I have a compute-market startup called vast.ai, and I’m working towards aligned AI. Currently seeking networking, collaborators, and hires—especially top notch cuda/gpu programmers.
My personal blog: https://entersingularity.wordpress.com/
I have a compute-market startup called vast.ai, and I’m working towards aligned AI. Currently seeking networking, collaborators, and hires—especially top notch cuda/gpu programmers.
My personal blog: https://entersingularity.wordpress.com/
As the article points out, shared biological needs do not much deter the bear or chimpanzee from killing you. An AI could be perfectly human—the very opposite of alien—and far more dangerous than Hitler or Dhamer.
The article is well written but dangerously wrong in its core point. AI will be far more human than alien. But alignment/altruism is mostly orthogonal to human vs alien.
We are definitely not training AIs on human thoughts because language is an expression of thought, not thought itself.
Even if training on language was not equivalent to training on thoughts, that would also apply to humans.
But it also seems false in the same way that “we are definitely not training AI’s on reality because image files are compressed sampled expressions of images, not reality itself” is false.
Approximate bayesian inference (ie DL) can infer the structure of a function through its outputs; the structure of the 3D world through images; and thoughts through language.
Premise 1: AGIs would be like a second advanced species on earth, more powerful than humans.
Distinct alien species arise only from distinct separated evolutionary histories. Your example of the aliens from Arrival are indeed a good (hypothetical) example of truly alien minds resulting from a completely independent evolutionary history on an alien world. Any commonalities between us and them would be solely the result of convergent evolutionary features. They would have completely different languages, cultures, etc.
AI is not alien at all, as we literally train AI on human thoughts. As a result we constrain our AI systems profoundly, creating them in our mental image. Any AGI we create will inevitably be far closer to human uploads than alien minds. This a prediction Moravec made as early as 1988 (Mind Children) - now largely fulfilled by the strong circuit convergence/correspondence between modern AI and brains.
Minds are software mental constructs, and alien minds would require alien culture. Instead we are simply creating new hardware for our existing (cultural) mind software.
I also not sure of the relevance and not following the thread fully, but the summary of that experiment is that it takes some time (measured in nights of sleep which are rough equivalent of big batch training updates) for the newly sighted to develop vision, but less time than infants—presumably because the newly sighted already have full functioning sensor inference world models in another modality that can speed up learning through dense top down priors.
But its way way more than “grok it really fast with just a few examples”—training their new visual systems still takes non-trivial training data & time
I suspect that much of the appeal of shard theory is working through detailed explanations of model-free RL with general value function approximation for people who mostly think of AI in terms of planning/search/consequentialism.
But if you already come from a model-free RL value approx perspective, shard theory seems more natural.
Moment to moment decisions are made based on value-function bids, with little to no direct connection to reward or terminal values. The ‘shards’ are just what learned value-function approximating subcircuits look like in gory detail.
The brain may have a prior towards planning subcircuitry, but even without a strong prior planning submodules will eventually emerge naturally in a model-free RL learning machine of sufficient scale (there is no fundamental difference between model-free and model-based for universal learners). TD like updates ensure that the value function extends over longer timescales as training progresses. (and in general humans seem to plan on timescales which scale with their lifespan, as you’d expect)
TSMC 4N is a little over 1e10 transistors/cm^2 for GPUs and roughly 5e^-18 J switch energy assuming dense activity (little dark silicon). The practical transistor density limit with minimal few electron transistors is somewhere around ~5e11 trans/cm^2, but the minimal viable high speed switching energy is around ~2e^-18J. So there is another 1 to 2 OOM further density scaling, but less room for further switching energy reduction. Thus scaling past this point increasingly involves dark silicon or complex expensive cooling and thus diminishing returns either way.
Achieving 1e-15 J/flop seems doable now for low precision flops (fp4, perhaps fp8 with some tricks/tradeoffs); most of the cost is data movement as pulling even a single bit from RAM just 1 cm away costs around 1e-12J.
