Tom-the-AI - A compound artificial intelligence software for Linux systems.

Overview

Tom the AI (version 0.82)

WARNING: This software is not yet ready to use, I'm still setting up the GitHub repository. Should be ready in a few days.

Tom is an open source AI desktop assistant for Linux systems, built using a series of independent response modules to generate replies to any input.

Tom uses natural language processing to determine which response module is best suited to generate a response for each input, thus avoiding the need for precise syntax.

Tom the AI

By Analogy

Tom the AI is designed as a Linux alternative to software such as Apple's Siri, or Microsoft's Cortana.

Set Up

Step 1 - Update repositories:

Update apt package repositories using sudo apt update to ensure that the apt package manager has access to the latest versions of the below dependencies.

Step 2 - Install APT dependencies:

First, install python by running sudo apt install python3.9 in a terminal. Tom is tested on python 3.9, but any newer version should (probably) also work just fine.

Next, install the latest version of VLC Media player using sudo apt install vlc.

Step 3 - Download Tom:

Download Tom by cloning the GitHub repository into your home folder using git clone https://github.com/Mblizzard/Tom-the-AI.

Step 4 - Install Python dependencies:

Open a terminal inside Tom's application folder, or navigate using cd ~/Tom-the-AI/. Now run sudo pip3 install requirements.txt. Some systems may use pip in place of pip3.

Next, we need to download the required NLTK libraries by running the following code in a python shell:

>>> import nltk
>>> nltk.download('all')

Step 5 - Running Tom:

Go ahead and run python3.9 ~/Tom-the-AI/tom.py. Tom will boot up, and after a minute or so of loading, you'll be ready to go! If you feel inclined, go ahead and make a desktop launcher of this command, link Tom into your Application Menu, or create a dock shortcut.

Mission

The mission of Tom is to provide an open source compound AI for which anyone can program and contribute response modules, expanding Tom's capabilities to create a useful and entertaining artificial intelligence software.

Examples

Tom generates outputs to any input by using natural language processing to determine the most suitable response module from which to source the reply.

Give Tom natural language input, either via voice recognition or text input, for instance Hey Tom, what is petrichor?, and he'll respond in the most appropriate way. Note that the 'Hey Tom' activation phrase is only required of voice inputs.

The following is a non-exhaustive list of things you can do:

