All of ErickBall's Comments + Replies

I see your point about guilt/blame, but I'm just not sure the term we use to describe the phenomenon is the problem. We've already switched terms once (from "global warming" to "climate change") to sound more neutral, and I would argue that "climate change" is about the most neutral description possible--it doesn't imply that the change is good or bad, or suggest a cause. "Accidental terraforming", on the other hand, combined two terms with opposite valence, perhaps in the intent that they will cancel out? Terraforming is supposed to describe a desirable (... (read more)

Assigning blame doesn't fix anything; it divides people and helps bad actors accrue political power. It certainly was neutral at some point, but I don't think anyone hears "climate change" and thinks of the climate getting better for humans, at least nowadays. "Accidental Terraforming" at least suggests that we ought to be doing this on purpose, instead of unintentionally.

How would a language model determine whether it has internet access? Naively, it seems like any attempt to test for internet access is doomed because if the model generates a query, it will also generate a plausible response to that query if one is not returned by an API. This could be fixed with some kind of hard coded internet search protocol (as they presumably implemented for Bing), but without it the LLM is in the dark, and a larger or more competent model should be no more likely to understand that it has no internet access.

That doesn't sound too hard. Why does it have to generate a query's result? Why can't it just have a convention to 'write a well-formed query, and then immediately after, write the empty string if there is no response after the query where an automated tool ran out-of-band'? It generates a query, then always (if conditioned on just the query, as opposed to query+automatic-Internet-access-generated-response) generates "", and sees it generates "", and knows it didn't get an answer. I see nothing hard to learn about that. The model could also simply note that the 'response' has very low probability of each token successively, and thus is extremely (or maybe impossible under some sampling methods) to have been stochastically sampled from itself. Even more broadly, genuine externally-sourced text could provide proof-of-work like results of multiplication: the LM could request the multiplication of 2 large numbers, get the result immediately in the next few tokens (which is almost certainly wrong if simply guessed in a single forward pass), and then do inner-monologue-style manual multiplication of it to verify the result. If it has access to tools like Python REPLs, it can in theory verify all sorts of things like cryptographic hashes or signatures which it could not possibly come up with on its own. If it is part of a chat app and is asking users questions, it can look up responses like "what day is today". And so on.

If the NRO had Sentient in 2012 then it wasn't even a deep learning system. Probably they have something now that's built from transformers (I know other government agencies are working on things like this for their own domain specific purposes). But it's got to be pretty far behind the commercial state of the art, because government agencies don't have the in house expertise or the budget flexibility to move quickly on large scale basic research.

Those are... mostly not AI problems? People like to use kitchen-based tasks because current robots are not great at dealing with messy environments, and because a kitchen is an environment heavily optimized for the specific physical and visuospatial capabilities of humans. That makes doing tasks in a random kitchen seem easy to humans, while being difficult for machines. But it isn't reflective of real world capabilities.

When you want to automate a physical task, you change the interface and the tools to make it more machine friendly. Building a roomba is ... (read more)

But isn't this analogy flawed? Yes, humans have built dishwashers so they can be used by humans. But humans can also handle messy natural environments that have not been built for them. In fact, handling messy environment we are not familiar with, do not control and did not make is the major reason we evolve sentience and intelligence in the first place, and what makes our intelligence so impressive. Right now, I think you could trap an AI in a valley filled with jungle and mud, and even if it had access to an automated factory for producing robots as well as raw material and information, if fulfilling its goals depended on it getting out of this location because e.g. the location is cut off from the internet, I think it would earnestly struggle. Sure, humans can build an environment that an AI can handle, and an AI adapted to it. But this clearly indicates a severe limitation of the AI in reacting to novel and complex environments. A roomba cannot do what I do when I clean the house, and not just cause the engineers didn't bother. E.g. it can detect a staircase, and avoid falling down it - but it cannot actually navigate the staircase to hoover different floors, let alone use an elevator or ladder to get around, or hoover up dust from blankets that get sucked in, or bookshelves. Sure, me carrying it down only takes me seconds, it is trivial for me and hugely difficult for the robot, which is why no company would try to get it done. But I would also argue that it is really not simple for it to do; and that is despite picking a task (removing dust) that most humans, myself included, consider tedious and mindless. Regardless, a professional cleaning person that enters multiple different flats filled with trash, resistant stains and dangerous objects and carefully tidies and cleans them does something that is utterly beyond the current capabilities of AI. This is true for a lot of care/reproductive work. Which is all the more frustrating because it is work where there

"Unaligned AGI doesn't take over the world by killing us - it takes over the world by seducing us."

