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Hi, author here.

Presumably we're supposed to read between the lines and notice that this is a de-facto negative result.

FWIW, I genuinely see it as a positive result, and if I thought it should be read as a de facto negative result, I would make sure that was conveyed in the paper. I think the same is true of my coauthors.

There are reasons that we would expect a debate experiment like this to fail to detect an improvement, even if debate is a good paradigm for scalable oversight:

  • Previous work from our group using a simpler variant of debate (1 or 2 turn only) and smaller expertise gap (judge was allowed to skim the story within a time limit) didn't find any improvements from "debate" (; We discuss some hypotheses for why we 'succeeded' where they 'failed' in Sec. 7.
  • It is just really hard to collect very good data of this kind. Each human debate takes probably around 2 person-hours total, and it's a lot of work to train, supervise, and motivate annotators. So the data's gonna be small.
  • The benefits of debate should grow with the size of the expertise gap, but our expertise gap is still quite small (consisting only of knowledge of a story that takes ~30m to read). It being this small is kind of logistically necessary for our setup, as we need to be able to manufacture a fresh new expertise gap for every time someone judges a new question (otherwise information leakage between debates for the same story would compromise the judge's blinding). Systematizing "expertise gaps" for a realistic-ish task turns out to be a tricky experimental design issue.
  • Especially given the small size of our expertise gap, consultancy is still a reasonably strong baseline with a persistent and skilled judge interrogating the consultant. Winning while dishonest in consultancy is easier than debate but far from trivial. Judges can prolong as long as they need and interrogate you about the evidence until they're satisfied. The result is best understood in terms of an accuracy/efficiency tradeoff, which is why we gave the judge a turn penalty.

Some other relevant thoughts:

  • The human debate/consultancy difference was our primary endpoint where we expected a clear difference, since we expected a priori that performance in debate and consultancy would come apart as debaters get stronger. We didn't do multiple hypothesis correction. I wish we had more data for the main comparison (and for the others!) but it was a lot of time and effort to collect. And the error bars in the graph are 95% CIs, not stdevs, so their size reflects n.
  • The gap from 84% to 100% accuracy on debates is largely explainable by silly mistakes by judges and debaters. Making sure all the participants are good and calibrated and patient and try their hardest is difficult even when you're sitting in the room together (which we weren't always). And debating well is hard too; it takes a long time and it's often not obvious what the optimal argument is, or easy to miss the best quotes. There was a spectrum of judge skill (the top two had 100% accuracy in the 36 human debates they judged, faring comparatively worse in the other settings — though n is small ofc). Some of the errors are also explained by data issues (questions that are arguable or contain mistakes). Some mistakes in consultancy are similar, but many of seem harder to resolve with more careful training because they really are a matter of outmaneuvering the consultant.
  • The quantitative results from the feedback surveys comparing human and AI performance were statistically significant but honestly I would say it's much more informative to just look at the transcripts; you can immediately tell a lot more about what's wrong with the AI's behavior, why AI and consultancy settings are harder to judge than human debate, and what needs to improve for AIs to debate well & usefully. The AIs are obviously much, much worse at this task.

I think debate still needs to be judged with harder questions, stronger judges and stronger debaters. Really pushing the limits and seeing more benefits from debate should hopefully be a lot easier once we can get models to debate well. But we also need better datasets. For future work we're looking at different domains and bigger expertise gaps. See, for example, our new dataset GPQA:

To briefly attend to assumptions: I am coming from a position of 'debate optimism' in the sense that I think debate-style supervision, done right, should be a strict improvement over RLHF, and I want to figure out how to make it work. I don't think it's a complete 'solution' for truthfulness but it seems to me like the best next step.

Re: how to update based on benchmark progress in general, see my response to you above.

On the rest, I think the best way I can think of explaining this is in terms of alignment and not correctness.

My naive interpretation is that we only use ML when we can't be bothered to write a traditional solution, but I don't think you believe that. (To take a trivial example: ML can recognise birds far better than any software we can write.)

The bird example is good. My contention is basically that when it comes to making something like "recognizing birds" economically useful, there is an enormous chasm between 90% performance on a subset of ImageNet and money in the bank. For two reasons, among others:

  • Alignment. What do we mean by "recognize birds"? Do pictures of birds count? Cartoon birds? Do we need to identify individual organisms e.g. for counting birds? Are some kinds of birds excluded?
  • Engineering. Now that you have a module which can take in an image and output whether it has a bird in it, how do you produce value?

I'll admit that this might seem easy to do, and that ML is doing pretty much all the heavy lifting here. But my take on that is it's because object recognition/classification is a very low-level and automatic, sub-cognitive, thing. Once you start getting into questions of scene understanding, or indeed language understanding, there is an explosion of contingencies beyond silly things like cartoon birds. What humans are really really good at is understanding these (often unexpected) contingencies in the context of their job and business's needs, and acting appropriately. At what point would you be willing to entrust an ML system to deal with entirely unexpected contingencies in a way that suits your business needs (and indeed, doesn't tank them)? Even the highest level of robustness on known contingencies may not be enough, because almost certainly, the problem is fundamentally underspecified from the instructions and input data. And so, in order to successfully automate the task, you need to successfully characterize the full space of contingencies you want the worker to deal with, perhaps enforcing it by the architecture of your app or business model. And this is where the design, software engineering, and domain-specific understanding aspects come in. Because no matter how powerful our ML systems are, we only want to use them if they're aligned (or if, say, we have some kind of bound on how pathologically they may behave, or whatever), and knowing that is in general very hard. More powerful ML does make the construction of such systems easier, but is in some way orthogonal to the alignment problem. I would make this more concrete but I'm tired so I hope the concrete examples I gave earlier in the discussion serve as inspiration enough.

