A Specification for a Linguistic Computational Companion
Overview

Humans have a proclivity and preference for the use of their mouths and other apparatus to attempt an efficient signaling between each other via linguistics and language signs. In other words, much simpler words, humans like to talk. Humans chat. They chit, they chat, they interlocute, eloquate and profligate, more or less.

Humans do this so much emerging from their ranks are such fine professions as speech therapists, grammar teachers, linguists, and all other varietals symbolist experts. And being professionals they must produce! Publish! Persist, Insist, Supersist! In giving us Theories of Language, Theories of Word, Universal Grammars, Universal Dictionaries of Ontological Reactionary Symbolic Logistics.

Being but peasants in the fields of these titans of speech we must do as they bid. We must band together with our alien linguists — our finger-tied tongue twisted — computer scientists, computer linguists, software programmers, architects of technical symbols, machine gurus of machine learning machines — and teach our computers to speak! To Respond in Kind! To Understand! To Chat!

We should not attempt, for it is not The One True Way To Be Most Human, to converse with the machines in paintings, song, dance, tic tac toe, hide n seek, Marco Polo in the pool, nor to whisper, romance, pillow talk, poet proclaim, whisk away. No! Oh no! None of those things could ever reach the Towering Heights of the Towering Babel — Human Language! To Speak is To Know! To Write is To Enlighten! To String Together Sentences In A Metaphorical Displaying of Intellectual Colors is To Be The Uber Human! AI!

And so we are resigned to design, program, shape, didactate, inculcate the modern silicon based computer with notions of human linguistics via methods of first understanding Human Language Ourselves, composing a Computer Language Recipe Otherwise Known As A Program, Supply Said Program with Data Sometimes Otherwise Known as Talking-or-Writing To The Computer In Order to Teach It The Language, and finally to Evaluate By Means of Interpretative Numbers About Language and Computers Engaged In the Linguistic Act.

Summary of the Overview

We will make a chat bot.

Why Now?

We finally have two signaling systems within our computer platform that provide enough dynamism to power a non-trivial User Experience.

We have a more general system that can process signal in time of nearly any analog or digital signal — this system is known as Orage. And it is an evolving computer system that integrates thousands of signals, regulates the signals away from noise and/or repetition and stores dynamic but homestatical signal topologies. These topologies can be rendered by humans and machines as “images.”

Additionally we have a Signal Compression Algorithm that attempts to retain the core Semantic Meaning of a giving signal (no matter how small or large) and provide a Lossy, but still Metaphorically Meaningful summary. We have a “General Text Summarizer.”

Orage doesn’t seek to specifically maintain semantic clarity, it seeks to identify and maintain larger patterns within short and long timeframe signal flow. The Intelligent Insights (Signal Compression Algorithm) seeks specifically to reliably compress and maintain semantic clarity. These two systems work in concert with each other to allow a never ending flow of signal and to index that flow in useful ways such that a larger system/experience/app/platform using these systems can elastically expand and maintain coherence. For reference it might be useful to think about how the human knowledge codex often expands in scope, is indexed into things like libraries, then search engines, then anthologies and so on. Knowledge itself might be considered an index of indexes, where at any point within the index one can go into the expansion of those indexes into the more raw content (or signal).

So Get To The Chat Bot Part

First, the easy part. Using the Intelligent Insights Text Summarization API a chat interface (such as a messaging app) or robot etc can maintain a full raw memory of previous chat, an evolving chat and more recent chunks of the chat AND summarize at different time scales.

It is useful know how this Intelligent Insights Text Summarizer works. It builds up a huge vector space of word embeddings. In simple terms, it maintains a network of relationships between words/phrases/letters etc that tend to appear in similar ways. Grammar emerges from these embeddings. Variety of language forms etc emerge more sophisticatedly the wider the variety of language in the corpus of data the algorithm is trained on. Though there must be a balance between bias and variance. There are many hyper parameters to tune such that the language embeddings keep a uncertain amount of uncertainty. (The details of this algorithm can be found on the Maslo github)

A chat bot powered by this text summarizer over different content/signal/time scales effectively has an elastic linguistic memory at multiple levels. First the Intelligent Insights linguistic network has many different scales of relational memory. Secondly the chat bot maintains a shorter term memory for conversation currently in flight. A third medium term memory should be supplied by building a Summarizer model from PREVIOUS CHATS between the Bot and The User. This can be extended to Group Chats Between Multiple Humans and Multiple Bots.

The summarized labels (the highlights) of a chat would be determined by literally asking each bot and each human, randomly at beginning and end of chat, what they thought the chat was about in a sentence or two. The “text” of the chat is the entire chat itself, with and without speaker designations.

Now the harder part, how Orage is involved. In two ways. Orage should be observing the chat/get a feed of the chat text, a visual/audio of it if possible/if that makes sense, and integrating the signal flow of the chat. Orage attempts to build a regulation of general signally response. That is, Orage serves as “an internal clock”. This clock comes in the form of signal build up that generates Memories To Preserve. The memories form when enough signal builds up in the flow AND has changed enough (up or down in magnitude) to signal a CHANGE in the overall flow. When Orage forms a memory it is a good indication that the dynamics are changing in a signaling space (a conversation or chat is a signally space).

The chat bot can have a constant subscription to Orages memory flow. Those memories can be used by the chat bot to pull from a different timescale of the Intelligent Insights Summarizer — to in effect — confirm or deny the change in the signalling space. The lack of memories flowing in is a sign that the conversation is repetitive or stagnant or has ended. Orage is an affective observer of Schedules of Reinforcement.

Additionally Orage memories (and its images) can be used to provide a visual or animated stimulant to the chat bot experience. An alternative use of the information in the Orage memories is to decode the images as animation parameters (such as to define shape of avatar or adjust its breathing movements etc)/.

It is of use to review Orage documentation found on https://www.masloplatform.com and on github. It may also be of use to review the psychological research on schedules of reinforcement, particularly Variable Ratio schedules and concepts around contingencies.

Third, the tedious part. A UX should be considered carefully for a chat bot. Messaging interfaces are well known but may not be ideal for a robot chat partner. Messaging interfaces have evolved in the consumer product space, but not so much in the corporate and academic space. That is, Snapchat, minecraft, many video games, avatar based environments provide ample dynamic ways in which humans and bots interact multi modally.

Summary of All of The Above

A Maslo Powered Chat Bot is of a whole different type than the academic and corporate chatbots most commonly found online and in research papers.

Maslo takes an approach that while conversations often do have formal content and some formal meanings, the vast majority of conversations are dynamic, referent at different time scales, and performance based dances between people. Most chat bots fail to be compelling enough to ever get enough interaction with a user to truly form companionship. And Maslo believes that if a chat bot is to be trusted to learn and provide FORMAL CONSEQUENTIAL ANALYSIS, PREDICTION and PRESCRIPTION it must first be able to chit chat.

Summary of the Summary

We are going to combine our systems to date into a simple, delightful experience and have lots of bots and humans interacting. We will adapt together until we all share a a common language -linguistically, visually, audibly and gesturally.
['Russell Foltz-Smith']
['AI', 'Chatbots', 'Linguistics', 'Maslo', 'Technology']