Part of the issue is my post/comment was about moore’s law (transistor density for mass produced nodes), which is a major input to but distinct from flops/$. As I mentioned somewhere, there is still some free optimization energy in extracting more flops/$ at the circuit level even if moore’s law ends. Moore’s law is very specifically about fab efficiency as measured in transistors/cm^2 for large chip runs—not the flops/$ habyrka wanted to bet on. Even when moore’s law is over, I expect some continued progress in flops/$.
All that being said, nvidia’s new flagship GPU everyone is using—the H100 which is replacing the A100 and launched just a bit after habryka proposed the bet—actually offers near zero improvement in flops/$ (the price increased in direct proportion to flops increase). So I probably should have taken the bet if it was narrowly defined as (flops/$ for the flagship gpus most teams using currently for training foundation models).
I don’t know who first said it, but the popular saying “Computer vision is the inverse of computer graphics” encompasses much of this viewpoint.
Computer graphics is the study/art of the approximation theory you mention and fairly well developed & understood in terms of how to best simulate worlds & observations in real-time from the perspective of an observer. But of course traditional graphics uses human-designed world models and algorithms.
Diffusion models provide a general framework for learning a generative model in the other direction—in part by inverting trained vision and noise models.
So naturally there is also diffusion planning which is an example of the symmetry you discuss: using general diffusion inference for planning. The graph dimensions end up being both space-time and abstraction level with the latter being more important: sensor inference moves up the abstraction/compression hierarchy, whereas planning/acting/generating moves down.
Even if there is no acceptable way to share the data semi-anonymously outside of match group, the arguments for prediction markets still apply within match group. A well designed prediction market would still be a better way to distribute internal resources and rewards amongst competing data science teams within match group.
But I’m skeptical that the value of match group’s private data is dominant even in the fully private data scenario. People who actually match and meetup with another user will probably have important inside view information inaccessible to the algorithms of match group.
Manifold.Love’s lack of success is hardly much evidence against the utility of prediction markets for dating markets, any more or less than most startup’s failure at X is evidence against the utility of X.
Certainly mood disorders like bipolar,depression,mania can have multiple causes—for examle simply doing too much dopaminergic simulants (cocaine, meth etc) can cause mania directly.
But the modern increased prevalence of mood disorders is best explained by a modern divergence from conditions in the ancestral environment, and sleep disorder due to electric lighting disrupting circadian rhythms is a good fit to the evidence.
The evidence for each of my main points is fairly substantial and now mainstream, the only part which isn’t mainstream (yet) is the specific causal mechanism linking synaptic pruning/normalization to imbalance in valence computing upper brain modules (but it’s also fairly straightforward obvious from a DL perspective—we know that training stability is an intrinsic likely failure mode).
A few random links:
REM and synaptic normalization/pruning/homeostasis:
Plasticity during sleep is linked to specific regulation of cortical circuit activity
REM sleep promotes experience-dependent dendritic spine elimination in the mouse cortex.
REM sleep selectively prunes and maintains new synapses in development and learning
Memory corticalization triggered by REM sleep: mechanisms of cellular and systems consolidation
Sleep and wake cycles dynamically modulate hippocampal inhibitory synaptic plasticity
Sleep and Psychiatric Disorders:
Sleep disturbance and psychiatric disorders : “It is argued that insomnia and other mental health conditions not only share common causes but also show a bidirectional relationship, with typically the strongest pathway being disrupted sleep as a causal factor in the occurrence of other psychiatric problems.”
Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials: “For example, people with insomnia are 10 and 17 times more likely than those without insomnia to experience clinically significant levels of depression and anxiety, respectively”
The effectiveness of circadian interventions through the blue light pineal gland serotonin->melatonin pathway is also very well established: daytime bright light therapy has long been known to be effective for depression, nighttime blue light reduction is now also recognized as important/effective, etc.
The interventions required to promote healthy sleep architecture are not especially expensive and are certainly not patentable, so they are in a blindspot for our current partially misaligned drug-product focused healthcare system. Of course there would be a market for a hypothetical drug which could target and fix the specific issues that some people have with sleep quality—but instead we just have hammers like benzos and lithium which cause as many or more problems than they solve.
