# Overview of strong human intelligence amplification methods


How can we make many humans who are very good at solving difficult problems?





# Summary (table of made-up numbers)

I made up the made-up numbers in this table of made-up numbers; therefore, the numbers in this table of made-up numbers are made-up numbers.

![](https://i.imgur.com/A6SbX6e.png)

![](https://i.imgur.com/kfsxN28.png)


# Call to action

If you have a shitload of money, there are some projects you can give money to that would make supergenius humans on demand happen faster. If you have a fuckton of money, there are projects whose creation you could fund that would greatly accelerate this technology.

If you're young and smart, or are already an expert in either stem cell / reproductive biology, biotech, or anything related to brain-computer interfaces, there are some projects you could work on.

If neither, think hard, maybe I missed something.

You can DM me or gmail me at tsvibtcontact. 

# Context

## The goal

What empowers humanity is the ability of humans to notice, recognize, remember, correlate, ideate, tinker, explain, test, judge, communicate, interrogate, and design. To increase human empowerment, improve those abilities by improving their source: human brains. 

AGI is going to destroy the future's promise of massive humane value. To prevent that, create humans who can navigate the creation of AGI. Humans alive now can't figure out how to make AGI that leads to a humane universe.

These are desirable virtues: philosophical problem-solving ability, creativity, wisdom, taste, memory, speed, cleverness, understanding, judgement. These virtues depend on mental and social software, but can also be enhanced by enhancing human brains. 

How much? To navigate the creation of AGI will likely require solving philosophical problems that are beyond the capabilities of the current population of humans, given the available time (some decades). Six standard deviations is 1 in 10^9, seven standard deviations is 1 in 10^12. So the goal is to create many people who are 7 SDs above the mean in cognitive capabilities. That's "strong human intelligence amplification". (Why not more SDs? There are [many downside risks](https://tsvibt.blogspot.com/2022/08/downside-risks-of-genomic-selection.html) to changing the process that creates humans, so going further is an unnecessary risk.)

It is my conviction that this is the only way forward for humanity.

## Constraint: Algernon's law

[Algernon's law](https://gwern.net/drug-heuristic): If there's a change to human brains that human-evolution could have made, but didn't, then it is net-neutral or net-negative for inclusive relative genetic fitness. If intelligence is ceteris paribus a fitness advantage, then a change to human brains that increases intelligence must either come with other disadvantages or else be inaccessible to evolution. 

Ways around Algernon's law, increasing intelligence anyway:

* We could apply a stronger selection pressure than human-evolution applied. The selection pressure that human-evolution applied to humans is capped (somehow) by the variation of IGF among all germline cells. So it can only push down [mutational load](https://en.wikipedia.org/wiki/Genetic_load) to some point.
* Maybe (recent, perhaps) human-evolution selected against intelligence beyond some point. 
* We could come up with better design ideas for mind-hardware.
* We could use resources that evolution didn't have. We have metal, wires, radios, basically unlimited electric and metabolic power, reliable high-quality nutrition, mechanical cooling devices, etc.
* Given our resources, some properties that would have been disadvantages are no longer major disadvantages. E.g. a higher metabolic cost is barely a meaningful cost. 
* We have different values from evolution; we might want to trade away IGF to gain intelligence.

## How to know what makes a smart brain

### Figure it out ourselves

* We can test interventions and see what works.
* We can think about what, mechanically, the brain needs in order to function well.
* We can think about thinking and then think of ways to think better.

### Copy nature's work

* There are seven billion natural experiments, juz runnin aroun doin stuff. We can observe the behaviors of the humans and learn what circumstances of their creation leads to fewer or more cognitive capabilities.
* We can see what human-evolution invested in, aimed at cognitive capabilities, and add more of that.

# Brain emulation

## The approach

> Method: figure out how neurons work, scan human brains, make a simulation of a scanned brain, and then use software improvements to make the brain think better.

The idea is to have a human brain, but with the advantages of being in a computer: faster processing, more scalable hardware, more introspectable (e.g. read access to all internals, even if they are obscured; computation traces), reproducible computations, A/B testing components or other tweaks, low-level optimizable, process forking. This is a "figure it out ourselves" method——we'd have to figure out what makes the emulated brain smarter.

## Problems 

* While we have some handle on the fast (<1 second) processes that happen in a neuron, no one knows much about the slow (>5 second) processes. The slow processes are necessary for what we care about in thinking. People working on brain emulation mostly aren't working on this problem because they have enough problems as it is.

