As an exercise on programming Android, I've made an app to log predictions you make and keep score of your results. Like PredictionBook, but taking more of a personal daily exercise feel, in line with this post.

The "statistics" right now are only a score I copied from the old Credence calibration game, and a calibration bar chart.

I'm hoping for suggestionss for features and criticism on the app design.

Here's the link for the apk (v0.4), and here's the source code repository. You can download it at Google Play Store.

Pending/Possible/Requested Features:

• Set check-in dates for predictions
• Tags (and stats by tag)
• Stats by timeframe
• Beeminder integration
• Trivia questions you can answer if you don't have any personal prediction to make
• Ring pie chart to choose probability

Edit:

2015-08-26 - Fixed bug that broke on Android 5.0.2 (thanks Bobertron)

2015-08-28 - Change layout for landscape mode, and add a better icon

2015-08-31 -

• Daily notifications
• Buttons at the expanded-item-layout (ht dutchie)
• Show points won/lost in the snackbar when a prediction is answered
• Translation to portuguese

A well-known American federal appellate judge, Alex Kozinski, has written a commentary on systemic biases and institutional myths in the criminal justice system.

The basic thrust of his criticism will be familiar to readers of the sequences and rationalists generally. Lots about cognitive biases (but some specific criticisms of fingerprints and DNA evidence as well). Still, it's interesting that a prominent federal judge -- the youngest when appointed, and later chief of the Ninth Circuit -- would treat some sacred cows of the judiciary so ruthlessly. 

This is specifically a criticism of U.S. criminal justice, but, ceteris paribus, much of it applies not only to other areas of U.S. law, but to legal practices throughout the world as well.

One fallacy that I see frequently in works of popular science -- and also here on LessWrong -- is the belief that we have strong evidence of the way things were in pre-history, particularly when one is giving evidence that we can explain various aspects of our culture, psychology, or personal experience because we evolved in a certain way. Moreover, it is held implicit that because we have this 'strong evidence', it must be relevant to the topic at hand. While it is true that the environment did effect our evolution and thus the way we are today, evolution and anthropology of pre-historic societies is emphasized to a much greater extent than rational thought would indicate is appropriate. 

As a matter of course, you should remember these points whenever you hear a claim about prehistory:

• Most of what we know (or guess) is based on less data than you would expect, and the publish or perish mentality is alive and well in the field of anthropology.

• Most of the information is limited and technical, which means that anyone writing for a popular audience will have strong motivation to generalize and simplify.

• It has been found time and time again that for any statement that we can make about human culture and behavior that there is (or was) a society somewhere that will serve as a counterexample. 

• Very rarely do anthropologists or members of related fields have finely tuned critical thinking skills or a strong background on the philosophy of science, and are highly motivated to come up with interpretations of results that match their previous theories and expectations. 

Results that you should have reasonable levels of confidence in should be framed in generalities, not absolutes. E.g., "The great majority of human cultures that we have observed have distinct and strong religious traditions", and not "humans evolved to have religion". It may be true that we have areas in our brain that evolved not only 'consistent with holding religion', but actually evolved 'specifically for the purpose of experiencing religion'... but it would be very hard to prove this second statement, and anyone who makes it should be highly suspect. 

Perhaps more importantly, these statements are almost always a red herring. It may make you feel better that humans evolved to be violent, to fit in with the tribe, to eat meat, to be spiritual, to die at the age of thirty.... But rarely do we see these claims in a context where the stated purpose is to make you feel better. Instead they are couched in language indicating that they are making a normative statement -- that this is the way things in some way should be. (This is specifically the argumentum ad antiquitatem or appeal to tradition, and should not be confused with the historical fallacy, but it is certainly a fallacy). 

It is fine to identify, for example, that your fear of flying has a evolutionary basis. However, it is foolish to therefore refuse to fly because it is unnatural, or to undertake gene therapy to correct the fear. Whether or not the explanation is valid, it is not meaningful. 

Obviously, this doesn't mean that we shouldn't study evolution or the effects evolution has on behavior. However, any time you hear someone refer to this information in order to support any argument outside the fields of biology or anthropology, you should look carefully at why they are taking the time to distract you from the practical implications of the matter under discussion. 

Markets are powerful decentralized optimization engines - it is known.  Liberals see the free market as a kind of optimizer run amuck, a dangerous superintelligence with simple non-human values that must be checked and constrained by the government - the friendly SI.  Conservatives just reverse the narrative roles.

In some domains, where the incentive structure aligns with human values, the market works well.  In our current framework, the market works best for producing gadgets. It does not work so well for pricing intangible information, and most specifically it is broken when it comes to health.

We treat health as just another gadget problem: something to be solved by pills.  Health is really a problem of knowledge; it is a computational prediction problem.  Drugs are useful only to the extent that you can package the results of new knowledge into a pill and patent it.  If you can't patent it, you can't profit from it.

So the market is constrained to solve human health by coming up with new patentable designs for mass-producible physical objects which go into human bodies.  Why did we add that constraint - thou should solve health, but thou shalt only use pills?  (Ok technically the solutions don't have to be ingestible, but that's a detail.)

The gadget model works for gadgets because we know how gadgets work - we built them, after all.  The central problem with health is that we do not completely understand how the human body works - we did not build it.  Thus we should be using the market to figure out how the body works - completely - and arguably we should be allocating trillions of dollars towards that problem.

The market optimizer analogy runs deeper when we consider the complexity of instilling values into a market.  Lawmakers cannot program the market with goals directly, so instead they attempt to engineer desireable behavior by ever more layers and layers of constraints.  Lawmakers are deontologists.

As an example, consider the regulations on drug advertising.  Big pharma is unsafe - its profit function does not encode anything like "maximize human health and happiness" (which of course itself is an oversimplification).  If allowed to its own devices, there are strong incentives to sell subtly addictive drugs, to create elaborate hyped false advertising campaigns, etc.  Thus all the deontological injunctions.  I take that as a strong indicator of a poor solution - a value alignment failure.

What would healthcare look like in a world where we solved the alignment problem?

To solve the alignment problem, the market's profit function must encode long term human health and happiness.  This really is a mechanism design problem - its not something lawmakers are even remotely trained or qualified for.  A full solution is naturally beyond the scope of a little blog post, but I will sketch out the general idea.

To encode health into a market utility function, first we create financial contracts with an expected value which captures long-term health.  We can accomplish this with a long-term contract that generates positive cash flow when a human is healthy, and negative when unhealthy - basically an insurance contract.  There is naturally much complexity in getting those contracts right, so that they measure what we really want.  But assuming that is accomplished, the next step is pretty simple - we allow those contracts to trade freely on an open market.

There are some interesting failure modes and considerations that are mostly beyond scope but worth briefly mentioning.  This system probably needs to be asymmetric.  The transfers on poor health outcomes should partially go to cover medical payments, but it may be best to have a portion of the wealth simply go to nobody/everybody - just destroyed.

In this new framework, designing and patenting new drugs can still be profitable, but it is now put on even footing with preventive medicine.  More importantly, the market can now actually allocate the correct resources towards long term research.

