"AI Safety for Fleshy Humans" an AI Safety explainer by Nicky Case

I think this is really quite good, and went into way more detail than I thought it would. Basically my only complaints on the intro/part 1 are some terminology and historical nitpicks. I also appreciate the fact that Nicky just wrote out her views on AIS, even if they're not always the most standard ones or other people dislike them (e.g. pointing at the various divisions within AIS, and the awkward tension between "capabilities" and "safety"). 

I found the inclusion of a flashcard review applet for each section super interesting. My guess is it probably won't see much use, and I feel like this is the wrong genre of post for flashcards.^[1] But I'm still glad this is being tried, and I'm curious to see how useful/annoying other people find it.

I'm looking forward to parts two and three.

Nitpicks:^[2]

Logic vs Intuition:

I think "logic vs intuition" frame feels like it's pointing at a real thing, but it seems somewhat off. I would probably describe the gap as explicit vs implicit or legible and illegible reasoning (I guess, if that's how you define logic and intuition, it works out?). 

Mainly because I'm really skeptical of claims of the form "to make a big advance in/to make AGI from deep learning, just add some explicit reasoning". People have made claims of this form for as long as deep learning has been a thing. Not only have these claims basically never panned out historically, these days "adding logic" often means "train the model harder and include more CoT/code in its training data" or "finetune the model to use an external reasoning aide", and not "replace parts of the neural network with human-understandable algorithms". (EDIT for clarity: That is, I'm skeptical of claims that what's needed to 'fix' deep learning is by explicitly implementing your favorite GOFAI techniques, in part because successful attempts to get AIs to do more explicit reasoning look less like hard-coding in a GOFAI technique and more like other deep learning things.) 

I also think this framing mixes together "problems of game theory/high-level agent modeling/outer alignment vs problems of goal misgeneralization/lack of robustness/lack of transparency" and "the kind of AI people did 20-30 years ago" vs "the kind of AI people do now".

This model of logic and intuition (as something to be "unified") is quite similar to a frame of the alignment problem that's common in academia. Namely, our AIs used to be written with known algorithms (so we can prove that the algorithm is "correct" in some sense) and performed only explicit reasoning (so we can inspect the reasoning that led to a decision, albeit often not in anything close to real time). But now it seems like most of the "oomph" comes from learned components of systems such as generative LMs or ViTs, i.e. "intuition". The "goal" is to a provably* safe AI, that can use the "oomph" from deep learning while having enough transparency/explicit enough thought processes. (Though, as in the quote from Bengio in Part 1, sometimes this also gets mixed in with capabilities, and become how AIs without interpretable thoughts won't be competent.)

Has AI had a clean "swap" between Logic and Intuition in 2000?

To be clear, Nicky clarifies in Part 1 that this model is an oversimplification. But as a nitpick, I think if you had to pick a date, I'd probably pick 2012, when a conv net won the ImageNet 2012 competition in a dominant matter, and not 2000.  

Even more of a nitpick, but the examples seem pretty cherry picked?

For example, Nicky uses the example of deep blue defeating kasparov as an example of a "logic" based AI. But in that case, almost all Chess AIs are still pretty much logic based. Using Stockfish as an example, Stockfish 16's explicit alpha-beta search both is using a reasoning algorithm that we can understand, and does the reasoning "in the open". Its neural network eval function is doing (a small amount of) illegible reasoning. While part of the reasoning has become illegible, we can still examine the outputs of the alpha-beta search to understand why certain moves are good/bad. (But fair, this might be by far the most widely known non-deep learning "AI". The only other examples I can think of are Watson and recommender systems, but those were still using statistical learning techniques. I guess if you count MYCIN or SHRDLU or ELIZA...?)

(And modern diffusion models being unable to count or spell seem like a pathology specific to that class of  generative model, and not say, Claude Opus.)

FOOM vs Exponential vs Steady Takeoff

Ryan already mentioned this in his comment. [LW(p) · GW(p)]

Even less important and more nitpicky nitpicks:

When did AIs get better than humans (at ImageNet)?

In footnote [3], Nicky writes:

In 1997, IBM's Deep Blue beat Garry Kasparov, the then-world chess champion. Yet, over a decade later in 2013, the best machine vision AI was only 57.5% accurate at classifying images. It was only until 2021, three years ago, that AI hit 95%+ accuracy. 

