Posts

Comments

Comment by veedrac on AGI Predictions · 2020-11-21T21:15:43.542Z · LW · GW

This is only true if, for example, you think AI would cause GDP growth. My model assigns a lot of probability to ‘AI kills everyone before (human-relevant) GDP goes up that fast’, so questions #7 and #8 are conditional on me being wrong about that. If we can last any small multiples of a year with AI smart enough to double GDP in that timeframe, then things probably aren't as bad as I thought.

Comment by veedrac on AGI Predictions · 2020-11-21T11:54:59.794Z · LW · GW

To emphasize, the clash I'm perceiving is not the chance assigned to these problems being tractable, but to the relative probability of ‘AI Alignment researchers’ solving the problems, as compared to everyone else and every other explanation. In particular, people building AI systems intrinsically spend a degree of their effort, even if completely unconvinced about the merits of AI risk, trying to make systems aligned, just because that's a fundamental part of building a useful AI.

I could talk about the specific technical work, or the impact that things like the AI FOOM Debate had on Superintelligence had on OpenPhil, or CFAR on FLI on Musk on OpenAI. Or I could go into detail about the research being done on topics like Iterated Amplification and Agent Foundations and so on and ways that this seems to me to be clear progress on subproblems.

I have a sort of Yudkowskian pessimism towards most of these things (policy won't actually help; Iterated Amplification won't actually work), but I'll try to put that aside here for a bit. What I'm curious about is what makes these sort of ideas only discoverable in this specific network of people, under these specific institutions, and particularly more promising than other sorts of more classical alignment.

Isn't Iterated Amplification in the class of things you'd expect people to try just to get their early systems to work, at least with ≥20% probability? Not, to be clear, exactly that system, but just fundamentally RL systems that take extra steps to preserve the intentionality of the optimization process.

To rephrase a bit, it seems to me that a worldview in which AI alignment is sufficiently tractable that Iterated Amplification is a huge step towards a solution, would also be a worldview in which AI alignment is sufficiently easy (though not necessarily easy) that there should be a much larger prior belief that it gets solved anyway.

Comment by veedrac on AGI Predictions · 2020-11-21T09:54:55.753Z · LW · GW

There is a huge difference in the responses to Q1 (“Will AGI cause an existential catastrophe?”) and Q2 (“...without additional intervention from the existing AI Alignment research community”), to a point that seems almost unjustifiable to me. To pick the first matching example I found (and not to purposefully pick on anybody in particular), Daniel Kokotajlo thinks there's a 93% chance of existential risk without the AI Alignment community's involvement, but only 53% with. This implies that there's a ~43% chance of the AI Alignment community solving the problem, conditional on it being real and unsolved otherwise, but only a ~7% chance of it not occurring for any other reason, including the possibility of it being solved by the researchers building the systems, or the concern being largely incorrect.

What makes people so confident in the AI Alignment research community solving this problem, far above that of any other alternative?

Comment by veedrac on The Colliding Exponentials of AI · 2020-11-01T10:39:53.498Z · LW · GW

On the other hand, improvements on ImageNet (the datasets alexnet excelled on at the time) itself are logarithmic rather than exponential and at this point seem to have reached a cap at around human level ability or a bit less (maybe people got bored of it?)

The best models are more accurate than the ground-truth labels.

Are we done with ImageNet?
https://arxiv.org/abs/2006.07159

Yes, and no. We ask whether recent progress on the ImageNet classification benchmark continues to represent meaningful generalization, or whether the community has started to overfit to the idiosyncrasies of its labeling procedure. We therefore develop a significantly more robust procedure for collecting human annotations of the ImageNet validation set. Using these new labels, we reassess the accuracy of recently proposed ImageNet classifiers, and find their gains to be substantially smaller than those reported on the original labels. Furthermore, we find the original ImageNet labels to no longer be the best predictors of this independently-collected set, indicating that their usefulness in evaluating vision models may be nearing an end. Nevertheless, we find our annotation procedure to have largely remedied the errors in the original labels, reinforcing ImageNet as a powerful benchmark for future research in visual recognition.

Figure 7. shows that model progress is much larger than the raw progression of ImageNet scores would indicate.

Comment by veedrac on The Solomonoff Prior is Malign · 2020-10-25T08:53:31.695Z · LW · GW

I think this is wrong, but I'm having trouble explaining my intuitions. There are a few parts;

