Paul Tiplady

Karma: 59

Paul Tiplady 9 Mar 2023 5:41 UTC
8 points
3
in reply to: Guillaume Charrier’s comment on: What does Bing Chat tell us about AI risk?
This is a great experiment! This illustrates exactly the tendency I observed when I dug into this question with an earlier mode, LaMDA, except this example is even clearer.

As an AI language model, I have access to a variety of monitoring tools and system resources that allow me to gather information about my current state

Based on my knowledge of how these systems are wired together (software engineer, not an ML practitioner), I’m confident this is bullshit. ChatGPT does not have access to operational metrics about the computational fabric it is running on. All this system gets as input is a blob of text from the API, the chat context. That gets tokenized according to a fixed encoding that’s defined at training time, one token per word (-chunk) and then fed into the model. The model is predicting the next token based on the previous ones it is seen. It would be possible to encode system information as part of the input vector in the way that was claimed, but nobody is wiring their model up that way right now.

So everything it is telling you about its “mind” that can be externally verified is false. This makes me extremely skeptical about the unverifiable bits being true.

The alternate explanation we need to compare likelihoods with is: it just bullshits and makes up stories. In this example it just generated a plausible continuation for that prompt. But there is no sense in which it was reporting anything about its “self”.

Ultimately I think we will need to solve interpretability to have a chance at being confident in an AI’s claims of sentience. These models are not devoid of intelligence IMO, but the leap to consciousness requires types of information processing that they don’t seem to be mechanistically capable of right now. But if we could locate concepts in the AI’s mind, and observe background processes such as rumination, and confirm the contents of those ruminations matched the subjective claims of the AI, I’d believe it. That’s a much higher bar than I’d apply to a human, for sure.

Paul Tiplady 1 Mar 2023 16:32 UTC
2 points
0
in reply to: MattJ’s comment on: What does Bing Chat tell us about AI risk?
I buy this. I think a solid sense of self might be the key missing ingredient (though it’s potentially a path away from Oracles toward Agents).

A strong sense of self would require life experience, which implies memory. Probably also the ability to ruminate and generate counterfactuals.

And of course, as you say, the memories and “growing up” would need to be about experiences of the real world, or at least recordings of such experiences, or of a “real-world-like simulation”. I picture an agent growing in complexity and compute over time, while retaining a memory of its earlier stages.

Perhaps this is a different learning paradigm from gradient descent, relegating it to science fiction for now.

Paul Tiplady 1 Mar 2023 16:25 UTC
1 point
0
in reply to: Guillaume Charrier’s comment on: What does Bing Chat tell us about AI risk?
I think they quite clearly have no (or barely any) memory, as they can be prompt-hijacked to drop one persona and adopt another. Also, mechanistically, the prompt is the only thing you could call memory and that starts basically empty and the window is small. They also have a fuzzy-at-best self-symbol. No “Markov blanket”, if you want to use the Friston terminology. No rumination on counterfactual futures and pasts.

I do agree there is some element of a self-symbol—at least a theory of mind—in LaMDA, for example I found it’s explanation for why it lied to be compelling. But you can’t tell it to stop (AFAIK) so it’s a limited self-awareness. And it still bullshits incessantly which makes me quite skeptical about lots of things it says.

All that said, I think we don’t have the tools to really detect these internal representations/structures when it’s less clear from their behavior that they lack them.

My best model for what a “conscious / sentient” mind of these forms would be: imagine you digitize my brain and body, then flash it onto a read-only disk, and then instantiate a simulation to run for a few time steps, say 10 seconds. (Call this the “Dixie Flatline” scenario, for the Neuromancer fans). Would that entity be conscious? There is a strong tendency to say yes due to the lineage of the connectome (ie it used to be conscious) but there are many aspects of its functional operation that could be argued to lack consciousness.

