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/
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.
No, and that’s a reasonable ask.
To a first approximation my futurism is time acceleration; so the risks are the typical risks sans AI, but the timescale is hyperexponential ala roodman. Even a more gradual takeoff would imply more risk to global stability on faster timescales than anything we’ve experience in history; the wrong AGI race winners could create various dystopias.
Yes, but it’s because the things you’ve outlined seem mostly irrelevant to AGI Omnicide Risk to me? It’s not how I delineate the relevant parts of the classical view, and it’s not what’s been centrally targeted by the novel theories
They are critically relevant. From your own linked post ( how I delineate ) :
We only have one shot. There will be a sharp discontinuity in capabilities once we get to AGI, and attempts to iterate on alignment will fail. Either we get AGI right on the first try, or we die.
If takeoff is slow (1) because brains are highly efficient and brain engineering is the viable path to AGI, then we naturally get many shots—via simulation simboxes if nothing else, and there is no sharp discontinuity if moore’s law also ends around the time of AGI (an outcome which brain efficiency—as a concept—predicts in advance).
We need to align the AGI’s values precisely right.
Not really—if the AGI is very similar to uploads, we just need to align them about as well as humans. Note this is intimately related to 1. and the technical relation between AGI and brains. If they are inevitably very similar then much of the classical AI risk argument dissolves.
You seem to be—like EY circa 2009 - in what I would call the EMH brain camp, as opposed to the ULM camp. It seems given the following two statements, you would put more weight on B than A:
A. The unique intellectual capabilities of humans are best explained by culture: our linguistically acquired mental programs, the evolution of which required vast synaptic capacity and thus is a natural emergent consequence of scaling.
B. The unique intellectual capabilities of humans are best explained by a unique architectural advance via genetic adaptations: a novel ‘core of generality’[1] that differentiates the human brain from animal brains.
This is a EY term; and if I recall correctly he still uses it fairly recently.
Said pushback is based on empirical studies of how the most powerful AIs at our disposal currently work, and is supported by fairly convincing theoretical basis of its own. By comparison, the “canonical” takes are almost purely theoretical.
You aren’t really engaging with the evidence against the purely theoretical canonical/classical AI risk take. The ‘canonical’ AI risk argument is implicitly based on a set of interdependent assumptions/predictions about the nature of future AI:
fast takeoff is more likely than slow, downstream dependent on some combo of:
continuation of Moore’s Law
feasibility of hard ‘diamondoid’ nanotech
brain efficiency vs AI
AI hardware (in)-dependence
the inherent ‘alien-ness’ of AI and AI values
supposed magical coordination advantages of AIs
arguments from analogies: namely evolution
These arguments are old enough that we can now update based on how the implicit predictions of the implied worldviews turned out. The traditional EY/MIRI/LW view has not aged well, which in part can be traced to its dependence on an old flawed theory of how the brain works.
For those who read HPMOR/LW in their teens/20′s, a big chunk of your worldview is downstream of EY’s and the specific positions he landed on with respect to key scientific questions around the brain and AI. His understanding of the brain came almost entirely from ev psych and cognitive biases literature and this model in particular—evolved modularity—hasn’t aged well and is just basically wrong. So this is entangled with everything related to AI risk (which is entirely about the trajectory of AI takeoff relative to human capability).
It’s not a coincidence that many in DL/neurosci have a very different view (shards etc). In particular the Moravec view that AI will come from reverse engineering the brain, that progress is entirely hardware constrained and thus very smooth and predictable, that is the view turned out to be mostly all correct. (his late 90′s prediction of AGI around 2028 is especially prescient)
So it’s pretty clear EY/LW was wrong on 1. - the trajectory of takeoff and path to AGI, and Moravec et al was correct.
Now as the underlying reasons are entangled, Moravec et al was also correct on point 2 - AI from brain reverse engineering is not alien! (But really that argument was just weak regardless.) EY did not seriously consider that the path to AGI would involve training massive neural networks to literally replicate human thoughts.
Point 3 Isn’t really taken seriously outside of the small LW sphere. By the very nature of alignment being a narrow target, any two random Unaligned AIs are especially unlikely to be aligned with each other. The idea of a magical coordination advantage is based on highly implausible code sharing premises (sharing your source code is generally a very bad idea, and regardless doesn’t and can’t actually prove that the code you shared is the code actually running in the world—the grounding problem is formidable and unsolved)
The problem with 4 - the analogy from evolution—is that it factually contradicts the doom worldview—evolution succeeded in aligning brains to IGF well enough despite a huge takeoff in the speed of cultural evolution over genetic evolution—as evidence by the fact that humans have one of the highest fitness scores of any species ever, and almost certainly the fastest growing fitness score.