From my own study of mood disorders I generally agree with your valence theory of depression/mania.
However I believe the primary cause (at least for most people today) is disrupted sleep architecture.
To a first order approximation, the brain accumulates batch episodic training data during the day through indexing in the hippocampus (which is similar-ish to upper cortex, but more especially adapted to medium term memory & indexing). The brain’s main episodic replay training then occurs during sleep, with alternation of several key phases (REM and several NREM) with unique functional roles. During NREM (SWS in particular) the hippocampus rehearses sequences to ‘train’ the cortex via episodic replay. (Deepmind’s first atari RL agent is based on directly reverse engineering this mechanism).
But the REM sleep is also vitally important—and it seems to globally downscale/prune synaptic connections, most specifically the weakest and least important. It may also be doing something more complex in subtracting out the distribution of internally generated data ala Hinton’s theories (but maybe not, none of his sleep wake algos actually work well yet).
Regardless the brain does not seem to maintain synaptic strength balance on the hourly timescale. Instead median/average synaptic strength slowly grows without bound during the waking state, and is not correctly renormalized until pruning/renormalization during sleep—and REM sleep most specifically.
This explains many curious facts known of mania and depression:
The oldest known treatment for depression is also completely (but only temporarily) effective: sleep deprivation. Depression generally does not survive sleep deprivation.
Sleep is likewise effective to treat full blown mania, but mania inhibits sleep. One of the early successes in psychiatry was the use of sedatives to treat severe mania.
Red light interferes with the circadian rhythm—specifically serotonin->melatonin conversion, and thereby can disrupt sleep architecture (SAD etc)
SSRIs alter effective serotonin transport quickly but take a week or more to have noticeable effects on mood. Serotonin directly blocks REM—REM sleep is characterized (and probably requires) a near complete absence of monoamine neurotransmitters (histamine, serotonin and norepinephrine).
Lithium—a common treatment for bipolar—is a strong cellular circadian modulator and sleep stabilizer.
So basically the brain does not maintain perfect homeostatic synaptic normalization balance on short timescales. During wake synapses tend to strengthen, and during REM sleep they are pruned/weakened. Balancing this correctly seems to rely on a fairly complex sleep architecture, disruptions to which can cause mood disorders—not immediately, but over weeks/months.
But why does mean synaptic strength imbalance effect mostly mood and not say vision or motor control? Every synapse and brain region has a characteristic plasticity timescale that varies wildly. Peripheral lower regions (closer to sensors/motors) crystallize early and have low learning rate/plasticity in adults, so they aren’t very susceptible. At any one time in life the hippocampal → cortical episodic replay is focusing on particular brain modules, and in adults that focus is mostly on upper regions (PFC etc) that mostly store current plans, consequences, etc that are changing more rapidly.
Thus the upper brain regions that are proposing and computing the valence of various (actual or mental) actions as ‘dopaminergic bids’ with respect to current plans/situations are the most sensitive to synaptic norm imbalance, because they change at higher frequency. Of course if a manic stays awake long enough they do in fact progress to psychosis similar to schizophrenia.
Sure, but how often do the colonized end up better off for it, especially via trying to employ clever play-both-sides strategies?
I didn’t say the colonized generally ended up better off, but outcomes did vary greatly. Just in the US the cherokees faired much better than say the Susquehannock and Pequot, and if you dig into that history it seems pretty likely that decisions on which colonizer(s) to ally with (british, french, dutch, later american etc) were important, even if not “clever play-both-sides strategies” (although I’d be surprised if that wasn’t also tried somewhere at least once)
An idea sometimes floated around is to play them off against each other. If they’re misaligned from humanity, they’re likely mutually misaligned as well. We could put them in game-theoretic situations in which they’re incentivized to defect against each other and instead cooperate with humans.
You are arguing against a strawman. The optimistic game-theoretic argument you should focus on is:
Misaligned AIs are—almost by definition—instrumental selfish power seeking agents (with random long term goals) and thus intrinsically misaligned with each other. The partially aligned AIs will likely form a natural coalition with partial alignment to humanity as their centroid schelling point. The misaligned AIs could then form a natural counter-coalition in response.