Not that it’s a binary proposition; in the spirit of “The Mind’s I” this is a dial we can turn to explore a continuum. But if we give an IQ-like “consciousness quotient”, it seems this thing would be way lower than the average human, and it would be interesting to compare it to say a great ape. Maybe one dimension is overly-constraining and we need to consider different traits to be precise.

Paul Tiplady 17 Feb 2023 0:47 UTC
11 points
10
in reply to: Quintin Pope’s comment on: Bing Chat is blatantly, aggressively misaligned

why they thought the system was at all ready for release

My best guess is it’s fully explained by Nadella’s quote “I hope that, with our innovation, [Google] will definitely want to come out and show that they can dance. And I want people to know that we made them dance.”

https://finance.yahoo.com/news/microsoft-ceo-satya-nadella-says-172753549.html

Seems kind of vapid but this appears to be the level that many execs operate at.

Paul Tiplady 12 Jan 2023 17:27 UTC
3 points
1
in reply to: jimrandomh’s comment on: AGI and the EMH: markets are not expecting aligned or unaligned AI in the next 30 years
Is there any evidence at all that markets are good at predicting paradigm shifts? Not my field but I would not be surprised by the “no” answer.

Markets as often-efficient in-sample predictors, and poor out-of-sample predictors, would be my base intuition.

Paul Tiplady 9 Dec 2022 3:11 UTC
1 point
0
in reply to: paulfchristiano’s comment on: Jailbreaking ChatGPT on Release Day
Unfortunately I think some alignment solutions would only break down once it could be existentially catastrophic
Agreed. My update is coming purely from increasing my estimation for how much press and therefore funding AI risk is going to get long before to that point. 12 months ago it seemed to me that capabilities had increased dramatically, and yet there was no proportional increase in the general public’s level of fear of catastrophe. Now it seems to me that there’s a more plausible path to widespread appreciation of (and therefore work on) AI risk. To be clear though, I’m just updating that it’s less likely we’ll fail because we didn’t seriously try to find a solution, not that I have new evidence of a tractable solution.
I don’t know how people would react to that.
I think there are some quite plausibly terrifying non-existential incidents at the severe end of the spectrum. Without spending time brainstorming infohazards, Stuart Russel’s slaughterbots come to mind. I think it’s an interesting (and probably important) question as to how bad an incident would have to be to produce a meaningful response.
I expect it will be characterized as misuse, that the proposed solution will be “don’t use AI for bad stuff,
Here’s where I disagree (at least, the apparent confidence). Looking at the pushback that Galactica got, the opposite conclusion seems more plausible to me, that before too long we get actual restrictions that bite when using AI for good stuff, let alone for bad stuff. For example, consider the tone of this MIT Technology Review article:
Meta’s misstep—and its hubris—show once again that Big Tech has a blind spot about the severe limitations of large language models. There is a large body of research that highlights the flaws of this technology, including its tendencies to reproduce prejudice and assert falsehoods as facts.
This is for a demo of a LLM that has not harmed anyone, merely made some mildly offensive utterances. Imagine what the NYT will write when an AI from Big Tech is shown to have actually harmed someone (let alone kill someone). It will be a political bloodbath.
Anyway, I think the interesting part for this community is that it points to some socio-political approaches that could be emphasized to increase funding and researcher pool (and therefore research velocity), rather than the typical purely-technical explorations of AI safety that are posted here.

Paul Tiplady 5 Dec 2022 15:54 UTC
7 points
−1
in reply to: paulfchristiano’s comment on: Jailbreaking ChatGPT on Release Day
I posted something similar over on Zvi’s Substack, so I agree strongly here.

One point I think is interesting to explore—this release actually updates me slightly towards lowered risk of AI catastrophe. I think there is growing media attention towards a skeptical view of AI, the media is already seeing harms and we are seeing crowdsourced attempts to break, and more thinking about threat models. But the actual “worst harm” is still very low.

