Category: Machine Learning
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A gentle introduction to embeddings at the inaugural GenAI Network Melbourne meetup
I was thrilled to help kick-off the GenAI Network Melbourne meetup at their first meeting recently. I presented a talk titled Semantic hide and seek – a gentle introduction to embeddings, based on my experiments with Semantle, other representation learning, and some discussion of what it means to use Generative AI in developing new products…
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LLM WTF
What Token Follows (WTF) when generating text with a Large Language Model (LLM)? This notebook (you can run in Colab) and companion slide deck is my perfunctory (don’t say tokenistic) attempt to demystify GenAI for a general technology audience, specifically: how text is generated by LLMs. The premise of the notebook is to demonstrate and…
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Perspectives edition #27
I was thrilled to contribute to Thoughtworks Perspectives edition #27: Power squared: How human capabilities will supercharge AI’s business impact. There are a lot of great quotes from my colleagues Barton Friedland and Ossi Syd in the article, and here’s one from me: The ability to build or consume solutions isn’t necessarily going to be…
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A coding saga with Bard
Though but a footnote in the epic of coding with AI, I though it worth musing on my recent experience with Bard. Bard currently uses the LaMDA model, which is capable of generating code, but not optimised for it. The story might be different with Codey as protagonist (or is that antagonist?) I didn’t produce…
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Smarter Semantle Solvers
A little smarter, anyway. I didn’t expect to pick this up again, but when I occasionally run the first generation solvers online, I’m often equal parts amused and frustrated by rare words thrown up that delay the solution – from amethystine to zigging. The solvers used the first idea that worked; can we make some…
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22 rules of generative AI
Thinking about adopting, incorporating or building generative AI products? Here are some things to think about, depending on your role or roles. I assume you’re bringing your own product idea(s) based on an understanding of an opportunity or problems for customers. These rules therefore focus on the solution space. Solutions with generative AI typically involve…
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Reasoning About Machine Creativity
With the current interest in generative AI, I wanted to write a short post updating the framing I took in my older talk Reasoning About Machine Intuition (2017), which was intended for broad audiences to understand the impact and best application of AI solutions from multiple digital delivery perspectives. Bicycles and automobiles share some features…
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Nerfing along
NeRFs provide many benefits for 3D content: the rendering looks natural while the implementation is flexible. So I wanted to get hands on, and build myself a NeRF. I wanted to understand what’s possible to reproduce in 3D from just a spontaneous video capture. I chose a handheld holiday video from an old iPhone X…
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Synthesising Semantle Solvers
Picking up threads from previous posts on solving Semantle word puzzles with machine learning, we’re ready to explore how different solvers might play along with people while playing the game online. Maybe you’d like to play speed Semantle against an artificially intelligent opponent, maybe you’d like a left-of-field hint on a tricky puzzle, or maybe…
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Second Semantle Solver
In the post Sketching Semantle Solvers, I introduced two methods for solving Semantle word puzzles, but I only wrote up one. The second solver here is based the idea that the target word should appear in the intersection between the cohorts of possible targets generated by each guess. To recap, the first post: Solution source…