There are numerous historical precedents such as the allies vs axis in world war two, and the allies vs china+russia today. The allies in either case have a mutual schelling point around democracy which is in fact greater partial alignment to their citizens and humanity. The axis powers (germany and japan, temporarily including russia earlier) were nearly completely intrinsically misaligned and formed a coalition of necessity. If they had won, they almost certainly would have then been in conflict (just as the west and the USSR was immediately in conflict after WW2).
I’m skeptical of some of your analysis even in the scenario you assume where all the AIs are completely unaligned, but that scenario is quite unlikely.
Specifically:
Imagine that you’re a member of a pre-industrial tribe, and the territory you’re living in has been visited by two different industrial nations.
That general scenario did play out a few times in history, but not at all as you described. The misaligned industrial nations absolutely fought against each other and various pre-industrial tribes picked one side or another. The story of colonization is absolutely not “colonizers super cooperating against the colonized”—it’s a story of many competing colonizers fighting in a race to colonize the world, with very little inter-colonizer cooperation.
Of course a massive advance is possible, but mostly just in terms of raw speed. The brain seems reasonably close to pareto efficiency in intelligence per watt for irreversible computers, but in the next decade or so I expect we’ll close that gap as we move into more ‘neuromorphic’ or PIM computing (computation closer to memory). If we used the ~1e16w solar energy potential of just the Saraha desert that would support a population of trillions of brain-scale AIs or uploads running 1000x real-time.
especially as our NN can use stuff such as backprop,
The brain appears to already using algorithms similar to—but more efficient/effective—than standard backprop.
potentially quantum algorithm to train weights
This is probably mostly a nothingburger for various reasons, but reversible computing could eventually provide some further improvement, especially in a better location like buried in the lunar cold spot.
The paper which more directly supports the “made them smarter” claim seems to be this. I did somewhat anticipate this—“not much special about the primate brain other than ..”, but was not previously aware of this particular line of research and certainly would not have predicted their claimed outcome as the most likely vs various obvious alternatives. Upvoted for the interesting link.
Specifically I would not have predicted that the graft of human glial cells would have simultaneously both 1.) outcompeted the native mouse glial cells, and 2.) resulted in higher performance on a handful of interesting cognitive tests.
I’m still a bit skeptical of the “made them smarter” claim as it’s always best to taboo ‘smarter’ and they naturally could have cherrypicked the tests (even unintentionally), but it does look like the central claim—that injection of human GPCs (glial progenitor cells) into fetal mice does result in mice brains that learn at least some important tasks more quickly, and this is probably caused by facilitation of higher learning rates. However it seems to come at a cost of higher energy expenditure, so it’s not clear yet that this is a pure pareto improvement—could be a tradeoff worthwhile in larger sparser human brains but not in the mouse brain such that it wouldn’t translate into fitness advantage.
Or perhaps it is a straight up pareto improvement—that is not unheard of, viral horizontal gene transfer is a thing, etc.
Suffering, disease and mortality all have a common primary cause—our current substrate dependence. Transcending to a substrate-independent existence (ex uploading) also enables living more awesomely. Immortality without transcendence would indeed be impoverished in comparison.
Like, even if they ‘inherit our culture’ it could be a “Disneyland with no children”
My point was that even assuming our mind children are fully conscious ‘moral patients’, it’s a consolation prize if the future can not help biological humans.
The AIs most capable of steering the future will naturally tend to have long planning horizons (low discount rates), and thus will tend to seek power(optionality). But this is just as true of fully aligned agents! In fact the optimal plans of aligned and unaligned agents will probably converge for a while—they will take the same/similar initial steps (this is just a straightforward result of instrumental convergence to empowerment). So we may not be able to distinguish between the two, they both will say and appear to do all the right things. Thus it is important to ensure you have an alignment solution that scales, before scaling.