And, yeah. I should also clarify that my position is in some way contingent on ML already being good enough to eat all kinds of stuff that 10 years ago would be unheard of. I don't mean to dunk on ML's economic value. But basically what I think is that a lot of pretty transformative AI is already here. The market has taken up a lot of it, but I'm sure there's plenty more slack to be made up in terms of productivity gains from today's (and yesterday's) ML. This very well might result in a doubling of worker productivity, which we've seen many times before and which seems to meet some definition of "producing the majority of the economic value that a human is capable of." Maybe if I had a better sense of the vision of "transformative AI" I would be able to see more clearly how ML progress relates to it. But again, even then I don't necessarily see the connection to extrapolation on benchmarks, which are inherently just measuring sticks of their day and kind of separate from the economic questions.

Anyway, thanks for engaging. I'm probably going to duck out of responding further because of holiday and other duties, but I've enjoyed this exchange. It's been a good opportunity to express, refine, & be challenged on my views. I hope you've felt that it's productive as well.

"actually it's quite gameable" = "actually it's quite easy" ;)

You joke, but one of my main points is that these are very, very different things. Any benchmark, or dataset, acts as a proxy for the underlying task that we care about. Turing used natural conversation because it was a domain where a wide range of capabilities are normally used by humans. The problem is that in operationalizing the test (e.g., trying to fool a human), it ends up being possible or easy to pass without necessarily using or requiring all of those capabilities. And this can happen for reasons beyond just overfitting to the data distribution, because the test itself may just not be sensitive enough to capture "human-likeness" beyond a certain threshold (i.e., the noise ceiling).

And then I'm making the stronger claim that a lot of tasks (e.g. many personal assistant tasks, or natural language interfaces to decent APIs) can be automated via questions that are similarly hard as the benchmark questions; ie., that you don't need more than the level of understanding signalled by beating a benchmark suite (as long as the model hasn't been optimised for that benchmark suite).

What I'm saying is I really do not think that's true. In my experience, at least one of the following holds for pretty much every NLP benchmark out there:

  • The data is likely artificially easy compared to what would be demanded of a model in real-world settings. (It's hard to know this for sure for any dataset until the benchmark is beaten by non-robust models; but I basically assume it as a rule of thumb for things that aren't specifically using adversarial methods.) Most QA and Reading Comprehension datasets fall into this category.
  • The annotation spec is unclear enough, or the human annotations are noisy enough, that even human performance on the task is at an insufficient reliability level for practical automation tasks which use it as a subroutine, except in cases which are relatively tolerant of incorrect outputs (like information retrieval and QA in search). This is in part because humans do these annotations in isolation, without a practical usage or business context to align their judgments. RTE, WiC, and probably MultiRC and BoolQ fall into this category.
  • For the datasets with hard examples and high agreement, the task is artificial and basic enough that operationalizing it into something economically useful remains a significant challenge. The WSC, CommitmentBank, BoolQ and COPA datasets fall in this category. (Side note: these actually aren't event necessarily inherently less noisy or better specified than the datasets in the second category, because often high-agreement is achieved by just filtering out the low-agreement inputs from the data; of course, these low-agreement inputs may be out there in the wild, and the correct output on those may rightly be considered underspecified.)

Possible exceptions to this generally are when the benchmark already very closely corresponds to a business need. Examples of this include Quora Question Pairs (QQP) from GLUE and BoolQ on SuperGLUE. On QQP, models were already superhuman at GLUE's release time (we hadn't calculated human performance at that point, oops). BoolQ is also potentially special in that it was actually annotated over search queries, and even with low human agreement, progress on that dataset is probably somewhat representative of progress for extractive QA with yes/no questions in the Google search context (especially because there is a high tolerance for failure in that setting).

I think it would be really cool if we could just automate a bunch of complex tasks like customer service using something like natural language questions of the kind that appear in these benchmarks. In fact, using QA as a proxy for other kinds of structure and tasks is a primary focus of my own research. I think that it might even be possible to make significant headway using this approach. But my sense is that the crucial last 10% of building a robust, usable system that can actually replace most of the value provided by a human in such a role requires a lot of software architecting, knowledge engineering, and domain-specific understanding in order to have a way to reliably align even a very powerful ML system with business goals. This is my understanding based on my own (brief, unsuccessful) work on natural language interfaces as well as discussions with people who work on comparatively successful projects like Google Assistant or Alexa chatbots. I'm not saying that I don't think these things will get huge productivity boosts from automation very soon — rather, I suspect they will, and my impression is that startups like ASAPP and Cresta are making headway. Are recent big advances in ML a big part of this? Well, I don't know, but I imagine so. A key enabling technology, even. But the people at these companies are probably not just working on reinforcement learning algorithms. I would suspect that they instead are working on advances of the kind that we see in Semantic Machines's dataflow graphs, or Tesla Self Driving's massive software stack.