[Scaling law theories]
I’m not aware of these—do you have any references?
Sure: here’s a few: quantization model, scaling laws from the data manifold, and a statistical model.
True but misleading? Isn’t the brain’s “architectural prior” a heckuva lot more complex than the things used in DL?
The full specification of the DL system includes the microde, OS, etc. Likewise much of the brain complexity is in the smaller ‘oldbrain’ structures that are the equivalent of a base robot OS. The architectural prior I speak of is the complexity on top of that, which separates us from some ancient earlier vertebrate brain. But again see the brain as a ULM post, which cover the the extensive evidence for emergent learned complexity from simple architecture/algorithms (now the dominant hypothesis in neuroscience).
I’m not convinced these DL analogies are useful—what properties do brains and deepnets share that renders the analogies useful here?
Most everything above the hardware substrate—but i’ve already provided links to sections of my articles addressing the convergence of DL and neurosci with many dozens of references. So it’d probably be better to focus exactly on what specific key analogies/properties you believe diverge.
DL is a pretty specific thing
DL is extremely general—it’s just efficient approximate bayesian inference over circuit spaces. It doesn’t imply any specific architecture, and doesn’t even strongly imply any specific approx inference/learning algorithm (as 1st and approx 2nd order methods are both common).
E.g. what if working memory capacity is limited by the noisiness of neural transmission, and we can reduce the noisiness through gene edits?
Training to increase working memory capacity has near zero effect on IQ or downstream intellectual capabilities—see gwern’s reviews and experiments. Working memory capacity is important in both brains and ANNs (transformers), but it comes from large fast weight synaptic capacity, not simple hacks.
Noise is important for sampling—adequate noise is a feature, not a bug.
Sure—I’m not saying no improvement is possible. I expect that the enhancements from adult gene editing should encompass most all of the brain tweaks you can get from drugs/diet. But those interventions will not convert an average brain into an Einstein.
The brain—or more specifically the brains of very intelligent people—are already very efficient, so I’m also just skeptical in general that there are many remaining small tweaks that take you past the current “very intelligent”. Biological brains beyond the human limit are of course possible, but probably require further significant size expansion amongst other infeasible changes.
Sleep is very important, less isn’t really better—most of the critical cortex learning/training happens during sleep through episodic replay, SWRs and REM etc.
ANNs and BNNs operate on the same core principles; the scaling laws apply to both and IQ in either is a mostly function of net effective training compute and data quality.
How do you know this?
From study of DL and neuroscience of course. I’ve also written on this for LW in some reasonably well known posts: starting with The Brain as a Universal Learning Machine, and continuing in Brain Efficiency, and AI Timelines specifically see the Cultural Scaling Criticality section on the source of human intelligence, or the DL section of simboxes. Or you could see Steven Byrne’s extensive LW writings on the brain—we are mostly in agreement on the current consensus from computational/systems neuroscience.
The scaling laws are extremely well established in DL and there are strong theoretical reasons (and increasingly experimental neurosci evidence) that they are universal to all NNs, and we have good theoretical models of why they arise. Strong performance arises from search (bayesian inference) over a large circuit space. Strong general performance is strong performance on many many diverse subtasks which require many many specific circuits built on top of compressed/shared base circuits down a heirarchy. The strongest quantitative predictor of performance is the volume of search space explored which is the product of C * T (capacity and data/time). Data quality matters in the sense that the search volume quantitative function of predictive loss only applies to tasks similar enough to the training data distribution.
In comparing human brains to DL, training seems more analogous to natural selection than to brain development. Much simpler “architectural prior”, vastly more compute and data.
No—biological evolution via natural selection is very similar to technological evolution via engineering. Both brains and DL systems have fairly simple architectural priors in comparison to the emergent learned complexity (remember whenever I use the term learning, I use it in a technical sense, not a colloquial sense) - see my first early ULM post for a review of the extensive evidence (greatly substantiated now by my scaling hypothesis predictions coming true with the scaling of transformers which are similar to the archs I discussed in that post).
so to the extent this could work at all, it is mostly limited to interventions on children and younger adults who still have significant learning rate reserves
There’s a lot more to intelligence than learning.
