rdlecler1

that wavelength penetrates the skin. you need to be around 222nm for human safety

uviquity has prototypes of a 220nm solid state chip they’ll commercialize next year (we’re an investor). a single far-uvc photon will destroy the covid virus.

https://uviquity.com/

You clearly have deep understanding of base reality and the emergence of complexity. So please share an actual argument.

It’s the other way around. Waymo may do the job exquisitely, but exquisite is also expensive. Telsa has always constrained their system to lower cost hardware. It’ll be easier for Tesla to eventually punch through than for Waymo to cut their costs to that of a Tesla robotaxi.

In other words, it turns out the implementation details don’t matter all that much.

Power eventually turns a high trust society into a low trust society when that power is used to one’s own advantage.

Think different. No kidding. No one is going to think this.

YC is putting through 200+ companies per batch and making a tiny investment. It’s far more efficient to swallow the occasional bad bet than to do a lot due diligence… Now the VCs that came in after… that’s another story.

This is a great point. LLMs may flood an app with more features without making it better. At the end I do the day you still need to make something people love. AI may help you build that faster if you know what do build, but it’s not going to make a bad product great.

Remember there is still survival bias here. We’re not seeing the complications from people who died of the infection.

Do driverless cars make all this moot?

Super interesting.

This sits in a larger field of complexity theory and complex adaptive systems. There was also some interesting work on “Artificial Life” although that research program seems to have fallen out of favor. My introduction in 1995 was the book Chaos and then Stuart Kauffman’s At Home in the Universe. Wolframs New Kind of Science was also interesting.

I could have bogged the essay down with qualifiers to address all the potential straw man objections, but that didn't seem productive. It's easy to take an uncharitable view on this, but I do explain more about GRNs later in the essay. I worked with them for 8 years, and yes, they do act like the rudimentary brains of the cell, and that's the reason this system is selected again and again by evolution.

I'm not arguing that this approximator is necessary (not sufficient) for this class of networks. I've proposed some conjectures on what we might expect to see, but there are certainly other salient ingredients and common principles that we haven't discovered, and I think it's important to hunt for them.

This is a great question, and I don't yet have an answer. I'm going to butcher this description, so please be charitable, but functionally, the attention mechanism reduces the dimensions and uses the coincidence between the Q and K linear layers to narrow down to a subset of the input, and then the softmax amplifies the signal.

One unsatisfying argument might be that this might fall into implementation details for this particular class. Another prediction might be that an attention mechanism is an essential element of these networks that appears in other networks of this class. Another is that this is a decent approximation, but has limitations, and we'll figure out how the brain does it and replace it with that.

More generally there’s graph neural networks, for instance, but not you’re including many dynamic networks that are not open-ended or evolvable. The idea is to identify common dynamics and add constraints on the types of networks that are included to find general principles within that class. Kisen the constraints, you make the class too broad and can’t identify common principles.

Genetic systems code gene regulatory networks. I spent most of the essay on them.

Those neurons are being trained the day we were born. Reality corresponds to about 11 million bits per second. What I suspect’s happening is that we train higher and higher levels of abstraction and we get to a point where new knowledge is involves training a new permutation of a few high level neurons.

Activation functions are implementation details. See appendix for the general formula.

No, graphs are too inclusive.

The original papers were published in scientific journals. More assertive claims aren’t kosher.

You’re right. Writing is hard—especially when you’re cutting across disciplines. I wasn’t happy with the writing, but I stand by the claims.

I don’t know if this exists outside of spacetime, but I have a suspicion that UACs didn’t begin with gene regulatory networks, but are more fundamental part of a computational universe hypothesis.

Are those just implementation details?

The topology needs to be information bearing. Weights of 0.0001 are likely spurious and if other weights are so relatively big they can effectively make the other fan in weights spurious as well.

No, too inclusive.

Despite vast implementation constraints spanning diverse biological systems, a clear pattern emerges the repeated and recursive evolution of Universal Activation Networks (UANs). These networks consist of nodes (Universal Activators) that integrate weighted inputs from other units or environmental interactions and activate at a threshold, resulting in an action or an intentional broadcast. Minimally, Universal Activator Networks include gene regulatory networks, cell networks, neural networks, cooperative social networks, and sufficiently advanced artificial neural networks.

Evolvability and generative open-endedness define Universal Activation Networks, setting them apart from other dynamic networks, complex systems or replicators. Evolvability implies robustness and plasticity in both structure and function, differentiable performance, inheritable replication, and selective mechanisms. They evolve, they learn, they adapt, they get better and their open-enedness lies in their capacity to form higher-order networks subject to a new level of selection.