To make all this concrete, let's use an example of a trillion dollar health question - one that our current system is especially ill-posed to solve:

What are the long-term health effects of abnormally low levels of solar radiation?  What levels of sun exposure are ideal for human health?

This is a big important question, and you've probably read some of the hoopla and debate about vitamin D.  I'm going to soon briefly summarize a general abstract theory, one that I would bet heavily on if we lived in a more rational world where such bets were possible.

In a sane world where health is solved by a proper computational market, I could make enormous - ridiculous really - amounts of money if I happened to be an early researcher who discovered the full health effects of sunlight.  I would bet on my theory simply by buying up contracts for individuals/demographics who had the most health to gain by correcting their sunlight deficiency.  I would then publicize the theory and evidence, and perhaps even raise a heap pile of money to create a strong marketing engine to help ensure that my investments - my patients - were taking the necessary actions to correct their sunlight deficiency.  Naturally I would use complex machine learning models to guide the trading strategy.

Now, just as an example, here is the brief 'pitch' for sunlight.

If we go back and look across all of time, there is a mountain of evidence which more or less screams - proper sunlight is important to health.  Heliotherapy has a long history.

Humans, like most mammals, and most other earth organisms in general, evolved under the sun.  A priori we should expect that organisms will have some 'genetic programs' which take approximate measures of incident sunlight as an input.  The serotonin -> melatonin mediated blue-light pathway is an example of one such light detecting circuit which is useful for regulating the 24 hour circadian rhythm.

The vitamin D pathway has existed since the time of algae such as the Coccolithophore.  It is a multi-stage pathway that can measure solar radiation over a range of temporal frequencies.  It starts with synthesis of fat soluble cholecalciferiol which has a very long half life measured in months. ^{[1] [2]}

The rough pathway is:

• Cholecalciferiol (HL ~ months) becomes 
• 25(OH)D (HL ~ 15 days) which finally becomes 
• 1,25(OH)₂ D (HL ~ 15 hours)

The main recognized role for this pathway in regards to human health - at least according to the current Wikipedia entry - is to enhance "the internal absorption of calcium, iron, magnesium, phosphate, and zinc".  Ponder that for a moment.

Interestingly, this pathway still works as a general solar clock and radiation detector for carnivores - as they can simply eat the precomputed measurement in their diet.

So, what is a long term sunlight detector useful for?  One potential application could be deciding appropriate resource allocation towards DNA repair.  Every time an organism is in the sun it is accumulating potentially catastrophic DNA damage that must be repaired when the cell next divides.  We should expect that genetic programs would allocate resources to DNA repair and various related activities dependent upon estimates of solar radiation.

I should point out - just in case it isn't obvious - that this general idea does not imply that cranking up the sunlight hormone to insane levels will lead to much better DNA/cellular repair.  There are always tradeoffs, etc.

One other obvious use of a long term sunlight detector is to regulate general strategic metabolic decisions that depend on the seasonal clock - especially for organisms living far from the equator.  During the summer when food is plentiful, the body can expect easy calories.  As winter approaches calories become scarce and frugal strategies are expected.

So first off we'd expect to see a huge range of complex effects showing up as correlations between low vit D levels and various illnesses, and specifically illnesses connected to DNA damage (such as cancer) and or BMI.  

Now it turns out that BMI itself is also strongly correlated with a huge range of health issues.  So the first key question to focus on is the relationship between vit D and BMI.  And - perhaps not surprisingly - there is pretty good evidence for such a correlation ^[3][4] , and this has been known for a while.

Now we get into the real debate.  Numerous vit D supplement intervention studies have now been run, and the results are controversial.  In general the vit D experts (such as my father, who started the vit D council, and publishes some related research^[5]) say that the only studies that matter are those that supplement at high doses sufficient to elevate vit D levels into a 'proper' range which substitutes for sunlight, which in general requires 5000 IU day on average - depending completely on genetics and lifestyle (to the point that any one-size-fits all recommendation is probably terrible).

The mainstream basically ignores all that and funds studies at tiny RDA doses - say 400 IU or less - and then they do meta-analysis over those studies and conclude that their big meta-analysis, unsurprisingly, doesn't show a statistically significant effect.  However, these studies still show small effects.  Often the meta-analysis is corrected for BMI, which of course also tends to remove any vit D effect, to the extent that low vit D/sunlight is a cause of both weight gain and a bunch of other stuff.

So let's look at two studies for vit D and weight loss.

First, this recent 2015 study of 400 overweight Italians (sorry the actual paper doesn't appear to be available yet) tested vit D supplementation for weight loss.  The 3 groups were (0 IU/day, ~1,000 IU / day, ~3,000 IU/day).  The observed average weight loss was (1 kg, 3.8 kg, 5.4 kg). I don't know if the 0 IU group received a placebo.  Regardless, it looks promising.

On the other hand, this 2013 meta-analysis of 9 studies with 1651 adults total (mainly women) supposedly found no significant weight loss effect for vit D.  However, the studies used between 200 IU/day to 1,100 IU/day, with most between 200 to 400 IU.  Five studies used calcium, five also showed weight loss (not necessarily the same - unclear).  This does not show - at all - what the study claims in its abstract.

In general, medical researchers should not be doing statistics.  That is a job for the tech industry.

Now the vit D and sunlight issue is complex, and it will take much research to really work out all of what is going on.  The current medical system does not appear to be handling this well - why?  Because there is insufficient financial motivation.

Is Big Pharma interested in the sunlight/vit D question?  Well yes - but only to the extent that they can create a patentable analogue!  The various vit D analogue drugs developed or in development is evidence that Big Pharma is at least paying attention.  But assuming that the sunlight hypothesis is mainly correct, there is very little profit in actually fixing the real problem.

There is probably more to sunlight that just vit D and serotonin/melatonin.  Consider the interesting correlation between birth month and a number of disease conditions^[6].  Perhaps there is a little grain of truth to astrology after all.

Thus concludes my little vit D pitch.  

In a more sane world I would have already bet on the general theory.  In a really sane world it would have been solved well before I would expect to make any profitable trade.  In that rational world you could actually trust health advertising, because you'd know that health advertisers are strongly financially motivated to convince you of things actually truly important for your health.

Instead of charging by the hour or per treatment, like a mechanic, doctors and healthcare companies should literally invest in their patients long-term health, and profit from improvements to long term outcomes.  The sunlight health connection is a trillion dollar question in terms of medical value, but not in terms of exploitable profits in today's reality.  In a properly constructed market, there would be enormous resources allocated to answer these questions, flowing into legions of profit motivated startups that could generate billions trading on computational health financial markets, all without selling any gadgets.

So in conclusion: the market could solve health, but only if we allowed it to and only if we setup appropriate financial mechanisms to encode the correct value function.  This is the UFAI problem next door.

This is some old work I did for SI. See also Notes on the Psychology of Power.

