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- Author: Nathan Zimmerman
- Category: article
- URL: element84.com/machine-l…
- Author: Naomi Klein
- Category: article
- URL: www.theguardian.com/us-news/n…
- Author: turing.ac.uk
- Category: article
- URL: www.turing.ac.uk/blog/why-…
- Author: par piketty
- Category: article
- URL: www.lemonde.fr/blog/pike…
- Author: Ethan Mollick
- Category: rss
- URL: www.oneusefulthing.org/p/against…
- Author: Aravind
- Category: rss
- URL: newsletter.terrawatchspace.com/last-week…
🔗 Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning
Some of the arguments made here would ring obvious to traditional spatial analysts, others to traditional remote sensers and, I suppose, many might seem “bread and butter” for the ML crowd. But it is not each individual argument that is the point here; it is putting them together, now, and in a contemporary and fresh framework that makes this paper worth a read. Well worth it indeed.
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Authors: Esther Rolf, Konstantin Klemmer, Caleb Robinson, Hannah Kerner Category: article URL: arxiv.org/abs/2402….
🔗 Why We’re Talking About a Centralized Vector Embeddings Catalog Now
The white paper mentioned below is well worth a read. It puts in much more eloquent words many of the reasons why I’m very excited about the new generation of satellite foundation models and the potential embeddings have to make satellite data more useful, useable, and used! A lot of food for thought and great argumentation for why we need to think about satellite images more and more like abstract tables than like images of pixels.
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our team published a detailed white paper in which we make the case for how Earth Observation (EO) data providers such as NASA can dramatically improve access to their data by creating a centralized vector embeddings catalog
🔗 The rise of end times fascism
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The governing ideology of the far right in our age of escalating disasters has become a monstrous, supremacist survivalism.
Three recent material developments have accelerated end times fascism’s apocalyptic appeal. The first is the climate crisis. While some high-profile figures might still publicly deny or minimize the threat, global elites, whose ocean-front properties and datacenters are intensely vulnerable to rising temperatures and sea levels, are well-versed in the ramifying perils of an ever-heating world. The second is Covid-19: epidemiological models had long predicted the possibility of a pandemic devastating our globally networked world; the actual arrival of one was taken by many powerful people as a sign that we have officially arrived at what US military analysts forecasted as “the Age of Consequences”. No more predictions, it’s going down. The third factor is the rapid advancement and adoption of AI, a set of technologies that have long been associated with sci-fi terrors about machines turning on their makers with ruthless efficiency – fears expressed most forcefully by the same people who are developing these technologies. All of these existential crises are layered on top of escalating tensions between nuclear-armed powers.
The startup country contingent is clearly foreseeing a future marked by shocks, scarcity and collapse.
the most powerful people in the world are preparing for the end of the world, an end they themselves are frenetically accelerating.
contemporary far-right movements lack any credible vision for a hopeful future. The average voter is offered only remixes of a bygone past, alongside the sadistic pleasures of dominance over an ever-expanding assemblage of dehumanized others.
But it also opens up powerful possibilities for resistance. To bet against the future on this scale – to bank on your bunker – is to betray, on the most basic level, our duties to one another, to the children we love, and to every other life form with whom we share a planetary home.
bunkered nation lies at the heart of the Maga agenda, and of end times fascism.
We should think of this less as old-school imperialism than super-sized prepping, at the level of the national state.
End times fascism is a darkly festive fatalism – a final refuge for those who find it easier to celebrate destruction than imagine living without supremacy.
🔗 Why we still need small language models - even in the age of frontier AI
This is a cool example of how less can be more. The article is also surprisingly informative for a post of these characteristics.
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In a six week sprint, we set out to see how far a small, open-weight language model could be pushed using lightweight tools and without massive infrastructure. By combining retrieval-augmented generation, reasoning trace fine-tuning, and budget forcing at inference time, our 3B model achieved near-frontier reasoning performance on real-world health queries – and is small enough to run locally on a laptop. We’re open-sourcing everything, and we believe this approach has enormous potential for public sector and compute-constrained environments.
🔗 Trump, national-capitalism at bay
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Let’s be clear: Trump’s national capitalism likes to flaunt its strength, but it is actually fragile and at bay. Europe has the means to confront it, provided it regains confidence in itself, forges new alliances and calmly analyzes the strengths and limitations of this ideological framework.
This is the first weakness of national capitalism: when powers reach a boiling point, they end up devouring each other. The second is that the dream of prosperity promised by national capitalism always ends up disappointing public expectations because it is, in reality, built on exacerbated social hierarchies and an ever-growing concentration of wealth.
When measured in terms of purchasing power parity, the reality is very different: the productivity gap with Europe disappears entirely.
The reality is that the US is on the verge of losing control of the world, and Trump’s rhetoric won’t change that.
In the face of Trumpism, Europe must, first and foremost, remain true to itself.
Europe must heed the calls from the Global South for economic, fiscal and climate justice.
🔗 Against "Brain Damage”
The final quote of “[o]ur fear of AI “damaging our brains” is actually a fear of our own laziness” has a lot of power, although it also oversees the mirrors of “nudges” that technology creates to act lazily.
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ways of using AI to help, rather than hurt, your mind.
If you outsource your thinking to the AI instead of doing the work yourself, then you will miss the opportunity to learn.
the harm happens even when students have good intentions.
we have increasing evidence that, when used with teacher guidance and good prompting based on sound pedagogical principles, AI can greatly improve learning outcomes.
Moving away from asking the AI to help you with homework to helping you learn as a tutor is a useful step.
find more in the Wharton Generative AI Lab prompt library.
while AI is more creative than most individuals, it lacks the diversity that comes from multiple perspectives.
The deeper risk is that AI can actually hurt your ability to think creatively by anchoring you to its suggestions. This happens in two ways.
the anchoring effect. Once you see AI’s ideas, it becomes much harder to think outside those boundaries.
Second, as the MIT study showed, people don’t feel as much ownership in AI generated ideas, meaning that you will disengage
how do you get AI’s benefits without the brain drain? The key is sequencing. Always generate your own ideas before turning to AI.
Every post I write, like this one, I do a full draft entirely without any AI
Only when it is done do I turn to a number of AI models and give it the completed post and ask it to act as a reader: Was this unclear at any point, and how, specifically could I clarify the text for a non-technical reader? And sometime like an editor: I don’t like how this section ends, can you give me 20 versions of endings that might fit better.
there is the option to have it help make us better. One interesting example is using AI as a facilitator.
If you want to keep the human part of your work: think first, write first, meet first.
Our fear of AI “damaging our brains” is actually a fear of our own laziness.
🔗 Last Week in Earth Observation: May 26, 2025
Aravind has a really interesting take on why the hyperscalers are moving into the weather model space.
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I think the real story here is not about weather accuracy, and it is definitely not about replacing ECMWF or NOAA in the future.
This is really about weather becoming part of the cloud infrastructure, about turning forecasting into a cloud-native service that’s deeply embedded within their compute ecosystems.
For sectors such as energy, insurance, agriculture, logistics and finance, weather is not just data, it is a key decision driver. If you can offer native, on-demand, customisable forecasts, users will start building their products and workflows around you: models, simulations, dashboards, alerts and triggers, aka a sticky service layer.
TL;DR: I think Google and Microsoft are trying to make weather foundational by turning it into a programmable infrastructure layer, that powers the horizontal layer of weather intelligence and climate services.