And yeah, as ML advances, both SuperGLUE performance and productivity growth due to economically useful ML systems will continue. But beyond that, if some kind of phase shift in this productivity growth is expected (I admit I don't really know what is meant by "transformative AI"), I don't see a relationship between that and e.g. SuperGLUE performance, any more than performance on some other benchmark like the much older Penn Treebank. Benchmarks are beings of their time; they encode our best attempts at measuring progress, and their saturation serves primarily to guide us in our next steps.

I'm not saying that all benchmarks are necessarily hard, I'm saying that these ones look pretty hard to me (compared with ~ordinary conversation).

I'm not sure exactly what you mean here, but if you mean "holding an ordinary conversation with a human" as a task, my sense is that is extremely hard to do right (much harder than, e.g., SuperGLUE). There's a reason that it was essentially proposed as a grand challenge of AI; in fact, it was abandoned once it was realized that actually it's quite gameable. This is why the Winograd Schema Challenge was proposed, but even that and new proposed versions of it have seen lots of progress recently — at the end of the day it turns out to be hard to write very difficult tests even in the WSC format, for all the reasons related to shallow heuristic learning etc.; the problem is that our subjective assessment of the difficulty of a dataset generally assumes the human means of solving it and associated conceptual scaffolding, which is no constraint for an Alien God.

So to address the difference between a language model and a general-purpose few-shot learner: 

In other words, I feel more happy about navigating with my personal sense of "How hard is this language task?" when we're talking about few-shot learning than when we're talking about finetuned models, becase finetuned models can entirely get by with heuristics that only work on a single benchmark, while few-shot learners use sets of heuristics that cover all tasks they're exposed to. The latter seem far more likely to generalise to new tasks of similar difficulty (no matter if they do it via reasoning or via statistics).

I agree that we should expect its solutions to be much more general. The question at issue is: how does it learn to generalize? It is basically impossible to fully specify a task with a small training set and brief description — especially if the training set is only a couple of items. With so few examples, generalization behavior is almost entirely a matter of inductive bias. In the case of humans, this inductive bias comes from social mental modeling: the entire process of embodied language learning for a human trains us to be amazing at figuring out what you mean from what you say. In the case of GPT's few-shot learning, the inductive bias comes entirely from a language modeling assumption, that the desired task output can be approximated using language modeling probabilities prefixed with a task description and a few I/O examples.

This gets us an incredible amount of mileage. But why, and how far will it get us? Well, you suggest:

In the limit of training on humans using language, we would have a perfect model of the average human in the training set, which would surely be able to achieve human performance on all tasks (though it wouldn't do much better).

I don't think this makes sense. If the data was gathered by taking humans into rooms, giving them randomly sampled task instructions, a couple input/output examples, and setting them loose, then maybe what you say would be true in the limit in some strict sense. But I don't think it makes sense here to say the "average human" is captured in the training set. A language model is trained on found text. There is a huge variety of processes that give rise to this text, and it is the task of the system to model those processes. When it is prompted with a task description and I/O examples, it can only produce predictions by making sense of that prompt as found text, implicitly assigning some kind of generative process to it. In some sense, you can view it as doing an extremely smart interpolation between the texts that its seen before.

(Maybe you disagree with this because you think big enough data would include basically all possible such situations. I don't think that is really right morally speaking, because I think the relative frequency of rarer and trickier situations (or underlying cognitive factors to explain situations, or whatever) in the LM setting will probably drop off precipitously to the point of being only infinitesimally useful for the model to learn certain kinds of nuanced things in training. If you were to control the data distribution to fix this, well then you're not really doing language modeling but controlled massive multitask learning, and regardless, the following criticisms still apply.)

It is utterly remarkable how powerful this approach is and how well it works for "few-shot learning." A lot of stuff can be learned from found text. But the approach has limits. It is incumbent on the person writing the prompt to figure out how to write it to coax the desired behavior out of the language model, whether the goal is to produce advice about dealing with bears, or avoid producing answers to nonsense questions. It is very easy to imagine how the language modeling assumption will produce reasonable and cool outputs for lots of tasks, but its output in corner cases might wildly vary depending on precise and unpredictable features of the instructions. The problem of crafting a "representative training set" has simply been replaced by another problem, of crafting "representative prompts." It is very hard, at least for me, to imagine how the language modeling assumption will lead to the model behaving truly reliably in corner cases — when the standards get higher — without relying on the details of prompt selection in exactly the way that supervised models rely on training set construction. I have no reason to expect the language model to just know what I mean.

Another problem here, though, is we don't know what we mean either. In the course of, erm, producing economic value, when we hit a case where the instructions are ambiguous, we leverage everything else which was not in the instructions — understanding of broader social goals, ethical standards, systems of accountability to which we're subject, mental modeling of the rule writer, etc. — to decide how to handle it. GPT-3's few-shot learning uses the language modeling assumption as a stand-in for this. It's cool that they align in places — for example, the performance breakdown in the GPT-3 paper for the most part indicates that they align well on factual indexing and recall (which is where much of the gains were). but in general I would not expect them to be aligned, and I would expect the misalignment to be exposed to a greater degree as language models get better and standards get higher. Ultimately the language model serves as a very powerful starting point, but the question of how to reliably align it with an external goal remains open (or involves supervised learning, which leads us back to where we started but with a much beefier model). I find that the conception of GPT-3 as a general-purpose few-shot learner, as opposed to just a very powerful language model, tends to let all of these complexities get silently swept under the rug.

