Ron Wolf ron-wolf

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 impressed 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 labor costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We

Everything here is written according to runit in mind, please modify it according to your init(openrc/s6).

Disclaimer: I am a physicist, not a computer scientist. I have no experience with creating programming languages. I am leading a research group that is all in on Julia. I have been following developments in many computer languages for many years, and evaluated many options reasonably deeply before deciding to move us to Julia. This are my thoughts on some of the most important pain points right now from the perspective of a user of the language, advanced libraries and occasional contributor and writer of libraries.

The problem statement

Currently Julia lacks a good way to express how things should behave. In many languages types are used to express this information. In Julia, in order to make maximum use of the languages dynamicism, we are encouraging to write code as generically as possible, and defer specifying types to the users of libraries as much as possible. This allows the injection of unanticipated behaviours deep inside our code.

 # Do
function norm(x, y)
  (x - y) ^ 2

This gist will no longer be updated as the changelog will be on the official website.

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	"""
	MIT License

	Copyright (c) 2023 David Buchanan

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is

	#!/usr/bin/env bash
	#
	# add smart quotes to Hugo Markdown source files, using the
	# reference implementation of CommonMark's CLI tool:
	# https://github.com/commonmark/commonmark-spec
	# Notes:
	# - assumes TOML front matter
	# - converts footnote-style links to inline
	# - normalizes ordered/unordered list formatting
	#

	#include <stdint.h>
	#include <stdio.h>
	#include <stdlib.h>

	// munged from https://github.com/simontime/Resead

	namespace sead
	{
	class Random
	{

	/* base32 */
	/*
	Copyright (c) 2011, Chris Umbel

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is
	furnished to do so, subject to the following conditions:

	import numpy as np
	import tensorflow as tf
	from tensorflow import keras
	from tensorflow.keras.models import Sequential
	from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D
	from tensorflow.keras.layers import MaxPooling2D, BatchNormalization

	keras.backend.clear_session()
	np.random.seed(1000)
	tf.random.set_seed(1000)

	# Example of two-line ZSH prompt with four components.
	#
	# top-left top-right
	# bottom-left bottom-right
	#
	# Components can be customized by editing set-prompt function.
	#
	# Installation:
	#
	# (cd && curl -fsSLO https://gist.githubusercontent.com/romkatv/2a107ef9314f0d5f76563725b42f7cab/raw/two-line-prompt.zsh)