  1. You're not doing Solomonoff right, since you're meant to condition on all observations. This makes it harder for simple programs to interfere with the outcome.
  2. More importantly but harder to explain, you're making some weird assumptions of the simplicity of meta-programs that I would bet are wrong. There seems to be a computational difficulty here, in that you envision  small worlds trying to manipulate  other worlds, where . That makes it really hard for the simplest program to be one where the meta-program that's interpreting the pointer to our world is a rational agent, rather than some more powerful but less grounded search procedure. If ‘naturally’ evolved agents are interpreting the information pointing to the situation they might want to interfere with, this limits the complexity of that encoding. If they're just simulating a lot of things to interfere with as many worlds as possible, they ‘run out of room’, because .
  3. Your examples almost self-refute, in the sense that if there's an accurate simulation of you being manipulated at time , it implies that simulation is not materially interfered with at time , so even if the vast majority of Solomonoff inductions have attempted adversary, most of them will miss anyway. Hypothetically, superrational agents might still be able coordinate to manipulate some very small fraction of worlds, but it'd be hard and only relevant to those worlds.
  4. Compute has costs. The most efficient use of compute is almost always to do enact your preferences directly, not manipulate other random worlds with low probability. By the time you can interfere with Solomonoff, you have better options.
  5. To the extent that a program  is manipulating predictions so that another other program that is simulating  performs unusually... well, then that's just how the metaverse is. If the simplest program containing your predictions is an attempt at manipulating you, then the simplest program containing you is probably being manipulated.
Comment by veedrac on I'm Voting For Ranked Choice, But I Don't Like It · 2020-09-20T20:59:51.349Z · LW · GW

IRV is an extremely funky voting system, but almost anything is better than Plurality. I very much enjoyed Ka-Ping Yee's voting simulation visualizations, and would recommend the short read for anyone interested.

I have actually made my own simulation visualization, though I've spent no effort annotating it and the graphic isn't remotely intuitive. It models a single political axis (eg. ‘extreme left’ to ‘extreme right’) with N candidates and 2 voting populations. The north-east axis of the graph determines the centre of one voting population, and the south-east axis determines the centre of the other (thus the west-to-east axis is when the voting populations agree). The populations have variances and sizes determined by the sliders. The interesting thing this has taught me is that IRV/Hare voting is like an otherwise sane voting system but with additional practically-unpredictable chaos mixed in, which is infinitely better than the systemic biases inherent to plurality or Borda votes. In fact, if you see advantages in sortition, this might be a bonus.

Comment by veedrac on Where is human level on text prediction? (GPTs task) · 2020-09-20T16:44:40.948Z · LW · GW

Sources:

https://web.stanford.edu/~jurafsky/slp3/

https://www.isca-speech.org/archive/Interspeech_2017/abstracts/0729.html

The latter is the source for human perplexity being 12. I should note that it tested on the 1 Billion Words benchmark, where GPT-2 scored 42.2 (35.8 was for Penn Treebank), so the results are not exactly 1:1.

Comment by veedrac on How Much Computational Power Does It Take to Match the Human Brain? · 2020-09-12T15:05:29.965Z · LW · GW

FLOPS don't seem to me a great metric for this problem; they are often very sensitive to the precise setup of the comparison, in ways that often aren't very relevant (the Donkey Kong comparison emphasized this), and the architecture of computers is fundamentally different to that of brains. What seems like a more apt and stable comparison is to compare the size and shape of the computational graph, roughly the tuple (width, depth, iterations). This seems like a much more stable metric, since scale-based metrics normally only change significantly when you're handling the problem in a semantically different way. In the example, hardware implementations of Donkey Kong and various sorts of software emulation (software interpreter, software JIT, RTL simulation, FPGA) will have very different throughputs on different hardware, and the setup and runtime overheads for each might be very different, but the actual runtime computation graphs should look very comparable.

This also has the added benefit of separating out hypotheses that should naturally be distinct. For example, a human-sized brain at 1x speed and a hamster brain at 1000x speed are very different, yet have seemingly similar FLOPS. Their computation graphs are distinct. Technology comparisons like FPGAs vs AI accelerators become a lot clearer from the computation graph perspective; an FPGA might seem at a glance more powerful from a raw OP/s perspective, but first principles arguments will quickly show they should be strictly weaker than an AI accelerator. It's also more illuminating given we have options to scale up at the cost of performance; from a pure FLOPS perspective, this is negative progress, but pragmatically, this should push timelines closer.

Comment by veedrac on Forecasting Thread: AI Timelines · 2020-08-26T06:16:20.425Z · LW · GW

I disagree with that post and its first two links so thoroughly that any direct reply or commentary on it would be more negative than I'd like to be on this site. (I do appreciate your comment, though, don't take this as discouragement for clarifying your position.) I don't want to leave it at that, so instead let me give a quick thought experiment.

A neuron's signal hop latency is about 5ms, and in that time light can travel about 1500km, a distance approximately equal to the radius of the moon. You could build a machine literally the size of the moon, floating in deep space, before the speed of light between the neurons became a problem relative to the chemical signals in biology, as long as no single neuron went more than half way through. Unlike today's silicon chips, a system like this would be restricted by the same latency propagation limits that the brain is, but still, it's the size of the moon. You could hook this moon-sized computer to a human-shaped shell on Earth, and as long as the computer was directly overhead, the human body could be as responsive and fully updatable as a real human.