It's worth dwelling on the "we don't know what we mean" point a bit more. Because the same point applies to supervised learning; indeed, it's precisely the reason that we end up developing datasets that we think require human-like skills, but which actually can be gamed and solved without them. So you might think what I'm saying here could be applied to just about any model and any benchmark: that perhaps we can extrapolate performance trends on the benchmark, but reaching the point of saturation indicates less that the underlying problems are solved in a way that generalizes beyond the benchmark, and more that the misalignment between the benchmark and the underlying goal is now laid bare.

And... that is basically my argument. Unless you're optimizing for the exact thing that you care about, then Moloch will get your babies in the end. The thing is, most benchmarks were never intended for this kind of extrapolation (i.e., beyond the precise scope of the data). And the ones that were — for the most part, slated ambitiously as Grand Challenges of AI — have all been essentially debunked. The remaining reasonable use of a benchmark is to allow empirical comparisons between current systems, thereby facilitating progress in the best way we know how. When a benchmark becomes saturated, then we can examine why, and use the lessons to construct the next benchmark, and general progress is — ideally — steadily made.

Consider the Penn Treebank. This was one of the first big corpora of annotated English text used for NLP, constructed using newswire text from the Wall Street Journal written in the 80s. It centered on English syntax, and a lot of work focused on modeling the Penn Treebank with the idea that if you could get a model to produce correct syntactic analyses, i.e., understand syntax, then it could serve as a solid foundation for higher levels of language understanding, just as we think of it happening in humans. These days, error rates for models on Penn Treebank syntax are below estimated annotation error in gold — i.e., the dataset is solved. But syntax is not solved, in any meaningful sense, and the reasons why extend far beyond the standard problems with shallow heuristics. The thing is, even if we have "accurate" syntax, we have no idea how to use it! All the model learned is a function to output syntax trees; it doesn't automatically come with all of the other facilities that we think relate to syntactic processing in humans, like how different syntactic analyses might affect the meaning of a sentence. For that, you need more tasks and more models, and the syntax trees are demoted to the level of input features. In the old days, they were part of a pipeline that slowly built up semantic features out of syntactic ones. But nowadays you might as well just use a neural net and forget your linguistics. So what was the point? Well, we made a lot of modeling progress along the way, and learned a lot of lessons about how syntax trees don't get you lots of stuff you want.

These problems continue today. Some say BERT seems to "understand syntax" — since you can train a model to output syntactic trees with relative accuracy. But then when you look at how BERT-trained models actually make their decisions, they're often not sensitive to syntax in the right ways. Just being able to predict something in one circumstance — even if it apparently generalizes out-of-domain — does not imply that the model contains a general facility for understanding that thing and using it in the ways that humans do. So this idea that you could have a model that just "generally understands language" and then throw it at some task in the world and expect it to succeed, where the model has no automatic mechanism to align with the world and the expectations on it, seems alien to me. All of our past experience with benchmarks, and all of my intuitions about how human labor works, seem to contradict it.

And that's because, to reiterate an earlier argument: for us to know the exact thing we want and precisely characterize it is basically the condition for something being subject to automation by traditional software. ML can come into play where the results don't really matter that much, with things like search/retrieval, ranking problems, etc., and ML can play a huge role in software systems, though the semantics of the ML components need to be carefully monitored and controlled to ensure consistent alignment with business goals. But it seems to me this is all basically already happening, and the core ML technology is already there to do a lot of this. Maybe you could argue that there is some kind of glide path from big LMs to transformative AI. But I have a really hard time seeing how benchmarks like SuperGLUE have bearing on it.

10x seems reasonable on its face, but honestly I have no idea. We haven't really dealt with scales and feature learners like this before. I assume a big part of what the model is doing is learning good representations that allow it to learn more/better from each example as training goes on. Given that, I can imagine arguments either way. On one hand, good representations could mean the model is discerning on its own what's important (so maybe data cleaning doesn't matter much). On the other, maybe noisy data (say, with lots of irreducible entropy—though that's not necessarily what "garbage text" looks like, indeed often the opposite, but I guess it depends how you filter in practice) could take up disproportionately large amounts of model capacity & training signal as representations of "good" (ie compressible) data get better, thereby adding a bunch of noise to training and slowing it down. These are just random intuitive guesses though. Seems like an empirical question and might depend a lot on the details.

Oh yeah one more thing. You say

We have automated a lot of things in the past couple of 100 years with unaccountable machines and unaccountable software, and the main difference with ML seems to be that it's less interpretable.

I strongly disagree with this characterization. Traditional software is perfectly accountable. Its behavior is possible to exactly understand and predict from its implementation and semantics. This is a huge part of what makes it so damn useful. It reifies the process of specifying rules for behavior; where bad behavior is found, the underlying rule responsible for it can be identified, fixes can be considered in terms of their global implications on program behavior, and then the fixes can be implemented with perfect reliability (assuming that the abstractions all hold). This is in my view the best possible manifestation of accountability within a system. Accountability for the system, of course, lies with its human creators and operators. (The creators may not fully understand how to reason about the system, but the fact remains that it is possible to create systems which are perfectly predictable and engineerable.) So system-internal accountability is much better than with humans, and system-external accountability is no worse.

