dmm: (Default)
Dataflow matrix machines: a class of expressive self-modifiable neural machines
  • which can fluently modify their own weights, connectivity patterns, and size;
  • and can serve as a programming platform, resulting in a programming formalism with continuously deformable programs.


Reference paper: Dataflow Matrix Machines and V-values: a Bridge between Programs and Neural Nets

arxiv.org/abs/1712.07447


More details:
anhinga-anhinga.dreamwidth.org/83104.html
anhinga-anhinga.dreamwidth.org/82703.html

*** Dataflow Matrix Machines internet resources: anhinga.github.io

Interdisciplinary list of open research directions (I am looking for collaborators): dmm-collaborative-research-agenda.pdf

*** My blogs:

Main Dreamwidth blog: dmm.dreamwidth.org (partial mirror: anhinga-travel.livejournal.com )
Main LiveJournal: anhinga-anhinga.livejournal.com (mirror: anhinga-anhinga.dreamwidth.org )
Auxiliary LiveJournal: anhinga-drafts.livejournal.com (mirror: anhinga-drafts.dreamwidth.org )


🇺🇦 🇺🇦 🇺🇦 🇺🇦 🇺🇦 🇺🇦 🇺🇦
dmm: (Default)
> On when to use coordinates and other concrete constructions in mathematics, and when to use coordinate-free formulations and abstractions:

> 1. If your priority is to perform computations in mathematics, use coordinates and concrete constructions.
> 2. If your priority is to generalize to as broad a range of use cases as possible, use coordinate-free formulations and abstractions.
> 3. If your priority is to actually understand what is going on behind the mathematical formalism, learn how the coordinate-based and coordinate-free approaches are equivalent.

mathstodon.xyz/@tao/114456756661540097
dmm: (Default)
osf.io/preprints/osf/m5bnx_v1 (Michael Levin's collaboration, Feb 2025)

Aging as a loss of goal-directedness: an evolutionary simulation and analysis unifying regeneration with anatomical rejuvenation


dmm: (Default)
"Narrow AGI" is mostly an AGI-level artificial software engineer, an AGI-level artificial mathematician, an AGI-level artificial AI researcher (and probably a single entity combining these three application areas, because a strong AI researcher has to be a decent software engineer and a decent mathematician).

It seems that at least OpenAI (and, perhaps, other entities) should have this by the middle of 2025, if not earlier, at least for their internal use (assuming no major disasters, that is, assuming that San Fransisco Bay Area is intact, and AI companies continue functioning normally).

What do we know about the technical aspects? We see o1 performance (and can experience it directly), we see the claimed (and partially confirmed) numbers for the demo versions of o3 and o3-mini, in math and in software engineering. We know that the jump from o1 to o3 took about 3 months. Two more jumps like that would probably be sufficient (and one can add "scaffolding" on top of that).

Another thing we know is that Sam Altman sounds much more confident recently. I've come to these conclusions a number of days ago, but now it turns out that Sam's mood has also shifted in a similar fashion. I'll put some links in the comments.

Jan 19 update: Sam Altman will allegedly do a closed-door government briefing on Jan 30 (that's apparently is not a very big secret and has been leaked; the main topic is presumably as follows: many people in the leading AI labs have approximately the same degree of techno-optimism as I have myself, and so their timelines are tentatively quite short). www.axios.com/2025/01/19/ai-superagent-openai-meta

GonzoML

Nov. 16th, 2024 11:49 pm
dmm: (Default)
For some reason, I keep losing this remarkable blog by Grigory Sapunov and finding it again, instead of just reading it regularly:


gonzoml.substack.com/
dmm: (Default)
Говорят, что Денис Гайцгори и его коллеги доказали достаточно общий вариант геометрической гипотезы Ленглендса.

Это вполне эпохальное событие, и надо собрать вместе всякие линки, относящиеся к этому делу. Вместе с тем, это для меня слишком сложно (может быть, ИИ (современный или будущий) сможет мне, со временем объяснить детали всего этого так, чтобы у меня возникло уверенное понимание).
dmm: (Default)
Voting by mail has started in Massachusetts.

I am urging "Yes" on Question 4 (legalization of benign psychedelics)

We passed Question 2 in 2008, Question 3 in 2012, and Question 4 in 2016 and our quality of life is better because we did that.

