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LLMs are left-leaning models
Life, James Hein, Published on 07/05/2025
» A while back I wrote about the political bias in Large Language Models (LLMs). Since then the models have evolved and David Rozado has conducted more recent tests based on four of the popular political orientation tests. Using the Political Compass, Political Spectrum, Political Correctness and Eysenck tests, he worked with xAI Grok 3 beta, Google's Gemini 2.5 pro, Deepseek V3, OpenAI GPT 4.1 and Meta's Llama 4 Maverick. In all but one of the tests Grok 3 was closest to the centre, and on average was the clear leader. All the models were still located in the Left Libertarian quadrant, with Grok just sneaking into a more Conservative area with the Eysenck test. These tests are of course but one way to measure the political leanings of any LLM. Overall however, it does still indicate the left-leaning bias in all models tested so far. If you want to see more details, you can visit David Rozado's substack.
Try not to drop your phone
Life, James Hein, Published on 25/03/2020
» The big news these days is coronavirus, the family name that covers the latest version, Covid-19. The impacts on tech are fairly obvious -- a lot of stuff is made in China these days. Apple, for example, is heavily invested in Chinese manufacturing and it has closed stores because the supply chain has run dry. Replacement iPhones are in short supply, so if you've damaged your phone, expect a potentially long wait before a full replacement is available. This also applies to replacement parts. So don't drop your phone. Employee travel is also discouraged while the virus spreads across the globe. At the time of this writing, stores in China have reopened and will not close. As an aside, the Australian Strategic Policy Institute (ASPI), a non-partisan think-tank, named Apple as one of 83 internationally known brands utilising the slave labour of Uighur Muslims.
AI -- what is it good for?
Life, James Hein, Published on 28/08/2019
» Why do we care about machine learning and the kind of Artificial Intelligence (AI) systems I mentioned last time? The amount of data being taken in by systems in modern times is outstripping the ability of humans to keep up. Enter machine learning systems to pre-process the information, highlight patterns and identify the bits and pieces that humans may find interesting. Key areas include fraud detection, whereby a set of rules is applied to data and flagged if those rules are detected. It also includes the age-old problem of the best delivery routes to bring manufactured goods to customers, with the additional benefit of using less fuel. A recent McKinsey report found that AI improved on "traditional analytics techniques" in 400 use cases across 19 industries and nine business functions. As far as the current situation, AI or machine learning is not an end state. Human intervention is still required to make sure the results make sense and also to ensure stuff isn't being missed in the process. There is also continuing training and refinement being regularly applied. It's a growing field and those organisations that are not at least aware of it could be left behind by their competitors. At least, until Skynet takes over.