snarles 29 Oct 2014 13:54 UTC
19 points
on: Wikipedia articles from the future
Simulated dream state experiments

Simulated dream state experiments (SDSEs) are computer simulation experiments involving simulated humans sentiences in a dream state. Since the passing of the Banford agreement (1) in 2035, SDSEs are the exclusive means of ethically conducting simulation experiments of simulated human sentiences without active consent (2), although contractual consent (3) is still universally required for SDSEs. SDSEs have widespread scientific, commercial, educational, political, military and legal purposes. Scientific studies using SDSEs have been used to develop accelerated dream learning techniques; SDSEs are also employed as part of the scientific process itself, as a means of controlling creative hypothesis bias (4). Commercial applications of SDSEs include screening of job applicants and simulated consumer testing. Simulated ordeals are a major use of SDSEs in legal, political, and military contexts, for the purpose of enforcing the ethical integrity or good faith of the subject.

Scientific status

SDSEs are widely accepted within the scientific community as a valid substitute for waking-state simulations. The rapid development of silicooneirology (5) provided the necessary understanding to influence dream states with a high degree of control, including fine control of the subject’s degree of lucidity. In a series of studies [1], [2], Walker et al. demonstrated high correlation between the behavior of one simulated set of subjects participating in a battery of waking-state simulated experiments and an identical set of copies participating in the equivalent SDSEs.

Controversy

SDSEs are still banned in the European Union and in most Latin American countries. Additionally, most religious organizations are against SDSEs. Pope Clementine VIII has stated that the use of SDSEs conflicts with the concept of faith.

A number of international scandals have erupted over the alleged use of SDSEs as a torture mechanism [3],[4]. Such abuse of SDSEs is considered a war crime by the updated Geneva convention.

Critics of SDSEs point out that the widespread acceptance of SDSEs is due to the universal emotional tendency of humans to devalue dream experiences as being “unreal.” However, SDSEs differ from natural dreams due to the external manipulation of the dream state. The status of SDSEs with long subjective time frames is also commonly disputed.

Author Jose Hernandez specifically criticized the effect of randomized SDSEs, especially simulation ordeals, on everyday life, in his book “Simulation Shock.” Hernandez argues that the ubiquity of SDSEs creates a sense of continual unease as to whether or not one is participating in an SDSE at any given moment. However, surveys conducted by sociologists [4], [5] find a general increase in self-reported happiness levels in employees at companies which began the use of randomized SDSEs.

(1): Bandford agreement: A provision of the UN council of human rights prohibiting the simulation of simulated human sentiences without active consent, even if contractual consent was given.

(2): Active consent: In the context of experiments on simulated human sentiences, active consent refers three conditions: 1. the subjects are aware of their simulated nature, 2. the subjects have the right to end the simulation at any time, 3. the subjects have the means to communicate to end the simulation at all times to the experimenters.

(3) Contractual consent: requires the subjects to be aware of their simulated nature, and provide informed consent to the experiment prior to the setup of the experiment. Additionally, the consenting individual (not the simulated copy) has the right to withdraw their simulated copy from the experiment at any time, and also has a number of rights regarding the confidentiality of all obtained data.

(4) Creative hypothesis bias: A statistical bias resulting from using the same data to formulate a set of hypotheses and to test those hypotheses. According to the Popperian (traditional) philosophy of science, an ideal scientific study completely avoids the creative hypothesis bias by completely separating the process of formulating hypotheses and testing those hypotheses, with an independent experiment for each hypothesis. In post-Popperian philosophy of science, creative hypothesis bias is controlled by use of SDSEs, in which a simulated copies of the original research team is presented with synthetically generated data (control group) or the original data (treatment group).

(5) Silicooneirology: The scientific study of sleep via simulation. In contrast to SDSEs, simulated sleep studies, by definition, make no attempt to interfere with the sleeping brain, providing only a pre-specified set of input signals specified by the Handbook of the International Society of Silicooneirology.