Deviant but not necessarily diseased or dysfunctional minds can demonstrate resistance to all treatment and attempts to change their mind (think No Universally Compelling Arguments; the premier example are probably psychopaths - no drug treatments are at all useful nor are there any therapies with solid evidence of even marginal effectiveness (one widely cited chapter, “Treatment of psychopathy: A review of empirical findings”, concludes that some attempted therapies merely made them more effective manipulators! We’ll look at that later.) While some psychopath traits bear resemblance to general characteristic of the powerful, they’re still a pretty unique group and worth looking at.

The main focus of my excerpts is on whether they are treatable, their effectiveness, possible evolutionary bases, and what other issues they have or don’t have which might lead one to not simply write them off as “broken” and of no relevance to AI.

(For example, if we were to discover that psychopaths were healthy human beings who were not universally mentally retarded or ineffective in gaining wealth/power and were destructive and amoral, despite being completely human and often socialized normally, then what does this say about the fragility of human values and how likely an AI will just be nice to us?)

This article presents an emerging architectural hypothesis of the brain as a biological implementation of a Universal Learning Machine.  I present a rough but complete architectural view of how the brain works under the universal learning hypothesis.  I also contrast this new viewpoint - which comes from computational neuroscience and machine learning - with the older evolved modularity hypothesis popular in evolutionary psychology and the heuristics and biases literature.  These two conceptions of the brain lead to very different predictions for the likely route to AGI, the value of neuroscience, the expected differences between AGI and humans, and thus any consequent safety issues and dependent strategies.

(The image above is from a recent mysterious post to r/machinelearning, probably from a Google project that generates art based on a visualization tool used to inspect the patterns learned by convolutional neural networks.  I am especially fond of the wierd figures riding the cart in the lower left. )

1. Intro: Two viewpoints on the Mind
2. Universal Learning Machines
3. Historical Interlude
4. Dynamic Rewiring
5. Brain Architecture (the whole brain in one picture and a few pages of text)
6. The Basal Ganglia
7. Implications for AGI
8. Conclusion

Intro: Two Viewpoints on the Mind

Few discoveries are more irritating than those that expose the pedigree of ideas.

-- Lord Acton (probably)

Less Wrong is a site devoted to refining the art of human rationality, where rationality is based on an idealized conceptualization of how minds should or could work.  Less Wrong and its founding sequences draws heavily on the heuristics and biases literature in cognitive psychology and related work in evolutionary psychology.  More specifically the sequences build upon a specific cluster in the space of cognitive theories, which can be identified in particular with the highly influential "evolved modularity" perspective of Cosmides and Tooby.

From Wikipedia:

Evolutionary psychologists propose that the mind is made up of genetically influenced and domain-specific^[3] mental algorithms or computational modules, designed to solve specific evolutionary problems of the past.^[4] 

From "Evolutionary Psychology and the Emotions":^[5]

An evolutionary perspective leads one to view the mind as a crowded zoo of evolved, domain-specific programs.  Each is functionally specialized for solving a different adaptive problem that arose during hominid evolutionary history, such as face recognition, foraging, mate choice, heart rate regulation, sleep management, or predator vigilance, and each is activated by a different set of cues from the environment.

If you imagine these general theories or perspectives on the brain/mind as points in theory space, the evolved modularity cluster posits that much of the machinery of human mental algorithms is largely innate.  General learning - if it exists at all - exists only in specific modules; in most modules learning is relegated to the role of adapting existing algorithms and acquiring data; the impact of the information environment is de-emphasized.  In this view the brain is a complex messy cludge of evolved mechanisms.

Additional indirect support comes from the rapid unexpected success of Deep Learning^[7], which is entirely based on building AI systems using simple universal learning algorithms (such as Stochastic Gradient Descent or other various approximate Bayesian methods^{[8][9][10][11]}) scaled up on fast parallel hardware (GPUs).  Deep Learning techniques have quickly come to dominate most of the key AI benchmarks including vision^[12], speech recognition^[13][14], various natural language tasks, and now even ATARI ^[15] - proving that simple architectures (priors) combined with universal learning is a path (and perhaps the only viable path) to AGI. Moreover, the internal representations that develop in some deep learning systems are structurally and functionally similar to representations in analogous regions of biological cortex^[16].

To paraphrase Feynman: to truly understand something you must build it. 

In this article I am going to quickly introduce the abstract concept of a universal learning machine, present an overview of the brain's architecture as a specific type of universal learning machine, and finally I will conclude with some speculations on the implications for the race to AGI and AI safety issues in particular.

Universal Learning Machines

A universal learning machine is a simple and yet very powerful and general model for intelligent agents.  It is an extension of a general computer - such as Turing Machine - amplified with a universal learning algorithm.  Do not view this as my 'big new theory' - it is simply an amalgamation of a set of related proposals by various researchers.

An initial untrained seed ULM can be defined by 1.) a prior over the space of models (or equivalently, programs), 2.) an initial utility function, and 3.) the universal learning machinery/algorithm.  The machine is a real-time system that processes an input sensory/observation stream and produces an output motor/action stream to control the external world using a learned internal program that is the result of continuous self-optimization.

There is of course always room to smuggle in arbitrary innate functionality via the prior, but in general the prior is expected to be extremely small in bits in comparison to the learned model.

The key defining characteristic of a ULM is that it uses its universal learning algorithm for continuous recursive self-improvement with regards to the utility function (reward system).  We can view this as second (and higher) order optimization: the ULM optimizes the external world (first order), and also optimizes its own internal optimization process (second order), and so on.  Without loss of generality, any system capable of computing a large number of decision variables can also compute internal self-modification decisions.

Conceptually the learning machinery computes a probability distribution over program-space that is proportional to the expected utility distribution.  At each timestep it receives a new sensory observation and expends some amount of computational energy to infer an updated (approximate) posterior distribution over its internal program-space: an approximate 'Bayesian' self-improvement.

The above description is intentionally vague in the right ways to cover the wide space of possible practical implementations and current uncertainty.  You could view AIXI as a particular formalization of the above general principles, although it is also as dumb as a rock in any practical sense and has other potential theoretical problems.  Although the general idea is simple enough to convey in the abstract, one should beware of concise formal descriptions: practical ULMs are too complex to reduce to a few lines of math.

A ULM inherits the general property of a Turing Machine that it can compute anything that is computable, given appropriate resources.  However a ULM is also more powerful than a TM.  A Turing Machine can only do what it is programmed to do.  A ULM automatically programs itself.

If you were to open up an infant ULM - a machine with zero experience - you would mainly just see the small initial code for the learning machinery.  The vast majority of the codestore starts out empty - initialized to noise.  (In the brain the learning machinery is built in at the hardware level for maximal efficiency).

Theoretical turing machines are all qualitatively alike, and are all qualitatively distinct from any non-universal machine.  Likewise for ULMs.  Theoretically a small ULM is just as general/expressive as a planet-sized ULM.  In practice quantitative distinctions do matter, and can become effectively qualitative.

