chadisfaction

The reverse turing test

I asked GPT-4 to come up with 10 questions to determine if the answerer was AI or human.

I provided my own answers for these questions and I also asked ChatGPT to answer them.

The result is that GPT-4 was able to correctly differentiate between AI and Human.

Extracting financial disclosure reports and police blotter narratives using OpenAI's Structured Output

tl;dr this demo shows how to call OpenAI's gpt-4o-mini model, provide it with URL of a screenshot of a document, and extract data that follows a schema you define. The results are pretty solid even with little effort in defining the data — and no effort doing data prep. OpenAI's API could be a cost-efficient tool for large scale data gathering projects involving public documents.

OpenAI announced Structured Outputs for its API, a feature that allows users to specify the fields and schema of extracted data, and guarantees that the JSON output will follow that specification.

For example, given a Congressional financial disclosure report, with assets defined in a table like this:

	# nokogiri requires open-uri
	require 'nokogiri'
	require 'open-uri'
	# csv will be used to export data
	require 'csv'
	require 'mechanize'
	# pp is useful to display mechanize objects
	require 'pp'

	"""A simple implementation of a greedy transition-based parser. Released under BSD license."""
	from os import path
	import os
	import sys
	from collections import defaultdict
	import random
	import time
	import pickle

	SHIFT = 0; RIGHT = 1; LEFT = 2;