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 
   .
buildseg is a building extraction plugin of QGIS based on PaddlePaddle.

buildseg buildseg is a Building Extraction plugin for QGIS based on PaddlePaddle. How to use Download and install QGIS and clone the repo : git clone

39 Dec 09, 2022
DANA paper supplementary materials

DANA Supplements This repository stores the data, results, and R scripts to generate these reuslts and figures for the corresponding paper Depth Norma

0 Dec 17, 2021
Vision Transformer for 3D medical image registration (Pytorch).

ViT-V-Net: Vision Transformer for Volumetric Medical Image Registration keywords: vision transformer, convolutional neural networks, image registratio

Junyu Chen 192 Dec 20, 2022
Ensembling Off-the-shelf Models for GAN Training

Data-Efficient GANs with DiffAugment project | paper | datasets | video | slides Generated using only 100 images of Obama, grumpy cats, pandas, the Br

MIT HAN Lab 1.2k Dec 26, 2022
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters

CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt

Paul Gavrikov 18 Dec 30, 2022
Selective Wavelet Attention Learning for Single Image Deraining

SWAL Code for Paper "Selective Wavelet Attention Learning for Single Image Deraining" Prerequisites Python 3 PyTorch Models We provide the models trai

Bobo 9 Jun 17, 2022
HyperPose is a library for building high-performance custom pose estimation applications.

HyperPose is a library for building high-performance custom pose estimation applications.

TensorLayer Community 1.2k Jan 04, 2023
Repository for the paper "Online Domain Adaptation for Occupancy Mapping", RSS 2020

RSS 2020 - Online Domain Adaptation for Occupancy Mapping Repository for the paper "Online Domain Adaptation for Occupancy Mapping", Robotics: Science

Anthony 26 Sep 22, 2022
DeiT: Data-efficient Image Transformers

DeiT: Data-efficient Image Transformers This repository contains PyTorch evaluation code, training code and pretrained models for DeiT (Data-Efficient

Facebook Research 3.2k Jan 06, 2023
This is the source code of the solver used to compete in the International Timetabling Competition 2019.

ITC2019 Solver This is the source code of the solver used to compete in the International Timetabling Competition 2019. Building .NET Core (2.1 or hig

Edon Gashi 8 Jan 22, 2022
Catch-all collection of generative art made using processing

Generative art with Processing.py Some art I have created for fun. Dependencies Processing for Python, see how to download/use here Packages contained

2 Mar 12, 2022
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

ENet This work has been published in arXiv: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. Packages: train contains too

e-Lab 344 Nov 21, 2022
A simple pytorch pipeline for semantic segmentation.

SegmentationPipeline -- Pytorch A simple pytorch pipeline for semantic segmentation. Requirements : torch=1.9.0 tqdm albumentations=1.0.3 opencv-pyt

petite7 4 Feb 22, 2022
Simultaneous Detection and Segmentation

Simultaneous Detection and Segmentation This is code for the ECCV Paper: Simultaneous Detection and Segmentation Bharath Hariharan, Pablo Arbelaez,

Bharath Hariharan 96 Jul 20, 2022
This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Reinforcement-trading This project uses Reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can

Deepender Singla 1.4k Dec 22, 2022
Benchmark for evaluating open-ended generation

OpenMEVA Contributed by Jian Guan, Zhexin Zhang. Thank Jiaxin Wen for DeBugging. OpenMEVA is a benchmark for evaluating open-ended story generation me

25 Nov 15, 2022
A Python script that creates subtitles of a given length from text paragraphs that can be easily imported into any Video Editing software such as FinalCut Pro for further adjustments.

Text to Subtitles - Python This python file creates subtitles of a given length from text paragraphs that can be easily imported into any Video Editin

Dmytro North 9 Dec 24, 2022
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

郭飞 3.7k Jan 03, 2023
Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning

Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning Kajetan Schweighofer1, Markus Hofmarcher1, Marius-Constantin D

Institute for Machine Learning, Johannes Kepler University Linz 17 Dec 28, 2022