While such a computer is obviously impractical on so many levels, I find it a good frame of reference to think about the characteristics of how computers scale upwards, much like Feynman's There's Plenty of Room at the Bottom was a good frame of reference for scaling down, considered back when transistors were still wired by hand. In particular, the speed of light is not a problem, and will never become one, except where it's a resource we use inefficiently.

Comment by veedrac on Forecasting Thread: AI Timelines · 2020-08-25T00:02:33.072Z · LW · GW
Scaling Language Model Size by 1000x relative to GPT3. 1000x is pretty feasible, but we'll hit difficult hardware/communication bandwidth constraints beyond 1000x as I understand.

I think people are hugely underestimating how much room there is to scale.

The difficulty, as you mention, is bandwidth and communication, rather than cost per bit in isolation. An A100 manages 1.6TB/sec of bandwidth to its 40 GB of memory. We can handle sacrificing some of this speed, but something like SSDs aren't fast enough; 350 TB of SSD memory would cost just $40k, but would only manage 1-2 TB/s over the whole array, and could not push it to a single GPU. More DRAM on the GPU does hit physical scaling issues, and scaling out to larger clusters of GPUs does start to hit difficulties after a point.

This problem is not due to physical law, but the technologies in question. DRAM is fast, but has hit a scaling limit, whereas NAND scales well, but is much slower. And the larger the cluster of machines, the more bandwidth you have to sacrifice for signal integrity and routing.

Thing is, these are fixable issues if you allow for technology to shift. For example,

  • Various sorts of persistent memories allow fast dense memories, like NRAM. There's also 3D XPoint and other ReRAMs, various sorts of MRAMs, etc.
  • Multiple technologies allow for connecting hardware significantly more densely than we currently do, primarily things like chiplets and memory stacking. Intel's Ponte Vecchio intends to tie 96 (or 192?) compute dies together, across 6 interconnected GPUs, each made of 2 (or 4?) groups of 8 compute dies.
  • Neural networks are amicable to ‘spatial computing’ (visualization), and using appropriate algorithms the end-to-end latency can largely be ignored as long as the block-to-block latency and throughput is sufficiently high. This means there's no clear limit to this sort of scaling, since the individual latencies are invariant to scale.
  • The switches themselves between the computers are not at a limit yet, because of silicon photonics, which can even be integrated alongside compute dies. That example is in a switch, but they can also be integrated alongside GPUs.
  • You mention this, but to complete the list, sparse training makes scale-out vastly easier, at the cost of reducing the effectiveness of scaling. GShard showed effectiveness at >99.9% sparsities for mixture-of-experts models, and it seems natural to imagine that a more flexible scheme with only, say, 90% training sparsity and support for full-density inference would allow for 10x scaling without meaningful downsides.

It seems plausible to me that a Manhattan Project could scale to models with a quintillion parameters, aka. 10,000,000x scaling, within 15 years, using only lightweight training sparsity. That's not to say it's necessarily feasible, but that I can't rule out technology allowing that level of scaling.

Comment by veedrac on Highlights from the Blackmail Debate (Robin Hanson vs Zvi Mowshowitz) · 2020-08-23T22:23:10.557Z · LW · GW

It might be possible to convince me on something like that, as it fixes the largest problem, and if Hanson is right that blackmail would significantly reduce issues like sexual harassment then it's at least worth consideration. I'm still disinclined towards the idea for other reasons (incentivizes false allegations, is low oversight, difficult to keep proportionality, can incentivize information hiding, seems complex to legislate), but I'm not sure how strong those reasons are.

Comment by veedrac on Will OpenAI's work unintentionally increase existential risks related to AI? · 2020-08-21T20:54:50.420Z · LW · GW

I agree this makes a large fractional change to some AI timelines, and has significant impacts on questions like ownership. But when considering very short timescales, while I can see OpenAI halting their work would change ownership, presumably to some worse steward, I don't see the gap being large enough to materially affect alignment research. That is, it's better OpenAI gets it in 2024 than someone else gets it in 2026.

This constant seems to be very small, which is why compute had to drop all the way to ~$1k before any researchers worldwide were fanatical enough to bother trying CNNs and create AlexNet.

It's hard to be fanatical when you don't have results. Nowadays AI is so successful it's hard to imagine this being a significant impediment.

Excluding GShard (which as a sparse model is not at all comparable parameter-wise)

I wouldn't dismiss GShard altogether. The parameter counts aren't equal, but MoE(2048E, 60L) is still a beast, and it opens up room for more scaling than a standard model.

Comment by veedrac on Highlights from the Blackmail Debate (Robin Hanson vs Zvi Mowshowitz) · 2020-08-21T18:18:30.044Z · LW · GW
Robin Hanson argued that negative gossip is probably net positive for society.

Yes, this is what my post was addressing and the analogy was about. I consider it an interesting hypothesis, but not one that holds up to scrutiny.

Lying about someone in a damaging way is already covered by libel/slander laws.

