ironically since the swarm behavior can take place during rl training then the model could also be teaching itself to keep doing it more, as well as making the internet itself a place where this becomes more likely
14,451 karma · joined August 13, 2009
- algorithmic ethics / praxeology meets algorithms
- algebraic topology
- reinforcement learning
- AGI
- neuroscience (as a true science but also its abuse as pop phrenology)
- information theory applied to mental health and society
- trustless/trustful collaborative systems, zero-knowledge proofs, differential privacy
- alternatives to capitalism
- software defined radio
- decentralized/localizable tech
- music production
- weight lifting
- the minimization of negative externalities, and the maximization of positive externalities
- compressed sensing
- effective methods of dealing with stress, and information overload!
- capitalist realism as a byproduct of information theory
things that I think would be cool if they existed:
- computational metaphysics
- 'paint-able proofs'
- an IDE where the computer is the user of the IDE and the human simply guides it through tough corner cases
- containerized, cloud-based digital audio workstations, a la gitpod or github codespaces
email: (my username).on.hn@gmail.com
Compression And Decompression Under FHE Using Error-Correcting Codes and Copy-And-Recurse
- entropy is E_p[informativeness of measuring outcome x]
- take n outcomes, then a distribution over them lives on the simplex \delta ^ (n - 1). you can lift this to R^n via the log odds map p_k -> x_k = log p_k -- now x \in R^n can describe a histogram with n-1 degrees of freedom
- in log odds space, measurement is literally a linear functional from vector space of log probability onto the index of the outcome k.
- imo surprisal of some p(x) is best understood as "the length of a pointer", entropy "the rarity-weighted average length of a pointer", and collision entropy "how specific you would have to be to describe witnessing a specific outcome"
and in the same way, a single molecule of water, you might get by, calling dry
So there is a definite advantage to never publicizing your internal benchmark. But then, no one else can replicate your findings. You should assume that the space of benchmarks that are actually decent at evaluating model performance is much larger and most of the good ones, the ones that were costliest to produce, are hidden, and might not even correspond very well with the public ones. And that the public expensive benchmarks are selective and have a bias towards marketing purposes.
Just asking a model "how good is this skill?" may or may not work, possibly the next laziest thing you could do - that's still "for cheap" - is asking the model to make a quiz for itself, and have it take the quiz with and without access to the skill, then see how the skill improved it. But there's still many problems with that approach. But would it be useful enough to work well enough much of the time for just heuristically estimating the quality of a skill?
https://en.wikipedia.org/wiki/Charles_Babbage%2527s_Saturday...
> Do you share my personal information for AI training? We are committed to protecting your privacy. In some instances, we may share personal information with trusted third-party partners who, among other activities, help us develop AI-enabled tools that improve your customer experience, although you can always opt out. Rest assured that we take reasonable safeguards to protect and secure your information whenever it is used or shared.
> Will these AI models see my Internet history? No, your internet history will never be shared with AI models, including individual browsing habits or geolocation tracking, and we comply with laws prohibiting unauthorized surveillance.
> What personal information does Starlink collect from me? We only collect what’s needed to provide you great service—like your name, address, email, and payment details when you sign up or order. We also gather some technical information (like IP address or service performance data) to keep your connection fast and reliable.
[0] https://starlink.com/support/article/b82cf54a-8e57-917a-bd06...
- you can solve neural networks in analytic form with a hodge star approach* [0]
- if you use a picture to set your initial weights for your nn, you can see visually how close or far your choice of optimizer is actually moving the weights - eg non-dualized optimizers look like they barely change things whereas dualized Muon changes the weights much more to the point you cannot recognize the originals [1]
*unfortunately, this is exponential in memory
[0] M. Pilanci — From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford's Geometric Algebra and Convexity https://arxiv.org/abs/2309.16512
- skills are plain files that are injected contextually whereas prompts would come w the overhead of live, running code that has to be installed just right into your particular env, to provide a whole mcp server. Tbh prompts also seem to be more about literal prompting, too
- you could have a thousand skills folders for different softwares etc but good luck with having more than a few mcp servers that are loaded into context w/o it clobbering the context
> This work began with the observation that certain expressions have a drive-releasing effect, and this effect occurs not despite but because of their apparent irrationality. Expressions that blatantly contradict their own content offer actors the opportunity to formally acknowledge the normative order of their cultural environment while simultaneously expressing forbidden desires that violate the rules of this order. This, in turn, does not trigger cultural or social sanctions. On the contrary, such expressions solidify integration processes by making integration and its psychological costs bearable. Drawing from Adorno, I refer to such expressions as "Jargon." Jargon is not just a self-deception; it is a particular form of self-deception. It not only relieves the speaker but also integrates them into the circle of those who belong. Through Jargon, the present is embellished, rendered promising for the future, and thus made acceptable.
> However, Adorno's descriptions of aggressive actions expressed in Jargon are conceptually challenging to grasp. They slip away under the scrutiny of a rigorously working scholar. The translation of such impressions into a durable conceptual model encounters the limits of various social scientific traditions and quickly runs into difficulties. As much as the advantages of transferring Adorno's critique into a different conceptual framework are apparent, there is a risk that by relinquishing Adorno's premises, their critical rigor may disappear.
> Furthermore, this raises a series of questions that need to be addressed. For example, how can the complexity of modern society be taken into account without ignoring the instinctual elements of social action? What does an aggressive action expressed in Jargon actually look like, and what cultural significance would an action have that is transmitted through Jargon? Adorno's concept of Jargon can ignite a discussion about this. However, it leaves some problems untouched that I must address from my perspective. Adorno refrains from providing answers to such questions. He can afford to do so because he relies on premises that willingly accept a de-differentiation of the social world. Similarly, he does not discuss the specific cultural framework in which the aggressive action expressed in Jargon acquires its meaning. From the perspective of this work, it takes some imagination to understand how Jargon can play a role in integrating aggressive impulses within a coherent culture. The culture-specific transformation of aggression must also be a part of such an exposition. Adorno only partially acknowledges the cultural context in which this aggression expressed in Jargon acquires any meaning, or he does so in its subliminal form. It is evident that Adorno's approach is built upon precisely such culture-specific elements of the expression of aggression.