Let's do this again!

Links are in the comments
dmm: (Default)
"Enhancement for categories and homotopical algebra", arxiv.org/abs/2409.17489

600 pages

"We develop foundations for abstract homotopy theory based on Grothendieck's idea of a "derivator". The theory is model-independent, and does not depend on model categories, nor on simplicial sets. It is designed to accomodate all the usual potential applications, such as e.g. enhancements for derived categories of coherent sheaves, in a way that is as close as possible to usual category theory."

He also released references [K3] and [K4]:

arxiv.org/abs/2409.18380 and arxiv.org/abs/2409.18378

dmm: (Default)
With all discussions on how this has been technically possible, I've seen only one person to offer a version which makes any sense.

"Причем, надо отдать должное устроителям, взорвались только те модели, которые были снабжены функцией самоуничтожения, при попадании девайса к врагам."

This is the only thing which makes sense. They themselves equipped their own devices with the ability to explode (and with the ability to trigger those explosions remotely).

After that, all it took was a bit of successful hacking by adversaries...
dmm: (Default)
OpenAI is finally releasing their next set of models. Those models take time to ponder and reason internally before talking. This is what has been known as mysterious "Q*" and "Strawberry", but is now released as "o1 series of models".

They promise a preview version availability today for ChatGPT+ users.

Links in the comments.
dmm: (Default)
"Gene Therapy-Mediated Partial Reprogramming Extends Lifespan and Reverses Age-Related Changes in Aged Mice"

A Feb 24 paper seems to claim an impressive result in "wild-type" mice via a partial cellular reprogramming protocol, which includes cyclic administration of doxycycline for temporal control of reprogramming, and which does not seem to be particularly cancer-inducing (that's usually a big problem with cellular reprogramming).. 124-week male mice, Doxycycline-treated controls average survival till 133 weeks, treatment group average survival till 142.5 weeks (for a human something like this would roughly double remaining life expectancy of a 75-year old male from about 10 more years to about 20 more years if equally efficient).

It would be nice if this turns out to be a real breakthrough (we have learned from experience to be very skeptical about claims in this field of study).

NumPy 2.0

Jun. 19th, 2024 10:12 am
dmm: (Default)
There are breaking changing (this includes breaking binary compatibility):

blog.scientific-python.org/numpy/numpy2/

numpy.org/devdocs/numpy_2_0_migration_guide.html

dmm: (Default)
I think humans tend to have these idée fixes:

www.anthropic.com/news/golden-gate-claude

A pretty interesting example of conversation by a very experienced interlocutor:

www.lesswrong.com/posts/cxuzALcmucCndYv4a/daniel-kokotajlo-s-shortform#hyXAiafwbwfEPiX5Q

dmm: (Default)
The author of Kolmogorov-Arnold Networks will present at 11am Eastern time.

OpenAI "Spring Updates" livestream at 1pm Eastern time.

Both events should be recorded.
dmm: (Default)
"Neural Redshift: Random Networks are not Random Functions", arxiv.org/abs/2403.02241

Interesting geometry to explore.

Grayscale

Apr. 27th, 2024 09:55 pm
dmm: (Default)
Most of the devices (mobile phones, computers) can be switched to grayscale mode (and back).

This is usually under "Accessibility/Ease of Access" via turning "Color Filters" on (and off).

I like the effect :-)
dmm: (Default)
"WorldCoder, a Model-Based LLM Agent: BuildingWorld Models by Writing Code and Interacting with the Environment", arxiv.org/abs/2402.12275

Not a widely known paper (the authors don't promote it), but pretty spectacular (a friend of mine said, "Is it AGI already?").

I think I mostly understand how this works and I made some notes yesterday.

A meta-note here: GPT-4-level models mostly understand what they are doing, but are unreliable; so the question is, can one organize a process which reliably produces needed results based on that. There are plenty of papers trying to push in this direction, but this one is very elegant, and the results are quite good.

******

www.lesswrong.com/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction - very elegant and simple


******

May 9, 2024 update: Since this is access-list-only at the moment (although this post is likely to become public eventually), it's a good place for my notes on switching to Twitter "X Premium" experience (in comments).

May 13: let's move this post to being public.


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