AGI Significance Paradox: As progress accelerates towards AGI, the number of people who realize the significance of each new breakthrough decreases.

Genes are to Evolution, Memes are to Culture, Dicenes are to General Intelligences.

Open-ended generative processes like evolution and human culture have a thing that is replicated and propagated by the process. For evolution, these are known as genes. For culture, these are known as memes.

It became obvious to me that there isn't an equivalent for individual brains. Is there something that is equivalent to this in general intelligent systems or biological brains?

I have come up with a term that describes these. The word I've invented is 'dicene'. This word is inspired by C.S. Peirce's dicent sign or dicisign. It is the combination then of dicent and gene, and thus dicene.

But why dicene over so many other possibilities like schemes, themes, intuitions, patterns, schemas, embeddings, attention, habits, explanations, ideas, meanings, representations, concepts, analogies, and lekta?

Star Wars has a simple dichotomy between good and evil. But as the stories have evolved, the nuances between these distinct poles also has acquired a richness that is hard to dismiss.

In the latest trilogy, we discover a dysfunctional civilization even after the defeat of the empire. The argument that the remnants of the empire make is that the galaxy stills seeks a need for order. Order and chaos are at seperate poles where evil is aligned with order.

We know however from D&D alignment charts that good and evil, order and chaos can exist in many combinations:

It is also interesting that the Sith (the Evil sorcerers) only come in pairs as opposed to the Jedi that can be entire communities. Apparently, to sustain evil one has to restrict agency and individuality to just a few. A community of evil-doers very quickly turns on itself.

So at one extreme we have evil, order, individuality and at the other we have good, chaos, collective. Which brings up the question, what is the definition of evil and good? Something that we took for granted ever since the first Star Wars movie.

This is an insightful article about what's wrong with Google as compared to Amazon, Microsoft, Apple and Facebook.

Which actually reminds me of the difference between organic and engineered designs:

Organic or biological designs are reusable designs from the ground up. They accommodate the needs of the environment because of the generality of the architecture. They don't pretend to solve just a narrow problem.

Architectures like biology that lead to general intelligence (i.e. like you) are from the ground up built from reusable components that encourage combinatorial mixing opportunities. One can never correctly guess the needs of the environment (i.e. market).

The opposite of generalization is pre-mature optimization. A company that has is genesis as a one-product company has a bias toward optimizing the entire stack. As a consequence, it compromises reusability and thus shuts of future opportunities of evolution.

Chess may be a good analogy for explaining the difference between a descriptive model of reality and a generative model of reality.

A descriptive model of chess will describe the rules (i.e. initial setup, allowed moves, points per piece, objectives). This tells you how a chessboard might evolve and also narrows the combinations of realistic chess positions.

In contrast, a generative model involves the actual gameplay between competing players that follow the rules. It generates new styles and patterns. It establishes new strategies and tactics. It generates new openings, middle gameplay, and end game tactics.

A generative model is an evolutionary model that leads to emergent behavior. As a consequence, a master of chess is able to recall the layout of chess positions in a real game in an instant, yet is unable to remember pieces placed in random positions.

That is because causality is present in the movements of pieces in a chessboard. An intuitive system like the human mind is able to capture that causality as a means to understanding and recall.

Here's more on the new mitigation scenario for 1.5C. How does it work? What would society look like? Are we willing to do what's required to stop climate breakdown? See thread.

1. Most models assume we need to keep growing the economy indefinitely. The problem is this makes it impossible to transition to zero emissions quickly enough; so they speculate on geoengineering and negative-emissions technologies to save us. Scientists reject this as too risky.

2. By contrast, this scenario proposes that high-income countries don't *need* more growth, and can scale down unnecessary production and consumption. This reduces energy demand (from 140 EJ in 2020 to 40 EJ in 2050), and enables a rapid transition to renewable energy.

3. What does it require? In the global North (Annex 1 countries)
•a shift from private cars to public transportation
•reduction in flights
•smaller average house size

Meanwhile, consumption in the global South (non-Annex 1 countries) rises to converge.

4. Significant reduction of aggregate material use. How? Longer product lifespans; regionalization of production and consumption; ban on advertising; a shift in taxes from labour to resources. All of this allows us to reduce ground freight, which cuts energy use.

1) OBSERVATION: The frequencies are changing. I would have to assess stats on the electromagnetic field to see its fluctuations. All I know is, I am highly sensitive to energy/frequencies, and they are changing, because…

2) 2 Weeks ago, my dog was effected by it as well. He is hearing things that aren’t technically audible. I can tell by his body language and sounds. One week ago I began feeling a pressure in my head and ears. It wasn’t painful at all, but it was intense for a bit.

3) It reminded me of taking off in an airplane a bit. I felt like I was in a vacuum. It went on and off for a couple of days. I’ve picked up on frequencies and high pitched sounds (not audible to most) for 30 years, and today my right ear began to ring for longer than usual.

4) This is nothing unusual for me, with exception of the duration. This happens often with me. No, it’s not tinnitus. Only this time, when I spoke, there was reverb creating a high pitch in my ear from my voice. This lasted for about 30 minutes. It did not hurt, just annoying.

5) So yes, the frequencies are changing. What this means exactly, I am unsure of at the moment. Some will say electromagnetic fields, others will say ascension, and some will say pole shift. I honestly can’t say at this very moment.

With @franklowery our new study on attributes of T cells that contribute to successful cell therapy in cancer patients in @ScienceMagazine today w/ colleagues @slgoff_SB, @NCI_CCR_SB @theNCI a TL;DR thread on key findings with caveats :) 1/10

has a long history of using tumor infiltrating T cells (TILs) to treat cancers since well.. before I was even born. We analyzed our most successful melanoma ACT trial for cell surface phenotypes in TIL infusion products of patients (aPD1/immunotherapy naive) /2

Surprisingly we found a CD39- TIL subset (CD39-CD69-, DN) associated with ACT-response. TBH we were expecting the opposite (CD39+). We only included CD39 bcz multiple groups (e.g. Simoni et al, 2018) had reported CD39+ as enriching for anti-tumor/neoantigen reactive T cells. /3

CD39- DN TILs RNA/epigenetics resemble stem-like memory progenitors, and in vitro were able to self-renew, and give rise to other CD39+ subsets. OTOH the most dominant subset of patient infusion products were CD39+ CD69+ (DP) and these guys were terminally differentiated.. /4

So, to clarify we specifically analyzed tumor-specific mutation-reactive Tcells. Turns out, ACT-responders had pool of neoantigen-reactive TILs in the CD39- phenotype, while non-responders did not (despite other irrelevant CD39- Tcells) -> not all CD39- T cells are bystanders /5

Next up at #enigma2021, Sanghyun Hong will be speaking about "A SOUND MIND IN A VULNERABLE BODY: PRACTICAL HARDWARE ATTACKS ON DEEP LEARNING"

(Hint: speaker is on the

In recent years ML models have worked from research labs to production, which makes ML security important. Adversarial ML research studies how to mess with ML

For example by messing with the training data (c.f. Tay which became super-racist super-fast) or by foiling ML models by changing inputs in ways humans can't see.

Prior work considers ML models in a standalone, mathematical way
* looks at the robustness in an isolated manner
* doesn't look at the whole ecosystem and how the model is used -- ML models are running in real hardware with real software which has real vulns!

This talk focuses on hardware-level vulnerabilities. This is particularly interesting because these can break cryptographic guarantees (because those are outside of their threat models)
e.g. fault injection attacks, side-channel attacks