Most of the weirder suggestions happen later in the recommendations (lower down on the page). I think the algorithm thinks to itself “The user appears tired of music and probably wants to watch something else”.
lsusr
Karma: 581
[Question] How do you do hyperparameter searches in ML?
[Personal Experiment] Training YouTube’s Algorithm
The Trouble with Physics does address zero-point energy as a possible explanation/alternative for dark energy. Your points 1 and 2 are correct. The problem is that the cosmological constant calculated from vacuum energy is many orders of magnitude greater than the observed cosmological constant.
I didn’t know that. Thank you. I have corrected the original article.
If physicists of the sort I’d like to talk to are around at all that’s good enough for me.
Thank you for the correction. AlphaStar is not completely stateless (even ignoring fog-of-war-related issues).
I think the issue here is more about a lack of ‘reasoning’ skills than time-scales: the network can’t think conceptually...
This is exactly what I mean. The problem I’m trying to elucidate is that today’s ML techniques can’t create good conceptual bridges from short time-scale data to long time-scale data (and vice-versa). In other words, that they cannot generalize concepts from one time scale to another. If we want to take ML to the next level then we’ll have to build a system that can. We may disagree about how to best phrase this but I think we’re on the same page concerning the capabilities of today’s ML systems.
As for connectome-specific harmonic waves, yes, my suggestion is to store slow-changing data in the largest eigenvectors of the Laplacian. The problem with LSTM (and similar RNN systems) is that there’s a combinatorial explosion[1] when you try to backpropagate their state cells. This is the computational cliff I mentioned in the article.
The human brain has no known mechanism for conventional backpropagation in the style of artificial neural networks. I believe no such mechanism exists. I hypothesize instead that the human brain doesn’t run into the aforementioned computational cliff because there’s no physical mechanism to hit that cliff.
So if the human brain doesn’t use backpropagation then what does it use? I think a combination of Laplacian eigenvectors and predictive modeling. If everything so far is true then this sidesteps the RNN computational cliff. I think it uses something involving resonance[2] between state networks instead, but we can reach this conclusion without knowing how the human brain works.
This is promising for a two related reasons: one involving power and the other involving trainability.
Concerning power: I think resonance could provide a conceptual bridge between shorter time-scales to longer time-scales. This solves the problem of fractal organization in the time domain and provides a computational mechanism for forming logic/concepts and then integrating them with larger/smaller parts of the internal conceptual architecture.
Concerning trainability: You don’t have to backpropagate when training the human brain (because you can’t). If CSHW and predictive modeling is how the human brain gradient ascends then this could completely sidestep the aforementioned computational cliff involved in training RNNs. Such a machine would require a hyperlinearly smaller quantity of training data to solve complex problems.
I think these two ideas work together; the human brain sidesteps the computational cliff because it uses concepts (eigenvectors) in place of raw low-level associations.
Machine Learning Can’t Handle Long-Term Time-Series Data
Fixed. Thanks.
[Book Review] The Trouble with Physics
I didn’t know what “straussian” means so I looked it up.
The “Straussian” approach to the history of political philosophy is articulated primarily in the writings of Leo Strauss. Strauss wrote extremely careful, detailed studies of canonical philosophical works along with essays explaining his approach. The most controversial claim Strauss made was that philosophers in the past did not always present their thoughts openly and explicitly. They used an “art of writing” to entice potential philosophers to begin a life of inquiry by following the hints the authors gave about their true thoughts and questions. The overriding purpose of Strauss’s own studies was to prove that philosophy in its original Socratic form is still possible by showing the persistence of certain fundamental problems throughout the history of philosophy. The most pertinent of those problems, not merely to political philosophy but to human life as a whole, was the problem of justice. Strauss also insisted that “historicism” is based on a philosophical account of the character and limitations of human knowledge and that it can be refuted, therefore, only on the basis of a philosophical argument. ― The Straussian Approach by Catherine Zuckert in The Oxford Handbook of the History of Political Philosophy
But I can’t tell exactly what ‘people who engage in philosophy’ means and why it’s in quotes. It sounds like the title of an essay but a web search doesn’t find anything.
Do you feel comfortable giving an example of such a memeplex?
Suffering is indeed an antimeme—and a broad-ranging one too. This is a new addition to my collection. Thanks.
I didn’t know what the substitution effect is either so here’s a definition.
The substitution effect is the decrease in sales for a product that can be attributed to consumers switching to cheaper alternatives when its price rises. ― Source
I appreciate this article very much. I read the whole thing and was disappointed when I realized Curtis Yarvin hadn’t finished the series yet. It already has many great insights and illuminating points. I’ll be digesting the implications for a while.
Diversity of approaches is important in this game. My favorite things about it is how Yarvin attacks a closely-related problem from a different perspective. In particular:
He focuses on the political economy. (I deliberately de-emphasize politics when choosing where to focus.)
He debugs things from first principles. “It is always better to debug forward.” (I prefer to debug backwards.)
I agree with almost everything he says. I disagree with his claim that it is “always” better to debug forward. Debugging forward is better when you have a small dataset, as is the case with the historic sweep of broad political ideologies (the subject of Yavin’s writing). I think when you’re dealing with smaller problems, like niche technical decisions, there’s a greater diversity data and therefore a greater opportunity to figure things out inductively.
These are all good points. By “mainstream” I’m referring to to the information you acquire by being part of a culture; the stuff you learn just because “that’s how it’s done”. This definition of “mainstream” is necessarily subjective because it can be defined only from the perspective of a specific culture or subculture. To someone growing up Amish, “mainstream” (in this context) is Amish. “Mainstream” is important because holes in this kind of knowledge are uniquely difficult to identify.
I don’t think there’s a spectrum between “no-platforming” and “unimportant”. I think they’re opposites in a way that doesn’t come full-circle, but haven’t thought about this hard enough to be sure. It’s certainly worth exploring.
Using the word “anti-meme” absolutely could be used to defend ideas from valid criticism, as illustrated by Isnasene’s comment to a similar post. Lisp is indeed an excellent example here. I think the library problem was its biggest issue and this got overlooked by early proselytizers.
I believe I’ve had kesho experiences too. This easily meets the criteria of “spiritual experience” and “mystical perception”, though it has no hallucinatory component.
I hadn’t noticed utilitarianism and ethical vegetarianism check these boxes. I wrote this series hoping for exactly this kind of insight. Thanks!
Your comment on the cross-cultural application of utilitarianism makes this extra insightful. I have edited the original post to acknowledge that antimemes are not always culture-specific.
Defining “Antimeme”
That’s a good idea. I hadn’t thought about it like that.
I think you’re right. I write about this topic precisely because it could “provide some high-value insight to a few people”. I trust we can figure out a special set of rules for discussing antimemes to make it work. The tiny amount of progress in this field so far suggests there could be lots of low-hanging fruit.
I think it depends on your problem. If you have lots of compute power, high dimensionality and powerful higher-order emergent behavior from hyperparameters then Bayesian optimization makes sense. And vice-versa.