Just as the simplest possible Turing Machine is in fact quite simple, the simplest possible Universal Learning Machine is also probably quite simple.  A couple of recent proposals for simple universal learning machines include the Neural Turing Machine^[16] (from Google DeepMind), and Memory Networks^[17].  The core of both approaches involve training an RNN to learn how to control a memory store through gating operations. 

Historical Interlude

At this point you may be skeptical: how could the brain be anything like a universal learner?  What about all of the known innate biases/errors in human cognition?  I'll get to that soon, but let's start by thinking of a couple of general experiments to test the universal learning hypothesis vs the evolved modularity hypothesis.

In a world where the ULH is mostly correct, what do we expect to be different than in worlds where the EMH is mostly correct?

One type of evidence that would support the ULH is the demonstration of key structures in the brain along with associated wiring such that the brain can be shown to directly implement some version of a ULM architecture.

From the perspective of the EMH, it is not sufficient to demonstrate that there are things that brains can not learn in practice - because those simply could be quantitative limitations.  Demonstrating that an intel 486 can't compute some known computable function in our lifetimes is not proof that the 486 is not a Turing Machine.

Nor is it sufficient to demonstrate that biases exist: a ULM is only 'rational' to the extent that its observational experience and learning machinery allows (and to the extent one has the correct theory of rationality).  In fact, the existence of many (most?) biases intrinsically depends on the EMH - based on the implicit assumption that some cognitive algorithms are innate.  If brains are mostly ULMs then most cognitive biases dissolve, or become learning biases - for if all cognitive algorithms are learned, then evidence for biases is evidence for cognitive algorithms that people haven't had sufficient time/energy/motivation to learn.  (This does not imply that intrinsic limitations/biases do not exist or that the study of cognitive biases is a waste of time; rather the ULH implies that educational history is what matters most)

The genome can only specify a limited amount of information.  The question is then how much of our advanced cognitive machinery for things like facial recognition, motor planning, language, logic, planning, etc. is innate vs learned.  From evolution's perspective there is a huge advantage to preloading the brain with innate algorithms so long as said algorithms have high expected utility across the expected domain landscape.  

On the other hand, evolution is also highly constrained in a bit coding sense: every extra bit of code costs additional energy for the vast number of cellular replication events across the lifetime of the organism.  Low code complexity solutions also happen to be exponentially easier to find.  These considerations seem to strongly favor the ULH but they are difficult to quantify.

Neuroscientists have long known that the brain is divided into physical and functional modules.  These modular subdivisions were discovered a century ago by Brodmann.  Every time neuroscientists opened up a new brain, they saw the same old cortical modules in the same old places doing the same old things.  The specific layout of course varied from species to species, but the variations between individuals are minuscule. This evidence seems to strongly favor the EMH.

Throughout most of the 90's up into the 2000's, evidence from computational neuroscience models and AI were heavily influenced by - and unsurprisingly - largely supported the EMH.  Neural nets and backprop were known of course since the 1980's and worked on small problems^[18], but at the time they didn't scale well - and there was no theory to suggest they ever would.  

Theory of the time also suggested local minima would always be a problem (now we understand that local minima are not really the main problem^[19], and modern stochastic gradient descent methods combined with highly overcomplete models and stochastic regularization^[20] are effectively global optimizers that can often handle obstacles such as local minima and saddle points^[21]).  

The other related historical criticism rests on the lack of biological plausibility for backprop style gradient descent.  (There is as of yet little consensus on how the brain implements the equivalent machinery, but target propagation is one of the more promising recent proposals^[22][23].)

Many AI researchers are naturally interested in the brain, and we can see the influence of the EMH in much of the work before the deep learning era.  HMAX is a hierarchical vision system developed in the late 90's by Poggio et al as a working model of biological vision^[24].  It is based on a preconfigured hierarchy of modules, each of which has its own mix of innate features such as gabor edge detectors along with a little bit of local learning.  It implements the general idea that complex algorithms/features are innate - the result of evolutionary global optimization - while neural networks (incapable of global optimization) use hebbian local learning to fill in details of the design.

Dynamic Rewiring

In a groundbreaking study from 2000 published in Nature, Sharma et al successfully rewired ferret retinal pathways to project into the auditory cortex instead of the visual cortex.^[25]  The result: auditory cortex can become visual cortex, just by receiving visual data!  Not only does the rewired auditory cortex develop the specific gabor features characteristic of visual cortex; the rewired cortex also becomes functionally visual. ^[26] True, it isn't quite as effective as normal visual cortex, but that could also possibly be an artifact of crude and invasive brain rewiring surgery.

The ferret study was popularized by the book On Intelligence by Hawkins in 2004 as evidence for a single cortical learning algorithm.  This helped percolate the evidence into the wider AI community, and thus probably helped in setting up the stage for the deep learning movement of today.  The modern view of the cortex is that of a mostly uniform set of general purpose modules which slowly become recruited for specific tasks and filled with domain specific 'code' as a result of the learning (self optimization) process.

The next key set of evidence comes from studies of atypical human brains with novel extrasensory powers.  In 2009 Vuillerme et al showed that the brain could automatically learn to process sensory feedback rendered onto the tongue^[27].  This research was developed into a complete device that allows blind people to develop primitive tongue based vision.

In the modern era some blind humans have apparently acquired the ability to perform echolocation (sonar), similar to cetaceans.  In 2011 Thaler et al used MRI and PET scans to show that human echolocators use diverse non-auditory brain regions to process echo clicks, predominantly relying on re-purposed 'visual' cortex.^[27] 

The echolocation study in particular helps establish the case that the brain is actually doing global, highly nonlocal optimization - far beyond simple hebbian dynamics.  Echolocation is an active sensing strategy that requires very low latency processing, involving complex timed coordination between a number of motor and sensory circuits - all of which must be learned.

Somehow the brain is dynamically learning how to use and assemble cortical modules to implement mental algorithms: everyday tasks such as visual counting, comparisons of images or sounds, reading, etc - all are task which require simple mental programs that can shuffle processed data between modules (some or any of which can also function as short term memory buffers).

To explain this data, we should be on the lookout for a system in the brain that can learn to control the cortex - a general system that dynamically routes data between different brain modules to solve domain specific tasks.

But first let's take a step back and start with a high level architectural view of the entire brain to put everything in perspective.

Brain Architecture

Below is a circuit diagram for the whole brain.  Each of the main subsystems work together and are best understood together.  You can probably get a good high level extremely coarse understanding of the entire brain is less than one hour.

(there are a couple of circuit diagrams of the whole brain on the web, but this is the best.  From this site.)

The human brain has ~100 billion neurons and ~100 trillion synapses, but ultimately it evolved from the bottom up - from organisms with just hundreds of neurons, like the tiny brain of C. Elegans.

We know that evolution is code complexity constrained: much of the genome codes for cellular metabolism, all the other organs, and so on.  For the brain, most of its bit budget needs to be spent on all the complex neuron, synapse, and even neurotransmitter level machinery - the low level hardware foundation.

