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502 articles · updated 24/7 from 100+ sources
The Convergence of Proprietary and Open Source LLMs
A few months ago, I wrote about building LLM systems with self-hosted, open source models that beat…
The AI summer
My old boss Marc Andreessen liked to say that every failed idea from the Dotcom bubble would work now. It…
The VR winter continues
It can feel a little odd to write about anything other than generative AI these days, but I sometimes remind…
Apple intelligence and AI maximalism
No-one outside Apple has really used any Apple Intelligence features yet. It won't launch until the autumn,…
Building AI products
I will fly to India on Monday for a brief trip, and so I just spent an hour struggling through a very buggy…
Ways to think about AGI
The manuscript for ‘A Logic Named Joe’ In 1946, my grandfather, writing as ‘Murray Leinster’,…
AI and problems of scale
There’s a story in one of Georges Simeon’s 1930s detective stories that I think about sometimes when…
How to Beat Proprietary LLMs With Smaller Open Source Models
IntroductionWhen designing systems that use text generation models, many people first turn to proprietary…
Looking for AI use-cases
This image comes from a book by Martin Honeysett called ‘Microphobia’, published in 1982. It’s full of…
A Guide to Structured Generation Using Constrained Decoding
IntroductionWe often want specific outputs when interacting with generative language models. This is…
The problem of AI ethics
In the late 1990s, the UK Post Office deployed a new point-of-sale computer system, built for it by Fujitsu.…
Who cares about tech regulation?
When I look at the engagement on this website, and in my newsletter, it’s very clear that anything I write…
Modern Data Engineering and the Lost Art of Data Modelling
It wasn't long ago that the constraints imposed by finite storage and compute meant that data modelling was a…
Machine Learning in the Life Sciences Has a Data Problem
The world is captivated by the unrelenting progress of generative artificial intelligence (AI). Yet amidst…
Approximating Shapley Values for Machine Learning
In a previous post, I explained the theory behind Shapley values. I also explained that calculating Shapley…
Gnillehcs' Model of Integration
In the 1960s, Thomas Schelling developed a computational model that demonstrated how even a mild preference…
How Shapley Values Work
Shapley values - and their popular extension, SHAP - are machine learning explainability techniques that are…
Industry Perspective: Tree-Based Models vs Deep Learning for Tabular Data
For tabular data, gradient boosted trees (GBTs) perform better than neural networks (NNs). This is common…
4 Pandas Anti-Patterns to Avoid and How to Fix Them
pandas is a powerful data analysis library with a rich API that offers multiple ways to perform any given…
Supervised Clustering: How to Use SHAP Values for Better Cluster Analysis
Cluster analysis is a popular method for identifying subgroups within a population, but the results are often…
Utility vs Understanding: the State of Machine Learning Entering 2022
The empirical utility of some fields of machine learning has rapidly outpaced our understanding of the…
Explaining Machine Learning Models: A Non-Technical Guide to Interpreting SHAP Analyses
With interpretability becoming an increasingly important requirement for machine learning projects, there's a…