yoavg

Some remarks on Large Language Models

Yoav Goldberg, January 2023

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.

Intro

Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We

tomshanley

license: mit

rtoal

JS First

About This Manifesto

Have you ever argued for or against teaching language X as the first language in a university computer science curriculum? If so, I hope that your arguments:

• were first and foremost about students, considering the question “What do we want students to gain from their experience with a first language?”, not “Is language X better than language Y?” because the latter question requires too much context and isn’t really answerable;
• kept in mind that ultimately we want to train polyglots, so the first language is never the only language; and
• took into account previous work from computing educators, and education theorists and practitioners in general.

teamdandelion

7
2
1
0
4
1
4
9
5
9

xnuk

이 문서가 여기저기 알려짐에 따라, 이곳에 여러가지 댓글이 달리고 있습니다. 개인적으로는 댓글창을 없애버리고 싶지만 그럴 수 없는 터라, 댓글을 달기 전에 한번씩만 더 생각해주셨으면 합니다.

• 개인적인 감상은 이곳이 아닌 다른 곳에 적어주세요.
• 동성애 혐오적인 댓글을 달지 마세요.
• 기타 "난해한 혀엉... 언어"와 관련없는 댓글을 달지 말아주세요.

위 사항들을 포함해 제 마음에 안 드는 댓글들은 임의로 삭제하고 있습니다. 양해 부탁드립니다.

---

krtx

#!/bin/bash

# tmux requires unrecognized OSC sequences to be wrapped with DCS tmux;
# <sequence> ST, and for all ESCs in <sequence> to be replaced with ESC ESC. It
# only accepts ESC backslash for ST.
function print_osc() {
    if [[ -n $TERM ]] ; then
        printf "\033Ptmux;\033\033]"
    else
        printf "\033]"

kevindavenport

haje01

도커와 AWS를 활용한 클라우드 딥러닝 환경 구축

글쓴이: 김정주(haje01@gmail.com)

최근 딥러닝 관련 패키지들은 대부분 CPU와 GPU를 함께 지원하고 있습니다. GPU를 사용하면 보다 빠르게 학습 결과를 낼 수 있지만, GPU를 활용하기 위해서는 NVIDIA계열의 그래픽 카드, 드라이버 S/W 그리고 CUDA의 설치를 필요로 합니다.

이 글에서는 AWS의 GPU 인스턴스와 도커를 활용해 딥러닝 패키지(Caffe)를 편리하게 사용하는 방법을 소개합니다.

---

dave-andersen

import tensorflow as tf
import numpy as np
import time

N=10000
K=4
MAX_ITERS = 1000

start = time.time()

zuzap

hello world

forked from NPashaP's block: BiPartite