For a tiny brain with 1000 neurons or less, the genome can directly specify each connection.  As you scale up to larger brains, evolution needs to create vastly more circuitry while still using only about the same amount of code/bits.  So instead of specifying connectivity at the neuron layer, the genome codes connectivity at the module layer.  Each module can be built from simple procedural/fractal expansion of progenitor cells.

So the size of a module has little to nothing to do with its innate complexity.  The cortical modules are huge - V1 alone contains 200 million neurons in a human - but there is no reason to suspect that V1 has greater initial code complexity than any other brain module.  Big modules are built out of simple procedural tiling patterns.

Very roughly the brain's main modules can be divided into six subsystems (there are numerous smaller subsystems):

• The neocortex: the brain's primary computational workhorse (blue/purple modules at the top of the diagram).  Kind of like a bunch of general purpose FPGA coprocessors.
• The cerebellum: another set of coprocessors with a simpler feedforward architecture.  Specializes more in motor functionality. 
• The thalamus: the orangish modules below the cortex.  Kind of like a relay/routing bus.
• The hippocampal complex: the apex of the cortex, and something like the brain's database.
• The amygdala and limbic reward system: these modules specialize in something like the value function.
• The Basal Ganglia (green modules): the central control system, similar to a CPU.

In the interest of space/time I will focus primarily on the Basal Ganglia and will just touch on the other subsystems very briefly and provide some links to further reading.

The neocortex has been studied extensively and is the main focus of several popular books on the brain.  Each neocortical module is a 2D array of neurons (technically 2.5D with a depth of about a few dozen neurons arranged in about 5 to 6 layers).

Each cortical module is something like a general purpose RNN (recursive neural network) with 2D local connectivity.  Each neuron connects to its neighbors in the 2D array.  Each module also has nonlocal connections to other brain subsystems and these connections follow the same local 2D connectivity pattern, in some cases with some simple affine transformations.  Convolutional neural networks use the same general architecture (but they are typically not recurrent.)

Cortical modules - like artifical RNNs - are general purpose and can be trained to perform various tasks.  There are a huge number of models of the cortex, varying across the tradeoff between biological realism and practical functionality.  

Perhaps surprisingly, any of a wide variety of learning algorithms can reproduce cortical connectivity and features when trained on appropriate sensory data^[27].  This is a computational proof of the one-learning-algorithm hypothesis; furthermore it illustrates the general idea that data determines functional structure in any general learning system.

There is evidence that cortical modules learn automatically (unsupervised) to some degree, and there is also some evidence that cortical modules can be trained to relearn data from other brain subsystems - namely the hippocampal complex.  The dark knowledge distillation technique in ANNs^[28][29] is a potential natural analog/model of hippocampus -> cortex knowledge transfer.

Module connections are bidirectional, and feedback connections (from high level modules to low level) outnumber forward connections.  We can speculate that something like target propagation can also be used to guide or constrain the development of cortical maps (speculation).

The hippocampal complex is the root or top level of the sensory/motor hierarchy.  This short youtube video  gives a good seven minute overview of the HC.  It is like a spatiotemporal database.  It receives compressed scene descriptor streams from the sensory cortices, it stores this information in medium-term memory, and it supports later auto-associative recall of these memories.  Imagination and memory recall seem to be basically the same.  

The 'scene descriptors' take the sensible form of things like 3D position and camera orientation, as encoded in place, grid, and head direction cells.  This is basically the logical result of compressing the sensory stream, comparable to the networking data stream in a multiplayer video game.

Imagination/recall is basically just the reverse of the forward sensory coding path - in reverse mode a compact scene descriptor is expanded into a full imagined scene.  Imagined/remembered scenes activate the same cortical subnetworks that originally formed the memory (or would have if the memory was real, in the case of imagined recall).

The amygdala and associated limbic reward modules are rather complex, but look something like the brain's version of the value function for reinforcement learning.  These modules are interesting because they clearly rely on learning, but clearly the brain must specify an initial version of the value/utility function that has some minimal complexity.

As an example, consider taste.  Infants are born with basic taste detectors and a very simple initial value function for taste.  Over time the brain receives feedback from digestion and various estimators of general mood/health, and it uses this to refine the initial taste value function.  Eventually the adult sense of taste becomes considerably more complex.  Acquired taste for bitter substances - such as coffee and beer - are good examples.

The amygdala appears to do something similar for emotional learning.  For example infants are born with a simple versions of a fear response, with is later refined through reinforcement learning.  The amygdala sits on the end of the hippocampus, and it is also involved heavily in memory processing.

See also these two videos from khanacademy: one on the limbic system and amygdala (10 mins), and another on the midbrain reward system (8 mins)

The Basal Ganglia

The Basal Ganglia is a wierd looking complex of structures located in the center of the brain.  It is a conserved structure found in all vertebrates, which suggests a core functionality.  The BG is proximal to and connects heavily with the midbrain reward/limbic systems.  It also connects to the brain's various modules in the cortex/hippocampus, thalamus and the cerebellum . . . basically everything.

All of these connections form recurrent loops between associated compartmental modules in each structure: thalamocortical/hippocampal-cerebellar-basal_ganglial loops.

Just as the cortex and hippocampus are subdivided into modules, there are corresponding modular compartments in the thalamus, basal ganglia, and the cerebellum.  The set of modules/compartments in each main structure are all highly interconnected with their correspondents across structures, leading to the concept of distributed processing modules.

Each DPM forms a recurrent loop across brain structures (the local networks in the cortex, BG, and thalamus are also locally recurrent, whereas those in the cerebellum are not).  These recurrent loops are mostly separate, but each sub-structure also provides different opportunities for inter-loop connections.

The BG appears to be involved in essentially all higher cognitive functions.  Its core functionality is action selection via subnetwork switching.  In essence action selection is the core problem of intelligence, and it is also general enough to function as the building block of all higher functionality.  A system that can select between motor actions can also select between tasks or subgoals.  More generally, low level action selection can easily form the basis of a Turing Machine via selective routing: deciding where to route the output of thalamocortical-cerebellar modules (some of which may specialize in short term memory as in the prefrontal cortex, although all cortical modules have some short term memory capability).

There are now a number of computational models for the Basal Ganglia-Cortical system that demonstrate possible biologically plausible implementations of the general theory^[28][29]; integration with the hippocampal complex leads to larger-scale systems which aim to model/explain most of higher cognition in terms of sequential mental programs^[30] (of course fully testing any such models awaits sufficient computational power to run very large-scale neural nets).

For an extremely oversimplified model of the BG as a dynamic router, consider an array of N distributed modules controlled by the BG system.  The BG control network expands these N inputs into an NxN matrix.  There are N² potential intermodular connections, each of which can be individually controlled.  The control layer reads a compressed, downsampled version of the module's hidden units as its main input, and is also recurrent.  Each output node in the BG has a multiplicative gating effect which selectively enables/disables an individual intermodular connection.  If the control layer is naively fully connected, this would require (N²)² connections, which is only feasible for N ~ 100 modules, but sparse connectivity can substantially reduce those numbers.

It is unclear (to me), whether the BG actually implements NxN style routing as described above, or something more like 1xN or Nx1 routing, but there is general agreement that it implements cortical routing. 

