Thoughts on technology, AI, machine learning, and professional life.
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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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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