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import openai
import numpy as np
from openai.embeddings_utils import get_embedding

openai.api_key = "TOKEN"

def gpt3_embedding(content, engine='text-similarity-ada-001'):
		content = content.encode(encoding='ASCII',errors='ignore').decode()
		response = openai.Embedding.create(input=content,engine=engine)
		vector = response['data'][0]['embedding']  # this is a normal list

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State of AI Report
October 11, 2022
#stateofai
stateof.ai
Ian Hogarth
Nathan Benaich

About the authors
Nathan is the General Partner of Air Street Capital, a venture capital firm investing in AI-first technology and life science companies. He founded RAAIS and London.AI (AI community for industry and research), the RAAIS Foundation (funding open-source AI projects), and Spinout.fyi (improving university spinout creation). He studied biology at Williams College and earned a PhD from Cambridge in cancer research. 
Nathan Benaich

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Analyzing 5 Billion Games of Set with FeatureBase

FeatureBase is a binary-tree database built on Roaring Bitmaps. This makes it suitable for running analytics on massive data sets in real time. If you've never used FeatureBase before, you can get it running locally in about 5 minutes.

Today, we're going to take a look at using FeatureBase to simulate and analyze a very large number of Set games in real-time.

Set (the game)

Set is a card game designed by Marsha Falco in 1974 and published by Set Enterprises in 1991. The deck consists of 81 unique cards that vary in four features across three possibilities for each kind of feature: number of shapes (one, two, or three), shape (diamond, squiggle, oval), shading (solid, striped, or open), and color (red, green, or purple).

In a game of Set, the cards are shuffled and then 12 cards are drawn from the top of the deck and placed on the table. Game play then com

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FeatureBase in 5 Minutes

FeatureBase is a B-tree database built on Roaring Bitmaps. This makes it suitable for running analytics on massive data sets in real time. It's also perfect for use in machine learning applications, where a fast feature store is desired during training or inference. FeatureBase itself may be considered a type of machine learning model which is trained on large amounts of other model's inference data, over time.

This guide is designed to get you started using FeatureBase on a Mac or Linux/UNIX based system. It covers downloading and starting the FeatureBase server as well as ingesting and querying a small amount of data.

You may also want to reference the documentation for FeatureBase after you finish running through this guide.

Install

Start by heading over to the downloads on the Github repo and select the buil

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import openai
import numpy as np
from openai.embeddings_utils import get_embedding

openai.api_key = ""

def gpt3_embedding(content, engine='text-similarity-ada-001'):
    content = content.encode(encoding='ASCII',errors='ignore').decode()
    response = openai.Embedding.create(input=content,engine=engine)
    vector = response['data'][0]['embedding']  # this is a normal list

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// google docs app
var line = "{{line}}";

if ("{{text}}" == "" && "{{view}}" == "") {
	// just nothing
	url = "https://docs.google.com/";
} else if ("{{text}}" == "" && "{{view}}" != ""){
	// view
	var view = "{{view}}".substring(1); // trim the | off (fix this)
	if (view == "sheets") {

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import datetime
import logging
import os
import socket
import time
from random import randrange

import urllib.parse

from google.cloud import ndb

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import datetime
from dateutil.relativedelta import relativedelta
import re

# timewarp.py
# Kord Campbell
# Copyright 2020, Mitta Corp.
# All Rights Reserved.

# description: common name to solr timerange converter in python3

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import datetime
import os
import requests

from google.cloud import ndb

import flask_login

from lib.util import random_string, random_number, generate_token, random_name
from lib.solr import doc_count

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{
  "timestamp": "{{timestamp}}",
  "request_url": "/g",
  "result": "success",
  "response": [{
    "source_type": "spool",
    "created": "{{created}}",
    "updated": "{{updated}}",
    "name": "{{name}}",
    "upload_url": "{{upload_url}}",