Of course in actuality the BG architecture is considerably more complex, as it also must implement reinforcement learning, and the intermodular connectivity map itself is also probably quite sparse/compressed (the BG may not control all of cortex, certainly not at a uniform resolution, and many controlled modules may have a very limited number of allowed routing decisions).  Nonetheless, the simple multiplicative gating model illustrates the core idea.  

This same multiplicative gating mechanism is the core principle behind the highly successful LSTM (Long Short-Term Memory)^[30] units that are used in various deep learning systems.  The simple version of the BG's gating mechanism can be considered a wider parallel and hierarchical extension of the basic LSTM architecture, where you have a parallel array of N memory cells instead of 1, and each memory cell is a large vector instead of a single scalar value.

The main advantage of the BG architecture is parallel hierarchical approximate control: it allows a large number of hierarchical control loops to update and influence each other in parallel.  It also reduces the huge complexity of general routing across the full cortex down into a much smaller-scale, more manageable routing challenge.

Implications for AGI

These two conceptions of the brain - the universal learning machine hypothesis and the evolved modularity hypothesis - lead to very different predictions for the likely route to AGI, the expected differences between AGI and humans, and thus any consequent safety issues and strategies.

In the extreme case imagine that the brain is a pure ULM, such that the genetic prior information is close to zero or is simply unimportant.  In this case it is vastly more likely that successful AGI will be built around designs very similar to the brain, as the ULM architecture in general is the natural ideal, vs the alternative of having to hand engineer all of the AI's various cognitive mechanisms.

In reality learning is computationally hard, and any practical general learning system depends on good priors to constrain the learning process (essentially taking advantage of previous knowledge/learning).  The recent and rapid success of deep learning is strong evidence for how much prior information is ideal: just a little.  The prior in deep learning systems takes the form of a compact, small set of hyperparameters that control the learning process and specify the overall network architecture (an extremely compressed prior over the network topology and thus the program space).

The ULH suggests that most everything that defines the human mind is cognitive software rather than hardware: the adult mind (in terms of algorithmic information) is 99.999% a cultural/memetic construct.  Obviously there are some important exceptions: infants are born with some functional but very primitive sensory and motor processing 'code'.  Most of the genome's complexity is used to specify the learning machinery, and the associated reward circuitry.  Infant emotions appear to simplify down to a single axis of happy/sad; differentiation into the more subtle vector space of adult emotions does not occur until later in development.

If the mind is software, and if the brain's learning architecture is already universal, then AGI could - by default - end up with a similar distribution over mindspace, simply because it will be built out of similar general purpose learning algorithms running over the same general dataset.  We already see evidence for this trend in the high functional similarity between the features learned by some machine learning systems and those found in the cortex.

Of course an AGI will have little need for some specific evolutionary features: emotions that are subconsciously broadcast via the facial muscles is a quirk unnecessary for an AGI - but that is a rather specific detail.

The key takeway is that the data is what matters - and in the end it is all that matters.  Train a universal learner on image data and it just becomes a visual system.  Train it on speech data and it becomes a speech recognizer.  Train it on ATARI and it becomes a little gamer agent.  

Train a universal learner on the real world in something like a human body and you get something like the human mind.  Put a ULM in a dolphin's body and echolocation is the natural primary sense, put a ULM in a human body with broken visual wiring and you can also get echolocation. 

Control over training is the most natural and straightforward way to control the outcome.  

To create a superhuman AI driver, you 'just' need to create a realistic VR driving sim and then train a ULM in that world (better training and the simple power of selective copying leads to superhuman driving capability).

So to create benevolent AGI, we should think about how to create virtual worlds with the right structure, how to educate minds in those worlds, and how to safely evaluate the results.

One key idea - which I proposed five years ago is that the AI should not know it is in a sim. 

New AI designs (world design + architectural priors + training/education system) should be tested first in the safest virtual worlds: which in simplification are simply low tech worlds without computer technology.  Design combinations that work well in safe low-tech sandboxes are promoted to less safe high-tech VR worlds, and then finally the real world.

A key principle of a secure code sandbox is that the code you are testing should not be aware that it is in a sandbox.  If you violate this principle then you have already failed.  Yudkowsky's AI box thought experiment assumes the violation of the sandbox security principle apriori and thus is something of a distraction. (the virtual sandbox idea was most likely discussed elsewhere previously, as Yudkowsky indirectly critiques a strawman version of the idea via this sci-fi story).  

The virtual sandbox approach also combines nicely with invisible thought monitors, where the AI's thoughts are automatically dumped to searchable logs.

Of course we will still need a solution to the value learning problem.  The natural route with brain-inspired AI is to learn the key ideas behind value acquisition in humans to help derive an improved version of something like inverse reinforcement learning and or imitation learning^[31] - an interesting topic for another day.

Conclusion

Ray Kurzweil has been predicting for decades that AGI will be built by reverse engineering the brain, and this particular prediction is not especially unique - this has been a popular position for quite a while.  My own investigation of neuroscience and machine learning led me to a similar conclusion some time ago.  

The recent progress in deep learning, combined with the emerging modern understanding of the brain, provide further evidence that AGI could arrive around the time when we can build and train ANNs with similar computational power as measured very roughly in terms of neuron/synapse counts.  In general the evidence from the last four years or so supports Hanson's viewpoint from the Foom debate.  More specifically, his general conclusion:

Future superintelligences will exist, but their vast and broad mental capacities will come mainly from vast mental content and computational resources. By comparison, their general architectural innovations will be minor additions.

The ULH supports this conclusion.

Current ANN engines can already train and run models with around 10 million neurons and 10 billion (compressed/shared) synapses on a single GPU, which suggests that the goal could soon be within the reach of a large organization.  Furthermore, Moore's Law for GPUs still has some steam left, and software advances are currently improving simulation performance at a faster rate than hardware.  These trends implies that Anthropomorphic/Neuromorphic AGI could be surprisingly close, and may appear suddenly.

What kind of leverage can we exert on a short timescale?

Introduction

A recent survey showed that the LessWrong discussion forums mostly attract readers who are predominantly either atheists or agnostics, and who lean towards the left or far left in politics. As one of the main goals of LessWrong is overcoming bias, I would like to come up with a topic which I think has a high probability of challenging some biases held by at least some members of the community. It's easy to fight against biases when the biases belong to your opponents, but much harder when you yourself might be the one with biases. It's also easy to cherry-pick arguments which prove your beliefs and ignore those which would disprove them. It's also common in such discussions, that the side calling itself rationalist makes exactly the same mistakes they accuse their opponents of doing. Far too often have I seen people (sometimes even Yudkowsky himself) who are very good rationalists but can quickly become irrational and use several fallacies when arguing about history or religion. This most commonly manifests when we take the dumbest and most fundamentalist young Earth creationists as an example, winning easily against them, then claiming that we disproved all arguments ever made by any theist. No, this article will not be about whether God exists or not, or whether any real world religion is fundamentally right or wrong. I strongly discourage any discussion about these two topics.

