jegp

You make some great points that I largely agree with. I'm wondering whether I'm misrepresenting you though, because I'm not arriving at the same "everything looks like a nail" conclusion.

One thing I'd like to surface is the distinction you're making between software and physics. You point out that social and medical sciences are only now becoming approachable with scientific methodology. We can now, to a higher degree than before at least, model and predict what'll happen in social and medical scenarios. Isn't that an excellent example of how subject matters can be virtualized? I can't find good arguments for why this would stop there; why wouldn't our scientific models improve to such a degree that we can predict and "interfacte" with more and more of reality. Put plainly: why can't experiments be done in code one day? Particularly if we imagine having better robotics.

Do you see my point about closing the gap between the theorizing and experimenting? Or are they fundamentally different things? The "reasonably skilled person" seems like an excellent heuristic > 2 years ago that's rapidly being replaced by automated thinking machines.

I've seen quite a few academics wrangling with patents. What should come first? Proliferation of the sciences or (potential) profit? Your point about the golden goose is pretty interesting and others in this thread has pointed to the misaligned incentive structure in academia. I'm curious, do you think things like JOSS could help open up data and "the golden goose"? I'm not sure the funding bodies I'm interacting with would respect this kind of initiative, but maybe it's just a matter of time.

I actually disagree that software is a static program. If a program always have the same behaviour, it's a pretty bad program. You need inputs and, as demonstrated by the recent LLM inputs, parameters you can tune to correctly address a specific context.

I would also contest the point about science being social. To me, science is the interplay between social constructions and physical reality. Which is why tests and experiments are so fundamental. Your point about software development as kind of like science is spot on: why not be exploratory/empirical in software development? Couldn't we imagine an LLM, say, that would go out and try to solve a question we frame? This seems strikingly close to autoresearch [1]

[1]: https://github.com/karpathy/autoresearch

In principle I would agree. But, on a more philosophical level, couldn't you make that same argument about C? Or even assembly? Or even digital computers? Less facetious, it seems to me that the particular abstraction is less important. As long as it's unambiguous and widespread.

Regarding the biggest: nixpkgs sits at around 140k packages, way more than others.

Best: I still argue that Docker and Conda are more accessible, but my point was to go for reproducible, declarative science. Nix environments are exactly that. They're not perfect and are, as you point out, not standardized. But they cover much more ground that Docker. If you trust the upstream nix repo, you can get bit-level equality at every single build you (or anyone else) does. Docker relies on huge binary blobs that you can't inspect and that can be pretty much arbitrarily swapped around.

I'm totally fine if Guix takes over. Or the next big thing. As long as it's declarative and reproducible.

I'd love to hear you expand on how I cherry pick terms and definitions.

The Turing Way seems great and I'm all for education. One of the most interesting conversation topics in this thread is, to me, how to create the necessary incentive structures. Know how is only part of the way. We need to secure the credit assignment for "openness" both in academia and industry.

This seems to be strongly US-centric. In other (welfare) countries, publicly funded registries are anonymized and made available to research. For every single case. Of course, there are tons of data we don't see, but that shouldn't be an argument for not trying. The 99.99% unpublished cases is because our models/explanations/knowledge can't efficiently condense the medical mystery into a diagnosis code.

Everything can be prevented given sufficient knowledge. That's not the point. The point is how to prevent as much as possible.

Well said! And thank you for recognizing the effort.

The important part here is, as you say, to light candles and insist on rigor. Coincidentally, history tells us that that also gets us further. So by pure memetic selection, this strategy should win

I agree that a lot of the practices in academia are misaligned with the original goal. But can't you say that for other systems/institutions as well? Point being, what about keeping the scientific method as the north star - as a good heuristic to avoid BS arguments and awarding low-quality research. And, crucially, to stay sane. My post is pretty naive, but I stand by the ideal of pushing knowledge as reproducible models.

100%. I'm not saying the vision we're talking about is there yet, I just think it's an enticing thought. The existence of (predatory) publishers is a sad and miserable joke in its own right.

I think the idea does: the point is that closed-source software isn't useful for anyone else than the (copyright)owner. In scientific standards, that is. Similar to esoteric and cryptic theories that resist accessible explanations.

:-) I can comfort you with the fact that the text is not AI generated. I did use AI to proof read it and I liked some of its suggestions to improve the flow of the text. English isn't my first language, so this is a great help for me.

Erm. Why?

Thanks! I appreciate that. Your point about DeepMind and frontier models is spot on. When they "embody"/build on the science done before them we get absolutely mindblowing synergies. But I wonder what happens when the LLMs become way smarter that us: why even loop us in? I guess that's related to the recent field medalist letter https://mathandai.org/

Thank you for engaging. This is a much more insightful take.

