Tag: Language Insufficiency Hypothesis

What’s in a word?

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The winding down seems to take longer than the winding up. I’m trying to finish my Language Insufficiency Hypothesis manuscript, and I’m hung up on word choices. I’ve shared this chart before. My conundrum is the name of the Y-axis.

I’ve been calling it Language Effectiveness, but I’ve been losing sleep. Rather than effectiveness, should it rather be efficiency or efficacy? These are all near-synonyms, but they have nuanced differences, so I turned to ChatGPT.

The terms efficiency, efficacy, and effectiveness are often used interchangeably but have distinct meanings, especially in professional, scientific, and practical contexts. Here is a breakdown of their differences:

Efficiency

  • Definition: The ability to achieve a desired result with minimal waste of resources (time, money, energy, materials).
  • Focus: Resource utilisation.
  • Key Question: How well are resources being used to achieve the goal?
  • Example: A fuel-efficient car travels further on less petrol, minimising waste.

In short, efficiency measures how economically resources are used to accomplish a task.

Efficacy

  • Definition: The capacity to produce the desired result under ideal conditions.
  • Focus: Capability and potential.
  • Key Question: Does it work as intended when tested in controlled settings?
  • Example: A new medication shows high efficacy in clinical trials, curing 95% of patients in controlled environments.

In short, efficacy refers to the theoretical or tested success of an intervention under optimal conditions.

Effectiveness

  • Definition: The degree to which something produces the desired result in real-world conditions.
  • Focus: Practical success.
  • Key Question: Does it work in everyday use or the real world?
  • Example: A medication might be effective in treating 80% of patients in a hospital, accounting for factors like adherence and side effects.

In short, effectiveness measures how well something performs when applied practically, outside idealised settings.

Comparison

  • Efficiency concerns how something is done (minimal resource use).
  • Efficacy assesses if it can be done successfully (ideal conditions).
  • Effectiveness evaluates how well it works in real situations (practical application).

Think of a machine:

  • Its efficacy is its ability to perform a task perfectly when running in optimal conditions.
  • Its efficiency is how little energy or resources it uses while running.
  • Its effectiveness is how well it performs the task in a chaotic, real-world factory.

Interestingly, this was a helpful inquiry. Notice that the chart has three curves. My first choice seems to capture the entire framework, but the other two better capture the curves themselves.

The lower curve reflects efficiency — though effectiveness given the real-world circumstances. The middle curve represents efficacy. This is because conditions are ideal if the speaker and listener are on the same page and the speaker is a skilled communicator. The chart illustrates that even in this perfect situation, language communication cannot reach one hundred per cent for abstract concepts. The top curve illustrates the cognitive phenomenon that makes some people believe they communicate at a higher level than they do. This is akin to the illusory superiority cognitive bias, where people tend to believe they are above average.

I’m leaning towards naming the bottom curve language effectiveness and the middle curve the language efficacy horizon. Please stand by.

NB: If the cover image makes no sense, it’s because I entered ‘efficiency effectiveness efficacy’ into Midjourney, and this was one of the images it spat out.

Slice of Life

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This is a timeline of foundational ideas on which I’ve built my Language Insufficiency Hypothesis. I spent a day compositing this timeline in Adobe Illustrator. I hadn’t used Illustrator in decades. It’s got a lot of options, so I’ve been leveraging ChatGPT as a help guide. It seems the UI/UX could be improved, but I’m sure I’ll get used to it. I’ve got another couple dozen to go. I’m hoping a learning/efficiency curve kicks in.

WordPress wouldn’t accept or render my first few file types, even though they are listed as acceptable – SVG, PNG, TIF, WEBP – so I opted for gold, old-fashioned BMP, so it’s pretty hefty for inline rendering on a blog. I want to share, and so here is a late draft.

I’m no graphic artist, so it’s relatively primitive. I’ve been experimenting with colours, but the book is black and white, so I’ll probably just keep it the same.

There are a lot of data points to fit on this timeline, and I’m limited to a 6″ x 9″ form factor. Except for the first 3 entries, the items are to-scale by year. I have more information, but I can’t really fit it in. In fact, this started as two timelines. There was so much cross-over that I merged them into one, forcing me to remove some meta information and lose some detail. Design is hard.

It looks fine on the page. I don’t want to reduce the font sizes to cram in more info. I’ll probably put the more descriptive copy in an appendix, if not on a facing page.

Language Insufficiency Hypothesis, Chapter One

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Well, a word cloud anyway.

I’m busy focusing on the manuscript for Chapter One of my Language Insufficiency Hypothesis. Scanning the copy—over 4,000 words—I noticed some repetition. My eye caught ‘fundamental’, which I use often in speech as well. I decided to try a word cloud to illustrate common words.

As this is a hypothesis on the limitations of language, the fact that these are prominent is no surprise, but 34 instances of ‘fundamental’ seem fundamentally off—see what I did there? And this is only the first chapter of eight.

I’ll review the content to see if I can remove some instances—perhaps entire sentences—or choose a synonym to see where it leads.

The manuscript is in its fourth full revision, and it feels closer to completion. I aim for an early 2025 release date, but time will tell.

Ass usual, I employ AI (Claude, in this case) to do some heavy lifting.

