FAIR was founded in 2015 and Llama's first release was in 2023. Musk co-founded OpenAI in 2015 but no reasonable person credits ChatGPT in 2022 to him.
There's nothing ambiguous about this question[1][2]. The tool simply gives different responses at random.
And why should a "superintelligent" tool need to be optimized for riddles to begin with? Do humans need to be trained on specific riddles to answer them correctly?
It's called problem decomposition and agentic coding systems do some of this by themselves now: generate a plan, break the tasks into subgoals, implement first subgoal, test if it works, continue.
That's nice if it works, but why not look at the plan yourself before you let the AI have its go at it? Especially for more complex work where fiddly details can be highly relevant. AI is no good at dealing with fiddly.
That's what you can do. Tell the AI to make a plan in an MD file, review and edit it, and then tell another AI to execute the plan. If the plan is too long, split it into steps.
This has been a well integrated feature in cursor for six months.
As a rule of thumb, almost every solution you come up with after thirty seconds of thought for a online discussion, has been considered by people doing the same thing for a living.
There’s nothing stopping you from reviewing the plan or even changing it yourself. In the setup I use the plan is just a markdown file that’s broken apart and used as the prompt.
A 'language model' only has meaning in so far as it tells you this thing 'predicts natural language sequences'. It does not tell you how these sequences are being predicted or any anything about what's going on inside, so all the extra meaning OP is trying to place by calling them Language Models is well...misplaced. That's the point I was trying to make.
How do you join two datasets using r-trees? In a business setting, having a static and constant projection is critical. As long as you agree on zoom level, joining two datasets with S2 and H3 is really easy.
This data is indeed not irregularly distributed, in fact the fun thing about geospatial data is that you always know the maximum extent of it.
About your binary tree comment: yes this is absolutely valid, but consider then that binary trees also are a bad fit for distributed computing, where data is often partitioned at the top level (making it no longer a binary tree but a set of binary trees) and cross-node joins are expensive.
reply