The readme for the chatbot reads in part:
A neural chatbot using sequence to sequence model with attentional decoder. This is a fully functional chatbot.
This is based on Google Translate Tensorflow model https://github.com/tensorflow/models/blob/master/tutorials/rnn/translate/
Sequence to sequence model by Cho et al.(2014)
Created by Chip Huyen as the starter code for assignment 3, class CS 20SI: “TensorFlow for Deep Learning Research” cs20si.stanford.edu
The detailed assignment handout and information on training time can be found at http://web.stanford.edu/class/cs20si/assignments/a3.pdf
Dialogue is lacking but this chatbot could be trained to appear to government forces as a live “jihadist” following and conversing with other “jihadists.” Who may themselves be chatbots.
Unlike the expense of pilots for a fleet of drones, a single user could “pilot” a group of chatbots, creating an over-sized impression in cyberspace. The deeper the modeling of human jihadists, the harder it will be to distinguish virtual jihadists.
I say “jihadists” for headline effect. You could create interacting chatbots for right/left wing hate groups, gun owners, churches, etc., in short, anyone seeking to dilute surveillance.
(Unlike the ACLU or EFF, I don’t concede there are any legitimate reasons for government surveillance. The dangers of government surveillance far exceed any possible crime it could prevent. Government surveillance is the question. The answer is NO.)
From the webpage:
Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.
This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.