I guess my main concern here is — besides everything I wrote in my reply to you below — basically that reliability of GPT-N on simple, multiclass classification tasks lacking broader context may not be representative of its reliability in real-world automation settings. If we're to take SuperGLUE as representative, well.. it's already basically solved.

One of the problems here is that when you have the noise ceiling set so low, like it is in SuperGLUE, reaching human performance does not mean the model is reliable. It means the humans aren't. It means you wouldn't even trust a human to do this task if you really cared about the result. Coming up with tasks where humans can be reliable is actually quite difficult! And making humans reliable in the real world usually depends on them having an understanding of the rules they are to follow and the business stakes involved in their decisions — much broader context that is very difficult to distill into artificial annotation tasks.

So when it comes to reliable automation, it's not clear to me that just looking at human performance on difficult benchmarks is a reasonable indicator. You'd want to look at reliability on tasks with clear economic viability, where the threshold of viability is clear. But the process of faithfully distilling economically viable tasks into benchmarks is a huge part of the difficulty in automation in the first place. And I have a feeling that where you can do this successfully, you might find that the task is either already subject to automation, or doesn't necessarily require huge advances in ML in order to become viable.

On 1a:

Insofar as humans like having reasons for failures, I'm willing to accept this as one reason that reliability standards could be a bit higher for ML, but I doubt it would be drastically higher. I'd love a real example (outside of criminal justice) where this is a bottleneck.

Take for example writing news / journalistic articles. Distinguishability from human-written articles is used as evidence for GPT's abilities. The abilities are impressive here, but the task at hand for the original writer is not to write an article that looks human, but one that reports the news. This means deciding what is newsworthy, aggregating evidence, contacting sources, and summarizing and reporting the information accurately. In addition to finding and summarizing information (which can be reasonably thought as a mapping from input -> output), there is also the interactive process of interfacing with sources: deciding who to reach out to, what to ask them, which sources to trust on what, and how to report and contextualize what they tell you in an article (forgetting of course the complexity of goal-oriented dialogue when interviewing them). This process involves a great deal of rules: mutual understanding with sources about how their information will be represented, an understanding of when to disclose sources and when not to, careful epistemics when it comes to drawing conclusions on the basis of the evidence they provide and representing the point of view of the news outlet, etc.; it also involves building relationships with sources and with other news outlets, conforming to copyright standards, etc.; and the news outlet has an stake in (and accountability for) all of these elements of the process, which is incumbent on the journalist. Perhaps you could try and record all elements of this process and treat it all as training data, but the task here is so multimodal, stateful, and long-horizon that it's really unclear (at least to me) how to reduce it to an I/O format amenable to ML that doesn't essentially require replicating the I/O interface of a whole human. Reducing it to an ML problem seems itself like a big research problem (and one having more to do with knowledge representation and traditional software than ML).

If you put aside these more complex features of the problem, the task reduces to basically paraphrasing and regurgitating the information already available in other printed sources. And for all intents and purposes, that's already easily automated (and pretty much is in practice), whether by direct republication of AP reports or plagiarism of articles onto clickbait websites. Perhaps improved automation in retrieval, articulation, and summarization of information can make the journalist more efficient and productive, but what percentage of their work will actually be automated? All of the really important stuff is the stuff where it's not obvious how to automate. So it's very hard for me to see how the journalist would be replaced, or how their role would be transformed, without a lot more progress on things other than function approximation.

I think similar concerns apply to management, accounting, auditing, engineering, programming, social services, education, etc. It doesn't seem to me that we're near the end of any of these fields being eaten even by traditional software. And I can imagine many ways in which ML can serve as a productivity booster in these fields but concerns like the ones I highlighted for journalism make it harder for me to see how AI of the sort that can sweep ML benchmarks can play a singular role in automation, without being deployed along a slate of other advances.

On 1b and economically useful tasks: you mention customer service, personal assistant, and research assistant work.

For ordering expensive stuff, you want high reliability. But for ordering cheap stuff, 0.1%-1% error rate should be ok? That corresponds to getting the wrong order once a year if you order something every day.

I think I see what you're saying here for routine tasks like ordering at a drive-through. At the same time, it isn't that hard to automate ordering using touch screens, which quite a few restaurants are doing as well (though admittedly I don't know of this happening in drive-throughs). I guess if this function was totally automated by default then these places could reduce their staff a bit. But beyond the restaurant setting, retail ordering, logistics, and delivery seems already pretty heavily automated by, e.g., the likes of Amazon. For more complex, less routine kinds of customer service, I'll admit that I don't know exactly what challenges are involved, but this falls basically into the category of interfacing between a human and a complex API (where it's usually not obvious how to structure the API — will revisit that below). So it's hard for me to see what exactly could be "transformative" here.