This article has two main purposes:

1. To show an interesting example where the scientific method can lead to wrong conclusions

2. To overcome a certain specific bias, namely, that the pre-modern Catholic Church was opposed to the concept of the Earth orbiting the Sun with the deliberate purpose of hindering scientific progress and to keep the world in ignorance. I hope this would prove to also be an interesting challenge for your rationality, because it is easy to fight against bias in others, but not so easy to fight against bias on yourselves.

The basis of my claims is that I have read the book written by Galilei himself, and I'm very interested (and not a professional, but well read) in early modern, but especially 16-17^th century history.

Geocentrism versus Heliocentrism

I assume every educated person knows the name of Galileo Galilei. I won't waste the space on the site and the time of the readers to present a full biography about his life, there are plenty of on-line resources where you can find more than enough biographic information about him.

The controversy?

What is interesting about him is how many people have severe misconceptions about him. Far too often he is celebrated as the one sane man in an era of ignorance, the sole propagator of science and rationality when the powers of that era suppressed any scientific thought and ridiculed everyone who tried to challenge the accepted theories about the physical world. Some even go as far as claiming that people believed the Earth was flat. Although the flat Earth theory was not propagated at all, it's true that the heliocentric view of the Solar System (the Earth revolving around the Sun) was not yet accepted.

However, the claim that the Church was suppressing evidence about heliocentrism "to maintain its power over the ignorant masses" can be disproved easily:

- The common people didn't go to school where they could have learned about it, and those commoners who did go to school, just learned to read and write, not much more, so they wouldn't care less about what orbits around what. This differs from 20-21^th century fundamentalists who want to teach young Earth creationism in schools - back then in the 17^th century, there would be no classes where either the geocentric or heliocentric views could have been taught to the masses.

- Heliocentrism was not discovered by Galilei. It was first proposed by Nicolaus Copernicus almost 100 years before Galilei. Copernicus didn't have any affairs with the Inquisition. His theories didn't gain wide acceptance, but he and his followers weren't persecuted either.

- Galilei was only sentenced to house arrest, and mostly because of insulting the pope and doing other unwise things. The political climate in 17th century Italy was quite messy, and Galilei did quite a few unfortunate choices regarding his alliances. Actually, Galilei was the one who brought religion into the debate: his opponents were citing Aristotle, not the Bible in their arguments. Galilei, however, wanted to redefine the Scripture based on his (unproven) beliefs, and insisted that he should have the authority to push his own views about how people interpret the Bible. Of course this pissed quite a few people off, and his case was not helped by publicly calling the pope an idiot.

- For a long time Galilei was a good friend of the pope, while holding heliocentric views. So were a couple of other astronomers. The heliocentrism-geocentrism debates were common among astronomers of the day, and were not hindered, but even encouraged by the pope.

- The heliocentrism-geocentrism debate was never an ateism-theism debate. The heliocentrists were committed theists, just like  the defenders of geocentrism. The Church didn't suppress science, but actually funded the research of most scientists.

- The defenders of geocentrism didn't use the Bible as a basis for their claims. They used Aristotle and, for the time being, good scientific reasoning. The heliocentrists were much more prone to use the "God did it" argument when they couldn't defend the gaps in their proofs.

The birth of heliocentrism.

By the 16th century, astronomers have plotted the movements of the most important celestial bodies in the sky. Observing the motion of the Sun, the Moon and the stars, it would seem obvious that the Earth is motionless and everything orbits around it. This model (called geocentrism) had only one minor flaw: the planets would sometimes make a loop in their motion, "moving backwards". This required a lot of very complicated formulas to model their motions. Thus, by the virtue of Occam's razor, a theory was born which could better explain the motion of the planets: what if the Earth and everything else orbited around the Sun? However, this new theory (heliocentrism) had a lot of issues, because while it could explain the looping motion of the planets, there were a lot of things which it either couldn't explain, or the geocentric model could explain it much better.

The proofs, advantages and disadvantages

The heliocentric view had only a single advantage against the geocentric one: it could describe the motion of the planets by a much simper formula.

However, it had a number of severe problems:

- Gravity. Why do the objects have weight, and why are they all pulled towards the center of the Earth? Why don't objects fall off the Earth on the other side of the planet? Remember, Newton wasn't even born yet! The geocentric view had a very simple explanation, dating back to Aristotle: it is the nature of all objects that they strive towards the center of the world, and the center of the spherical Earth is the center of the world. The heliocentric theory couldn't counter this argument.

- Stellar parallax. If the Earth is not stationary, then the relative position of the stars should change as the Earth orbits the Sun. No such change was observable by the instruments of that time. Only in the first half of the 19^th century did we succeed in measuring it, and only then was the movement of the Earth around the Sun finally proven.

- Galilei tried to used the tides as a proof. The geocentrists argued that the tides are caused by the Moon even if they didn't knew by what mechanisms, but Galilei said that it's just a coincidence, and the tides are not caused by the Moon: just as if we put a barrel of water onto a cart, the water would be still if the cart was stationary and the water would be sloshing around if the cart was pulled by a horse, so are the tides caused by the water sloshing around as the Earth moves. If you read Galilei's book, you will discover quite a number of such silly arguments, and you'll see that Galilei was anything but a rationalist. Instead of changing his views against overwhelming proofs, he used  all possible fallacies to push his view through.

Actually the most interesting author in this topic was Riccioli. If you study his writings you will get definite proof that the heliocentrism-geocentrism debate was handled with scientific accuracy and rationality, and it was not a religious debate at all. He defended geocentrism, and presented 126 arguments in the topic (49 for heliocentrism, 77 against), and only two of them (both for heliocentrism) had any religious connotations, and he stated valid responses against both of them. This means that he, as a rationalist, presented both sides of the debate in a neutral way, and used reasoning instead of appeal to authority or faith in all cases. Actually this was what the pope expected of Galilei, and such a book was what he commissioned from Galilei. Galilei instead wrote a book where he caricatured the pope as a strawman, and instead of presenting arguments for and against both world-views in a neutral way, he wrote a book which can be called anything but scientific.

By the way, Riccioli was a Catholic priest. And a scientist. And, it seems to me, also a rationalist. Studying the works of such people like him, you might want to change your mind if you perceive a conflict between science and religion, which is part of today's public consciousness only because of a small number of very loud religious fundamentalists, helped by some committed atheists trying to suggest that all theists are like them.

Finally, I would like to copy a short summary about this book:

In 1651 the Italian astronomer Giovanni Battista Riccioli published within his Almagestum Novum, a massive 1500 page treatise on astronomy, a discussion of 126 arguments for and against the Copernican hypothesis (49 for, 77 against). A synopsis of each argument is presented here, with discussion and analysis. Seen through Riccioli's 126 arguments, the debate over the Copernican hypothesis appears dynamic and indeed similar to more modern scientific debates. Both sides present good arguments as point and counter-point. Religious arguments play a minor role in the debate; careful, reproducible experiments a major role. To Riccioli, the anti-Copernican arguments carry the greater weight, on the basis of a few key arguments against which the Copernicans have no good response. These include arguments based on telescopic observations of stars, and on the apparent absence of what today would be called "Coriolis Effect" phenomena; both have been overlooked by the historical record (which paints a picture of the 126 arguments that little resembles them). Given the available scientific knowledge in 1651, a geo-heliocentric hypothesis clearly had real strength, but Riccioli presents it as merely the "least absurd" available model - perhaps comparable to the Standard Model in particle physics today - and not as a fully coherent theory. Riccioli's work sheds light on a fascinating piece of the history of astronomy, and highlights the competence of scientists of his time.