* Experiments here, the sort that would give 0-to-1 end-to-end feedback about whether the whole thing is working, would be extremely expensive; and unit tests are much harder to calibrate (what reference to use?).

* Partial success could constitute a major AGI advance, which would be extremely dangerous. Unlike most of the other approaches listed here, brain emulations wouldn't be hardware-bound (skull-size bound).

* The potential for value drift——making a human-like mind with altered / distorted / alien values——is much higher here than with the other approaches. This might be especially selected for: subcortical brain structures, which are especially value-laden, are more physiologically heterogeneous than cortical structures, and therefore would require substantially more scientific work to model accurately. Further: because the emulation approach is based on copying as much as possible and then filling in details by seeing what works, many details will be filled in by non-humane processes (such as the shaping processes in normal human childhood).

Fundamentally, brain emulations are a 0-to-1 move, whereas the other approaches take a normal human brain as the basic engine and then modify it in some way. The 0-to-1 approach is more difficult, more speculative, and riskier.

# Genomic approaches

These approaches look at the 7 billion natural experiments and see which genetic variants correlate with intelligence. IQ is a very imperfect but measurable and sufficient proxy for problem-solving ability. Since >7 of every 10 IQ points are explained by genetic variation, we can extract a lot of what nature knows about what makes brains have many capabilities. We can't get that knowledge about capable brains in a form usable as engineering (to build a brain from scratch), but we can at least get it in a form usable as scores (which genomes make brains with fewer or more capabilities). These are "copy nature's work" approaches.

## Adult brain gene editing

### The approach

> Method: edit IQ-positive variants into the brain cells of adult humans.

See "[Significantly Enhancing ...](https://www.lesswrong.com/posts/JEhW3HDMKzekDShva/significantly-enhancing-adult-intelligence-with-gene-editing)".

### Problems

* Delivery is difficult.

* Editors damage DNA.

* The effect is greatly attenuated, compared to germline genetics. In adulthood, learning windows have been passed by; many genes are no longer active; damage that accumulates has already been accumulated; many cells don't receive the edits. This adds up to an optimistic ceiling somewhere around +2 or +3 SDs.

## Germline engineering

This is the way that will work. (Note that there are [many downside risks to germline engineering](https://tsvibt.blogspot.com/2022/08/downside-risks-of-genomic-selection.html), though AFAICT they can be alleviated to such an extent that the tradeoff is worth it by far.)

### The approach

> Method: make a baby from a cell that has a genome that has many IQ-positive genetic variants.

Subtasks:

* *Know what genome would produce geniuses.* This is already solved well enough. Because there are already polygenic scores for IQ that explain >12% of the observed variance in IQ ([pgscatalog.org/score/PGS003724/](https://pgscatalog.org/score/PGS003724/)), 10 SDs of raw selection power would translate into trait selection power at a rate greater than √(1/9) = 1/3, giving >3.3 SDs of IQ trait selection power, i.e. +50 IQ points. 

* *Make a cell with such a genome.* This is probably not that hard——via CRISPR editing stem cells, via [iterated meiotic selection](https://denovo.substack.com/p/meiosis-is-all-you-need), or via chromosome selection. My [math](https://tsvibt.blogspot.com/2022/08/the-power-of-selection.html#3-selecting-all-of-many-components) and simulations show that several methods would achieve strong intelligence amplification. If induced meiosis into culturable cells is developed, IMS can provide >10 SDs of raw selection power given very roughly $10^5 and a few months.

* *Know what epigenomic state (in sperm / egg / zygote) leads to healthy development.* This is not fully understood——it's an open problem that can be worked on.

* *Given a cell, make a derived cell (diploid mitotic or haploid meiotic offspring cell) with that epigenomic state.* This is not fully understood——it's an open problem that can be worked on. This is the main bottleneck.

These tasks don't necessarily completely factor out. For example, some approaches might try to "piggyback" off the natural epigenomic reset by using chromosomes from natural gametes or zygotes, which will have the correct epigenomic state already.

See also Branwen, "[Embryo Selection ...](https://gwern.net/embryo-selection)".

More information on request. Some of the important research is happening, but there's always room for more funding and talent.

### Problems

* It takes a long time; the baby has to grow up. (But [we probably have time](https://tsvibt.blogspot.com/2023/07/views-on-when-agi-comes-and-on-strategy.html), and delaying AGI only helps if you have an out.)

* Correcting the epigenomic state of a cell to be developmentally competent is unsolved.

* The baby can't consent, unlike with other approaches, which work with adults. (But the baby can also be made genomically disposed to be exceptionally healthy and sane.)