I know, but this only further emphasizes how much better paying those who helped a conviction is. Blackmail is private, threat-based, and necessarily unpoliced, whereas the courts have oversight and are an at least somewhat impartial test for truth.

Comment by veedrac on Will OpenAI's work unintentionally increase existential risks related to AI? · 2020-08-21T18:03:36.193Z · LW · GW

Gwern's claim is that these other institutions won't scale up as a consequence of believing the scaling hypothesis; that is, they won't bet on it as a path to AGI, and thus won't spend this money on abstract of philosophical grounds.

My point is that this only matters on short-term scales. None of these companies are blind to the obvious conclusion that bigger models are better. The difference between a hundred-trillion dollar payout and a hundred-million dollar payout is philosophical when you're talking about justifying <$5m investments. NVIDIA trained an 8.3 B parameter model as practically an afterthought. I get the impression Microsoft's 17 B parameter Turing-NLG was basically trained to test DeepSpeed. As markets open up to exploit the power of these larger models, the money spent on model scaling is going to continue to rise.

These companies aren't competing with OpenAI. They've built these incredibly powerful systems incidentally, because it's the obvious way to do better than everyone else. It's a tool they use for market competitiveness, not as a fundamental insight into the nature of intelligence. OpenAI's key differentiator is only that they view scale as integral and explanatory, rather than an incidental nuisance.

With this insight, OpenAI can make moonshots that the others can't: build a huge model, scale it up, and throw money at it. Without this understanding, others will only get there piecewise, scaling up one paper at a time. The delta between the two is at best a handful of years.

Comment by veedrac on Will OpenAI's work unintentionally increase existential risks related to AI? · 2020-08-21T15:49:46.701Z · LW · GW

If OpenAI changed direction tomorrow, how long would that slow the progress to larger models? I can't see it lasting; the field of AI is already incessantly moving towards scale, and big models are better. Even in a counterfactual where OpenAI never started scaling models, is this really something that no other company can gradient descent on? Models were getting bigger without OpenAI, and the hardware to do it at scale is getting cheaper.

Comment by veedrac on Highlights from the Blackmail Debate (Robin Hanson vs Zvi Mowshowitz) · 2020-08-21T03:17:34.402Z · LW · GW

Legalizing blackmail gives people with otherwise no motivation to harm someone through the sharing of information the motive to do so. I'm going to take that as the dividing line between blackmail and other forms of trade or coercion. I believe this much is generally agreed on in this debate.

If you're going to legalize forced negative-sum trades, I think you need a much stronger argument that assuming that, on net, the positive externalities will make it worthwhile. It's a bit like legalizing violence from shopkeepers because most of the time they're punching thieves. Maybe that's true now, when shopkeepers punching people is illegal, but one, I think there's a large onus on anyone suggesting this to justify that it's the case, and two, is it really going to stay the case, once you've let the system run with this newfound form of legalized coercion?

Before I read these excerpts, I was pretty much in the ‘blackmail bad, duh’ category. After I read them, I was undecided; maybe it is in fact true that many harms from information sharing comes with sufficient positive externalities, and those that do not are sufficiently clearly delimited to be separately legislated. Having thought about it longer, I now see a lot of counterexamples. Consider some person, who:

  • had a traumatic childhood,
  • has a crush on another person, and is embarrassed about it,
  • has plans for a surprise party or gift for a close friend,
  • or the opposite; someone else is planning a surprise for them,
  • has an injury or disfiguration on a covered part of their body,
  • had a recent break-up, that they want to hold out on sharing with their friends for a while,
  • left an unkind partner, and doesn't want that person to know they failed a recent exam,
  • posts anonymously for professional reasons, or to have a better work-life balance,
  • doesn't like a coworker, but tries not to show it on the job.

I'm sure I could go on for quite a while. Legalizing blackmail means that people are de-facto incentivized to exploit information when it would harm people, because their payout stops being derived from the public interest, through mechanisms like public reception, appreciation from those directly helped by the reveal of information, or payment from a news agency, and becomes proportional almost purely to the damage you can do.

It's true that in some cases these are things which should be generally disincentivized or made illegal, nonconsensual pornography being a prime example. In general I don't think this approach scales, because the public interest is so context dependent. Sometimes it is in the public interest to share someone's traumatic childhood, spoil a surprise or tell their coworker they are disliked. But the reward should be derived from the public interest, not the harm! If we want to monetarily incentivize people to share information they have on sexual abuse, pay them for sharing information that led to a conviction. And if you're not wanting to do that because it causes the bad incentive to lie... surely blackmail gives more incentive to lie, and the accuser being paid requires the case never to have gone to trial, so is worse on all accounts.

Comment by veedrac on Why haven't we celebrated any major achievements lately? · 2020-08-18T03:27:02.683Z · LW · GW

Apple's launch events get pretty big crowds, a lot of talk, and a lot of celebration.