Whenever I use the word learning, without further clarification, I mean learning as in bayesian learning or deep learning, not in the colloquial sense. My definition/sense of learning encompasses all significant changes to synapses/weights and is all encompassing.
Combinatorial search, unrolling the consequences of your beliefs, noticing things, forming new abstractions.
Brains are very slow so have limited combinatorial search, and our search/planning is just short term learning (short/medium term plasticity). Again it’s nearly all learning (synaptic updates).
if DCAI doesn’t kill everyone, it’s because technical alignment was solved, which our current civilization looks very unlikely to accomplish)
I find the standard arguments for doom implausible—they rely on many assumptions contradicted by deep knowledge of computational neuroscience and DL.
https://www.lesswrong.com/posts/FEFQSGLhJFpqmEhgi/does-davidad-s-uploading-moonshot-work
I was at the WBE2 workshop with Davidad but haven’t yet had time to write about progress (or lack thereof); I think we probably mostly agree that the type of uploading moonshot he discusses there is enormously expensive (not just in initial R&D, but also in recurring per scan costs). I am actually more optimistic than more pure DL based approaches will scale to much lower cost, but “much lower cost” is still on order of GPT4 training cost just to process the scan data through a simple vision ANN—for a single upload.
We can roughly bin brain tissue into 3 developmental states:
juvenile: macro structure formation—brain expanding, neural tissue morphogenesis, migration, etc
maturing: micro synaptic structure formation, irreversible pruning and myelination
mature: fully myelinated, limited remaining plasticity
Maturation proceeds inside out with the regions closest to the world (lower sensory/motor) maturing first, proceeding up the processing hierarchy, and ending with maturation of the highest levels (some prefrontal, associative etc) around age ~20.
The human brain’s most prized intellectual capabilities are constrained (but not fully determined) mostly by the upper brain regions. Having larger V1 synaptic capacity may make for a better fighter pilot through greater visual acuity, but STEM capability is mostly determined by capacity & efficiency of upper brain regions (prefrontal, associative, etc and their cerebellar partners).
I say constrains rather than determines because training data quantity/quality also obviously constrains. Genius level STEM capability requires not only winning the genetic lottery, but also winning the training run lottery.
Brain size only correlates with intelligence at 0.3-0.4.
IQ itself only correlates with STEM potential (and less so as you move away from the mean) but sure there are many ways to make a brain larger that do not specifically increase synaptic capacity&efficiency of the specific brain regions most important for STEM capability. Making neurons larger, increasing the space between them, increasing glial size or counts, etc. But some brain size increase methods will increase the size of STEM linked brain regions, so 0.3-0.4 seems about right.
The capacity&efficiency of the most important brain regions is mostly determined by genes effecting the earliest stage 1 - the architectural prior. These regions won’t fully be used until ~20 years later due to how the brain trains/matures modules over time, but most of the genetic influence is in stage 1 - i’d guess 75%.
I’d guess the next 20% of genetic influence is on stage 2 factors that effect synaptic efficiency and learning efficiency, and only 5% influence on 3 via fully mature/trained modules.
Yes a few brain regions (hippocampus, BG, etc) need to maintain high plasticity (with some neurogenesis) even well in to adulthood—they never fully mature to stage 3. But that is the exception, not the rule.
Brains are constantly evolving and adapting throughout the lifespan.
Not really—See above. At 45 most of my brain potential is now fully spent. I’m very unlikely to ever be a highly successful chess player or physicist or poet etc. Even learning a new human language is very slow and ineffective compared to a child. It’s all about depletion of synaptic learning potential reserves.
The colloquial use of the word ‘learning’ as in ‘learning’ new factual information is not at all what I mean and is not relevant to STEM capability. I am using ‘learning’ in the more deep learning sense of learning deep complex circuits important for algorithmic creativity, etc.
As a concrete specific example, most humans learn to multiply large numbers by memorizing lookup tables for multiplication of individual digits and memorizing a slow serial mental program built on that. But that isn’t the only way! It is possible to learn more complex circuits which actually do larger sum addition&multiplication directly—and some mentats/savants do acquire these circuits (with JVN being a famous likely example).
STEM capability is determined by deep learning many such circuits, not ‘learning’ factual knowledge.