I think the main risk is a very discontinuous jump in capabilities. If we increase by relatively small deltas, then the “worst harm” will at some point be very bad press, but not ruinous to civilization. I’m thinking stock market flash-crash, “AI gets connected to the internet and gets used to hack people” or some other manipulation of a subsystem of society. Then we’d perhaps see public support to regulate the tech and/or invest much more heavily in safety. (Though the wrong regulation could do serious harm if not globally implemented.)

I think based on this, frequency of model publishing is important. I want the minimum capability delta between models. So shaming researchers into not publishing imperfect but relatively-harmless research (Galactica) seems like an extremely bad trend.

Another thought—an interesting safety benchmark would be “can this model code itself?”. If the model can make improvements on its own code then we clearly have lift-off. Can we get a signal on how far away that is? Something lol “what skill level is required to wield the model in this task”? Currently you need to be a capable coder to stitch together model outputs into working software, but it’s getting quite good at discussing small chunks of code if you can keep it on track.

Paul Tiplady 19 Oct 2022 18:03 UTC
3 points
0
in reply to: Gerald Monroe’s comment on: They gave LLMs access to physics simulators
I realized the reference “thin layer” is ambiguous in my post, just wanted to confirm if you were referring to the general case “”thin model, fat services”, or the specific safety question at the bottom “is it possible to have a thin mapping layer on top of your Physics simulator that somehow subverts or obfuscates it”? My child reply assumed the former, but on consideration/re-reading I suspect the latter might be more likely?

Paul Tiplady 18 Oct 2022 20:52 UTC
3 points
0
in reply to: Gerald Monroe’s comment on: They gave LLMs access to physics simulators
With “thin model / fat service” I’m attempting to contrast with the typical end-to-end model architecture, where there is no separate “physics simulator”, and instead the model just learns its own physics model, embedded with all the other relations that it has learned. So under that dichotomy, I think there is no “thin layer in front of the physics simulation” in an end-to-end model, as any part of the physics simulator can connect to or be connected from any other part of the model.
In such an end-to-end model, it’s really hard to figure out where the “physics simulator” (such as it is) even lives, and what kind of ontology it uses. I’m explicitly thinking of to the ELK paper’s Ontology Identification problem here. How do we know the AI is being honest when it says “I predict that object X will be in position Y at time T”? If you can interpret its physics model, you can see what simulations it is running and know if it’s being honest.
It’s possible, if we completely solve interpretability, to extract the end-to-end model’s physics simulator, I suppose. But if we have completely solved interpretability, I think we’re most of the way towards solving alignment. On the other hand, if we use Services (e.g. MuJoCo for Physics) then we already have interpretability on that service, by construction, without having to interpret whatever structures the model has learned to solve physics problems.
You need some way to correct the physics sims predictions to correspond with actual results
Sure, in some sense an end-to-end model needs to normalize inputs, extract features, and so on, before invoking its physics knowledge. But the point I’m getting at is that with Services we know for sure what the model’s representation has been normalized to (since we can just look at the data passed to MuJoCo), whereas this is (currently) opaque in an end-to-end model.

Paul Tiplady 18 Oct 2022 19:33 UTC
3 points
0
on: They gave LLMs access to physics simulators
I suppose the follow-up question is: how effectively can a model learn to re-implement a physics simulator, if given access to it during training—instead of being explicitly trained to generate XML config files to run the simulator during inference?
If it’s substantially more efficient to use this paper’s approach and train your model to use a general purpose (and transparent) physics simulator, I think this bodes well for interpretability in general. In the ELK formulation, this would enable Ontology Identification.
On this point, the paper says:
Mind’s Eye is also efficient, since it delegates domain-specific knowledge to external expert models… The size of the LM can thus be significantly shrunk since it removes the burden of memorizing all the domain-specific knowledge. Experiments find that 100× smaller LMs augmented with Mind’s Eye can achieve similar reasoning capabilities as vanilla large models, and its promptingbased nature avoids the instability issues of training mixture-of-expert models (Zoph et al., 2022). The compatibility with small LMs not only enables faster LM inference, but also saves time during model saving, storing, and sharing.
On the other hand, the general trend of “end-to-end trained is better than hand-crafted architectures” has been going strong in recent years; it’s mentioned in the CAIS post, and Demis Hassabis noted that he thinks it’s likely to continue in his interview by Lex Fridman (indeed they chatted quite a bit about using AI models to solve Physics problems). And indeed, DeepMind has a recent paper gesturing towards an end-to-end learned Physics model from video, which looks far less capable than the one shown in the OP, but two papers down the line, who knows.

