Thoughts on AI

hello@bill.dev (Bill) — Fri, 06 Feb 2026 18:08:38 +0000

As I look around the co-working space around me, ‘AI’ is everywhere. To my left, someone on Zoom discussing the future of AI powered financial advice. To my right, two people pairing. Pairing on unpicking the mess generated by code generation. Directly in front of me, someone with two laptops. On one, Claude Code is burning through tokens while on the other they watch a show on Netflix.

I’ve found myself increasingly conflicted about Artificial Intelligence (AI). There appear to be two camps; the AI fanboys and the AI haters. Forced to pick one, I’d be closer to a hater than a fanboy. But I’m not forced to pick. My thoughts on AI are more nuanced. Not only that, they vary constantly. I’ll be honest, I find it difficult to avoid falling victim to recency bias, over-indexing on the most recent thing I’ve read. Unfortunately exposing myself to both extremes hasn’t helped me develop my own nuanced position. It’s left me feeling exhausted, bouncing around without settling. What do I think about AI?

@EmilyMBenderChatGPWhy2023

hello@bill.dev (Bill) — Wed, 04 Oct 2023 09:27:05 +0100

Author: Emily M. Bender
Full Title: ChatGP-why: When, if ever, is synthetic text safe, appropriate, and desirable?
Slides: Bender-GRAILE-2023.pdf
Talk: YouTube

Notes

There is a long list of examples of where the use of ChatGPT hasn’t turned out the way people expect.
Language form does not contain meaning and this explains why language models don’t understand.
Large language models are corpus models
Claude Shannon worked on early language models
Unigram language model models frequency of single words
Bigram language model models frequency of words given previous word
Trigram language model models frequency of words given previous two words
Good uses for language models include:
- Spell checkers
- Machine transcription
- Machine translation
- Text input
Neural networks are made of of perceptrons
A perceptron is a simplified model of a neuron
Transformer architecture is an arrangement or perceptrons
Language models use word embeddings
The number of words in training data determines the size of a language model
Generative AI is a mis-use of a classification and ranking tool
Generative AI produces plausible output not intelligence
In order to determine whether a machine can understand and infer meaning, we need definitions understanding and meaning.
Language competency makes it hard to separate form from meaning
Form refers to the marks on a page for language, the arrangement of pixels for images or video, etc.
Language meaning is the relationship between form and something external
Understanding is the recovery of communicative intent from form
Virtual assistants can understand limited instructions
Language models exposed only to form can never learn meaning
Language models do not learn the same way as babies
Babies learn the relationship between form (sound, mouth movement) and meaning by forming connections with external cues that hint at communicative intent.
The Octopus Paper show that form does not contain meaning
Large language models have a significant environmental impact
Environmental cost of large language models impacts marginalised communities
The contents of the internet do not represent a balanced view of humanity
The young and those from developed countries are more likely to have contributed to the volume of work available on the internet.
Sampling the internet without bias is hard
Large language models are too big
Generative AI output does not contain communicative intent
We bring our own understanding to language form
When reading generative text, it is important to remember that the inference of meaning is our own.
A Stochastic Parrot refers to the stitching together of form without meaning
Coherence is in the eye of the beholder
Synthetic text lacks accountability
There is no Who behind generative text
Generative AI pollutes the information ecosystem
Information retrieval is a terrible use-case for a large language model
The more accurate generative text becomes the more dangerous it is
Chatbots hide the sources of the information they regurgitate
Responsible use-cases for generative AI include:
- where the only thing that matters is form
- text must not confuse author with a person
- text needs to clearly articulate biases
- consider labor practices
- consider data theft
Good use-cases for generative AI include:
- a dialogue partner for language learning
- a non-playable character
- writing support
Good use-cases for generative text must consider the costs
Be a critical consumer of AI
We need to understand how the AI technology was evaluated in the context in which it is being used.
We need to understand who benefits from the use of AI instead of a human?
You are responsible for your use of generative text
We must insist on transparency of source material in the training data.
Talk to students about what generative AI is
Use of generative AI in education is a missed learning opportunity
Use of generative AI by students indicates broader problem

Further Information

Three podcasts worth subscribing to on AI:

Ai on Bill Glover

Thoughts on AI

@EmilyMBenderChatGPWhy2023

Notes

Further Information