Ai

Science progresses via a collapsing of ontology into the new. A new framework emerges that alters how we define reality (ontology). The old ontology collapses under the weight of what it can no longer explain. The new order of meaning emerges, whereby yesterday’s observations are redeemed from their isolation and reinterpreted as signs of a deeper order. Examples of this include the shift from the geocentric (Earth-centered) to the heliocentric model of the universe. Or the transition from Newtonian mechanics to Einsteinian relativity. ...

Second brains are have moved from personal archives into agent-readable cognitive infrastructure. The central constraint is dual legibility. The system has to make sense to me, in Obsidian, and it has to make sense to Claude in the repo. Throughout history, scholars, scientists, philosophers, and writers have kept some version of an external thinking system. Commonplace books serve as working scrapbooks to store arguments, fragments, and half-formed ideas, to be returned to later. They were not kept as diaries in the introspective sense. John Locke even wrote A New Method of Making Common-Place-Books, offering a system for arranging quotations, ideas, and speeches by subject and category. Cognitive scientist Don Norman, in Things That Make Us Smart, explored how humans become smarter by building external aids that extend memory and reasoning. ...

… when people ask about the meaning of life as if it were the job of our cosmos to give meaning to our existence, they’re getting it backward: It’s not our Universe giving meaning to conscious beings, but conscious beings giving meaning to our Universe. I remember reading Life 3.0 before starting university and feeling like someone had cracked open the edge of reality for the first time. It made intelligence feel like the deepest force in the universe. I didn’t think every prediction would literally happen. Up until then, I still viewed the world in relatively normal categories: careers, countries, companies, technology. Tegmark reframed all of it as information systems recursively optimizing themselves. Humans stopped looking like the endpoint of evolution and started looking like a transitional phase. ...

This is pt.2. Pt.1 covers the raw materials overview. The first post catalogued what AI hardware is made from. This one tries to answer a more specific question: where in the supply chain does the actual geopolitical leverage sit? The assumption most people carry is that it sits at extraction. For almost every mineral that matters for AI hardware, China’s share of refining and processing is substantially higher than its share of mining. Often dramatically so. ...

I wanted to think about potential metrics for modelling POS - the following is a living document, open iterative work on this subject. Related posts will update on findings, failures and further ideas to be tested! Predicting probability of success (POS) in biotech should start with biology. Evaluating the mechanism of action (MOA) of therapeutic targets, differentiating between upstream and downstream interventions, and, how the position of a target within a biological pathway influences POS. Most models overweight market size, management quality, or phase transition statistics and underweight whether the mechanism actually has durable control over the disease system. The key question is where the target sits within the network. ...

I want to talk about a rabbit hole I have fallen down since reading a paper on The Promises and Predicaments of Federated Learning in Healthcare. Last year, I had the privilege of working with an awesome team, we explored ideas applying machine learning to tackle data interoperability challenges within our healthcare system. The problem: disparate data formats, strict privacy regulations, and the sheer volume of sensitive patient information scattered across multiple institutions. Our thesis was that the near-term impact of data interoperability is obvious, time saved and admin (huge burden to the NHS) reduced. We validated this with our study across thousands of hospital reported EHR systems, and with firsthand NHS clinical experience in the team. But the bigger picture? The data unlock. ...

Attachments Scenario Impact of AI.pdf

LLMs like GPT-o1 are demonstrating remarkable capabilities in knowledge and reasoning. Solving challenging mathematical problems, assisting scientists in writing proofs, retrieving related works, generating code, and discovering patterns. These feats hint at a future where AI doesn’t just follow human instructions but contribute creatively to human endeavours. The Promise and the Question A growing number of researchers propose autonomous agents that can generate and validate new ideas independently. CrewAI is a framework designed to build autonomous multi-agent systems that can generate and validate ideas without human intervention. ...

A recent study involving 759 startups across four randomised control trials found that teaching entrepreneurs to adopt a scientific approach significantly boosts revenue. Founders trained in this way become quicker to abandon bad ideas, make strategic pivots more effectively, and enhance their overall performance. Key Insights: Entrepreneurs who apply scientific thinking are more likely to terminate non-viable projects earlier, conserving resources and time. These entrepreneurs pivot more effectively, making a few well-considered changes rather than none or many unfocused ones. ...

Attachments TALOS pitch deck.pdf