kastnerkyle

raise ValueError("DEPRECATED/FROZEN - see https://github.com/kastnerkyle/tools for the latest")

# License: BSD 3-clause
# Authors: Kyle Kastner
# Harvest, Cheaptrick, D4C, WORLD routines based on MATLAB code from M. Morise
# http://ml.cs.yamanashi.ac.jp/world/english/
# MGC code based on r9y9 (Ryuichi Yamamoto) MelGeneralizedCepstrums.jl
# Pieces also adapted from SPTK
from __future__ import division
import numpy as np

Nicholas-Swift

class Node():
    """A node class for A* Pathfinding"""

    def __init__(self, parent=None, position=None):
        self.parent = parent
        self.position = position

        self.g = 0
        self.h = 0

JustinShenk

#!/bin/bash
# Add to instance metadata with `gcloud compute instances add-metadata \
#   instance-name --metadata-from-file startup-script=idle-shutdown.sh` and reboot
# NOTE: requires `bc`, eg, sudo apt-get install bc
# Modified from https://stackoverflow.com/questions/30556920/how-can-i-automatically-kill-idle-gce-instances-based-on-cpu-usage
threshold=0.1

count=0
wait_minutes=60
while true

onlurking

Programming as Theory Building

Peter Naur

Peter Naur's classic 1985 essay "Programming as Theory Building" argues that a program is not its source code. A program is a shared mental construct (he uses the word theory) that lives in the minds of the people who work on it. If you lose the people, you lose the program. The code is merely a written representation of the program, and it's lossy, so you can't reconstruct

rickhull

  # MONITORING: services run on loopback interface
  #             nginx reverse proxy exposes services to network
  #             - grafana:3010
  #             - prometheus:3020
  #             - loki:3030
  #             - promtail:3031

  # prometheus: port 3020 (8020)
  #
  services.prometheus = {

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

mxkrn

"""
This script uses the Metaphor (https://metaphor.systems) API to fetch the latest research results for a query of choice.
Given the popularity of Slack as a communication channel, it also provides the option to forward this information
to Slack using a web hook URL.

Usage:
1. Set the environment variables METAPHOR_API_KEY and SLACK_URL
2. Create a python>=3.7 environment
3. Install dependencies: `pip install metaphor-python requests`
4. Run: `python metaphor-research-digest.py --days 7 --num-results 10 --publish-to-slack`