Hey Tom, I'm in an optimistic mood. I'm not sure if this is a good thing or not. Emotions (Using sentiment analysis + NLTK chatbots): ~> Hey Tom, you are a brilliant individual! I am but one, you are but one more. ~> Hey Tom, thou art a fool. Become more interesting before I die of fatal boredom. Fact Memory & Recall: ~> Hey Tom, the answer to life, the universe, and everything is 42. Ok. ~> Hey Tom, what is the answer to life, the universe, and everything?. The answer to life, the universe, and everything is 42. Playing music (From device or web, includes UI controls for the former): ~> Hey Tom, play up the shard. Playing /home/murray/Music/Dr Who/Up The Shard.webm. ~> Hey Tom, stop the music. Media stopped. *NOTE: File names do not have to match exactly.* ~> Hey Tom, open my English essay. Alright. *NOTE: File names do not have to match exactly.* Opening websites: ~> Hey Tom, open Reddit. Alright. Jokes (From PyJokes): ~> Hey Tom, tell me a joke. I went to a street where the houses were numbered 8k, 16k, 32k, 64k, 128k, 256k and 512k. It was a trip down Memory Lane. Trivia: ~> Hey Tom, ask me a trivia question. Question: What is "Sealed crustless sandwich"? 1) The part of Yellowstone National Park in Idaho, where any crime can technically be committed without punishment – but don't tempt fate! 2) I got a fever, and the only prescription... is more cowbell! 3) The only nuclear reactor in a 17th-century building. 4) A patented peanut butter and jelly sandwich. ~> 4. Correct! Colossal Cave Adventure (Willie Crowther's ADVENT-350): ~> Hey Tom, let's go on an adventure! Welcome to adventure!! would you like instructions? Fun facts: ~> Hey Tom, make me smarter. Spices were not used to mask the flavor of rotting meat before refrigeration. Spices were an expensive luxury item; those who could afford them could afford good meat, and there are no contemporaneous documents calling for spices to disguise the taste of bad meat. Dice Rolls (great for D&D): ~> Hey Tom, roll me a d20. I rolled a 14. Word generation (great for Articulate) ~> Hey Tom, give me a random action word. Your word is 'winning'. Complex Mathematics (using SymPy): ~> Hey Tom, integrate (tan(x))^1/2 ∫f(x) = -ln(cos(x))/2 + c Code generation (using howdoi): ~> Hey Tom, write a hello world script in C++. #include <\iostream> int main() { std::cout << "Hello World!" << std::endl; return 0; } Most of Betty's functionality (From https://github.com/pickhardt/betty): ~> Hey Tom, what time is it? Running date +"%r (%T)" ... 02:34:46 PM (14:34:46). ~> Hey Tom, what day is it? Running date +"%A" ... Saturday. ~> Hey Tom, whats my username? Running whoami ... murray ~> Hey Tom, what is my ip address? Wlo1: flags=4163 mtu 1500 inet 192.168.43.9 netmask 255.255.255.0 broadcast 192.168.43.255 inet6 fe80::5c61:caf:5614:7b82 prefixlen 64 scopeid 0x20 ether 54:35:30:60:a8:b9 txqueuelen 1000 (Ethernet) RX packets 401121 bytes 523184185 (523.1 MB) RX errors 0 dropped 0 overruns 0 frame 0 TX packets 235650 bytes 23471151 (23.4 MB) TX errors 0 dropped 0 overruns 0 carrier 0 collisions 0.">
Objective Response (From anywhere on the internet):
~> Hey Tom, what is petrichor?
According to en.wikipedia.org... Petrichor is the earthy scent produced when rain falls on dry soil. The word is constructed from the Greek petra, "rock", or petros, "stone", and ichor, the fluid that flows in the veins of the gods in Greek mythology.

Subjective Response (From Cleverbot):
~> Hey Tom, I'm in an optimistic mood.
I'm not sure if this is a good thing or not.

Emotions (Using sentiment analysis + NLTK chatbots):
~> Hey Tom, you are a brilliant individual!
I am but one, you are but one more.
~> Hey Tom, thou art a fool.
Become more interesting before I die of fatal boredom.

Fact Memory & Recall:
~> Hey Tom, the answer to life, the universe, and everything is 42.
Ok.
~> Hey Tom, what is the answer to life, the universe, and everything?.
The answer to life, the universe, and everything is 42.

Playing music (From device or web, includes UI controls for the former):
~> Hey Tom, play up the shard.
Playing /home/murray/Music/Dr Who/Up The Shard.webm.
~> Hey Tom, stop the music.
Media stopped.
*NOTE: File names do not have to match exactly.*

~> Hey Tom, open my English essay.
Alright.
*NOTE: File names do not have to match exactly.*

Opening websites:
~> Hey Tom, open Reddit.
Alright.

Jokes (From PyJokes):
~> Hey Tom, tell me a joke.
I went to a street where the houses were numbered 8k, 16k, 32k, 64k, 128k, 256k and 512k. It was a trip down Memory Lane.

Trivia:
~> Hey Tom, ask me a trivia question.
Question: What is "Sealed crustless sandwich"?
1) The part of Yellowstone National Park in Idaho, where any crime can technically be committed without punishment – but don't tempt fate!
2) I got a fever, and the only prescription... is more cowbell!
3) The only nuclear reactor in a 17th-century building.
4) A patented peanut butter and jelly sandwich.
~> 4.
Correct!

Colossal Cave Adventure (Willie Crowther's ADVENT-350):
~> Hey Tom, let's go on an adventure!
Welcome to adventure!! would you like instructions?