* It's the most politically contentious approach.

# Signaling molecules for creative brains

## The approach

> Method: identify master signaling molecules that control brain areas or brain developmental stages that are associated with problem-solving ability; treat adult brains with those signaling molecules.

Due to evolved modularity, organic systems are governed by [genomic regulatory networks](https://en.wikipedia.org/wiki/Gene_regulatory_network). Maybe we can isolate and artificially activate GRNs that generate physiological states that produce cognitive capabilities not otherwise available in a default adult's brain. The hope is that there's a very small set of master regulators that can turn on larger circuits with strong orchestrated effects, as is the case with hormones, so that treatments are relatively simple, high-leverage, and discoverable. For example, maybe we could replicate the signaling context that activates childish learning capabilities, or maybe we could replicate the signaling context that activates parietal problem-solving in more brain tissue.

I haven't looked into this enough to know whether or not it makes sense. This is a "copy nature's work" approach: nature knows more about how to make brains that are good at thinking, than what is expressed in a normal adult human.

## Problems

* Who knows what negative effects might result.

* Learning windows might be irreversibly lost after childhood, e.g. by long-range connections being irrecoverably pruned.

# Brain-brain electrical interface approaches

Brain-computer interfaces don't obviously give an opportunity for large increases in creative philosophical problem-solving ability. See the discussion in "[Prosthetic connectivity](https://tsvibt.blogspot.com/2022/11/prosthetic-connectivity.html)". The fundamental problem is that we, programming the computer part, don't know how to write code that does transformations that will be useful for neural minds.

But brain-brain interfaces——adding connections between brain tissues that normally aren't connected——might increase those abilities. These approaches use electrodes to read electrical signals from neurons, then transmit those signals (perhaps compressed/filtered/transformed) through wires / fiber optic cables / EM waves, then write them to other neurons through other electrodes. These are "copy nature's work" approaches, in the sense that we think nature made neurons that know how to arrange themselves usefully when connected with other neurons.

## Problems with all electrical brain interface approaches

* *The butcher number.* Current electrodes kill more neurons than they record. That doesn't scale safely to millions of connections.
* *Bad feedback.* Neural synapses are not strictly feedforward; there is often reciprocal signaling and regulation. Electrodes wouldn't communicate that sort of feedback, which might be important for learning.


## Massive cerebral prosthetic connectivity

![](https://i.imgur.com/gFYorDf.png)

Source: [https://www.neuromedia.ca/white-matter/](https://www.neuromedia.ca/white-matter/)

Half of the human brain is white matter, i.e. neuronal axons with fatty sheaths around them to make them transmit signals faster. White matter is ~1/10 the volume of rodent brains, but ~1/2 the volume of human brains. Wiring is expensive and gets minimized; see "Principles of Neural Design" by Sterling and Laughlin. All these long-range axons are a huge metabolic expense. That means fast, long-range, high bandwidth (so to speak——there are many different points involved) communication is important to cognitive capabilities. See [here](https://www.sciencedirect.com/topics/immunology-and-microbiology/white-matter).

A better-researched comparison would be helpful. But vaguely, my guess is that if we compare long-range neuronal axons to metal wires, fiber optic cables, or EM transmissions, we'd see (amortized over millions of connections): axons are in the same ballpark in terms of energy efficiency, but slower, lower bandwidth, and more voluminous. This leads to:

> Method: add many millions of read-write electrodes to several brain areas, and then connect them to each other.

See "[Prosthetic connectivity](https://tsvibt.blogspot.com/2022/11/prosthetic-connectivity.html)" for discussion of variants and problems. The main problem is that current brain implants furnish <10^4 connections, but >10^6 would probably be needed to have a major effect on problem-solving ability, and electrodes tend to kill neurons at the insertion site. I don't know how to accelerate this, assuming that Neuralink is already on the ball well enough.

## Human / human interface

> Method: add many thousands of read-write electrodes to several brain areas in two different brains, and then connect them to each other.

If one person could think with two brains, they'd be much smarter. Two people connected is not the same thing, but could get some of the benefits. The advantages of an electric interface over spoken language are higher bandwidth, lower latency, less cost (producing and decoding spoken words), and potentially more extrospective access (direct neural access to inexplicit neural events). But it's not clear that there should be much qualitative increase in philosophical problem-solving ability. 

A key advantage over prosthetic connectivity is that the benefits might require a couple ooms fewer connections. That alone makes this method worth trying, as it will be probably be feasible soon.