[1],[2],...: fake references

snarles 15 May 2011 1:37 UTC
19 points
on: Talk about your research
My current project involves, in a broad sense, finding ways to fit complex (or mathematically inconvenient) models to data. If you can’t fit your models to data, what’s the point of the model, or the data?

Specifically, I’m working on statistical inference on protein interaction networks. I’m trying to find ways to fit generative models (such as preferential attachment, duplication-mutation-complementation) to network data in the form of adjacency matrices. Surprisingly little work on the subject has been done so far; a promising approach is Approximate Bayesian Computation.

Peer-to-peer “knowledge exchanges”

snarles8 Aug 2015 15:33 UTC

17 points

30 comments1 min readLW link

The trouble with Bayes (draft)

snarles19 Oct 2015 20:50 UTC

15 points

58 comments14 min readLW link

snarles 15 May 2011 13:19 UTC
14 points
in reply to: PhilGoetz’s comment on: Talk about your research
What we are trying to do is infer the evolutionary mechanisms by which organisms evolve increasingly complex systems of interacting proteins. A protein interaction network is a map of the organism’s proteins and their interactions in the form of a graph: we represent each protein with a node, and draw an edge between two proteins if they can form a complex.

One of the first observations made about protein networks is that the degree distributions of their graphs seem to obey a power law: there are few nodes which have a huge number of interaction partners which act as ‘hubs’ for the networks. Furthermore, hubs are less likely to have interactions with other hubs.

One evolutionary mechanism for explaining this power law is the preferential attachment model. In this model, the interaction network random gains new proteins at random: the added protein forms a single edge with an existing protein in the network, and it is disproportionally likely to form an edge with a ‘hub.’ (Technically speaking, the probability of choosing a particular existing node is proportional to the degree of that particular existing node.)

The preferential attachment model is explains the power law distribution, but is too simplistic by itself: it can only explain networks which have tree-like structures.

Another model for network growth is DMC (duplication-mutation-complementation), also called DDA (duplication-divergence-attachment), a model based on the biological phenomenon of gene duplication.

In DMC, there is a duplication step and a mutation step. In the duplication step, we duplicate one of the existing proteins at random. Say protein O was the existing protein and protein P is the duplicate. The duplicate interacts with all of the interaction partners of the original protein. If protein O formed complexes OE, OF, OG, then the duplicate forms complexes PE, PF, PG.

In the mutation step, the original copy and duplicate copy degenerate in a complementary fashion. They can lose the ability to form certain complexes, so long as the copy provides a backup copy of that particular complex. For example, protein O can lose the ability to form complex OE, but as long as its copy P can still form PE, the organism can continue to be viable. Thus an organism might go from having complexes {OE,OF,OG,PE,PF,PG} after the duplication step to having {PE,OF,OG} after the mutation step.

We can propose models for network generation based on preferential attachment, duplication-mutation-complementation, or a mixture of the two, as was proposed in Ratmann (2007).

For any particular model we want to see how well it explains the observed data. Given models M1, M2, M3, we can compare the probability of observing the data given the models, P(D|M1), P(D|M2), P(D|M3). We say that the model with the highest probability is the model which best explains the data.

However, a particular model might have a parameter q. For example, in the duplication-mutation-complementation model, we might want to vary the rate at which complexes are lost in the mutation step. For a model M with a parameter q, the probability of observing the data will depend on the parameter. For a certain parameter value, we write P(D|M,q) for this probability.

Now there are two different approaches to calculating P(D|M) in this situation. The Bayesian approach is to assume a prior distribution (such as a uniform distribution) on the parameter, P(q), and then compute P(D|M) = Integral[P(D|M,q)P(q) dq] over the parameter space.

The classical approach of maximum likelihood is simply to take P(D|M) = max[P(D|M,q)] over the parameter space. Though if we are comparing models, we will want to apply a penalty factor for the complexity of the model, so in fact we will take P(D|M)= c max[P(D|M,q)] where c is a complexity penalty (e.g. according to Aikake Information Criterion).