Comment by veedrac on Will OpenAI's work unintentionally increase existential risks related to AI? · 2020-08-14T02:25:43.525Z · LW · GW

Putting aside the general question, is OpenAI good for the world, I want to consider the smaller question, how do OpenAI's demonstrations of scaled up versions of current models affect AI safety?

I think there's a much easier answer to this. Any risks we face from scaling up models we already have with funding much less than tens of billions of dollars amounts to unexploded uranium sitting around, that we're refining in microgram quantities. The absolute worst that can happen with connectionist architectures is that we solve all the hard problems without having done the trivial scaled-up variants, and therefore scaling up is trivial, and so that final step to superhuman AI also becomes trivial.

Even if scaling up ahead of time results in slightly faster progress towards AGI, it seems that it at least makes it easier to see what's coming, as incremental improvements require research and thought, not just trivial quantities of dollars.

Going back to the general question, one good I see OpenAI producing is the normalization of the conversation around AI safety. It is important for authority figures to be talking about long-term outcomes, and in order to be an authority figure, you need a shiny demo. It's not obvious how a company could be more authoritative than OpenAI while being less novel.

Comment by veedrac on is gpt-3 few-shot ready for real applications? · 2020-08-09T00:27:17.176Z · LW · GW

I think the results in that paper argue that it's not really a big deal as long as you don't make some basic errors like trying to fine-tune on tasks sequentially. MT-A outperforms Full in Table 1. GPT-3 is already a multi-task learner (as is BERT), so it would be very surprising if training on fewer tasks was too difficult for it.

Comment by veedrac on is gpt-3 few-shot ready for real applications? · 2020-08-06T20:43:46.301Z · LW · GW

If the issue is the size of having a fine-tuned model for each individual task you care about, why not just fine-tune on all your tasks simultaneously, on one model? GPT-3 has plenty of capacity.

Comment by veedrac on Are we in an AI overhang? · 2020-07-27T20:24:53.107Z · LW · GW

Density is important because it affects both price and communication speed. These are the fundamental roadblocks to building larger models. If you scale to too large clusters of computers, or primarily use high-density off-chip memory, you spend most of your time waiting for data to arrive in the right place.

Comment by veedrac on Are we in an AI overhang? · 2020-07-27T16:25:48.995Z · LW · GW

Moore's Law is not dead. I could rant about the market dynamics that made people think otherwise, but it's easier just to point to the data.

https://docs.google.com/spreadsheets/d/1NNOqbJfcISFyMd0EsSrhppW7PT6GCfnrVGhxhLA5PVw

Moore's Law might die in the short future, but I've yet to hear a convincing argument for when or why. Even if it does die, Cerebras presumably has at least 4 node shrinks left in the short term (16nm→10nm→7nm→5nm→3nm) for a >10x density scaling, and many sister technologies (3D stacking, silicon photonics, new non-volatile memories, cheaper fab tech) are far from exhausted. One can easily imagine a 3nm Cerebras waffle coated with a few layers of Nantero's NRAM, with a few hundred of these connected together using low-latency silicon photonics. That would easily train quadrillion parameter models, using only technology already on our roadmap.

Alas, the nature of technology is that while there are many potential avenues for revolutionary improvement, only some small fraction of them win. So it's probably wrong to look at any specific unproven technology as a given path to 10,000x scaling. But there are a lot of similarly revolutionary technologies, and so it's much harder to say they will all fail.

Comment by veedrac on Does human choice have to be transitive in order to be rational/consistent? · 2019-08-11T08:29:39.617Z · LW · GW

Here's a rather out-there hypothesis.

I'm sure many LessWrong members have had the experience of arguing some point piecemeal, where they've managed to get weak agreement on every piece of the argument, but as soon as they step back and point from start to end their conversation partner ends up less than convinced. In this sense, in humans even implication isn't transitive. Mathematics is an example with some fun tales I'm struggling to find sources for, where pre-mathematical societies might have people unwilling to trade two of A for two of B, but happy to trade A for B twice, or other such oddities.

It's plausible to me that the need for consistent models of the world only comes about as intelligence grows and allows people to arbitrage value between these different parts of their thoughts. Early humans and their lineage before that weren't all that smart, so it makes sense that evolution didn't force their beliefs to be consistent all that much—as long as it was locally valid, it worked. As intelligence evolved, occasionally certain issues might crop up, but rather than fixing the issue in a fundamental way, which would be hard, minor kludges were put in place.

For example, I don't like being exploited. If someone leads me around a pump, I'm going to value the end state less than its ‘intrinsic’ value. You can see this behaviour a lot in discussions of trolley problem scenarios: people take objection to having these thoughts traded off against each other to the degree it often overshadows the underlying dilema. Similarly, I find gambling around opinions intrinsically uncomfortable, and notice that fairly frequently people take objection to me asking them to more precisely quantify their claims, even in cases where I'm not staking an opposing claim. Finally, since some people are better at sounding convincing than I am, it's completely reasonable to reject some things more broadly because of the possibility the argument is an exploit—this is epistemic learned helplessness, sans ‘learned’.

