FT: “Tim Minshall’s new book Your Life Is Manufactured presents a timely argument about the perils of losing touch with the art of making things…Minshall is an academic expert on innovation and technology, who leads a specialist manufacturing institute at Cambridge university. His book is part admiring hymn to the sophistication of modern factories and logistics systems, and part warning about the ways our modern global manufacturing system can go awry. “This system has developed two emergent properties we are less happy with: it is mind-bogglingly complex and worryingly fragile,” he writes. By emergent he means that when manufacturing supply chains are disrupted — as happened during the Covid pandemic, for instance — their various independent parts are often affected simultaneously. Problems ripple back and forth through the system, from material suppliers and industrial facilities to logistics and consumers, increasing the risk of shortages, or even outright collapse.”
NYTimes: “Microsoft…says it has created a new state of matter in its quest to make a powerful machine, called a quantum computer, that could accelerate the development of everything from batteries to medicines to artificial intelligence…Microsoft’s scientists said they had built what is known as a “topological qubit” based on this new phase of physical existence, which could be harnessed to solve mathematical, scientific and technological problems. With the development, Microsoft is raising the stakes in what is set to be the next big technological contest, beyond today’s race over artificial intelligence. Scientists have chased the dream of a quantum computer — a machine that could exploit the strange and exceedingly powerful behavior of subatomic particles or very cold objects — since the 1980s.”
Kevin Bryan’s 50 “correct moderate takes.” Among them: “22) Future of India very bright – English, young, educated, democratic, globally focused, successful expats…50) But at the end of day, success more important than words. Strong countries and societies and global orders are not build on words & soft power, but on growing liberty & prosperity.”
FT: “Google has built an artificial intelligence laboratory assistant to help scientists accelerate biomedical research, as companies race to create specialised applications from the cutting-edge technology. The US tech group’s so-called co-scientist tool helps researchers identify gaps in their knowledge and propose new ideas that could speed up scientific discovery. “What we’re trying to do with our project is see whether technology like the AI co-scientist can give these researchers superpowers,” said Alan Karthikesalingam, a senior staff clinician scientist at Google.”
WSJ on overhead bins in planes: “First, don’t be selfish, even if no one is looking. The rule on most airlines is to put your larger carry-on in the overhead bin and the smaller personal item under your seat…Don’t move someone else’s stuff without care and, when possible, asking if it’s OK…Another way to be considerate to fellow passengers: Get your essentials out of your carry-on bag before you stow it…The bottom line: There isn’t enough space in the overhead bins for all of our carry-on bags, no matter the size. So take your share and nothing more.”
Siddharth Pai: “Training a language model involves two key phases: pre-training and post-training. The former is when an AI model absorbs vast amounts of publicly available text and learns to generate human-like responses. This process results in a base model that possesses broad knowledge but lacks fine-tuning. It is also computationally expensive and requires enormous processing power, so it has so far been dominated by well-funded companies such as OpenAI and Google DeepMind. The post-training phase refines the model’s responses, making them more accurate and useful. Supervised fine-tuning involves human trainers creating question-answer pairs to help the model recognize good responses. OpenAI also pioneered reinforcement learning with human feedback (RLHF), where human reviewers score AI-generated responses for further refinement. These approaches are effective but costly, as they take a lot of human labour. DeepSeek’s breakthrough was its elimination of human involvement in post-training.”
