Lior Fox, January 2023
TL;DR: I present examples of apparent "symbolic" capabilities of ChatGPT, and discuss some context and possible interpretations
ChatGPT probably requires no introduction at this stage. If you haven't had the chance to play with it yet, you should do so (as long as it free?). Before I dive in, it is perhaps better to clearly state what this post isn't about. I will not discuss:
| # Working example for my blog post at: | |
| # http://danijar.com/variable-sequence-lengths-in-tensorflow/ | |
| import functools | |
| import sets | |
| import tensorflow as tf | |
| from tensorflow.models.rnn import rnn_cell | |
| from tensorflow.models.rnn import rnn | |
| def lazy_property(function): |
| /* | |
| This example uses Scala. Please see the MLlib documentation for a Java example. | |
| Try running this code in the Spark shell. It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above. | |
| This example is paired with a blog post on LDA in Spark: http://databricks.com/blog | |
| Spark: http://spark.apache.org/ | |
| */ | |
| import scala.collection.mutable |
| import org.apache.spark.mllib.linalg.distributed.RowMatrix | |
| import org.apache.spark.mllib.linalg._ | |
| import org.apache.spark.{SparkConf, SparkContext} | |
| // To use the latest sparse SVD implementation, please build your spark-assembly after this | |
| // change: https://github.com/apache/spark/pull/1378 | |
| // Input tsv with 3 fields: rowIndex(Long), columnIndex(Long), weight(Double), indices start with 0 | |
| // Assume the number of rows is larger than the number of columns, and the number of columns is | |
| // smaller than Int.MaxValue |
| /* Merge sort in C */ | |
| #include<stdio.h> | |
| #include<stdlib.h> | |
| // Function to Merge Arrays L and R into A. | |
| // lefCount = number of elements in L | |
| // rightCount = number of elements in R. | |
| void Merge(int *A,int *L,int leftCount,int *R,int rightCount) { | |
| int i,j,k; |
| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |
| """ | |
| Implementation of pairwise ranking using scikit-learn LinearSVC | |
| Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich, | |
| T. Graepel, K. Obermayer. | |
| Authors: Fabian Pedregosa <fabian@fseoane.net> | |
| Alexandre Gramfort <alexandre.gramfort@inria.fr> | |
| """ |
| import nltk | |
| text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital | |
| computer or the gears of a cycle transmission as he does at the top of a mountain | |
| or in the petals of a flower. To think otherwise is to demean the Buddha...which is | |
| to demean oneself.""" | |
| # Used when tokenizing words | |
| sentence_re = r'''(?x) # set flag to allow verbose regexps | |
| ([A-Z])(\.[A-Z])+\.? # abbreviations, e.g. U.S.A. |
| """ | |
| (C) Mathieu Blondel - 2010 | |
| License: BSD 3 clause | |
| Implementation of the collapsed Gibbs sampler for | |
| Latent Dirichlet Allocation, as described in | |
| Finding scientifc topics (Griffiths and Steyvers) | |
| """ |