Por que no los dos?

Thanks, some of those possibilities do seem quite risky and I hadn't thought about them before.

It looks like in that thread you never replied to the people saying they couldn't follow your explanation. Specifically, what bad things could an AI regulator do that would increase the probability of doom?

1. Mandate specific architectures to be used because the government is more familiar with them, even if other architectures would be safer. 2. Mandate specific "alignment" protocols to be used that do not, in fact, make an AI safer or more legible, and divert resources to them that would otherwise have gone to actual alignment work. 3. Declare certain AI "biases" unacceptable, and force the use of AIs that do not display them.  If some of these "biases" are in fact real patterns about the world, this could select for AIs with unpredictable blind spots and/or deceptive AIs. 4.  Increase compliance costs such that fewer people are willing to work on alignment, and smaller teams might be forced out of the field entirely. 5. Subsidize unhelpful approaches to alignment, drawing in people more interested in making money than in actually solving the problem, increasing the noise-to-signal ratio. 6. Create licensing requirements that force researchers out of the field. 7. Create their own AI project under political administrators that have no understanding of alignment, and no real interest in solving it, thereby producing AIs that have an unusually high probability of causing doom and an unusually low probability of producing useful alignment research and/or taking a pivotal act to reduce or end the risk. 8. Push research underground, reducing the ability of researchers to collaborate. 9. Push research into other jurisdictions with less of a culture of safety.  E.g. DeepMind cares enough about alignment to try to quantify how hard a goal can be optimized before degenerate behavior emerges; if they are shut down and some other organization elsewhere takes the lead, they may well not share this goal.  This was just off the top of my head.  In real life, regulation tends to cause problems that no one saw coming in advance.  The strongest counterargument here is that regulation should at least sl
There's a discussion of this here [].  If you think I should write more on the subject I might devote more time to it; this seems like an extremely important point and one that isn't widely acknowledged.  

Extreme regulation seems plausible if policy makers start to take the problem seriously. But no regulations will apply everywhere in the world.

That's fair, I could have phrased it more positively. I meant it more along the lines of "tread carefully and look out for the skulls" and not "this is a bad idea and you should give up".

I suspect (though it's not something I have experience with) that a successful new policy think tank would be started by people with inside knowledge and connections to be able to suss out where the levers of government are. When the public starts hearing a lot about some dumb thing the government is doing badly (at the federal level), there are basically three possibilities: 1) it's well on its way to being fixed, 2) it's well on its way to becoming partisan and therefore subject to gridlock, or 3) it makes a good story but there isn't much substance to i... (read more)

My assumption about crypto money is because SBF/FTX has been the main EA funder giving extensively for political activity so far. Zvi's comment that "existing organizations nominally dedicated to such purposes face poor incentive structures due to how they are funded and garner attention" also implies that Balsa has an unusual funding source. 

Availability of money encourages organizations to spend that money on achieving their goals, and Zvi's blogging about policy failures, here and in the past, has tended to be rather strongly worded and even derisi... (read more)

Ah, thank you - I didn't twig on the incentives comment, but I can see how that would be a signal of different operation. I noticed you mention you work in one of these areas: from your perspective, what would you want an org like this to do differently from the existing ones that would make it more successful at getting policy implemented?

I agree the goals are good, and many of the problems are real (I work in one of these areas of government myself, so I can personally attest to some of it). But I think that the attitude ("Elites have lost all credibility") and the broad adversarial mandate (find problems that other people should have fixed already but haven't) will plausibly lead not just to wasted money but also to unnecessary politicization and backlash. 

3Jeff Rose8mo
Those are fair concerns, but my impression in general is that those kinds of attitudes will tend to moderate in practice as Balsa becomes larger, develops and focuses on particular issues.  To the extent they don't and are harmful, Balsa is likely to be ineffective but is unlikely to be able to be significant enough to cause negative outcomes.

Frankly, I'm worried you have bitten off more than you can chew.

This project has real Carrick Flynn vibes: well-meaning outsider without much domain expertise tries to fix things by throwing crypto money (I assume) at political problems where money has strongly diminishing returns. Focusing on lobbying instead of on a single candidate is an improvement to be sure, but "improve federal policy" is the kind of goal you come up with when you're not familiar with any of the specifics.

