Note: Although this article is written as a five-part series, it is not necessary to read each part in order, nor is it necessary to read all parts, as each part has been written as a standalone piece.

Introduction

In Part III of this series (linked below), inspired by a quote from Deep Learning with PyTorch (further below), to build toward propositions for (1) verified humans on social media platforms and (2) transparency regarding the use of language models to generate text, I discussed the possibility of a machine forming a thesis by exploring both mechanical desires and algorithmically generated worldviews.

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Note: Although this article is written as a five-part series, it is not necessary to read each part in order, nor is it necessary to read all parts, as each part has been written as a standalone piece.

Introduction

In Part II of this series (linked below), inspired by a quote from a guidebook called Deep Learning with PyTorch (further below), I explored the changing nature of communication with human language to build toward propositions for (1) verified humans on social media platforms and (2) transparency regarding the use of language models to generate text.

In this article, to answer the…

Note: Although this article is written as a five-part series, it is not necessary to read each part in order, nor is it necessary to read all parts, as each part has been written as a standalone piece.

Introduction

In Part I of this series (linked below), inspired by a quote from a guidebook called Deep Learning with PyTorch (further below), I embarked on a journey toward propositions for (1) verified humans on social media platforms and (2) transparency regarding the use of language models to generate text by discussing the unimportance of the semantic classification of the actions and capabilities…

Note: Although this article is written as a five-part series, it is not necessary to read each part in order, nor is it necessary to read all parts, as each part has been written as a standalone piece.

Introduction

As I read, I often get lost in the text, my thoughts diverging from the author’s words toward something related yet different. This process seems almost like a conversation between the author and me, with a person’s words provoking my response. At times, I enjoy this aspect of reading, but more often, it is quite distracting.

Still, if I find the thoughts…

Artificial Intelligence, Machine Intelligence, Opinion

An interesting concept from literary theory states that if a reader wants to make sense of a text, then he will find an interpretation of that text that is consistent with his own world view, or perhaps more precisely, with his view of the world he supposes the text to concern. Oftentimes, to fulfill such a desire requires the reader to fill gaps in his own knowledge, as well as gaps in the logic or rhetoric of the writer by reading between the lines. …

The Natural Language Processing (NLP) group at Stanford University made publicly available the list of papers from their CS 384 seminar on Ethics and Social Issues in Natural Language Processing, and so I have been on a bit of a reading binge trying to learn more about this fascinating and important topic.

In this article, I want to explore the use of analogies for identifying biases in word embeddings by focusing on two papers on the topic: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (2016) [1] and Fair Is Better than Sensational: Man Is…

Opinion

As machine learning becomes more ubiquitous and its software packages become easier to use, it is natural and desirable that the low-level technical details are abstracted away and hidden from the practitioner. However, this brings with it the danger that a practitioner becomes unaware of the design decisions and, hence, the limits of machine learning algorithms. [1]

In this article, I want to discuss my experience in a formal degree program in analytics and draw comparisons with an autodidactic approach (here, defined to include books, projects, YouTube tutorials, MOOCs, and any other à la carte learning that cannot be summed…

Language is always situated, i.e., it is uttered in a specific situation at a particular place and time, and by an individual speaker with all the characteristics outlined above. All of these factors can therefore leave an imprint on the utterance. [1]

I have a bit of an obsession with language and communication that is difficult to summarize with a pithy list, but I will try: At any given time, I am reading between 5 and 25 books, and I am learning between 1 and 4 languages, with varying degrees of success; I am growing a note of phrases I…