In this post: SFT and RLVR on Swedish MedQA (10 min read) To practice medicine in Sweden, foreign medical doctors who want to validate their license are first required to pass a multiple-choice test. Given around 140 questions with 5 answer options each, they must reach at least 60% accuracy to pass. This theory test is challenging, with a pass rate of just about one in four and only 54% of…
In this post: LLM self-hosting and serving with vLLM and SGLang (8 min read) As the ecosystem around open-weight LLMs expands, so do its options for inference serving. Open-source software enables self-hosting these models on varied hardware, from outright GPU clusters to workstations, laptops and even smartphones. This post shares a first-hand account of trying to get these models to speed, from…
In this post: A survey of the open LLM ecosystem (7 min read) Leading model providers like Anthropic, OpenAI and xAI have been pushing the envelope by developing ever more capable Large Language Models (LLMs). These closed-weight frontier models are proprietary and served through APIs or subscription plans. The big bet of the AI race, on which over $1 trillion have been invested , is that…
In this post: A reality check from leading researchers (6 min read) In the heat of the ongoing AI summer, a chilling effect is starting to spread from the growing cracks between marketing claims and the underlying technology. To a backdrop of comparisons with the dot com bubble, some of the most accomplished minds of the field are beginning to revise their projections for what can realistically be…
In this post: A look back at pioneering thoughts on AI research (7 min read) I recently stumbled upon a research proposal that must have raised a lot of eyebrows and received widespread attention in machine learning circles: ‘We propose that a 2 month, 10 man study of artificial intelligence be carried out […]. The study is to proceed on the basis of the conjecture that every aspect of learning or…
In this post: A book review (5 min read) In January this year, Chip Huyen published her newest book ‘AI Engineering’ , which quickly made waves online. Having read her previous book ‘Designing Machine Learning Systems’ (2022) , which I warmly recommend, I wondered what could possibly remain to be covered in 500+ additional pages. It really turned out to be something entirely different, and this…
In this post: From Fully Convolutional Networks to TotalSegmentator (10 min read) Amidst the ongoing hype around the growing capabilities of large language models, it can be curious to note how earlier predictions about machine learning have stood the test of time. Autonomous driving and radiology in particular were considered obvious candidates for automation, starting with the deep learning boom…
In this post: An attempt to reconstruct Ilya Sutskever's 2020 AI reading list (8 min read) I recently shared a summary of a viral AI reading list attributed to Ilya Sutskever, which laid claim to covering ‘ 90% of what matters ’ back in 2020. It boils down the reading items to barely one percent of the original word count to form the TL;DR I would have wished for before reading. The viral version…
In this post: Ilya Sutskever's AI Reading list in ~120 words per item (15 min read) Earlier this year, a reading list with about 30 papers was shared on Twitter . It reportedly forms part of a longer version originally compiled by Ilya Sutskever, co-founder and chief scientist of OpenAI at the time, for John Carmack in 2020 with the remark: ‘If you really learn all of these, you’ll know 90% of…