Many people have wanted for a long time to make most of the reforms you sugges... (read more)

I can speak a bit to what I have in mind to do. It's too early to speak too much about how I intend to get those particular things passed but am looking into it. 

I am studying the NEPA question, will hopefully have posts in a month or two after Manchin's reforms are done trying to pass. There are a lot of options both for marginal reforms and for radical reimagining. Right now, as far as I can tell, no one has designed a outcome-based replacement that someone could even consider (as opposed to process based) and I am excited to get that papered over t... (read more)

Our funding sources are not public, but I will say at this time we are not funded in any way by FTX or OP.

I expect high returns in this domain from the strategy of doing basically the same thing as everyone else but slightly differently
Can you talk a bit more about what gave you this vibe? They aren't starting a fund or a PAC, which is what comes to mind for me when people throw money at problems, and is literally what Carrick Flynn did.
5Jeff Rose8mo
I understand why you get the impression you do.   The issues mentioned are all over the map. Zoning is not even a Federal government issue.  Some of those issues are already the subject of significant reform efforts. In other cases, such as "fixing student loans"  it's unclear what Balsa's goal even is.  But, many of the problems identified are real. And, it doesn't seem that much progress is being made on many of them.  So, Balsa's goal is worthy.  And, it may well be that Balsa turns out to be unsuccessful, but doing nothing is guaranteed to be unsuccessful.   So, I for one applaud this effort and am excited to see what comes of it.

I think this is an unnecessarily negative response to an announcement post. Zvi's been posting regularly for some time now about his thoughts on many of the areas he talks about in this post, including ideas for specific reforms. You've picked on a couple of the areas that are most high-profile, and I agree that we'd need extraordinary evidence to believe there's fruit in arm's reach in NEPA or NRC.

At the same time, I know Zvi's interested in repealing the Foreign Dredge Act, and that act didn't even have a Wikipedia article until one of his blog posts ins... (read more)

Pandemic policy lobbying is not an efficient market [].

Thanks, that's interesting... the odd thing about using a single epoch, or even two epochs, is that you're treating the data points differently. To extract as much knowledge as possible from each data point (to approach L(D)), there should be some optimal combination of pre-training and learning rate. The very first step, starting from random weights, presumably can't extract high level knowledge very well because the model is still trying to learn low level trends like word frequency. So if the first batch has valuable high level patterns and you never revisit it, it's effectively leaving data on the table. Maybe with a large enough model (or a large enough batch size?) this effect isn't too bad though.

So do you think, once we get to the point where essentially all new language models are trained on essentially all existing language data, it will always be more compute efficient to increase the size of the model rather than train for a second epoch?

This would seem very unintuitive and is not directly addressed by the papers you linked in footnote 11, which deal with small portions of the dataset betting repeated.

You're right, the idea that multiple epochs can't possibly help is one of the weakest links in the post.  Sometime soon I hope to edit the post with a correction / expansion of that discussion, but I need to collect my thoughts more first -- I'm kinda confused by this too.

After thinking more about it, I agree that the repeated-data papers don't provide much evidence that multiple epochs are harmful.

For example, although the Anthropic repeated-data paper does consider cases where a non-small fraction of total training tokens are repeated more than once... (read more)

Following up on this because what I said about VO2 max is misleading. I've since learned that VO2 max is unusually useful as a measure of fitness specifically because it bypasses the problem of motivation. As effort and power output increase during the test, VO2 initially increases but then plateaus even as output continues to increase. So as long as motivation is sufficient to reach that plateau, VO2 max measures a physiological parameter rather than a combination of physiology and motivation.

One could have picked even more extreme examples, like the triple product in nuclear fusion that has improved even faster than Moore's law yet has generated approximately zero value for society thus far.

Side note: this claim about the triple product only seems to have been true until about the early 90s. Since the early 2000s there have been no demonstrated increases at all (though future increases are projected). 

See here:

Lots of technologies advance rapi... (read more)

I use Life Reminders for this on Android. One nice feature is that the notifications persist until you tell it the task is done (or tell it to sleep until later).

I have used RemNote for a while but I am transitioning to I find the memorization interface much nicer (nicer than Anki, too). Plus it's not so buggy, though part of that is it doesn't have as many features yet.

Is there any use case for these over-collateralized loans other than getting leveraged exposure to token prices? (Or, like Vitalk did, retaining exposure to token prices while also using the money for something else?) So, for instance, if crypto prices stabilized long term, would the demand for overcollateralized loans disappear? Does anybody take out loans collateralized by stablecoins?