If I'm reading yiur argument right, you're saying that deployed models are downstream versions (reified) of aa theory. Theory, being the actual object of science.

I think this misrepresents science. Science is the ability to build testable knowledge. From that,how would you separate the test from the science? In fact, in an ideal world, why wouldn't you want your theory to be put in a format that's executable? I'm not saying that those things are always the same, my (provocative) title is based on a dream where we can imagine theory and model coexist because software is now a thing.

Thanks for the well wishes

Arguments are fine. Vacuous statements need grounding. The follow-up is way more detailed

I agree the post is muddy about whether the relationship is bijective (equivalent, poodle=dog) or injective (onto, poodle is a dog). I make it slightly more precise in the statement "I posit that open source software is a necessary condition if we are to science in a computerized world". That's where the "is" comes from in the title. Throughout history, this definitely has not been the case. I'm arguing that's changing.

Did you read the post...?

Equivalent? In the analogy of the math or physics results it would be a mental model in someone's brain that you can't access or verify. You just hope it's true

TL;DR I claim that modern science is synonymous with open source software. This post explains why, why it matters, and what you can (and should) do next.

What's your stack? After reading this, self hosting suddenly appeals to me.

Great. If they can make some money out of this, why not?

I wonder what happens if they lock down some features as premium-only. The competition is tight in this space and there are tons of alternatives to Hyprland, like Sway, River, etc. Monetizing open-source code sounds like a dangerous path...

Is the code for the "feeling lucky" selection mechanism open? Or, do you know how they select papers at random?

That's a great point. I thought about something similar, but I also realized the arXiv numbers are growing like crazy, so I wonder how long it'll take for the (hardcoded) numbers to be deprecated. One could of course add some kind of cronjob to update the numbers, but that sounds like a lot of work...

Thanks for this! If I knew this existed, I wouldn't have built the page myself.

Conflicting motivations sounds like a very reasonable thing in large populations, in fact. But I have a hard time believing why workers wouldn't unanimously want better pay, better conditions, and more power. I would be curious to see any counter examples!

Isn't the question then about the lesser evil? It's wrong to deny unions on the basis that some people are corrupt. Some people in companies are corrupt too and the US lies squarely in the middle of the corruption index https://en.wikipedia.org/wiki/Corruption_Perceptions_Index?w... I don't see why America!=Norway is a relevant argument.

As an outsider, I'm astounded why workers aren't unionizing to a much higher degree. It's been proven to work [1] against the misinformation, discord, and wealth inequality that companies will, inevitably, cause. Despite the small union fee, the individual clearly stands to benefit[1]. Is it because people are cheap? Or not familiar with history? You'd think that tech workers were quite informed.

[1]: https://nordics.info/show/artikel/trade-unions-in-the-nordic...

Hey HackerNews community,

I wanted to share my excitement around our work on a "Neuromorphic Intermediate Representation" (NIR), published in Nature Communications: "NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. ... NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies."

Why This Matters:

Neuromorphic computing, inspired by the architecture and processes of biological nervous systems, has long held promise for achieving more efficient, scalable, and intelligent systems. But so far, there hasn't been any unifying computational model, which has (1) scattered the scientific efforts and (2) hindered reproducibility.

Enter NIR, which acts as a kind of neural assembly language — a common ground where diverse architectures of neuromorphic systems can communicate seamlessly. NIR models are directly interpretable by neuromorphic platforms, much like the earliest compilers that bridged the gap between assembly languages and digital processors.

Key Highlights:

    Seamless Translation Between Computational Realms: The NIR creates a natural mapping between continuous-time neural dynamics and discrete computational models, allowing for unprecedented interoperability between neuromorphic and conventional hardware.

    Enhanced Efficiency and Scalability: By utilizing principles from both analog and digital realms, this approach optimizes data processing, reducing power consumption while maintaining speed and accuracy. This could pave the way for more efficient AI models that run on edge devices and within IoT ecosystems.

    Inspired by Biology, Refined for Technology: NIR embraces the adaptive, decentralized nature of biological neural networks, bringing us closer to hardware that can support general intelligence rather than narrowly focused algorithms.

Why Now is the Time to Pay Attention:

This isn't just another incremental step in AI or computing. NIR could be the catalyst that helps us unlock the next generation of intelligent systems — ones that don't just mimic intelligence but are built to understand, learn, and grow from the complexity of the world around them. This is a chance for developers, researchers, and innovators to join a movement that takes computing closer to the elegance of the human brain.

I'm curious to hear your opinions and feedback!

Disclaimer: I'm the first author.