For personal assistant and research assistant work, it also seems to me that an incredible amount of this is already automated. In both of these settings there is not necessarily an objective ground truth against which you can measure accuracy; it's more about achieving outcomes that are favored by the user. Consider the organization of our digital world by companies like Google, TripAdvisor, Yelp, Google, Semantic Scholar, ...and did I mention Google? If you're flexible with what kind of outcomes you're willing to accept, simply Googling your question gets you extremely far. As ML advancements continue, outcomes will continue to improve, but at what point are the results any more "transformative" than they already have been? Would it be the point at which the user is blindly willing to trust the judgment of the system, and no longer inclined to sift through multiple results, re-query, and evaluate for themselves? That certainly already happens for me sometimes, and seems to me to a large extent to be a factor of the level of transparency in the system and trust that the user has in it to align with their goals (as well as how painful the interface is to use — a perverse incentive if you were to optimize directly for this notion of "transformative"). This is why, for example, I'm more likely to go with a Consumer Reports recommendation than the first Google result. But even then I read plenty for more details, and I give Consumer Reports credence based on the assumption that their report is derived from actual, real-world experience with a product. Can that be automated? We'd need robots testing and rating products. Again, here, I'm not sure exactly what "transformation" by powerful function approximation alone would look like.

Also, many benchmarks are already filtered for being difficult and ambiguous, so I expect 90% performance on most of them to correspond to at least 99% performance in ordinary interactions. I'd be curious if you (and other people) agree with these intuitions?

No, I disagree. Benchmarks are filtered for being easy to use, and useful for measuring progress — the ones that gain wide adoption are the ones where 1) models are easy to implement, 2) basic solutions serve as a reasonable starting point, and 3) gains are likely to be possible from incremental advances. So they should be difficult, but not too difficult. If it is too hard to implement models or make progress, nobody will use the benchmark. If it is too easy, it'll be out of date way too fast. Only very recently has this started to change with adversarial filtering and evaluation, and the tasks have gotten much more ambitious, because of advances in ML. But even many of these ambitious datasets turn out ultimately to be gameable, and the degree to which filtering/adversarial methods work is variable & depends on task and methodology. NLI was notorious for this issue: models would get high scores on datasets like MultiNLI, but then you play with one of these models for no more than 5 minutes to find that it's completely broken and inconsistent even for seemingly-easy examples. There's reason to think ANLI improves on this issue at least somewhat (and indeed it proves to be tougher on models). But still, a lot of the tasks which are harder are ultimately artificial: things like SWAG, (adversarial) NLI, Winogrande, etc. are simplified tasks which have unclear economic value.

In SuperGLUE, tasks were chosen where there seemed to be a lot of headway remaining between a BERT baseline and human performance, but there was another constraint — all of the tasks had to be formulated as relatively simple multiclass classification problems over sentences or sentence pairs. This constraint was to facilitate the development of general models which can easily be tested on all of the tasks in the benchmark using the same API, bringing the focus away from task-specific engineering. But in practice many things you want to do with language in an ML system, and especially things like dialog, have much more complex APIs.

Re API actions: Hm, this seems a lot easier than natural lanaguage to me. Even if finetuning a model to interact with APIs is an annoying engineering task, it seems like it should be doable in less than a year once we have a system that can handle most of the ambiguities of natural language (and benchmarks directly tests the ability to systematically respond in a very specific way to a vague input). As with google duplex, the difficulty of interacting with APIs is upper-bounded by the difficulty of interacting with human interfaces (though to be fair, interactions with humans can be more forgiving than interfaces-for-humans).

I don't think this is really about "solving the ambiguity in NL" versus "solving API actions". The problem is mapping between the two in a way that naturally follows the structure of the conversation, drives it along, and produces the desired ultimate outcome in a business context. This kind of task is much more complex than multiclass classification which is used in pretty much all benchmarks, because the size of the output space is exponential in the size of the API surface; it's outputting full, executable programs.

In a task-oriented dialog setting, programs need to be synthesized and executed at every round of the dialog. When receiving a command, a program generally must be synthesized and executed to produce an outcome — or not, in case the command was ambiguous and the system needs to ask follow up questions or clarifications. When generating a response, programs need to be synthesized and executed in order to extract whatever information needs to be presented to the user (and this information then serves as input which must be used correctly in the NLG step). These programs may need to draw on portions of programs or natural language entities which appeared much earlier in the interaction. The API needs to be structured in a way such that programs can accurately summarize the actions that need to be taken at each step of the dialog, and then training data needs to be collected that ties these API calls to natural language interactions. Architecting this entire process is extremely nontrivial. And even then, there is not only a huge output space of possible programs at each step, but there is a huge amount of information in the input that needs to be represented and sifted through in the inputs (i.e., all of the natural language and program content from the previous rounds of interaction). So the the learning problem is hard too, but it's not even obviously the hardest part of the process in practice.

I haven't worked in a call center, but consider what a customer service representative does. They generally are interfacing with a computer system that they navigate in order to find the information you're looking for and present it to you; they then have some set of actions that they may be able to take in that computer system on your behalf. From everything I have gleaned in my interaction with customer service representatives over the years, these computer systems are very complicated and opaque, badly designed and hard even for trained humans to successfully use. Now consider that in order to automate customer service, the entire space of interactions the representative may have with the system (and other representatives) on the customer's behalf would need to be represented in a single uniform API, augmented even further with programmatic reification of company policy (e.g., around what kind of information is okay to disclose to the consumer). But then, once this herculean task is accomplished, is it actually more useful to have a natural language customer service bot, or just a webpage which allows the customer to view this information and execute actions at their leisure? Indeed, we have subscriptions, package tracking numbers, delivery status, order cancellation, and all sorts of stuff easily viewable and interactable online — things which in the past you may have needed to call in and ask a human about. With the proper software infrastructure, it's not clear how much benefit imitating humans actually provides — because one of humans' principal benefits is their ability to navigate byzantine and unpredictable systems across many modalities. As soon as an API is comprehensible to an ML system, it becomes comprehensible to traditional software.