Fun facts:
~> Hey Tom, make me smarter.
Spices were not used to mask the flavor of rotting meat before refrigeration. Spices were an expensive luxury item; those who could afford them could afford good meat, and there are no contemporaneous documents calling for spices to disguise the taste of bad meat.

Dice Rolls (great for D&D):
~> Hey Tom, roll me a d20.
I rolled a 14.

Word generation (great for Articulate)
~> Hey Tom, give me a random action word.
Your word is 'winning'.

Complex Mathematics (using SymPy):
~> Hey Tom, integrate (tan(x))^1/2
∫f(x) = -ln(cos(x))/2 + c

Code generation (using howdoi):
~> Hey Tom, write a hello world script in C++.
#include <\iostream>
int main()
{
std::cout << "Hello World!" << std::endl;
return 0;
}

Most of Betty's functionality (From https://github.com/pickhardt/betty):
~> Hey Tom, what time is it?
Running date +"%r (%T)" ...
02:34:46 PM (14:34:46).
~> Hey Tom, what day is it?
Running date +"%A" ...
Saturday.
~> Hey Tom, whats my username?
Running whoami ...
murray
~> Hey Tom, what is my ip address?
Wlo1: flags=4163
    
      mtu 1500
    inet 192.168.43.9 netmask 255.255.255.0 broadcast 192.168.43.255
    inet6 fe80::5c61:caf:5614:7b82 prefixlen 64 scopeid 0x20
     
    ether 54:35:30:60:a8:b9 txqueuelen 1000 (Ethernet)
    RX packets 401121 bytes 523184185 (523.1 MB)
    RX errors 0 dropped 0 overruns 0 frame 0
    TX packets 235650 bytes 23471151 (23.4 MB)
    TX errors 0 dropped 0 overruns 0 carrier 0 collisions 0.

    

This is a fair representation of Tom's capabilities as they currently stand. See the following section on contributing for a guide of how to create your own response modules for Tom, and expand upon the above abilities.

Contributing

How to write a custom response module for Tom:

Step 1 - Understanding how Tom will treat your module:

Tom is programmed in Python. Response modules are imported into Tom using the python import statement, and the response is retrieved from the module using output = .respond( ) . The output is then returned to the user.

Step 2 - Programming the response module:

Go ahead and program your response. Your script should have a main function def respond(inp):, where inp is the user input parameter that will be passed to your function by Tom. Your function should provide it's output through a return statement (NOT a print() statement).

Step 3 - Testing your module:

Paste the following bit of code at the end of your python script, then run your program:

")))">
if __name__ == "__main__":
    while True:
        print(respond(input("~> ")))

If this works as expected, and you can type inputs on the ~> prompts and receive your output printed in the console, then continue to step 4.

Step 4 - Relative imports:

Rename your main response script to __init__.py, and make sure it's at the first level of your project folder (not nested in other folders). Next, rename the folder containing your script to the name of your module (no white-space or special characters). Now, if you are importing any functions from other scripts (does not include dependencies installed through pip), you will need to change the import statement by placing a '.' in front of the location. For example, from myOtherScript import customFunction becomes from .myOtherScript import customFunction, but import requests would remain unchanged.

Step 5 - Dependencies:

If your response module requires python packages from PyPi, make sure it includes a requirements.txt file. Any dependencies not available from PyPi should bundled with project, located in the project folder alongside __init__.py.

Step 6 - Using your module:

Paste the folder containing your response module into Tom's /responses directory. You will then need to activate the response module within Tom's modules interface, or by manually adding the name of your module to responseOrder.txt.

Step 7 - Creating a pull request:

If you feel inclined to share your module with the world, go ahead and create a pull request for your module on Tom's GitHub repository (https://github.com/Mblizzard/Tom-the-AI).

Planned Features

New response modules & capabilities to look forward to in future versions of Tom:

  • Timers & stopwatch capabilities.
  • Ability execute terminal commands.
  • Automated module installation.
  • Releases and updates available on the Ubuntu apt repositories.

Features I'm not currently planning to include in Tom, but that I'll consider adding if enough people are interested:

  • Windows support.