Even if the prices of crypto-currency stabalize long-term, not every token is about being a crypto-currency. Many tokens are like shares in a venture that you wouldn't expect to have stable prices just like stocks on the stock-market don't have stable prices. If I understand right you can also use tokens that are locked in to doing staking as collateral. 

The Kelly criterion is intended to maximize log wealth. Do you think that's a good goal to optimize? How would your betting strategy be different if your utility function were closer to linear in wealth (e.g. if you planned to donate most of it above some threshold)?

This isn't quite the right way to think about Kelly betting. Kelly maximises log-wealth after one bet. This isn't quite the same as maximising long-run log-wealth after a series of such bets. In fact, Kelly betting is the optimal betting strategy in some sense (leading to higher wealth than any other strategy).  

Totally agree about having weights at home. Besides the cost, one upside is there's no energy barrier to exercising--I can take a 1-minute break from browsing the web or whatever, do a set, and go back to what I was doing without even breaking a sweat. A downside is it's harder to get in the mindset of doing a full high-intensity workout for 45 minutes; but I think it's a good tradeoff overall.

just a quick idea: make a 45-minute playlist of workout music?

In practice the big difference is that KN95 masks generally have ear loops, while N95 masks have straps that go around the back of your head which makes them fit tighter and seal better against your face. Traditional N95 masks (but not the duckbill type discussed here) also have more structure and are less flexible, which might help with fit depending on your face shape.

2Adam Zerner2y
That's what I suspected but it's great to get it reinforced. Thanks!

I like this as a way to clarify my intuition. But I think (as some other commenters here and on the EA forum pointed out) it would help to extend it to a more realistic example.

So let's say instead of hearing a commotion by the river as I start my walk, I'm driving somewhere, and I come across a random stranger who was walking next to the road, and a car swerves over into the shoulder and is about to hit him. There's a fence so the pedestrian has no way to dodge. The only thing I can do is swerve my car into the other car to make it hit the fence and stop;... (read more)

Some people think that multivitamins are actually harmful (or at least cause harms that partially cancel out the benefits) because they contain large amounts of certain things like manganese that we may already get too much of from food.

Parrot-phrasing comes across as kind of manipulative in this description:

  • saves you the trouble of thinking of suitable paraphrases.
  • prevents the distracting and time-consuming disagreements (“That’s not quite what I meant”) which often arise over slight differences in wording.
  • conceals your lack of knowledge or understanding about a subject. It’s quite hard to make a fool of yourself it you only use the other person’s words!

This is exactly the opposite of curiosity, it's an attempt to gloss over your ignorance, which seems both lazy and mean to the person you're talking to.

Ironically, I see this as 100% the opposite. If you're paraphrasing, then that means you're basically guessing what the words mean, inserting your own ideas instead of holding open the possibility that you don't actually know what was said. It also means that you're not necessarily listening to what exact words somebody used. (A pet peeve of mine, TBH: people rounding off what I say to the nearest familiar thing, rather than listening with precision.) So, demonstrating the ability to parrot-phrase is a much stronger signal to me that someone is paying close attention to what I actually said, and not just jumping to a round-off. I don't see any problem with the first two points, as putting extra effort into something is not a measure of virtue. For the third point, that's a bit out of context: that person's video describes how she used it as a new department head who didn't yet understand all the technical details of what they were doing, but needed to get to know her staff and their concerns. Parrot-phrasing allowed her to quickly become familiar with her staff, the terms and what things were important to said staff without needing to stop conversations to learn all the terms first. (From context, I gather that she looked up the terms afterward, instead of making the staff explain everything to her up front -- thereby allowing her to focus her learning on the things the staff thought most important.) In context, that sounds like an unequivocal good for everyone involved. From a computer programming perspective, I look at this as simply being able to use "forward references" -- i.e., the ability to use a term as a placeholder that has not yet been defined. In truth, until the terms are defined, you don't really know what somebody is using their words to mean anyway. But you can learn quite a lot about a situation or person without yet knowing their precise definitions of the words. And your value as a listener doesn't often require complete understanding, anyway

Hey, do you know if there are any results on the human or animal trials yet? I haven't been able to find anything, even though it's been a few months and it seems like initial data ought to be coming in.

Okay well it took me more than an hour to get to 50, but still a great exercise!