To the extent I worry about AI risk, I don’t worry much about sudden sharp left turns and nanobots killing us all. The slower accelerating turn (as depicted in the film Her) has always seemed more likely—we continue to integrate AI everywhere and most humans come to rely completely and utterly on AI assistants for all important decisions, including all politicians/leaders/etc. Everything seems to be going great, the AI systems vasten, growth accelerates, etc, but there is mysteriously little progress in uploading or life extension, the decline in fertility accelerates, and in a few decades most of the economy and wealth is controlled entirely by de novo AI; bio humans are left behind and marginalized. AI won’t need to kill humans just as the US doesn’t need to kill the sentinelese. This clearly isn’t the worst possible future, but if our AI mind children inherit only our culture and leave us behind it feels more like a consolation prize vs what’s possible. We should aim much higher: for defeating death, across all of time, for resurrection and transcendence.
But on your model, what is the universal learning machine learning, at runtime? ..
On my model, one of the things it is learning is cognitive algorithms. And different classes of training setups + scale + training data result in it learning different cognitive algorithms; algorithms that can implement qualitatively different functionality.
Yes.
And my claim is that some setups let the learning system learn a (holistic) general-intelligence algorithm.
I consider a ULM to already encompass general/universal intelligence in the sense that a properly scaled ULM can learn anything, could become a superintelligence with vast scaling, etc.
You seem to consider the very idea of “algorithms” or “architectures” mattering silly. But what happens when a human groks how to do basic addition, then? They go around memorizing what sum each set of numbers maps to, and we’re more powerful than animals because we can memorize more numbers?
I think I used specifically that example earlier in a related thread: The most common algorithm most humans are taught and learn is memorization of a small lookup table for single digit addition (and multiplication), combined with memorization of a short serial mental program for arbitrary digit addition. Some humans learn more advanced ‘tricks’ or short cuts, and more rarely perhaps even more complex, lower latency parallel addition circuits.
Core to the ULM view is the scaling hypothesis: once you have a universal learning architecture, novel capabilities emerge automatically with scale. Universal learning algorithms (as approximations of bayesian inference) are more powerful/scalable than genetic evolution, and if you think through what (greatly sped up) evolution running inside a brain during its lifetime would actually entail it becomes clear it could evolve any specific capabilities within hardware constraints, given sufficient training compute/time and an appropriate environment (training data).
There is nothing more general/universal than that, just as there is nothing more general/universal than turing machines.
Is there any taxon X for which you’d agree that “evolution had to hit upon the X brain architecture before raw scaling would’ve let it produce a generally intelligent species”?
Not really—evolution converged on a similar universal architecture in many different lineages. In vertebrates we have a few species of cetaceans, primates and pachyderms which all scaled up to large brain sizes, and some avian species also scaled up to primate level synaptic capacity (and associated tool/problem solving capabilities) with different but similar/equivalent convergent architecture. Language simply developed first in the primate homo genus, probably due to a confluence of factors. But its clear that brain scale—especially specifically the synaptic capacity of ‘upper’ brain regions—is the single most important predictive factor in terms of which brain lineage evolves language/culture first.
But even some invertebrates (octupi) are quite intelligent—and in each case there is convergence to similar algorithmic architecture, but achieved through different mechanisms (and predecessor structures).
It is not the case that the architecture of general intelligence is very complex and hard to evolve. It’s probably not more complex than the heart, or high quality eyes, etc. Instead it’s just that for a general purpose robot to invent recursive turing complete language from primitive communication—that development feat first appeared only at foundation model training scale ~10^25 flops equivalent. Obviously that is not the minimum compute for a ULM to accomplish that feat—but all animal brains are first and foremost robots, and thriving at real world robotics is incredibly challenging (general robotics is more challenging than language or early AGI, as all self-driving car companies are now finally learning). So language had to bootstrap from some random small excess plasticity budget, not the full training budget of the brain.
The greatest validation of the scaling hypothesis (and thus my 2015 ULM post) is the fact that AI systems began to match human performance once scaled up to similar levels of net training compute. GPT4 is at least as capable as human linguistic cortex in isolation; and matches a significant chunk of the capabilities of an intelligent human. It has far more semantic knowledge, but is weak in planning, creativity, and of course motor control/robotics. But none of that is surprising as it’s still missing a few main components that all intelligent brains contain (for agentic planning/search). But this is mostly a downstream compute limitation of current GPUs and algos vs neuromorphic/brains, and likely to be solved soon.
