rassibassi
Joined 10 years ago
Joined 10 years ago
Presently, a large language model (LLM) uses the same amount of computing resources for both simple and complex problems, which is seen as a drawback. Imagine if an LLM could adjust its computational effort based on the complexity of the task. During inference, it might then perform a sort of search across the solution space. The "search" mentioned in the article means just that, a method of dynamically managing computational resources at the time of testing, allowing for exploration of the solution space before beginning to "predict the next token."
At OpenAI Noam Brown is working on this, giving AI the ability to "ponder" (or "search"), see his twitter post: https://x.com/polynoamial/status/1676971503261454340
""" Caching Libraries
joblib.Memory provides caching functions and works by explicitly saving the inputs and outputs to files. It is designed to work with non-hashable and potentially large input and output data types such as numpy arrays.
"""
From https://pypi.org/project/diskcache/https://github.com/stanfordnlp/dspy/blob/main/dsp/modules/ca...
Yet, that still means, knowledge of O cannot be synthesized.
Each of the multiplexed channels are individually limited by the Shannon limit, and with higher power the fiber's Kerr effect creates interference which creates a sweet spot for the optimal optical launch power.
the novelty here is that the spectral channels are all generated from a single laser source rather than a laser per channel
In an experiment like this, only the initial light source is modulated and therefore all channels carry the same data. The equipment for the transmitter and receiver chain is so expensive that university labs can barely afford one of each.
Not sure what the baud rate of a single channel was in their experiment but probably between 32-80Gb which is common for the lab equipment at Universities. The industry is knocking on 100-400Gb where for the actual decoding and signal processing there is massive parallelism applied to reduce the rate even more
Description: https://danishdesignaward.com/en/arkiver/nominee/adam-lenzin...
https://www.nts.live/shows/the-do-you-breakfast-show/episode...