CEV-tropes

snarles22 Sep 2014 18:21 UTC

12 points

15 comments1 min readLW link

Latex support?

snarles6 Sep 2011 12:42 UTC

12 points

9 comments1 min readLW link

snarles 4 May 2011 1:45 UTC
10 points
in reply to: Zetetic’s comment on: Discussion: Pathways for the Aspiring AGI Researcher?
Money is of course only one factor; I think you could easily make a career in statistics without compromising your interests.

There are many researchers in statistics who work on areas which are extremely relevant to artificial intelligence. One of the best examples is Cosma Shalizi (http://www.cscs.umich.edu/~crshalizi/), who developed the “Causal State Splitting Reconstruction” algorithm for automatically detecting patterns in time series.

Your background is also quite suited for statistics: both the computer science and the philosophy. Nowadays, the computational element is becoming more and more prominent in statistics; the best example is the rapid growth of Markov Chain Monte Carlo sampling for Bayesian analysis. MCMC methods originated in statistical physics, but they are now being employed to analyze complex probability models in exotic settings such as social networks.

As for the philosophical aspect of statistics (which is usually understated,) you would be surprised at how many foundational questions still remain on the question of how to draw conclusions from data. There are a few basic tenets for statistical procedures, e.g. the likelihood principle and the conditionality principle, but none of them are universally accepted as “axioms” and in fact some of them contradict each other. The choice of which principle to adopt results in an explosion of competing methodologies: frequentist, Bayesian, empirical Bayesian, conditional, “pragmatic” (cross-validation) etc. Yet the question of how to do inference from data is obviously a central question to be addressed for the goal of designing an inductive agent.

snarles 13 Dec 2009 19:04 UTC
9 points
on: The Amanda Knox Test: How an Hour on the Internet Beats a Year in the Courtroom
I’d like to suggest another type of rationality test for this site. The top contributors should randomly make posts that are flat-out wrong to see how they are received; and they should also randomly make legitimate posts under different names.

snarles 23 Oct 2015 15:55 UTC
8 points
on: The trouble with Bayes (draft)
Update from the author:

Thanks for all of the comments and corrections! Based on your feedback, I have concluded that the article is a little bit too advanced (and possibly too narrow in focus) to be posted in the main section of the site. However, it is clear that there is a lot of interest in the general subject. Therefore, rather than posting this article to main, I think it would be more productive to write a “Philosophy of Statistics” sequence which would provide the necessary background for this kind of post.

snarles 18 Mar 2012 1:02 UTC
8 points
on: Please advise the Singularity Institute with your domain-specific expertise!
I give out statistics-related opinions all the time which don’t seem to be especially well received. But, if it means anything, I have been admitted to one of the top 4 statistics Ph. D programs, and plan to start my doctoral studies next fall. I’ll sign up.