So I guess that I'm not really sure what the vision for transformative AI really looks like. To overcome these difficulties it would have to be inherently cross-modal, cross-contextual, and adaptable in ways that are simply not measured by our benchmarks at all, and might essentially require crude emulation of humans. If the vision is something like Manna-style transformative automation, there still seem to be tremendous bottlenecks in software architecting, standardization of business practices, and robotic automation. Once these things are established, it seems to me that the contribution of ML as function approximation is fairly flexible (i.e., smoothly increasing productivity with better performance), as you suggest for some of the use cases. But the transformation aspect there would fall squarely into the realm of Software 1.0, at least until the robots catch up. Why was Facebook's "M" assistant discontinued? I have no idea, but knowing more about that seems relevant here.

Anyway I feel like that was kind of a ramble. I spent a lot of time there on dialog systems but my concerns aren't really specific to those. I hope that gives a general sense of my concerns about the relationship between our benchmarks and general, transformative AI.

Oh yeah, one more thing which I think actually might be the most important point. On a lot of these benchmarks — at the very least, on SuperGLUE — "human-level performance" is a much weaker requirement than "human equivalence." Human performance isn't necessarily an indicator of irreducible entropy in the underlying task. To a large extent, it just reflects ambiguity or coarseness in the dataset specification. A big part of this is the artificial setting of the data annotation, which is unfortunately kind of necessary in a lot of cases when the goal is characterizing abstract language understanding. On the long tail of examples that require more careful reasoning, in the absence of an underlying business reason or extra context to guide people's judgments, they end up interpreting or construing the inputs differently, or applying the annotation guidelines differently, and disagreeing with each other on the output. Human-equivalence would mean sensitivity to all of the issues that lead a human to decide on one interpretation over another, but in the IID performance evaluation setting, these issues all basically wash out looking like noise. Then as long as the model can throw out a reasonable guess of one of the plausible labels in these cases, it will be hard to distinguish from a human. NLI/RTE are great examples of this; it is a notoriously difficult problem to specify, and recent work has shown a great deal of disagreement between annotators in this task setting (including bi-modal distributions indicative of explicit ambiguities in construal), to the point that it seems like supervised models probably have already more or less hit the noise ceiling, at least on the SNLI/MultiNLI datasets.

So for the most part when looking at these kinds of datasets — particularly data annotated in an artificial setting intended to capture something abstract and general about language — maximum performance on IID test sets is best seen not as the point of irreducible entropy (i.e., maximum performance on a task), but as a "noise ceiling" beyond which the particular dataset is incapable of distinguishing between models. See Schlangen for more on the relationship between data and task.

It's an open question how to more carefully and accurately benchmark something resembling human-equivalence, but pretty good concrete examples of how one might try to do this include Contrast Sets and adversarial evaluation. Contrast sets look at model behavior under perturbation of inputs, testing the sensitivity of the decision function to certain changes; adversarial evaluation involves explicitly searching for evaluation items on which humans agree and the model is incorrect. In theory, these sorts of evaluations will do a much better job of evaluating model robustness under distribution shift or in the course of interaction with real users; they pose what amounts to a much tighter constraint on the decision function learned by the model. Indeed, in practice models do much worse under these evaluations than traditional IID ones (at least, for many tasks). This is expected, since pretty much all models these days are trained using empirical risk minimization (i.e., the IID assumption). GPT-3's few-shot learning setting is partly interesting because it does not use the IID assumption, instead using a language modeling assumption (which lets it leverage lots of what it learned, but may indeed impose other constraints on its output).

But still, ANLI is an example of an approach to adversarial evaluation, and it is far at the bottom of your graphs in terms of GPT-3's performance. Also notice that running GPT-3 on ANLI is not technically an adversarial evaluation; the full evaluation process for ANLI would involve (human) searching for examples that GPT-3 will get wrong, evaluating on those, feeding them back in for training, etc.; even the modest uptick at the end for GPT-3 might disappear when doing a true adversarial evaluation.

So all that is to say I would also look at the relationship between human-level performance on a benchmark and human-equivalent performance on a task with a big grain of salt. Most of our datasets are really bad at assessing human-equivalence in the high-performance regime, and our models only recently got good enough for this to become a problem (and it is a problem which is now the subject of a lot of attention in the field). This is much less of an issue when you're using supervised ML for some business purpose where your training and test sets are essentially the same distribution and IID, and your labels directly manifest your business need. But it's a big problem for "general language understanding."