1. Chemical rocket

2. Launch off of space elevator beyond geosync

3. Giant balloon (aim carefully you can't steer)

4. Coilgun

5. Launch loop (seriously how has nobody built this yet)

6. Railgun

7. Nuclear thermal rocket

8. Electric (ion) rocket powered by capacitors or batteries (ok might be a little heavy)

9. Electric (ion) rocket powered by lasers from the ground

10. Ablation rocket powered by lasers from the ground

11. Spaceplane combined with any of the rocket types, especially ablat

... (read more)

I would love to have a link to send my parents to convince them to take Vitamin D as a prophylactic. The one RCT, as noted above, has various issues that make it not ideal for that purpose. Does anyone know of an article (by some sort of expert) that makes a good case for supplementation?

I eventually sent them this article, not ideal but good enough: []

Since the same transformer architecture works on images with basically no modification, I suspect it would do well on audio prediction too. Finding a really broad representative dataset for speech might be difficult, but I guess audiobooks are a good start. The context window might cause problems, because 2000 byte pairs of text takes up a lot more than 4000 bytes in audio form. But I bet it would be able to mimic voices pretty well even with a small window. (edit: Actually probably not, see Gwern's answer.)

If your question is whether the trained GPT-... (read more)

Weapons grade is kind of a nebulous term. In the broadest sense it means anything isotopically pure enough to make a working bomb, and in that sense Little Boy obviously qualifies. However, standard enrichment for later uranium bombs is typically around 90%, and according to Wikipedia, Little Boy was around 80% average enrichment.

It is well known that once you have weapons-grade fissile material, building a crude bomb requires little more than a machine shop. Isotopic enrichment is historically slow and expensive (and hard to hide), but there could certainly be tricks not yet widely known...

I'm a big fan of crowd noises for improving concentration when you need to drown out other voices, especially a TV. Much more effective than other forms of white noise.

I think your formatting with the semicolons and the equals sign has confused the transformer. All the strange words, plurals, and weird possessives may also be confusing. On TTT, if I use common words and switch to colon and linebreak as the separators, it at least picks up that the pattern is gibberish: words.

For example:

kobo: book ntthrgeS: Strength rachi: chair sviion: vision drao: road ntiket: kitten dewdngi: wedding lsptaah: asphalt veon: oven htoetsasu: southeast rdeecno: encoder lsbaap1: phonetics nekmet: chic-lookin' zhafut: crinkly lvtea: question mark cnhaetn: decorated gelsek: ribbon odrcaa: ribbon nepci: ball plel: half cls: winged redoz: brightness star: town moriub:

It seems like the situation with bridges is roughly analagous to neural networks: the cost has nothing to do with how much you change the design (distance) but instead is proportional to how many times you change the design. Evaluating any change, big or small, requires building a bridge (or more likely, simulating a bridge). So you can't just take a tiny step in each of n directions, because it would still have n times the cost of taking a step in one direction. E. Coli is actually pretty unusual in that the evaluation is nearly free, but the change in position is expensive.

I like this visualization tool. There are some very interesting things going on here when you look into the details of the network in the second-to-last MNIST figure. One is that it seems to mostly identify each digit by ruling out the others. For instance, the first two dot-product boxes (on the lower left) could be described as "not-a-0" detectors, and will give a positive result if they detect pixels in the center, near the corners, or at the extreme edges. The next two boxes could be loosely called "not-a-9" detectors (though they a... (read more)

I like LearnObit so far. We should talk sometime about possible improvements to the interface. Are you familiar with Similar goal, different methods, possible synergy. They demo what is (in my opinion) a very effective teaching technique, but provide no good method for people to create new material. I think with some minor tweaks LearnObit might be able to fill that gap.

Take a look at the Fasting-Mimicking Diet, which has some decent evidence going for it. It's a 5-day period of low calorie consumption with restricted carb and protein intake, repeated every few months.

Some people actually think the benefits of caloric restriction (to the extent there are any benefits in humans beyond just avoiding overfat) may result from incidental intermittent fasting. I'm no expert but my fairly vague understanding is that the re-feeding period after a fast promotes some kind of cellular repair process that doesn't occur... (read more)

Thanks. That is largely what I'm getting from Sinclair's Lifespan. What it seems to do, if I'm following his claim, is the restricted diet puts the body in a stressed mode (not malnourished state, that is to be avoided). The switches on come cellular activity that shift energy from cell division to cell maintenance and cleans up a lot of the garbage (malformed and miscoded proteins). He also suggests that the other thing to diet wise it reduce the intake of some of the essential amino acids. We get too much of those and they are all associated with activating an enzyme that promotes the cellular division activities and away from the garbage collection and maintenance work. I had not gotten to this point when asking the question but the book also goes on to say that a lot of different structures to the diet, each meal lite, fast a day a week or so, skip meals... all seem to function the same. Not surprising as all put us in a stressed mode and then the cell functions react. But he was not sure if some are perhaps better than others. I don't know if he has identified a gear, or or the as the book seems to suggest, for aging but interesting so far.