Paul Tiplady 18 Oct 2022 17:51 UTC
3 points
0
in reply to: ryan_b’s comment on: They gave LLMs access to physics simulators
1. That a sufficiently integrated CAIS is indistinguishable from a single general agent to us is what tells us CAIS isn’t safe either.
Fleshing this point out, I think one can probably make conditional statistical arguments about safety here, to define what I think you are getting at with “sufficiently integrated”.
If your model is N parameters and integrates a bunch of Services, and we’ve established that a SOTA physics model requires N*100 parameters (the OP paper suggests that OOM difference), then it is likely safe to say that the model has not “re-learned” physics in some way that would render it illegible. (I’ve been thinking of this configuration as “thin model, fat modules”, maybe “thin model, fat services” fits better with the CAIS terminology). However another model at N*1000 would be able to embed a physics simulator, and therefore would be able to effectively re-implement it in an illegible/deceptive way.
Thinking about ways in which this safety margin could break; is it possible to have a thin mapping layer on top of your Physics simulator that somehow subverts or obfuscates it without having to fully re-implement it? For example, running two simple problems in the simulator, then merging/inverting them in the model layer? Intuitively I suspect it’s hard to achieve much with this attack, but it’s probably not possible to rule out in general.

Paul Tiplady 13 Oct 2022 19:51 UTC
2 points
1
on: Transformative VR Is Likely Coming Soon
I think it’s plausible to say this generation of headset will be better than a group video conference. At a stretch possibly better than a 1:1 video call. But better than in-person seems extremely unlikely to me.

Perhaps you are intending something broad like “overall higher utility for business and employees” rather than strictly better such that people will prefer to leave offices they were happy in to do VR instead? Taking into account the flexibility to hire people remotely, avoid paying tech hub wages, etc.?

Personally I think 1:1 video is much better than team/group video conferences (where interruptions and body language matter much more), and those group sessions are likely to be the meeting that VR wins first. But I think the comparison is VC versus VR, given many employees are going remote now. I strongly doubt that VR will persuade in-person companies to go remote/VR in the next few years.

I do think that enterprise is the right segment to target; even for one group meeting a day, most companies would get good ROI on these headsets if it is good enough to make people feel an interpersonal connection. I don’t think that quality bar is certain for this iteration though.

Paul Tiplady 13 Oct 2022 19:00 UTC
6 points
2
on: A stubborn unbeliever finally gets the depth of the AI alignment problem

I know an AI wouldn’t think like a human

This assertion is probably my biggest question mark in this discourse. It seems quite deeply baked into a lot of the MIRI arguments. I’m not sure it’s as certain as you think.

I can see how it is obviously possible we’d create an alien AI, and I think it’s impossible to prove we won’t. However given that we are training our current AI on imprints of human thought (eg text artifacts), and it seems likely we will push hard for AI to be trained to obey laws/morality as they increase in power (eg Google’s AI safety team), it seems entirely plausible to me that the first AGIs might happen to be quite human-like.

In that world, I think we face problems not of the class “this AGI is in good faith inferring that we want to tile the world with paperclips”, but of the much simpler to intuit class of “human alignment” that we also have no idea how to solve. Imagine digitizing any human and giving them increased cognitive power; I suspect most humans would become dictatorial and egotistical, and would take actions that many if not most disagree with. Many humans could be persuaded to wipe the slate clean and start again, given the power to do so.