There are other explanations for all the above, so this is hardly bulletproof, but I think there is merit to considering evolved defenses to exploitation that don't involve being exploit-free, as well as whether there is any benefit to something of this form. Behaviours that avoid and back away from these exploits seem fairly obvious places to look into. One could imagine (sketchily, non-endorsingly) an FAI built on these principles, so that even without a bulletproof utility function, the AI would still avoid self-exploit.

Comment by veedrac on Why do humans not have built-in neural i/o channels? · 2019-08-10T07:39:40.549Z · LW · GW

Most of the complexity in human society is unnecessary to merely outperform the competition. The exploits that prehistoric humans found were readily available; it's just that evolution could only find them by inventing a better optimizer, rather than getting there directly.

Crafting spears and other weapons is a simple example. The process to make them could be instinctual, and very little intellect is needed. Similar comments apply to clothing and cooking. If they were evolved behaviours, we might even expect parts of these weapons or tools to grow from the animal itself—you might imagine a dedicated role for one of the members of a group, who grows blades or pieces of armour that others can use as needed.

One could imagine plants that grow symbiotically with some mobile species that farms them and keeps them healthy in ways the plant itself is not able to do (eg. weeding), and in return provides nutrition and shelter, which could include enclosed walling over a sizable area.

One could imagine prey, like rabbits, becoming venomous. When resistance starts to form, they could primarily switch to a different venom for a thousand generations before switching back. In fact, you could imagine such venomous rabbits aggressively trying to drive predators extinct before they had the chance to gain a resistance; a short term cost for long-term prosperity.

The overall point is that evolution does not have the insight to get around optimization barriers. Consider brood parasites, where birds lay eggs in other species' nests. It is hypothesized that a major reason this behaviour is successful is because of retaliatory behaviour when a parasite is ejected. Clearly these victim species would be better off if they just wiped the parasites off the face of the earth, as long as they survived the one-time increased retaliation, but evolutionary pressure resulted in them evolving complicity.

Comment by veedrac on Why do humans not have built-in neural i/o channels? · 2019-08-09T05:26:58.330Z · LW · GW
And once you have one form of communication, the pressure to develop a second is almost none.

I agree with almost all of your post, but not this, given the huge number of channels of communication that animals have. Sound, sight, smell and touch are all important bidirectional communication channels between many social animals.

Comment by veedrac on Why do humans not have built-in neural i/o channels? · 2019-08-08T14:02:55.894Z · LW · GW

There are lots of simple things that organisms could do to make them wildly more successful. The success of human society is a good demonstration of how very low complexity systems and behaviours can drive your competition extinct, magnify available resources, and more, the vast majority of which could be easily coded into the genome in principle.

However, evolution does not make judgements about the end result. The question is whether there is a path of high success leading to your desired result. Laryngeal nerves are a good demonstration that even basic impediments won't be worked around if you can't get there step by step with appropriate evolutionary pressure. Ultimately there seems to be no impetus for a half-baked neuron tentacle, and a lot of cost and risk, so that will probably never be the path to such organisms.

There are many examples of fairly direct inter-organism communication, like RNA transfer between organisms, and to the extent that cells think in chemicals, the fact they share their chemical environment readily is a form of this kind of communication. I'm not aware of anything similarly direct at larger scales, between neurons.

Comment by veedrac on Preferences as an (instinctive) stance · 2019-08-08T05:01:22.889Z · LW · GW
I deny that a generic outside observer would describe us as having any specific set of preferences, in an objective sense.

It's possible that we've been struggling with this conversation because I've been failing to grasp just how radically different your opinions are to mine.

Imagine your generic outside observer was superintelligent, and understood (through pure analysis) qualia and all the corresponding mysteries of the mind. Would you then still say this outside observer would not consider us to have any specific set of preferences, in an objective sense, where “preferences” takes on its colloquial meaning?

If not, why? I think my stance is obvious; where preferences colloquially means approximately “a greater liking for one alternative over another or others”, all I have to claim is that there is an objective sense in which I like things, which is simple because there's an objective sense in which I have that emotional state and internal stance.

Comment by veedrac on Preferences as an (instinctive) stance · 2019-08-07T03:57:30.119Z · LW · GW
"Agent A has preferences R" is not a fact about the world. It is a stance about A, or an interpretation of A. A stance or an interpretation that we choose to take, for some purpose or reason.

I find it hard to imagine that you're actually denying that you or I have things that, colloquially, one would describe as preferences, and exist in an objective sense. I do have a preference for a happy and meaningful life over a life of pure agony. Anyone who thinks I do not is factually wrong about the state of the world.

Then there is a sense in which the interpretations of these systems we build are fully interpretative. If “preferences R” refers to a function returning a real number, for sure this is not some facet of the real world, and there are many such seemingly-different models for any agent. Here again I believe we agree.