Having analysed the chapter, I’ve identified several places where “fundamental” could be substituted or removed to enhance the linguistic diversity and rhetorical effectiveness of the text. Here are some examples:

  1. “These limitations, we argue, manifest not merely as practical challenges but as fundamental features of how meaning systems operate…”
    • Alternative: “These limitations, we argue, manifest not merely as practical challenges but as intrinsic features of how meaning systems operate…”
  2. “This theoretical perspective fundamentally reframes several longstanding problems in linguistics and philosophy.”
    • Alternative: “This theoretical perspective radically reframes several longstanding problems in linguistics and philosophy.”
  3. “These patterns suggest inherent rather than circumstantial constraints – limitations that emerge from language’s structure itself rather than from inadequate definition or imprecise usage.”
    • Here, “fundamental” could be omitted entirely without losing the core meaning of the sentence.
  4. “Understanding these constraints as systematic features rather than implementation failures provides a crucial theoretical foundation for the Language Insufficiency Hypothesis.”
    • Alternative: “Understanding these constraints as systematic features rather than implementation failures provides a vital theoretical foundation for the Language Insufficiency Hypothesis.”
  5. “These cognitive constraints manifest not as mere practical impediments but as necessary features of how meaning is processed and transmitted through human neural networks.”
    • Here, “fundamental” could be replaced with “essential” or simply omitted.

Notice that 3 of the 5 recommendations didn’t include the offending word. Rather, they already employed synonyms. This gives us insights into how LLMs translate language with fuzzy logic. Perhaps that’s an article for another day.

Where you from, Homie?

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This skit is a comical take on in-group versus out-group language insufficiency. It’s a couple years old, so you may have seen it before.

This video illustrates how easy it is for miscommunication to occur in mixed-group settings.
Trigger Warning: The humour is a bit weak and the focus is on stereotypes. If this isn’t quite up your street, just move on. Nothing to see here.

Beware the Bots: A Cautionary Tale on the Limits of Generative AI

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Generative AI (Gen AI) might seem like a technological marvel, a digital genie conjuring ideas, images, and even conversations on demand. It’s a brilliant tool, no question; I use it daily for images, videos, and writing, and overall, I’d call it a net benefit. But let’s not overlook the cracks in the gilded tech veneer. Gen AI comes with its fair share of downsides—some of which are as gaping as the Mariana Trench.

First, a quick word on preferences. Depending on the task at hand, I tend to use OpenAI’s ChatGPT, Anthropic’s Claude, and Perplexity.ai, with a particular focus on Google’s NotebookLM. For this piece, I’ll use NotebookLM as my example, but the broader discussion holds for all Gen AI tools.

Now, as someone who’s knee-deep in the intricacies of language, I’ve been drafting a piece supporting my Language Insufficiency Hypothesis. My hypothesis is simple enough: language, for all its wonders, is woefully insufficient when it comes to conveying the full spectrum of human experience, especially as concepts become abstract. Gen AI has become an informal editor and critic in my drafting process. I feed in bits and pieces, throw work-in-progress into the digital grinder, and sift through the feedback. Often, it’s insightful; occasionally, it’s a mess. And herein lies the rub: with Gen AI, one has to play babysitter, comparing outputs and sending responses back and forth among the tools to spot and correct errors. Like cross-examining witnesses, if you will.

But NotebookLM is different from the others. While it’s designed for summarisation, it goes beyond by offering podcasts—yes, podcasts—where it generates dialogue between two AI voices. You have some control over the direction of the conversation, but ultimately, the way it handles and interprets your input depends on internal mechanics you don’t see or control.

So, I put NotebookLM to the test with a draft of my paper on the Language Effectiveness-Complexity Gradient. The model I’m developing posits that as terminology becomes more complex, it also becomes less effective. Some concepts, the so-called “ineffables,” are essentially untranslatable, or at best, communicatively inefficient. Think of describing the precise shade of blue you can see but can’t quite capture in words—or, to borrow from Thomas Nagel, explaining “what it’s like to be a bat.” NotebookLM managed to grasp my model with impressive accuracy—up to a point. It scored between 80 to 100 percent on interpretations, but when it veered off course, it did so spectacularly.

For instance, in one podcast rendition, the AI’s male voice attempted to give an example of an “immediate,” a term I use to refer to raw, preverbal sensations like hunger or pain. Instead, it plucked an example from the ineffable end of the gradient, discussing the experience of qualia. The slip was obvious to me, but imagine this wasn’t my own work. Imagine instead a student relying on AI to summarise a complex text for a paper or exam. The error might go unnoticed, resulting in a flawed interpretation.

The risks don’t end there. Gen AI’s penchant for generating “creative” content is notorious among coders. Ask ChatGPT to whip up some code, and it’ll eagerly oblige—sometimes with disastrous results. I’ve used it for macros and simple snippets, and for the most part, it delivers, but I’m no coder. For professionals, it can and has produced buggy or invalid code, leading to all sorts of confusion and frustration.

Ultimately, these tools demand vigilance. If you’re asking Gen AI to help with homework, you might find it’s as reliable as a well-meaning but utterly clueless parent who’s keen to help but hasn’t cracked a textbook in years. And as we’ve all learned by now, well-meaning intentions rarely translate to accurate outcomes.

The takeaway? Use Gen AI as an aid, not a crutch. It’s a handy tool, but the moment you let it think for you, you’re on shaky ground. Keep it at arm’s length; like any assistant, it can take you far—just don’t ask it to lead.