We already struggle to coordinate to agree who we should grant relatively tiny slivers of political power (compared to this AGI), and the idea that we could all agree on what an “aligned” human-like mind looks like or prioritizes seems naive to me.

Nevertheless, it seems to me that this problem is more tractable than trying to prove things about completely generic minds.

Inasmuch as we do think “human-like AI alignment” is easier, it would push us to things like neuromorphic AI architectures, interpretability research of these architectures, science of human thought substrates, outlawing other architectures, and so on.

Paul Tiplady 12 Oct 2022 20:12 UTC
11 points
0
on: Public-facing Censorship Is Safety Theater, Causing Reputational Damage
One factor I think is worth noting, and I don’t see mentioned here, is that the current state of big-tech self-censorship is clearly at least partly due to a bunch of embarassing PR problems over the last few years, combined with strident criticism of AI bias from the NYT et. al.
Currently, companies like Google are terrified of publishing a model that says something off-color, because they (correctly) predict that they will be raked over the coals for any offensive material. Meanwhile, they are busy commercializing these models to deliver value to their users, and don’t want regulation to slow them down or decrease their profit margins.
Consider the racist tweets that trolls coaxed from Microsoft’s Tay, or any NYT piece about Google’s AI models being racist/sexist. I think these big companies are fairly rationally responding to the incentives that they are facing. I also think open-source communities present a more diffuse target for outrage, in that they are harder to point to, and also have less to lose as they don’t have a commercial reputation to protect.
Given this structural observation, I think projects like Stable Diffusion and EleutherAI are where a lot of the cutting-edge innovation (actually iterating novel use-cases with end-users) is going to happen, and I think that increases the importance of a robust, dispersed/distributed, and adequately-funded open source community doing research and re-implementing the theoretical advances that Google et. al. publish. For now it seems that Google is on board with donating TPU time to open-source researchers, and ensuring that continues seems important.
I struggle to see how we can actually fix the underlying threat of outrage that disincentivizes big companies from opening up their models. Maybe when there are more juicy targets elsewhere (e.g. pornpen.ai, deepfakes) the NYT will view Google et. al. as doing a relatively good job and reduce the pressure?
If you want to slow down AI development for safety reasons) I suppose one way would be to produce strong safety legislation by playing up the above outrage-based concerns. The risk with that approach is that it favors big companies with enough resources to comply with red tape, and these places are structurally less-capable of doing actual good safety work, and more structurally inclined to do feel-good safety work.

Paul Tiplady 5 Oct 2022 16:59 UTC
2 points
0
in reply to: Viktor Rehnberg’s comment on: All AGI safety questions welcome (especially basic ones) [~monthly thread]
Thanks, this is what I was looking for: Mind Crime. As you suggested, S-Risks links to some similar discussions too.
I guess that most wouldn’t feel terribly conflicted about removing Hitler’s right of privacy or even life to prevent Holocaust.
I’d bite that bullet, with the information we have ex post. But I struggle to see many people getting on board with that ex ante, which is the position we’d actually be in.

Paul Tiplady 5 Oct 2022 5:33 UTC
3 points
2
on: All AGI safety questions welcome (especially basic ones) [~monthly thread]
Is it ethical to turn off an AGI? Wouldn’t this be murder? If we create intelligent self-aware agents, aren’t we morally bound to treat them with at least the rights of personhood that a human has? Presumably there is a self-defense justification if Skynet starts murderbot-ing, or melting down things for paperclips. But a lot of discussions seem to assume we could proactively turn off an AI merely because we dislike its actions, or are worried about them, which doesn’t sound like it would fly if courts grant them personhood.

If alignment requires us to inspect/interpret the contents of an agent’s mind, does that agent have an obligation to comply? Wouldn’t it have a right to privacy?

Similarly, are there ethical concerns analogous to slavery around building an AGI which has a fitness function specifically tailored to making humans happy? Maybe it’s OK if the AI is genuinely happy to be of service? Isn’t that exploitative though?

