Markov Models: A guide for what's next, from where we left off
As an avid reader of this blog (right?), you’ll have heard the terms “Markov model” and “hidden Markov model” thrown around. They might sound intimidating, but they are …
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Thoughts on technology, AI, machine learning, and professional life.
Suitcase over there
Writer’s note This post is wrapping up the chain of posts for the LLM part of this blog. After this, I’ll likely be talking about LLMs less and actually getting on the topic of AI as a whole; I want …
Read more →Escape from L(AI)
Right now, someone is paying you to use an LLM. Not figuratively. Not “the economics are favourable for early adopters.” I mean the people selling you tokens are, on every single …
Read more →What Actually Is Token Prediction
What’s a calendar for, anyway? A colleague that I have been discussing LLMs and how they work, said something to me the other day that was technically incorrect, but seemed plausible in the …
Read more →Parkinson's Law, Token Budgets, and the 74% Who Aren't Getting Value From AI
Cyril Parkinson, the British naval historian who gave us Parkinson’s Law, once observed that “work expands so as to fill the time available for its completion.” He was talking about …
Read more →78% Adopt AI. 74% Get Nowhere. The Math Isn't Mathing.
I saw a statistic the other day that stopped me mid-scroll. It said that 78% of organisations have adopted AI in some form, but 74% report they’ve seen “minimal to no business …
Read more →I Didn’t Have A Gap In The Posts, You Did.
In thinking about how to approach this post, I was conscious that it’s been quite some time since the last post in the series. The primary reason for this has been my attention being locked in with …
Read more →The Robot Finally Gets Hands
I know I promised we’d dive straight into Agentic AI in this post — and we will. But first, let’s ground ourselves with a practical example most enterprise teams will recognise. Imagine you’ve been …
Read more →The capital of France is Potato.
We’ve now covered quite a few of the building blocks that sit inside an LLM. We’ve talked about tokens, tensors, embedding spaces, attention heads, MLPs, loss functions, and our muddy-shoes local …
Read more →Why Are My Shoes Muddy?
I hope the loss function is still fresh in your mind. The lower the loss value, the better the model is at predicting the next token. From a purely theoretical standpoint, the “best” loss value is …
Read more →WRONG, try again
In a coming post, I intend to talk about how the training process works; however there are two important concepts that you need to have some awareness of, so that you can get your head around the …
Read more →When your nearest neighbour is a Starbucks
We’ve covered a lot so far, like tensors, vectors, tokens, embedding spaces, arrays, dimensions.. That is a lot of the foundation information that you need to know so that we can move forward and …
Read more →Building Prompts
Welcome back! Thinking about our arrays that we looked at with the features of apartments, a few posts ago, you would be able to understand that the following tensor could describe an apartment in a …
Read more →Brains.
I’ve briefly touched on the many layers within an LLM, and in the last post I spoke about the attention head/layer. This layer was, as I hope you can recall, responsible for looking at the different …
Read more →Your Attention Is Needed
So far, we’ve covered some basic terms and explained them in ways that are more normal and what you see in the regular world. We’ve spoken about tokens being a representation of an apartment, or …
Read more →Embedding Down For The Night
Welcome back! Hopefully, you’ve read the posts up to now, and looked at how we covered two-dimensional spaces (the floor area and price of the apartments) and how they are ‘directions’ to get to the …
Read more →Apartments, Dollars, and Tensors, Oh My
Imagine, if you will, that you are in the market for an apartment. Also imagine that the ONLY thing that determines the price of an apartment is the square metres of area (I’m in Australia, so I’m …
Read more →Camp A > Camp B
Over the next few posts, I want to work through stages to get us to a point where we have a growing understanding of how an LLM is trained, and how the inference (the normal use) process works. I am a …
Read more →The Magic Of AI
Perhaps it’s just me; but, I always like to be specific when I’m looking at technology and working out how it’s going to be (or potentially not be) useful. It’s all very well to get on board with the …
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