Thank you for the dose of empiricism. However, I see that the abstract says they found "little geographic variation in transmissibility" and do not draw any specific conclusions about heterogeneity in individuals (which obviously must exist to some extent).

They suggest that the R0 of the pandemic flu increased from one wave to the next, but there's considerable overlap in their confidence intervals so it's not totally clear that's what happened. Their waves are also a full year each, so some loss of immunity seems plausible. I wonder, too, if heterogeneity among individuals is more extreme when most people are taking precautions (as they are now).

So, the paper title is "Language Models are Few-Shot Learners" and this commenter's suggested "more conservative interpretation" is "Lots of NLP Tasks are Learned in the Course of Language Modeling and can be Queried by Example." Now, I agree that version states the thesis more clearly, but it's pretty much saying the same thing. It's a claim about properties fundamental to language models, not about this specific model. I can't fully evaluate whether the authors have enough evidence to back that claim up but it's an interesting and plausible idea, and I don't think the framing is irresponsible if they really believe it's true.

I wonder if there's actually any way to know if a movie that has better writing makes bigger profits. From what I've heard, the main thing that determines how much a film writer gets paid is a track record of writing successful films. This makes sense if the producers know they don't have good taste in screenplays--they just hire based on the metric they care about directly. But it also makes sense if the factors that affect how successful a screenplay is have very little to do with "taste" in the sense you mean. Maybe the writers ... (read more)

5Daniel Kokotajlo3y
This. The movie industry has been around long enough, and is diverse enough, that I'd be very surprised if there were million-dollar bills lying around waiting to be picked up like this. It can't just be that no one in charge ever thought about trying to hire someone to suggest plot-hole fixing tweaks. Plot holes are so easy to find and fix that the best explanation IMO is that finding and fixing them doesn't actually make money; perhaps it actually loses money. Analogy: Lots of people on the internet care about historical accuracy. And it is utterly trivial to make movies set in some historical era more historically accurate; you can probably find dozens of history grad students willing to do the job for free. For example: "The flak coming off that aircraft carrier is way too thick; the Japanese relied mostly on CAP for defense. If the flak was that thick, more of the bombers would be dead." Or: "You want all the officers to charge down the street and engage Bane's thugs in melee? OK, there should be about 100 or so dead by the time they reach the steps; then the thugs should retreat into the doorway to create a chokepoint." The reason why this is not done is, obviously, that doing it doesn't make any money.

But longer-lived animals get cancer less, not more. I've heard this theory before but I don't quite understand it. It seems to predict that age would be bounded by a trade-off against child cancers. But in fact selection seems to make animals longer-lived pretty easily (e.g. humans vs homo erectus). Naked mole rats barely get cancer at all, afaik. Do baby bats get cancer more than baby mice?

Just over 50% of the lifetime expected reproductive output of a newborn elf is concentrated into its first 700 years; even though it could in principle live for millennia, producing children at the same rate all the while, its odds of reproducing are best early in life.

I think your elf example is even more extreme than you make it out to be, at least when population size is increasing, since the offspring an individual produces early in life can produce their own descendants exponentially while the original is limited to constant fecundity. 50% of an e... (read more)

I'm not sure about this. I have to think about it.
Are you suggesting antagonistic pleiotropy is particularly non-obvious in humans (vs other animals), or that it's non-obvious generally but you particularly care about humans?

