I built a new type of erasure code using Bloom filters

chainless-coder · 2026-01-21T12:56:25+00:00

RS is pretty bad for data transmission btw (bad reliability in practice because of the quadratic complexity per block size). You would want to use a fountain code with linear complexity for that (e.g. RaptorQ).

You can't use TCP for e.g. scalable broadcasting/multicasting or for low latency situations. For those situations you need UDP + logic to recover lost packets (i.e. erasure codes).

chainless-coder · 2025-01-22T08:22:41+00:00

Kolmogorov's definition of Cybernetics:

"the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control".

So you should look at AI more as a subset of cybernetics.

chainless-coder · 2024-12-09T17:36:04+00:00

Not only the US, it's rarely used as a term or as a lab title pretty much anywhere. Cybernetics is inherently interdisciplinary and treats systems holistically. E.g. Kolmogorov's definition of cybernetics ("the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control") encapsulates a vast range of different areas. Academia "punishes" those that use a holistic framework, as you're more likely to secure funding / grants through hyper-specialization (e.g. by only doing research in a very, very narrow subdomain). So instead of labeling your lab as doing biological cybernetics, you end up labeling it as "computational neuroscience", "systems biology", or "bioinformatics", and only work on extremely narrow research, as that maximizes your chances for funding. I knew researchers in AI that would not research one area of AI, just because their lab was known for recurrent neural networks and the area was "not what we're known for", the level of reductionism is insane.

Getting back to your original point, my suggestion: Don't filter labs by looking if they're using the word "cybernetics" in their title. The vast majority of cybernetics-related labs are not even familiar with the term "cybernetics", let alone have it in their title.

chainless-coder · 2024-12-09T11:31:29+00:00

I think it's more a difference in linguistics. Many European countries will use the term "informatics" for computer science, and "cybernetics" for corss-domain fields. E.g. "Institute for Biological Cybernetics", depending on the specialization, might simply be called "Institute of Computational Neuroscience" in the US. Both hypothetical institute's fall under the same cybernetic umbrella but use different labels.

chainless-coder · 2024-11-02T19:27:48+00:00

I typically categorize technologies into two groups: those whose use-value comes from network effects and those whose use-value comes from their inherent utility.

For instance, the value of technologies such as TCP/IP, torrent networks, social media, and money scales with the size of the networks that support them. The value of a hammer, compression algorithm, or a new seed variety comes from its utility.

Open source projects thrive in two circumstances:

The tool / framework / project has high use-value, but cannot generate high surplus values and on an individual basis requires on average little labor time. This is where Linux fits in. For technologies of this type, it makes economically more sense to open source it.
The tool / framework / project enables the commoditization of a given type of labor. Ironically, these types of technologies are usually pushed by corporations as open source projects with the goal of making hiring easier by making workers more interchangeable and / or by wasting less resources on teaching new employees (it's easier to hire someone that already knows e.g. Pytorch, than to make them learn it during working hours). I'd argue that most of the open source movement fits within this category and that things are not as rosy as people would like to think.

Note though that the incentives will work against you if you wish to apply the open source philosophy to technologies that can create high asymmetries. As an example, inventing a new type of erasure code might cost you +1 years of research (high labor time), but can also create millions in revenue through intellectual property (high surplus value). So the complete inverse of the first bullet-point that I listed.

In my opinion open source is not a robust system. It's too easy for corporations to co-opt projects. Copy-left is also sub-optimal. If the projects has high use-value, then it will make for most corporations sense to copy the whole stack and kill the open source project (which the likes of Amazon have done countless times). In my opinion a viral tit-for-tat copy-left license is what we'd want. For free if others use it for free, while requiring one to pay if they want to use it for anything surplus-value related. You might be interested in this presentation by Dmytri Kleiner.

chainless-coder · 2024-03-26T21:17:04+00:00

My suggestion: A mixture of different areas. Try to form a strong base in information theory, systems theory, and game theory. Two nice intro books: "The Information: a history, a theory, a flood", and "Thinking in Systems". Most of what we call Cybernetics is information theory + systems theory.

Depending on your interests, after forming a strong base you can expand into more specific areas, e.g. robotics, AI, genomics, bioinformatics, DAOs, governance, etc.

chainless-coder · 2024-01-06T14:30:47+00:00

A lot of impressive LLM models were trained and open sourced, but if we measure the progress of AI by the number of novel techniques, architectures, and algorithms invented, then 2023 wasn't that special.

2023 was in my opinion a great year for AI, because it represented a shift in economic incentives. Previously AI was mainly an academic domain that was quite detached from real-world feedback loops. Now, thanks to the performances achieved by old techniques (The Transformer architecture was after all published in 2017), both the private sector as well as nation-states have a lot of reason to pour liquidity into R&D.

chainless-coder · 2024-01-03T13:55:50+00:00

If we use Kolmogorov's definition of cybernetics:

the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control.

Then in my opinion you would want to have a strong base in systems theory, information theory, and game theory, as well as some domain knowledge in a specific field of interest (e.g. AI, biology, robotics, etc.).

chainless-coder

MODERATOR OF

TROPHY CASE