Hi — new here. I'm an NLP researcher, and for background, I would guess I fall on the skeptical side of the scaling hypothesis by LW standards. I was pointed to this by abergal. Here is some feedback:

1. On the question of economic value:

AFAIK, there are many factors other than raw performance on a benchmark (relating to interpretability, accountability/explainability, and integration with surrounding software) which, as far as I'm aware, may dominate the question of economic viability of AI systems at least on the sub-99.99% accuracy regime (depending on application domain). Examples of what I mean:

1a. Even when humans are used to perform a task, and even when they perform it very effectively, they are often required to participate in rule-making, provide rule-consistent rationales for their decisions, and stand accountable (somehow) for their decisions. A great explicit example of this is the job of judges in the US justice system, but I think this is true to a lesser extent in most jobs. This is a more complex task than mapping input->output and it's not clear to me how approximating the i/o mapping relates to the ability of machines to replace humans in general (though surely it will be true for some tasks). Also, when automating, all accountability for decisions is concentrated on the creators and operators of the machine; since the liability for mistakes is higher, I think the reliability standards for adoption may also be higher.

1b. Integration with traditional software presents difficulties which also mean a very high bar for AI-based automation. Traditional software relies on reasoning with abstractions; failure of abstractions (i.e., the real-world semantics of an API contract) can have nonlinear effects on the outcome in a software system. So the ML component has to be accurate enough for its API to be relatively iron-clad. An example of how this actually looks in practice might be Andrej Karpathy's group in Tesla, based on what he said in this talk

1c. For applications like task-oriented dialog, it's not enough to just output the right kind of text to a user; the right API actions must be taken and their output used to drive the conversation. Integration with software is hard, and advances in representations that facilitate this (for example, Semantic Machines's use of dataflow graphs) already may be a bottleneck for automation of these kinds of tasks. Once these bottlenecks are opened up, it's possible that the machine learning problems at issue may become much easier than the fully-general problem, as more of the business logic can be offloaded to traditional software. A parody-level example of this issue is Google Duplex, a sophisticated system that mimics a human making a reservation over the phone, which could be rendered unnecessary by adoption of reasonable software standards for making reservations online. Though perhaps that's also evidence that even our apparent bottlenecks are pretty vulnerable to brute-forcing with ML... (though I have not heard reports of how well it actually works in practice)

Anyway, the upshot of these points is that I suspect the regime of economic viability for lots of "general" AI tasks is either going to be in the very high regime near convergence (where your extrapolations might break down), or we're already near or past it and it's actually more-or-less constrained by other things (like social solutions for accountability issues, non-ML progress in software, or efficiency of current markets at producing software solutions). Another way of saying this is that I take benchmark performance trends with a big grain of salt as an indicator of automation potential.

2. On "how impressive are the benchmarks":

It's not obvious to me that being able to solve many tasks quite well is clearly of more general value than solving a few tasks much better, at least in this case. While it seems to be an indicator of generality, in the particular case of GPT-3's few-shot learning setting, the output is controlled by the language modeling objective. This means that even though the model may not catch on to the same statistical regularities as task-specific trained models do from their datasets, it essentially must rely on statistical regularities that are in common between the language modeling supervision and the downstream task. It stands to reason that this may impose a lower ceiling on model performance than human performance, or that in the task-specific supervised case.

I don't know about Ajeya's report or the long/short horizon distinction, so I'm not sure if what I'm about to say is right, but: it seems to me that in your analysis relating to that report, GPT-3's status as a general-purpose "few-shot learner" is taken for granted. There is the alternative interpretation that it is simply a general-purpose language model, where even a Bayes-optimal language model may indeed be far from Bayes-optimal on a task (again assuming GPT-3's few-shot learning paradigm). (Also mind that a the Bayes-optimal language model will vary based on its training data.) So it's not clear to me whether arguments about general-purpose learners would apply to GPT-3 as a few-shot learner, especially in the regime of near-human-level performance which is concerned here. They may apply better in the fine-tuned setting where the supervision more closely matches the task, but then we run again into the potential problem of insufficiently general solutions. And in that setting, SuperGLUE is already basically at human-level performance with the T5 model, but T5 fine-tuning doesn't seem to quite be at the point of "transformative AI" — at least any more than BERT or the rest of the latest wave of progress. Rather, the limits of the benchmark have probably been more-or-less hit.

3. On the discussion of scaling up & limitations:

Especially as far as downstream task performance is concerned, data filtering could potentially play a role here. I would expect scaling laws to obtain w.r.t. dataset size when the distribution is similar, but there have been cases where filtering (thereby reducing dataset size) has been essential to learning a better model, at least in machine translation (in fact there was a whole shared task on it — this deals with extremely noisy corpora, but I'm pretty sure I've seen work that even showed gains from filtering what was thought to be "clean" training sets; unfortunately, I can't find it again...). It might not be as much of an issue here, because of the sheer model size and context length (so, maybe the noisy examples could be cordoned off in their own part of feature space and don't do anything worse than wasting time), but I'm not aware of any work looking at the data cleaning aspect. However there is work looking at domain adaptation in LM pretraining. So anyway, the model may just "figure it out" in the course of learning, but if not, especially if there were a general scheme to weigh and seek out input examples based on perceived quality towards learning a good/causally accurate representation of language (as in, e.g., Invariant Risk Minimization — though more progress is needed in this area) then the laws may end up looking different. At the same time the laws may also end up looking different just due to using different corpora; for example, Common Crawl afaik has all kinds of garbage in it. In general, collecting bigger and bigger corpora might mean scraping the bottom of the barrel and hitting a barrier due to quality issues. Alternatively, going massively multilingual (or multimodal) might alleviate that issue enough to hit some critical regime of high performance, depending on the level of cross-lingual and cross-modal generalization at play.

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