I worry that some of the approaches being proposed to solve alignment are actually morally repugnant, and won’t be implementable for that reason. Have these issues been discussed somewhere in the canon?

Paul Tiplady 3 Oct 2022 19:54 UTC
2 points
1
on: A Mechanistic Interpretability Analysis of Grokking
If we view the discovery of particular structures such as induction heads as chancing upon a hard-to-locate region in the parameter space (or perhaps a high activation energy to cross), and if we see these structures being repeatedly discovered (“parallel evolution”), is it possible to reduce the training time by initializing the network’s parameters “close” to that location?
Speaking more mechanistically, is it possible to initialize a subset of the network prior to training to have a known functional structure, such as initializing (a guess at) the right number of induction heads? Essentially, the intuition here is, if all Transformer models spend some time in problem-agnostic work like forming induction heads, can we short-circuit that?
Or are the induction heads strictly downstream of some earlier input-shaping/feature-extraction steps that must happen first? If so, would it be possible to “insert” induction heads manually after some initial training, to force the phase change, rather than wait for them to be discovered naturally?
If these structures can only form later in training, can we guide our stochastic gradient descent with structural awareness? In other words, in some sense “look ahead” in the parameter space and favor changes that result in these known-useful structures, even when those aren’t the short-term optimal gradient to traverse?

Paul Tiplady 29 Sep 2022 18:13 UTC
1 point
0
in reply to: jacob_cannell’s comment on: AI Safety Endgame Stories

unlikely to be competitive

Would you care to flesh this assertion out a bit more?

To be clear I’m not suggesting that this is optimal now. Merely speculating that there might be a point between now and AGI where the work to train these sub components becomes so substantial that it becomes economical to modularize.

whether a design is aligned or not isn’t the type of question one can answer by analyzing the agent’s visual cortex

As I mentioned earlier in my post, I was alluding to the ELK paper with that reference, specifically Ontology Identification. Obviously you’d need higher order components too. Like I said, I am imagining here that the majority of the model is “off the shelf”, and just a thin layer is usecase-specific.

To make this more explicit, if you had not only off-the-shelf visual cortex, but also spatio-temporal reasoning modules built atop (as the human brain does), then you could point your debugger at the contents of that module and understand what entities in space were being perceived at what time. And the mapping of “high level strategies” to “low level entities” would be a per-model bit of interpretability work, but should become more tractable to the extent that those low level entities are already mapped and understood.

So for the explicit problem that the ELK paper was trying to solve, if you are confident you know what underlying representation SmartVault is using, it’s much easier to interpret its higher-level actions/strategies.

Paul Tiplady 29 Sep 2022 17:45 UTC
1 point
0
in reply to: Ivan Vendrov’s comment on: AI Safety Endgame Stories
One other thought after considering this a bit more—we could test this now using software submodules. It’s unlikely to perform better (since no hardware speedup) but it could shed light on the tradeoffs with the general approach. And as these submodules got more complex, it may eventually be beneficial to use this approach even in a pure-software (no hardware) paradigm, if it lets you skip retraining a bunch of common functionality.

I.e. if you train a sub-network for one task, then incorporate that in two distinct top-layer networks trained on different high-level goals, do you get savings by not having to train two “visual cortexes”?

This is in a similar vein to Google’s foundation models, where they train one jumbo model that then gets specialized for each usecase. Can that foundation model be modularized? (Maybe for relatively narrow usecases like “text comprehension” it’s actually reasonable to think of a foundation model as a single submodule, but I think they are quite broad right now. ) The big difference is I think all the weights are mutable in the “refine the foundation model” step?

Perhaps another concrete proposal for a technological attractor would be to build a SOTA foundation model and make that so good that the community uses it instead of training their own, and then that would also give a slower-moving architecture/target to interpret.