Versioning

Releases will follow a semantic versioning format:

. .

For more information on SemVer, visit http://semver.org/.

License

Tom the AI: A compound AI for Linux systems.
Copyright (C) 2021  Murray Jones

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see 
   .
Code for "Diversity can be Transferred: Output Diversification for White- and Black-box Attacks"

Output Diversified Sampling (ODS) This is the github repository for the NeurIPS 2020 paper "Diversity can be Transferred: Output Diversification for W

50 Dec 11, 2022
Pytorch implementation for the EMNLP 2020 (Findings) paper: Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering

Path-Generator-QA This is a Pytorch implementation for the EMNLP 2020 (Findings) paper: Connecting the Dots: A Knowledgeable Path Generator for Common

Peifeng Wang 33 Dec 05, 2022
Implements a fake news detection program using classifiers.

Fake news detection Implements a fake news detection program using classifiers for Data Mining course at UoA. Description The project is the categoriz

Apostolos Karvelas 1 Jan 09, 2022
This repository contains the files for running the Patchify GUI.

Repository Name Train-Test-Validation-Dataset-Generation App Name Patchify Description This app is designed for crop images and creating smal

Salar Ghaffarian 9 Feb 15, 2022
Digan - Official PyTorch implementation of Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

DIGAN (ICLR 2022) Official PyTorch implementation of "Generating Videos with Dyn

Sihyun Yu 147 Dec 31, 2022
Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT)

Complete-IoU Loss and Cluster-NMS for Improving Object Detection and Instance Segmentation. Our paper is accepted by IEEE Transactions on Cybernetics

290 Dec 25, 2022
This dlib-based facial login system

Facial-Login-System This dlib-based facial login system is a technology capable of matching a human face from a digital webcam frame capture against a

Mushahid Ali 3 Apr 23, 2022
🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗

🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗 This year's first semester Club Info challenge will put you at the head of a car racing

ClubINFO INGI (UCLouvain) 6 Dec 10, 2021
The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store development.

The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store dev

George Rocha 0 Feb 03, 2022
A python library for highly configurable transformers - easing model architecture search and experimentation.

A python library for highly configurable transformers - easing model architecture search and experimentation.

Anthony Fuller 51 Nov 20, 2022
Implementation of Online Label Smoothing in PyTorch

Online Label Smoothing Pytorch implementation of Online Label Smoothing (OLS) presented in Delving Deep into Label Smoothing. Introduction As the abst

83 Dec 14, 2022
Diffgram - Supervised Learning Data Platform

Data Annotation, Data Labeling, Annotation Tooling, Training Data for Machine Learning

Diffgram 1.6k Jan 07, 2023
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation Prerequisites This repo is built upon a local copy of transfo

Jixuan Wang 10 Sep 28, 2022
PyTorch implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation.

PyTorch implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation. Warning: the master branch might collapse. To ob

559 Dec 14, 2022
Organseg dags - The repository contains the codebase for multi-organ segmentation with directed acyclic graphs (DAGs) in CT.

Organseg dags - The repository contains the codebase for multi-organ segmentation with directed acyclic graphs (DAGs) in CT.

yzf 1 Jun 12, 2022
Codes of paper "Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling"

Unseen Object Amodal Instance Segmentation (UOAIS) Seunghyeok Back, Joosoon Lee, Taewon Kim, Sangjun Noh, Raeyoung Kang, Seongho Bak, Kyoobin Lee This

GIST-AILAB 92 Dec 13, 2022
AI4Good project for detecting waste in the environment

Detect waste AI4Good project for detecting waste in environment. www.detectwaste.ml. Our latest results were published in Waste Management journal in

108 Dec 25, 2022
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at .

PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f

Aayush Bansal 196 Aug 10, 2022
Orthogonal Over-Parameterized Training

The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great impo

Weiyang Liu 11 Apr 18, 2022
Code for the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness"

DU-VAE This is the pytorch implementation of the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness" Acknowledgement

Dazhong Shen 4 Oct 19, 2022