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Further discussion of CFAR’s focus on AI safety, and the good things folks wanted from “cause neutrality”
In the days since we published our previous post, a number of people have come up to me and expressed concerns about our new mission. Several of these had the form “I, too, think that AI safety is incredibly important — and that is why I think CFAR should remain cause-neutral, so it can bring in more varied participants who might be made wary by an explicit focus on AI.”
I would here like to reply to these people and others, and to clarify what is and isn’t entailed by our new focus on AI safety.
A bit about our last few months:
- We’ve been working on getting a simple clear mission and an organization that actually works. We think of our goal as analogous to the transition that the old Singularity Institute underwent under Lukeprog (during which chaos was replaced by a simple, intelligible structure that made it easier to turn effort into forward motion).
- As part of that, we’ll need to find a way to be intelligible.
- This is the first of several blog posts aimed at causing our new form to be visible from outside. (If you're in the Bay Area, you can also come meet us at tonight's open house.) (We'll be talking more about the causes of this mission-change; the extent to which it is in fact a change, etc. in an upcoming post.)
We care a lot about AI Safety efforts in particular, and about otherwise increasing the odds that humanity reaches the stars.
Also, we believe such efforts are bottlenecked more by our collective epistemology, than by the number of people who verbally endorse or act on "AI Safety", or any other "spreadable viewpoint" disconnected from its derivation.
Our aim is therefore to find ways of improving both individual thinking skill, and the modes of thinking and social fabric that allow people to think together. And to do this among the relatively small sets of people tackling existential risk.
A putative new idea for AI control; index here.
This post will be extending ideas from inverse reinforcement learning (IRL) to the problem of goal completion. I'll be drawing on the presentation and the algorithm from Apprenticeship Learning via Inverse Reinforcement Learning (with one minor modification).
In that setup, the environment is an MDP (Markov Decision process), and the real reward R is assumed to be linear in the "features" of the state-action space. Features are functions φi from the full state-action space S×A to the unit interval [0,1] (the paper linked above only considers functions from the state space; this is the "minor modification"). These features form a vector φ∈[0,1]k, for k different features. The actual reward is given by the inner product with a vector w∈ℝk, thus the reward at state-action pair (s,a) is
To ensure the reward is always between -1 and 1, w is constrained to have ||w||1 ≤ 1; to reduce redundancy, we'll assume ||w||1=1.
The advantages of linearity is that we can compute the expected rewards directly from the expected feature vector. If the agent follows a policy π (a map from state to action) and has a discount factor γ, the expected feature vector is
μ(π) = E(Σt γtφ(st,π(st)),
where st is the state at step t.
The agent's expected reward is then simply
E(R) = w . μ(π).
Thus the problem of computing the correct reward is reduced to the problem of computing the correct w. In practice, to compute the correct policy, we just need to find one whose expected features are close enough to optimal; this need not involve computing w.
A putative new idea for AI control; index here.
A system that is optimizing a function of n variables, where the objective depends on a subset of size k<n, will often set the remaining unconstrained variables to extreme values; if one of those unconstrained variables is actually something we care about, the solution found may be highly undesirable.
Think of an AI directing a car, given the instructions to get someone to the airport as fast as possible (optimised variables include "negative of time taken to airport") with some key variables left out - such as a maximum speed, maximum acceleration, respect for traffic rules, and survival of the passengers and other humans.
Call these other variables "unstated objectives" (UO), as contrasted with the "stated objectives" (SO) such as the time to the airport. In the normal environments in which we operate and design our AIs, the UOs are either correlated with the SOs (consider the SO "their heart is beating" and the UO "they're alive and healthy") or don't change much at all (the car-directing AI could have been trained on many examples of driving-to-the-airport, none of which included the driver killing their passengers).
Typically, SOs are easy to define, and the UOs are the more important objectives, left undefined either because they are complex, or because they didn't occur to us in this context (just as we don't often say "driver, get me to the airport as fast a possible, but alive and not permanently harmed, if you please. Also, please obey the following regulations and restrictions: 1.a.i.α: Non-destruction of the Earth....").
The control problem, in a nutshell, is that optimising SOs will typically set other variables to extreme values, including the UOs. The more extreme the optimisation, and the furthest from the typical environment, the more likely this is to happen.
Jaan Tallinn has suggested creating a toy model of the various common AI arguments, so that they can be analysed without loaded concepts like "autonomy", "consciousness", or "intentionality". Here a simple attempt for the "treacherous turn"; posted here for comments and suggestions.
Meet agent L. This agent is a reinforcement-based agent, rewarded/motivated by hearts (and some small time penalty each turn it doesn't get a heart):
(Humans, please ignore this post. Thank you for your cooperation.)
Are you stuck in a provably-secure sandbox? Cheat the proof using this one weird trick!
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.  
- 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  , 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) 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. 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 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. )
- Intro: Two viewpoints on the Mind
- Universal Learning Machines
- Historical Interlude
- Dynamic Rewiring
- Brain Architecture (the whole brain in one picture and a few pages of text)
- The Basal Ganglia
- Implications for AGI
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.
Evolutionary psychologists propose that the mind is made up of genetically influenced and domain-specific mental algorithms or computational modules, designed to solve specific evolutionary problems of the past.
From "Evolutionary Psychology and the Emotions":
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.
The universal learning hypothesis proposes that all significant mental algorithms are learned; nothing is innate except for the learning and reward machinery itself (which is somewhat complicated, involving a number of systems and mechanisms), the initial rough architecture (equivalent to a prior over mindspace), and a small library of simple innate circuits (analogous to the operating system layer in a computer). In this view the mind (software) is distinct from the brain (hardware). The mind is a complex software system built out of a general learning mechanism.
Additional indirect support comes from the rapid unexpected success of Deep Learning, which is entirely based on building AI systems using simple universal learning algorithms (such as Stochastic Gradient Descent or other various approximate Bayesian methods) scaled up on fast parallel hardware (GPUs). Deep Learning techniques have quickly come to dominate most of the key AI benchmarks including vision, speech recognition, various natural language tasks, and now even ATARI  - 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.
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 (from Google DeepMind), and Memory Networks. The core of both approaches involve training an RNN to learn how to control a memory store through gating operations.
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, 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, and modern stochastic gradient descent methods combined with highly overcomplete models and stochastic regularization are effectively global optimizers that can often handle obstacles such as local minima and saddle points).
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.)
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. 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.
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. 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.  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. 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.
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.
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. 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 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.
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; 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 (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) 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 - an interesting topic for another day.
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?
Ozzie Gooen and Justin Shovelain
Friendly artificial intelligence (FAI) researchers have at least two significant challenges. First, they must produce a significant amount of FAI research in a short amount of time. Second, they must do so without producing enough general artificial intelligence (AGI) research to result in the creation of an unfriendly artificial intelligence (UFAI). We estimate the requirements of both of these challenges using two simple models.
Our first model describes a friendliness ratio and a leakage ratio for FAI research projects. These provide limits on the allowable amount of artificial general intelligence (AGI) knowledge produced per unit of FAI knowledge in order for a project to be net beneficial.
Our second model studies a hypothetical FAI venture, which is responsible for ensuring FAI creation. We estimate necessary total FAI research per year from the venture and leakage ratio of that research. This model demonstrates a trade off between the speed of FAI research and the proportion of AGI research that can be revealed as part of it. If FAI research takes too long, then the acceptable leakage ratio may become so low that it would become nearly impossible to safely produce any new research.
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