wound up writing a small tool to patch out all the lock prefixes.
> mean, "not enough data" is an explanation why neural networks can't do a bunch of things... One of the things humans are really good at is learning things from a few examples
I dispute the search space problem for something like folding clothes. Like a lot of human actions in space, folding clothes and other motor tasks are hierarchical sequences of smaller tasks that are strung together, similar to a sentence or paragraph of text.
We can probably learn things from each other from few examples because we are leaning on a large library of subtasks that all have learned or which are innate, and the actual novel learning of sequencing and ordering is relatively small to get to the new reward.
I expect soon we'll get AIs that have part of their training be unsupervised rl in a physics simulation, if it's not being done already.
> Like a lot of human actions in space, folding clothes and other motor tasks are hierarchical sequences of smaller tasks that are strung together
I disagree, you can model those tasks as hiearchical sequences of smaller tasks. But the terminal goal of folding clothes is to turn a pile of unfolded clothes into a neat pile of folded clothes.
The reason you would break down the task is because getting between those two states with the only reward signal being "the clothes are now folded" takes a lot of steps, and given the possible actions the robot can take, results in a large search space.
Reading this code for the first time, this seems to be a consequence of the separation between allocating and fd and "installing" a pointer to a file there. Allocating the fd already needs to acquire a lock. So if the install happens together with allocation, there wouldn't be a need to use synchronize_rcu to kick out other threads. The lock would do that.
> In other words the LLM does not contain the knowledge of what the words represent.
This is probably true for some words and concepts but not others. I think we find that llms make inhuman mistakes only because they don't have the embodied senses and inductive biases that are at the root of human language formation.
If this hypothesis is correct, it suggests that we might be able to train a more complete machine intelligence by having them participate in a physics simulation as one part of the training. I.e have a multimodal ai play some kind of blockworld game. I bet if the ai is endowed with just sight and sound, it might be enough to capture many relevant relationships.