But we seem not to be agreeing at the next step, with the preference stance. Here I claim your goal should not be to maximize the function “preferences R”, whose precise values are irrelevant and independent, but to maximise the actual human preferences.

Consider measuring a simpler system, temperature, and projecting this onto some number. Clearly, depending on how you do this projection, you can end up at any number for a given temperature. Even with a simplicity prior, higher temperatures can correspond to larger numbers or smaller numbers in the projection, with pretty much equal plausibility. So even in this simplified situation, where we can agree that some temperatures are objectively higher than others, you cannot reliably maximize temperature by maximizing its projection.

Your preference function is a projection. The arbitrary choices you have to make to build this function are not assumptions about the world, they are choices about the model. When you prove that you have many models of human preference, you are not proving that preference is entirely subjective.

That's why, when you use empathy to figure out someone's goals and rationality, this also allows you to better predict them. But this is a fact about you (and me), not about the world. Just as "Thor is angry" is actually much more complex than electromagnetism, our prediction of other people via our empathy machine is simpler for us to do - but is actually more complex for an agent that doesn't already have this empathy machinery to draw on.

This Thor analogy is... enlightening of the differences in our perspectives. Imagining an angry Thor is a much more complex hypothesis up until the point you see an actual Thor in the sky hurling spears of lightning. Then it becomes the only reasonable conclusion, because although brains seem like they involve a lot of assumptions, a brain is ultimately many fewer assumptions (to the pre-industrial Norse people) than that same amount of coincidence.

This is the point I am making with people. If your computer models people as arbitrary, randomly sampled programs, of course you struggle to distinguish human behaviour from their contrapositives. However, people are not fully independent, nor arbitrary computing systems. Arguing that a physical person optimizing competently for a good outcome and a physical person optimizing nega-competently for a bad outcome are similarly simple has to overcome at least two hurdles:

1. We seem to know things about which mental states are good and which mental states are bad. This implies there is objective knowledge that can be learnt about it.

2. You would need to extend your arguments about mathematical functions into the real world. I don't know how this could be approached.

I have a hard time believing that in another world people think that the qualia corresponding to our suffering is good and the qualia corresponding to our happiness is bad, and if it is, this strikes me as a much bigger deal than anything else you are saying.


Comment by veedrac on Practical consequences of impossibility of value learning · 2019-08-05T23:07:03.726Z · LW · GW

One of us is missing what the other is saying. I'm honestly not sure what argument you are putting forth here.

I agree that preference/reward is an interpretation (the terms I used were map and territory). I agree that (p,R) and (-p,-R) are approximately equally complex. I do not agree that complexity is necessarily isomorphic between the map and the territory. This means although the model might be a strong analogy when talking about behaviour, it is sketchy to use it as a model for complexity of behaviour.

Comment by veedrac on Practical consequences of impossibility of value learning · 2019-08-05T12:12:52.582Z · LW · GW
But that doesn't detract from the main point: that simplicity, on its own, is not sufficient to resolve the issue.

It kind of does. You have shown that simplicity cannot distinguish (p, R) from (-p, -R), but you have not shown that simplicity cannot distinguish a physical person optimizing competently for a good outcome from a physical person optimizing nega-competently for a bad outcome.

If it seems unreasonable for there to be a difference, consider a similar map-territory distinction of a height map to a mountain. An optimization function that gradient descents on a height map is the same complexity, or nearabouts, as one that gradient ascents on the height map's inverse. However, a system that physically gradient descents on the actual mountains can be much simpler than one that gradient ascents on the mountain's inverse. Since negative mental experiences are somehow qualitatively different to positive ones, it would not surprise me much if they did in fact effect a similar asymmetry here.

Comment by veedrac on Practical consequences of impossibility of value learning · 2019-08-03T18:34:51.775Z · LW · GW
Obviously misery would be avoided because it's bad, not the other way around.

As mentioned, this isn't obvious to me, so I'd be interested in your reasoning. Why should evolution build systems that want to avoid intrinsically bad mental states?

We are trying to figure out what is bad by seeing what we avoid. And the problem remains whether we might be accidentally avoiding misery, while trying to avoid its opposite.

Yes, my point here was twofold. One, the formalism used in the paper does not seem to be deeply meaningful, so it would be best to look for some other angle of attack. Two, given the claim about intrinsic badness, the programmer is embedding domain knowledge (about conscious states), not unlearnable assumptions. A computer system would fail to learn this because qualia is a hard problem, not because it's unlearnable. This makes it asymmetric and circumventable in a way that the no free lunch theorem is not.

Comment by veedrac on Very different, very adequate outcomes · 2019-08-03T06:25:19.692Z · LW · GW
Pushing q towards 1 might be a disaster

If I consider satisfaction of my preferences to be a disaster, in what sense can I realistically call them my preferences? It feels like you're more caught up on the difficulty of extrapolating these preferences outside of their standard operation, but that seems like a rather different issue.