## Interface with brain tissue in a vat 

> Method: grow neurons in vitro, and then connect them to a human brain.

The advantage of this approach is that it would in principle be scalable. The main additional obstacle, beyond any neural-neural interface approaches, is growing cognitively useful tissue in vitro. This is not completely out of the question——see "DishBrain"——but who knows if it would be feasible.

# Massive neural transplantation

## The approach

> Method: grow >10^8 neurons (or appropriate stem cells) in vitro, and then put them into a human brain.

There have been some experiments along these lines, at a smaller scale, aimed at treating brain damage. 

The idea is simply to scale up the brain's computing wetware. 

## Problems

* It would be a complex and risky surgery.
* We don't know how to make high-quality neurons in vitro.
* The arrangement of the neurons might be important, and would be harder to replicate. Using donor tissue might fix this, but becomes more gruesome and potentially risky.
* It might be difficult to get transplanted tissue to integrate. There's at least some evidence that [human cerebral organoids can integrate into mouse brains](https://www.nature.com/articles/s41380-020-00910-4.pdf).
* Problem-solving might be bottlenecked on long-range communication rather than neuron count.

# Support for thinking

Generally, these approaches try to improve human thinking by modifying the algorithm-like elements involved in thinking. They are "figure it out ourselves" approaches.

## The approaches

There is external support:

> Method: create artifacts that offload some elements of thinking to a computer or other external device.

E.g. the printing press, the text editor, the search engine, the typechecker.

There is mental software:

> Method: create methods of thinking that improve thinking.

E.g. the practice of mathematical proof, the practice of noticing rationalization, the practice of investigating boundaries.

There is social software:

> Method: create methods of social organization that support and motivate thinking.

E.g. a shared narrative in which such-and-such cognitive tasks are worth doing, the culture of a productive research group.

> Method: create methods of social organization that constitute multi-person thinking systems.

E.g. git.

## Problems

* The basic problem is that the core activity, human thinking, is not visible or understood. As a consequence, problems and solutions can't be shared / reproduced / analysed / refactored / debugged. [Philosophers couldn't even keep paying attention to the question](https://plato.stanford.edu/entries/abduction/). There are major persistent blind spots around [important cognitive tasks that have bad feedback](https://tsvibt.blogspot.com/2023/01/the-anthropomorphic-ai-dilemma.html#3-anthropomorphic-ai-is-scalable).
* Solutions are highly context dependent——they depend on variables that aren't controlled by the technology being developed. This adds to the unscalability of these solutions.
* The context contains strong adversarial memes, which limits these properties of solutions: speed (onboarding time), scope (how many people), mental energy budget (fraction of each person's energy), and robustness (stability over time and context). 

# FAQ

## What about weak amplification

Getting rid of lead poisoning should absolutely be a priority. It won't greatly increase humanity's maximum intelligence level though.

## What about ...

* BCIs? weaksauce
* Nootropics? weaksauce
* Brain training? weaksauce
* Transplanting bird neurons? Seems risky and unlikely to work.
* Something something bloodflow? weaksauce
* Transcranial magnetic stimulation? IDK, probably weaksauce. This is a "counting up from negative up to zero" thing; might remove inhibitions or trauma responses, or add useful noise that breaks anti-helpful states, or something. But it won't raise the cap on insight, probably——people sometimes get to have their peak problem solving sometimes anyway.
* Ultrasound? ditto
* Neurofeedback? Possibly... seems like a better bet than other stuff like this, but probably weaksauce. 
* Getting good sleep? weaksauce——good but doesn't make supergeniuses
* Gut microbiome? weaksauce
* Mnemonic systems? weaksauce
* Software exobrain? weaksauces
* LLMs? no
* Psychedelics? stop
* Buddhism? Aahhh, I don't think you get what this is about
* Embracing evil? go away
* Rotating armodafinil, dextromethorphan, caffeine, nicotine, and lisdexamfetamine? AAHHH NOOO
* [redacted]? Absolutely not. Go sit in the corner and think about what you were even thinking of doing.

## The real intelligence enhancement is ...

Look, I'm all for healing society, healing trauma, increasing collective consciousness, creating a shared vision of the future, ridding ourselves of malign egregores, blah blah. I'm all for it. But it's a difficult, thinky problem. ...So difficult that you might need some good thinking help with that thinky problem...

## Is this good to do?

Yeah, probably. There are [many downside risks](https://tsvibt.blogspot.com/2022/08/downside-risks-of-genomic-selection.html), but the upside is large and the downsides can be greatly alleviated.