snarles 12 Apr 2019 1:23 UTC
7 points
on: The Hard Work of Translation (Buddhism)
I started out as a self-identified rationalist, got fascinated by mysticism and ‘went native.’ Ever since, I have been watching the rationality from the sidelines to see if anyone else will ‘cross over’ as well.
I predict that if Romeo continues to work on methods for teaching meditation, that eventually he will also ‘go mystical’ and publicly rescind his claim that all perceived metaphysical insights can be explained as pathological disconnects with reality caused by neural rewiring. Conditional on his continuing to teach, I predict with 70% probability that it occurs within the next 10 years.
There are two theories that both lead me to make this prediction, which I call Theory M and Theory D. I ascribe probability 0.8 to Theory M and 0.15 to Theory D. I ascribe a probability of 0.02 to their intersection.
Theory M (‘Mystical’) is that there exists a truth that cannot be expressed in conventional language, and that truth has been rediscovered independently by hundreds of seekers throughout history, and that many of the most established spiritual traditions—Taoist, Buddhist, Hindu, Kabbalistic, Christian, Sufi, and many others—were founded for the purpose of disseminating that same truth. If Theory M is true, and conditional on Romeo continuing to teach, I predict that his role in the community will motivate him to deepen his practice and will also bring him in contact with teachers from other spiritual practices. These experiences will catalyze his own mystical insights.
Theory D (‘Delusional’) is that spiritual seekers expose themselves to mechanisms of self-delusion that are so strong that they would even convince someone who is initially highly identified with rationality and skepticism to start assigning a high probability that Theory M is true.
The main reason I place so much confidence in Theory M is similar to the reason why string theorists place so much confidence in string theory: so many aspects of reality that were previously baffling to me are suddenly very comprehensible and elegant when I understand them through Theory M. Secondary reasons are my personal experiences, but I concede that some aspect of these could be due to delusions so I will not defend them here.
The reason I have so much confidence in Theory D is that I consider myself to have a high capacity for rational thinking, that I am very well-informed in the ideas of the rationality community and I consider myself knowledgeable about a variety of disciplines necessary for understanding reality: neuroscience, psychology, sociology, statistics, philosophy, biology, physics, religious history. I hold a Ph. D. in Statistics and am currently working as a research scientist in a research institution specializing in mental health research. And yet I have daily experiences which reaffirm my confidence in Theory M. If theory M is not true, then I would conclude that the delusion I am suffering is incredibly strong, lies outside of all mental diseases which I know about and evades my own attempts to self-diagnose. I admit that I have little incentive to eliminate the delusion since it makes my life so pleasant. I admit that I perhaps could have protected myself from delusion even more solidly by embedding myself deeply in a physical social community that was dedicated to rationality. I have not done that. However, I am surrounded by scientists at my workplace.
If theory M is true, then I warmly wish all of you reading to discover its truth. If it is not true, I wish for my delusion to be eliminated so that I can stop living in a pleasant fantasy and re-align myself with rationalists who are trying to maximize their positive impact on the world.
I expect that my views will not find much support among you, but I challenge you to judge my claims by the professed standards of your own community. If you feel yourself strongly disagreeing with me, I challenge you to engage me as a fellow rationalist (or to point out how I have violated the community rules of discourse) rather than succumbing the knee-jerk reaction of dismissing a set of beliefs which make you uncomfortable.

snarles 25 Jul 2015 4:05 UTC
7 points
in reply to: jacob_cannell’s comment on: Steelmaning AI risk critiques
There is no way to raise a human safely if that human has the power to exponentially increase their own capabilities and survive independently of society.

snarles 11 Sep 2010 0:57 UTC
7 points
on: More art, less stink: Taking the PU out of PUA
It is an interesting question why the psychological or sociological research community has not yet paid any serious attention (as far as I know) to the pick-up community.

snarles 3 Jun 2010 14:45 UTC
7 points
in reply to: taw’s comment on: Open Thread: June 2010
I’m not a psychologist but I thought I could improve on the vagueness of the original discussion.

There are a few factors which determine “smartness” (or potential for success):
1. Speed. Having faster hardware.
2. Pattern Recognition. Being better at “chunking”.
3. Memory.
4. Creativity. (=”divergent” thinking.)
5. Detail-awareness.
6. Experience. Having incorporated many routines into the subconscious thanks to extensive practice.
7. Knowledge. (Quality is more important than quantity.)
The first five traits might be considered part of someone’s “talent.” Experience and knowledge, which I’ll group together as “training”, must be gained through hard work. Potential for success is determined by a geometric (rather than additive) combination of talent and training: that is, roughly,

potential for success=talent * training

All this math, of course, is not remotely intended to be taken at face value, but it’s merely the most efficient way to make my point.

The “super-smart” start life with more talent than average. The rule of the bell curve holds, so they generally do not have an overwhelming cognitive advantage over the average person. But they have enough talent to justify investing much more of their resources into training. This is because a person with 15 talent will gain 15 success for every unit of time they put into training, while a unit of training is worth 17 success for a person with 17 talent. The less time you have to spend, the more time costs, so all other things being equal, the person with more talent will put more time into training. Suppose the person with 15 talent puts 100 units of time into training, and the person with 17 talent puts 110 units of time into training. Then:

person with 15 talent * 100 training ⇒ 15000 success

person with 17 talent * 110 training ⇒ 18700 success

Which is 25% more success for only 13% more talent.

There’s probably some more formal work done along these lines, I’m not an economist either.