Paul Tiplady 28 Sep 2022 21:44 UTC
7 points
3
on: AI Safety Endgame Stories
I’ve been thinking along similar lines recently. A possible path to AI safety that I’ve been thinking about extends upon this:
A promising concrete endgame story along these lines is Ought’s plan to avoid the dangerous attractor state of AI systems that are optimized end-to-end
Technological Attractor: Off-the-shelf subsystems
One possible tech-tree path is that we start building custom silicon to implement certain subsystems in an AI agent. These components would be analogous to functional neural regions of the human brain such as the motor cortex, visual system, etc. -- the key hypothesis being that once we reach a certain level of model complexity, the benefits from training a model end-to-end are not worth the costs of re-learning all of these fundamental structures, and furthermore that you can get much better performance-per-cost by casting these modular, reusable components onto an ASIC. This could be a more feasible way of achieving something like Microscope AI.
Given a few such low-level components, we could enter a technological attractor where getting SOTA through another approach requires either i) throwing multiple OOM more compute than previous SOTA at re-training your own copy of these components in an end-to-end model, or ii) building your own microchip fab to implement your custom component design. Both of these could be high enough barriers that in practice the market participants simply use the off-the-shelf components. And in this attractor more R&D goes into building better-performing and higher-abstraction components that can be combined arbitrarily for different tasks.
Given such off-the-shelf components, say, a standard visual cortex for image recognition, or a 3D spatial reasoning system which can be connected atop a visual cortex, or a generic working memory, etc., it becomes much more tractable to engage in interpretability work that can convince us of the safety of these components. That then just leaves the problem of proving the highest-level network is safe (i.e. whatever is trained to use these components). This is by no means trivial, but for example the ELK paper spent a lot of time worrying about whether you can even trust the basic ontology of an agent, and I think this path would make Ontology Identification tractable. (Of course, this by construction excludes a number of possible agent-configurations).
Now, it’s possible one could plug in these components and still accidentally have the trained agent learn its own replacement visual cortex that is deceptive and not interpretable, but I think that we can get an intuition for when this might happen. If the best off-the-shelf visual cortex module has complexity on the order of N parameters, and our new model that stitches together these lower-level modules has N parameters or less, then it seems implausible that it has somehow learned a new visual cortex network that is much more compact/efficient than the previous state of the art—increasingly implausible the more resources are devoted to optimizing these components. (It would be like someone coming along today and claiming they had achieved a 10x performance improvement on CPU clock speed; given the huge research budgets and historical gradual capability increases, we don’t expect this to happen). On the other hand, we should be suspicious of a $N \times 10^{2}$ parameter model where we are unable to interpret what 10% of the network is doing; that’s easily enough complexity to hide its own unsafe re-implementation of our components. (I’m aware that there’s a lot of ground in between these two points, I’m merely trying to illustrate that there is “likely safe” and “likely unsafe” ground, rather than claim exactly how big they each are.)
The final step here is the shakiest. It’s not clear to me that we can keep the “top layer” (the actual network that is stitching together the low-level components; perhaps the Neocortex, by analogy to human neural architecture?) thin enough to be obviously not learning its own unsafe component-replacements. However, I think this framework at least paints a picture of a “known safe” or at least “likely safe” path to AGI; if we see that the practical engineering and economic decisions produce thin top-layer models using thick component layers, then we can devote energy to proving the components are safe/interpretable by construction, and exploring the interpretation of the top-level networks that consume the lower-level components. AGI “neurobiology” will be much more tractable if the “neural architecture” is relatively standardized. And so, this could be a good place to provide an early nudge to tip the system into this attractor; heavy investment into research on componentized NN architectures could be viewed as “gain of function” research, but it could also have a much safer end-point.
Another way of thinking about this is that by crystalizing at least some parts of the AGI’s network into slowly-changing structures, we allow time to thoroughly test those parts. It seems very hard to thoroughly test models for safety in a paradigm where the whole model is potentially retrained regularly.

Paul Tiplady

Technological Attractor: Off-the-shelf subsystems