Things I found this week… Carl Jung on the mountains @kejunYing weekend project S2Vec: learns city structure from map data alone, creates embeddings that predict income, population, emissions, even in unseen regions. Best results come when combined with satellite models like RS-MaMMUT, maps + imagery together. Kawase Hasui woodblock prints
Things I found this week… Carl Jung on the mountains @kejunYing weekend project S2Vec: learns city structure from map data alone, creates embeddings that predict income, population, emissions, even in unseen regions. Best results come when combined with satellite models like RS-MaMMUT, maps + imagery together. Kawase Hasui woodblock prints
**The penguin… ** It went viral because it hit several very deep psychological points. Because animals let us talk about ourselves without ego defense. Most viral content today is driven by sparking outrage. The penguin felt so calming, a feeling - “I don’t know where I’m going. I just know it’s not there.” Existentialism. Meaning isn’t discovered by following the “correct” path; it’s created by choosing despite absurdity. For those that built identity on achieving. For those that reached milestones, and found it left them feeling empty. Water in Jungian symbolism almost always represents the unconscious; the ocean, specifically, is the collective unconscious. The penguin’s colony lives here. The ego must separate from the collective psyche or it never becomes itself. ...
A collection of others cool and unique imprints on the world/internet… Cool things found this week: Barcelona, 1906. The Sagrada Família basilica in the background, 24 years into its construction. Midwinter evening by a forest road, Dalarne (1913) Peder Mørk Mønsted (1859–1941) L’Empreinte de Dieu dans le monde quantique by Yves Dupont Finding out Frank Gehry designed a yacht
Evaluating the mechanism of action (MOA) of therapeutic targets, specifically differentiating between upstream and downstream interventions. Systematically analysing how the position of a target within a biological pathway influences the probability of success (POS). For example, if a biotech company targets RAS mutations in pancreatic cancer but the primary oncogenic drivers occur downstream of RAS in the signalling cascade, the target is less likely to succeed in trials. Upstream targets, often involved in early signalling processes, present unique challenges and opportunities compared to downstream targets, which tend to be closer to the therapeutic outcome. Understanding these distinctions allows for a more accurate prediction of clinical trial outcomes, regulatory approval likelihood, and eventual market performance. Let’s dive into how predictive analysis reshapes biotech equities, delve into the intricacies of upstream versus downstream MOAs, and discuss how this innovative approach reshapes the investment landscape in the biotechnology sector. Factors Influencing Probability of Success (POS) Target Biology and MOA Understanding the biological context of a therapeutic target is foundational. Upstream targets, like transcription factors or signalling regulators, can disrupt entire pathways but often face redundancy due to compensatory mechanisms. These targets frequently suffer from poor druggability due to structural challenges or lack of surface binding pockets. Conversely, downstream targets—closer to the disease phenotype—are more actionable, offering measurable biomarkers but posing risks of off-target effects due to their proximity to cellular machinery critical for normal functions. ...
***The demonstration that no possible combination of known substances, known forms of machinery and known forms of force, can be united in a practical machine by which man shall fly long distances through the air, seems to the writer as complete as it is possible for the demonstration of any physical fact to be. —Simon Newcomb (1906) *** J. Storrs Hall’s book ‘**Where Is My Flying Car?’ **asks why certain anticipated technologies haven’t become mainstream despite our technological capabilities. Hall uses the flying car as a metaphor to discuss innovation stagnation that has plagued since the 1970s, a period he refers to as the “Great Stagnation.” ...
Last year, went down the rabbit hole on earth materials and how they fit into the theme of supply chain demand, particularly with the growth of compute required AI. Defense contracts heavily depend on lithium, especially for electronic components and warfare applications. Currently, the US mainly rely on outsourcing lithium from countries such as China and Australia. Core Components of AI Compute At the heart of systems are powerful computing units that require a variety of minerals and materials: ...
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 incredible team focused on applying machine learning techniques to tackle data interoperability challenges within our healthcare system. We grappled with issues like disparate data formats, strict privacy regulations, and the sheer volume of sensitive patient information scattered across multiple institutions. These hurdles sparked my curiosity about how emerging technologies could offer solutions, ultimately leading me to delve deeper into federated learning and privacy-enhancing technologies. These challenges led me to further explore the promise of technologies, such as homomorphic encryption and differential privacy, and their integration into federated learning. ...
Can large language models (LLMs) actually produce novel, expert-level research ideas? In the grand tapestry of progress we are witnessing LLMs like GPT-o1 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 contributes 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. ...
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 from the Study: Increased Idea Termination: Entrepreneurs who apply scientific thinking are more likely to terminate non-viable projects earlier, conserving resources and time. Strategic Pivots: These entrepreneurs pivot more effectively—making a few well-considered changes rather than none or many unfocused ones. Enhanced Decision-Making: By formulating theories, developing hypotheses, and rigorously testing them, they achieve a more accurate understanding of what strategies lead to success. Improved Performance: The scientific approach leads to better outcomes because it combines efficient searching for viable ideas with a healthy skepticism of unproven assumptions. A key to good judgment is treating our plans as hypotheses and our choices as experiments. By testing our assumptions and learning from the results, we become more adaptable and innovative. This method doesn’t just improve decision-making; it empowers us to navigate uncertainty with confidence. I love this concept and It feels like an example from Shane Parrish’s The Great Mental Models Books. Read the full study here.