The full article can be found under this link. I recommend it to everyone interested in the topic. It shows that geocentrists at that time had real scientific proofs and real experiments regarding their theories, and for most of them the heliocentrists had no meaningful answers.

Disclaimers:

- I'm not a Catholic, so I have no reason to defend the historic Catholic church due to "justifying my insecurities" - a very common accusation against someone perceived to be defending theists in a predominantly atheist discussion forum.

- Any discussion about any perceived proofs for or against the existence of God would be off-topic here. I know it's tempting to show off your best proofs against your carefully constructed straw-men yet again, but this is just not the place for it, as it would detract from the main purpose of this article, as summarized in its introduction.

- English is not my native language. Nevertheless, I hope that what I wrote was comprehensive enough to be understandable. If there is any part of my article which you find ambiguous, feel free to ask.

I have great hopes and expectations that the LessWrong community is suitable to discuss such ideas. I have experience with presenting these ideas on other, predominantly atheist internet communities, and most often the reactions was outright flaming, a hurricane of unexplained downvotes, and prejudicial ad hominem attacks based on what affiliations they assumed I was subscribing to. It is common for people to decide whether they believe a claim or not, based solely by whether the claim suits their ideological affiliations or not. The best quality of rationalists, however, should be to be able to change their views when confronted by overwhelming proof, instead of trying to come up with more and more convoluted explanations. In the time I spent in the LessWrong community, I became to respect that the people here can argue in a civil manner, listening to the arguments of others instead of discarding them outright.

I've heard of the concept of "weirdness points" many times before, but after a bit of searching I can't find a definitive post describing the concept, so I've decided to make one.  As a disclaimer, I don't think the evidence backing this post is all that strong and I am skeptical, but I do think it's strong enough to be worth considering, and I'm probably going to make some minor life changes based on it.

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Chances are that if you're reading this post, you're probably a bit weird in some way.

No offense, of course.  In fact, I actually mean it as a compliment.  Weirdness is incredibly important.  If people weren't willing to deviate from society and hold weird beliefs, we wouldn't have had the important social movements that ended slavery and pushed back against racism, that created democracy, that expanded social roles for women, and that made the world a better place in numerous other ways.

Many things we take for granted now as why our current society as great were once... weird.

Joseph Overton theorized that policy develops through six stages: unthinkable, then radical, then acceptable, then sensible, then popular, then actual policy.  We could see this happen with many policies -- currently same-sex marriage is making its way from popular to actual policy, but not to long ago it was merely acceptable, and not too long before that it was pretty radical.

Some good ideas are currently in the radical range.  Effective altruism itself is such a collection of beliefs typical people would consider pretty radical.  Many people think donating 3% of their income is a lot, let alone the 10% demand that Giving What We Can places, or the 50%+ that some people in the community do.

And that's not all.  Others would suggest that everyone become vegetarian, advocating for open borders and/or universal basic income, theabolishment of gendered language, having more resources into mitigating existential risk, focusing on research into Friendly AI, cryonicsand curing death, etc.

While many of these ideas might make the world a better place if made into policy, all of these ideas are pretty weird.

Weirdness, of course, is a drawback.  People take weird opinions less seriously.

The absurdity heuristic is a real bias that people -- even you -- have.  If an idea sounds weird to you, you're less likely to try and believe it,even if there's overwhelming evidence.  And social proof matters -- if less people believe something, people will be less likely to believe it.  Lastly, don't forget the halo effect -- if one part of you seems weird, the rest of you will seem weird too!

(Update: apparently this concept is, itself, already known to social psychology as idiosyncrasy credits.  Thanks, Mr. Commenter!)

...But we can use this knowledge to our advantage.  The halo effect can work in reverse -- if we're normal in many ways, our weird beliefs will seem more normal too.  If we have a notion of weirdness as a kind of currency that we have a limited supply of, we can spend it wisely, without looking like a crank.

All of this leads to the following actionable principles:

Recognize you only have a few "weirdness points" to spend.  Trying to convince all your friends to donate 50% of their income to MIRI, become a vegan, get a cryonics plan, and demand open borders will be met with a lot of resistance.   But -- I hypothesize -- that if you pick one of these ideas and push it, you'll have a lot more success.

Spend your weirdness points effectively.  Perhaps it's really important that people advocate for open borders.  But, perhaps, getting people to donate to developing world health would overall do more good.  In that case, I'd focus on moving donations to the developing world and leave open borders alone, even though it is really important.  You should triage your weirdness effectively the same way you would triage your donations.

Clean up and look good.  Lookism is a problem in society, and I wish people could look "weird" and still be socially acceptable.  But if you're a guy wearing a dress in public, or some punk rocker vegan advocate, recognize that you're spending your weirdness points fighting lookism, which means less weirdness points to spend promoting veganism or something else.

Advocate for more "normal" policies that are almost as good.   Of course, allocating your "weirdness points" on a few issues doesn't mean you have to stop advocating for other important issues -- just consider being less weird about it.  Perhaps universal basic income truly would be a very effective policy to help the poor in the United States.  But reforming the earned income tax credit and relaxing zoning laws would also both do a lot to help the poor in the US, and such suggestions aren't weird.

Use the foot-in-door technique and the door-in-face technique.  The foot-in-door technique involves starting with a small ask and gradually building up the ask, such as suggesting people donate a little bit effectively, and then gradually get them to take the Giving What We Can Pledge.  The door-in-face technique involves making a big ask (e.g., join Giving What We Can) and then substituting it for a smaller ask, like the Life You Can Save pledge or Try Out Giving.

Reconsider effective altruism's clustering of beliefs.  Right now, effective altruism is associated strongly with donating a lot of money and donating effectively, less strongly with impact in career choice, veganism, and existential risk.  Of course, I'm not saying that we should drop some of these memes completely.  But maybe EA should disconnect a bit more and compartmentalize -- for example, leaving AI risk to MIRI, for example, and not talk about it much, say, on 80,000 Hours.  And maybe instead of asking people to both give more AND give more effectively, we could focus more exclusively on asking people to donate what they already do more effectively.

Evaluate the above with more research.  While I think the evidence base behind this is decent, it's not great and I haven't spent that much time developing it.  I think we should look into this more with a review of the relevant literature and some careful, targeted, market research on the individual beliefs within effective altruism (how weird are they?) and how they should be connected or left disconnected.  Maybe this has already been done some?

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Also discussed on the EA Forum and EA Facebook group.