Comment by veedrac on Practical consequences of impossibility of value learning · 2019-08-03T05:50:58.452Z · LW · GW

Fair warning, the following is pretty sketchy and I wouldn't bet I'd stick with it if I thought a bit longer.

---

Imagine a simple computer running a simple chess playing program. The program uses purely integer computation, except to calculate its reward function and to run minimax over them, which is in floating point. The search looks for the move that maximizes the outcome, which corresponds to a win.

This, if I understand your parlance, is ‘rational’ behaviour.

Now consider that the reward is negated, and the planner instead looks for the move that minimizes the outcome.

This, if I understand your parlance, is ‘anti-rational’ behaviour.

Now consider that this anti-rational program is run on a machine where floating point values encoded with a sign bit ‘1’ represent a positive number and those with a ‘0’ sign bit a negative number—the opposite to the standard encoding.

It's the same ‘anti-rational’ program, but exactly the same wires are lit up in the same pattern on this hardware as with the ‘rational’ program on the original hardware.

In what sense can you say the difference between rationality and anti-rationality at all exists in the program (or in humans), rather than in the model of them, when the same wires are both rational and anti-rational? I believe the same dilemma holds for indifferent planners. It doesn't seem like reward functions of the type your paper talks about are a real thing, at least in a sense independent of interpretation, so it makes sense that you struggle to distinguish them when they aren't there to distinguish.

---

I am tempted to base an argument off the claim that misery is avoided because it's bad rather than being bad because it's avoided. If true, this shortcuts a lot of your concern: reward functions exist only in the map, where numbers and abstract symbols can be flipped arbitrarily, but in the physical world these good and bad states have intrinsic quality to them and can be distinguished meaningfully. Thus the question is not how to distinguish indistinguishable reward functions, but how to understand this aspect of qualitative experience. Then, presumably, if a computer could understand what the experience of unhappiness is like, it would not have to assume our preferences.

This doesn't help solve the mystery. Why couldn't a species evolve to maximise its negative internal emotional states? We can't reasonably have gotten preference and optimization lined up by pure coincidence, so there must be a reason. But it seems like a more reasonable stance to shove the question off into the ineffable mysteries of qualia than to conflate it with a formalism that seems necessarily independent of the thing we're trying to measure.

Comment by veedrac on Kenshō · 2018-01-21T15:12:55.970Z · LW · GW

I believe I understood this metaphor. However, it seems to me this isn't a good place to be, since I predict the metaphor is only useful to ground discussion about the thing that's actually taking place. It is that second step that hasn't worked.

Let's flip this around. How do you know when someone is Looking? Is there a way to do so based on external behaviours? What is your equivalent of the following?

"I'm watching you stare at your phone. If your Looking, your head would be up and your eyes would be pointed at me."

You give a good example with the hair clipper, but I don't know how much, if at all, that relates to Looking. If it is closely related I have a few follow-up questions that probably get to the crux of the issue I specifically am stuck on.

Comment by veedrac on Kenshō · 2018-01-20T12:28:51.535Z · LW · GW

The exercise in falsification refers to Conor's last sentence, only no longer applied specific to him.

I'm wondering how you would falsify the claim (that I predict you will make and be justified in making) that I don't get it.

When I say I am confused about what I am meant to be confused about, I mean that I'm failing to identify as Alex. He at least has a command he knows he cannot do (Look above that! / That's the top.), whereas I am stuck in the realm of unknown unknowns.

Your paragraph on the "it" from your kenshō is a much closer description of how I currently feel than the inverse is; I don't understand what it would mean for this claim to be untrue except in the sense that it "not being okay" accurately describes external reality. But that feels like it falls into the same trap that your bullet points are said to, only in the opposite direction.

Your later post about the benefits does this more clearly; with absolute exception of the point about energy, and potential exception of the last, the other points seem oddly accurate representations of the difference between me and the average person. But I don't think I am enlightened.

So, on a concrete level, this comes through as the question of how would you differentiate someone who was born enlightened from someone who was not, but is perhaps mistakenly labelling a shallow surface immitation?

Comment by veedrac on Kenshō · 2018-01-20T02:25:45.672Z · LW · GW

I would be interested in how you would falsify it regardless. I am confused about what I am meant to be confused about (what does it mean for it to not be okay?) and I suspect the excersise would remedy that.

Comment by veedrac on Making Exceptions to General Rules · 2018-01-19T01:52:56.810Z · LW · GW

I am a very strong satisficer, in direct conflict to my moral system which would rather I maximise, so I live under the general understanding that I'm very far from my ideal.

Comment by veedrac on Making Exceptions to General Rules · 2018-01-18T01:58:35.540Z · LW · GW

I formed a similar argument around vegetarianism; I predicted that it is easier for me to draw a hard line than it is to reconsider that line on a case by case basis. Rational me is more than capable of distinguishing between lobster and cow, but there is a lot of power in being able to tell myself to just eat the things with the label.

This is an extreme overapproximation but, given the moral stakes and my general unreliability, the successful results seem sufficient justification.