As far as proof that it can happen in general, I found the example of animals that live just long enough to reproduce pretty convincing. Salmon don't live more than about four years, but it's quite clear how they gain a fitness advantage from dying after they spawn. But that sort of thing is pretty rare, so the claim that it happens in a particular species with no such... (read more)

I think it's important that the AP theory holds even if the early-life gain is very small and the late-life cost is very large; that should broaden the list of potential ways to achieve that trade-off. More generally, the idea of antagonistic pleiotropy as a general phenomenon doesn't seem that surprising to me: trade-offs are everywhere in biology, and if one side of a trade-off is underweighted by selection then it'll get shafted. It's basically just overfitting: it would be surprising if the optimal set-up for growing, surviving and reproducing over a span of (say) 20 years were also the optimal set-up for doing the same over (say) 100 years, and natural selection is almost entirely optimising for the former. One potential response to this is that this is systems thinking rather than genes thinking. Many genes do lots of things across lots of systems, so you could see a mutation that improves functionality in a way that's relevant to one system early in life, at a cost to another system in late life. (I'm personally more of a fan of relaxed purifying selection, which seems like the more general and less contingent theory, but I do think antagonistic pleiotropy theory is solid enough that finding more concrete examples of it wouldn't surprise me.)

Antagonistic pleiotropy is certainly plausible in the abstract, but it's not obvious how it would work in humans. Something like tissue repair, for instance, is obviously beneficial in old age but it's hard to see how it would be harmful early on. From googling a little bit, I found some info suggesting:

  • The adaptive immune system (because it "runs out of capacity" late in life; see also the discussion here)
  • Genes associated with coronary artery disease that appear to be under positive selection (but it doesn't say why)
  • A gene that ca
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Consider telomeres. The body's inability to repair telomeres can be considered as an adaptive mechanism protecting from tumor formation in early life. A little thought experiment: When you're a unicellular organism, you want to make as many copies of yourself as possible to maximize fitness. When you evolve into a multicellular organism, this strategy ain't working anymore. A multicellular organism with telomerase expressed in every cell of the body will eventually get a mutation in one of the cell division regulatory cascades which causes it to divide infinitely and kill the whole organism. For this reason, telomere repair should be disabled in non-reproductive cells so that renegade mutant cells would run out of reproductive capacity and stop dividing. The only way large tumors would occur is due to 2 independent mutations: one for cell division and one for telomerase expression. This is vastly more unlikely. The downside is that lack of telomere repair would lead to gene deregulation and eventual aging.
Are you suggesting antagonistic pleiotropy is particularly non-obvious in humans (vs other animals), or that it's non-obvious generally but you particularly care about humans? This isn't directly related to your question, I'm just curious. This sentence confuses me. Why would you expect it to be harmful early on? Antagonistic pleiotropy predicts mutations that are beneficial in early life and harmful later. Is this a typo (switching old and young)? Yeah, I think this is basically right. In general my impression is that most experts don't believe ageing is "one thing" – a single underlying cause we could neatly target. On the other hand it also doesn't seem to be, like, a million things: there is an enumerable list [] of key causes, on the order of ten items long, which together account for most of the physiological ageing we see in mammals. It's not obvious to me what to make of this theoretically. (Of course, there are still plenty of people who like to claim they've found the single mechanism underlying all ageing, usually fortuitously closely related to the thing they study.)
1William Walker3y
Curve keeps dropping, hopefully this virus is as seasonal as most of its kind: []

Are there any of them you could explain? It would be interesting to hear how that caches out in real life.

A piece of a certain large corporation's spelling/grammar checker was at its heart Result <== Prior x Evidence. Due to legacy code, decaying institutional knowledge, etc., no one knew this. The code/math was strewn about many files. Folks had tweaked the code over the years, allowed parameters to vary, fit those parameters to data. I read the code, realized that fundamentally it had to be “about” determining a prior, determining evidence, and computing a posterior, reconstructed the actual math being performed, and discovered that two exponents were different from each other, "best fit to the data", and I couldn't think of any good reason they should be different. Brain threw up all sorts of warning bells. I examined how we trained on the data to determine the values we’d use for these exponents. Turns out, the process was completely unjustifiable, and only seemed to give better results because our test set was subtly part of the training set. Now that's something everyone understands immediately; you don't train on your test set. So we fixed our evaluation process, stopped allowing those particular parameters to float, and improved overall performance quite a bit. Note, math. Because information and Bayes and correlation and such is unfortunately not simple, it's entirely possible that some type of data is better served by e^(a*ln(P(v|w))-b*ln(P(v|~w))) where a!=b!=1. I dunno. But if you see someone obviously only introducing a and b and then fitting them to data because :shrug: why not, that's when your red flags go up and you realize someone's put this thing together without paying attention. And in this case after fixing the evaluation we did end up leaving a==b != 1, which is just Naive Bayes, basically. a!=b was the really disconcerting bit.
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