Now it is likely that one of the key factors for high intelligence is a slower and more efficient maturation cycle that maintains greater synaptic learning reserves far into adulthood—ala enhanced neotany, but that is also an example of genetic influence that only matters in stage 1 and 2. Maturation is largely irreversible—once most connections are pruned and the few survivors are strengthened/myelinated you can’t go back to the earlier immature state of high potential.
But it ultimately doesn’t matter, because the brain just learns too slowly. We are now soon past the point at which human learning matters much.
If this was actually the case then none of the stuff people are doing in AI safety or anything else would matter.
Huh? Oh—by learning there I meant full learning in the training sense—stages 1 and 2. Of course things adults do now matter, they just don’t matter through the process of training new improved human brains.
Current AI is less sample efficient, but that is mostly irrelevant as the effective speed is 1000x to 10000x greater.
By the time current human infants finish ~30 year biological training, we’ll by long past AGI and approaching singularity (in hyperexpoential models).
heritability of IQ increases with age (up until age 20, at least)
Straight forward result of how the brain learns. Cortical/cerebellar modules start out empty and mature inwards out—starting with the lowest sensory/motor levels closest to the world and proceeding up the hierarchy ending with the highest/deepest modules like prefrontal and associative cortex. Maturation is physically irreversible as it involves pruning most long-range connections and myelinating&strengthening the select few survivors. Your intelligence potential is constrained prenatally by genes influencing synaptic density/connectivity/efficiency in these higher regions, but those higher regions aren’t (mostly) finishing training until ~20 years age.
It would matter in a world without AI, but that is not the world we live in. Yes if you condition on some indefinite AI pause or something then perhaps, but that seems extremely unlikely. It takes about 30 years to train a new brain—so the next generation of humans won’t reach their prime until around the singularity, long after AGI.
Though I do agree that a person with the genes of a genius for 2 years
Most genius is determined prenatally and during ‘training’ when cortex/cerebellum modules irreversibly mature, just as the capabilities of GPT4 are determined by the initial code and the training run.
Too slow too matter now due to the slow speed of neurons and bio learning combined with where we are in AI.
It does not. Despite the title of that section it is focused on adult expression factors. The post in general lacks a realistic mechanistic model of how tweaking genes affects intelligence.
genes are likely to have an effect if edited in adults: the size of the effect of a given gene at any given time is likely proportional to its level of expression
Is similar to expecting that a tweak to the hyperparams (learning rate) etc of trained GPT4 can boost its IQ (yes LLMs have their IQ or g factor). Most all variables that affect adult/trained performance do so only through changing the learning trajectory. The low hanging fruit or free energy in hyperparams with immediate effect is insignificant.
Of course if you combine gene edits with other interventions to rejuvenate older brains or otherwise restore youthful learning rate more is probably possible, but again it doesn’t really matter much as this all takes far too long. Brains are too slow.
ANNs and BNNs operate on the same core principles; the scaling laws apply to both and IQ in either is a mostly function of net effective training compute and data quality. Genes determine a brain’s architectural prior just as a small amount of python code determines an ANN’s architectural prior, but the capabilities come only from scaling with compute and data (quantity and quality).
So you absolutely can not take datasets of gene-IQ correlations and assume those correlations would somehow transfer to gene interventions on adults (post training in DL lingo). The genetic contribution to IQ is almost all developmental/training factors (architectural prior, learning algorithm hyper params, value/attention function tweaks, etc) which snowball during training. Unfortunately developmental windows close and learning rates slow down as the brain literally carves/prunes out its structure, so to the extent this could work at all, it is mostly limited to interventions on children and younger adults who still have significant learning rate reserves.
But it ultimately doesn’t matter, because the brain just learns too slowly. We are now soon past the point at which human learning matters much.
If it only requires a simple hack to existing public SOTA, many others will have already thought of said hack and you won’t have any additional edge. Taboo superintelligence and think through more specifically what is actually required to outcompete the rest of the world.
Progress in DL is completely smooth as it is driven mostly from hardware and enormous number of compute-dependent small innovations (yes transformers were a small innovation on top of contemporary alternatives such as memory networks, NTMs etc and quite predictable in advance ).
Its easy to say something is “not that hard”, but ridiculous to claim that when the something is build an AI that takes over the world. The hard part is building something more intelligent/capable than humanity, not anything else conditioned on that first step.
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.