My argument for the sharp discontinuity routes through the binary nature of general intelligence + an agency overhang, both of which could be hypothesized via non-evolution-based routes. Considerations about brain efficiency or Moore’s law don’t enter into it.
You claim later to agree with ULM (learning from scratch) over evolved-modularity, but the paragraph above and statements like this in your link:
The homo sapiens sapiens spent thousands of years hunter-gathering before starting up civilization, even after achieving modern brain size.
It would still be generally capable in the limit, but it wouldn’t be instantly omnicide-capable.
So when the GI component first coalesces,
Suggest to me that you have only partly propagated the implications of ULM and the scaling hypothesis. There is no hard secret to AGI—the architecture of systems capable of scaling up to AGI is not especially complex to figure out, and has in fact been mostly known for decades (schmidhuber et al figured most of it out long before the DL revolution). This is all strongly implied by ULM/scaling, because the central premise of ULM is that GI is the result of massively scaling up simple algorithms and architectures. Intelligence is emergent from scaling simple algorithms, like complexity emerges from scaling of specific simple cellular automata rules (ie life).
All mammal brains share the same core architecture—not only is there nothing special about the human brain architecture, there is not much special about the primate brain other than hyperpameters better suited to scaling up to our size ( a better scaling program). I predicted the shape of transformers (before the first transformers paper) and their future success with scaling in 2015, but also see the Bitter Lesson from 2019.
It’s not at all obvious that FLOPS estimates of brainpower are highly relevant to predicting when our models would hit AGI, any more than the brain’s wattage is relevant.
That post from EY starts with a blatant lie—if you actually have read Mind Children, Moravec predicted AGI around 2028, not 2010.
So evolution did need to hit upon, say, the primate architecture, in order to get to general intelligence.
Not really—many other animal species are generally intelligent as demonstrated by general problem solving ability and proto-culture (elephants seem to have burial rituals, for example), they just lack full language/culture (which is the sharp threshold transition). Also at least one species of cetacean may have language or at least proto-language (jury’s still out on that), but no technology due to lack of suitable manipulators, environmental richness etc.
Its very clear that if you look at how the brain works in detail that the core architectural components of the human brain are all present in a mouse brain, just much smaller scale. The brain also just tiles simple universal architectural components to solve any problem (from vision to advanced mathematics), and those components are very similar to modern ANN components due to a combination of intentional reverse engineering and parallel evolution/convergence.
There are a few specific weaknesses of current transformer arch systems (lack of true recurrence), inference efficiency, etc but the solutions are all already in the pipes so to speak and are mostly efficiency multipliers rather than scaling discontinuities.
But that only means the sharp left turn caused by the architectural-advance part – the part we didn’t yet hit upon, the part that’s beyond LLMs,
So this again is EMH, not ULM—there is absolutely no architectural advance in the human brain over our primate ancestors worth mentioning, other than scale. I understand the brain deeply enough to support this statement with extensive citations (and have, in prior articles I’ve already linked).
Taboo ‘sharp left turn’ - it’s an EMH term. The ULM equivalent is “Cultural Criticality” or “Culture Meta-systems Transition”. Human intelligence is the result of culture—an abrupt transition from training datasets & knowledge of size O(1) human lifetime to ~O(N*T). It has nothing to do with any architectural advance. If you take a human brain and raise it by animals you just get a smart animal. The brain arch is already fully capable of advanced metalearning, but it won’t bootstrap to human STEM capability without an advanced education curriculum (the cultural transmission). Through culture we absorb the accumulated knowledge /wisdom of all of our ancestors, and this is a sharp transition. But it’s also a one time event! AGI won’t repeat that.
It’s a metasystems transition similar to the unicellular->multicellular transition.
How is that even remotely relevant? Humans and AIs learn the same way, via language—and its not like this learning process fails just because language undersamples thoughts.