But humans do not get 95% top-1 accuracy^[3] on imagenet! If you consult this paper from the imagenet creators (https://arxiv.org/abs/1409.0575), they note that:

. We found the task of annotating images with one of 1000 categories to be an extremely challenging task for an untrained annotator. The most common error that an untrained annotator is susceptible to is a failure to consider a relevant class as a possible label because they are unaware of its existence. (Page 31)

And even when using an human expert annotators, who did hundreds of validation image for practice, the human annotator still got a top-5 error of 5.1%, which was surpassed in 2015 by the original resnet paper (https://arxiv.org/abs/1512.03385) at 4.49% for ResNet 14 (and 3.57% for an ensemble of six resnets). 

(Also, good top-1 performance on imagenet is genuinely hard and may be unrepresentative of actually being good at vision, whatever that means Take a look at some of the "mistakes" current models make:)

1. ^{^}

   Using flashcards suggests that you want to memorize the concepts. But a lot of this piece isn't so much an explainer of AI safety, but instead an argument for the importance of AI Safety. Insofar as the reader is not here to learn a bunch of new terms, but instead to reason about whether AIS is a real issue, it feels like flashcards are more of a distraction than an aid.

2. ^{^}

   I'm writing this in part because I at some point promised Nicky longform feedback on her explainer, but uh, never got around to it until now. Whoops. 

3. ^{^}

   Top-K accuracy = you guess K labels, and are right if any of them are correct. Top 5 is significantly easier on image net than Top 1, because there's a bunch of very similar classes and many images are ambiguous.

@henry [LW · GW] (who seems to know Nicky) said on a duplicate link post of this: 

This is an accessible introduction to AI Safety, written by Nicky Case and the teens at Hack Club. So far, part 1/3 is completed, which covers a rough timeline of AI advancement up to this point, and what might come next.

If you've got feedback as to how this can be made more understandable, that'd be appreciated! Reach out to Nicky, or to me and I'll get the message to her.

Random error:

Exponential Takeoff:

AI's capabilities grow exponentially, like an economy or pandemic.

(Oddly, this scenario often gets called "Slow Takeoff"! It's slow compared to "FOOM".)

Actually, this isn't how people (in the AI safety community) generally use the term slow takeoff.

Quoting from the blog post by Paul:

Futurists have argued for years about whether the development of AGI will look more like a breakthrough within a small group (“fast takeoff”), or a continuous acceleration distributed across the broader economy or a large firm (“slow takeoff”).

[...]

(Note: this is not a post about whether an intelligence explosion will occur. That seems very likely to me. Quantitatively I expect it to go along these lines. So e.g. while I disagree with many of the claims and assumptions in Intelligence Explosion Microeconomics, I don’t disagree with the central thesis or with most of the arguments.)

Slow takeoff still can involve a singularity (aka an intelligence explosion).

The terms "fast/slow takeoff" are somewhat bad because they are often used to discuss two different questions:

• How long does it take from the point where AI is seriously useful/important (e.g. results in 5% additional GDP growth per year in the US) to go to AIs which are much smarter than humans? (What people would normally think of as fast vs slow.)
• Is takeoff discontinuous vs continuous?

And this explainer introduces a third idea:

• Is takeoff exponential or does it have a singularity (hyperbolic growth)?

This is good! I would recommend it to a friend!

Some feedback.

• An individual human can be inhumane, but the aggregate of human values kind of visibly isn't and in most ways couldn't be: Human cultures are getting more humane reliably as transparency/reflection and coordination increases over time, but also inevitably if you aggregate a bunch of concave values [LW · GW] it will produce a value system that treats all of the subjects of the aggregation pretty decently.
  A lot of the time, when people accuse us of conflating something, we equate those things because we have an argument that they're going to turn out to be equivalent.
  So emphasizing a difference between these two things could be really misleading, and possibly kinda harmful, given that it could undermine the implementation of the simplest/most arguably correct solutions to alignment (which are just aggregations of humans' values). This could be a whole conversation, but could we just not define humane values as being necessarily distinct from human values? How about this:
  • People are sometimes confused by 'Human values', as it seems to assume that all humans value the same things, but many humans have values that conflict with the preferences of other humans. When we say 'Humane values', we're defining a value system that does a decent job at balancing and reconciling the preferences of every human (Humans, Every one).
• [graph point for "systems programmer with mlp shirt"] would it be funny if there were another point, "systems programmer without mlp shirt", and it was pareto-inferior
• "What if System 2 is System 1". This is a great insight, I think it is, and I think the main reason nerdy types often fail to notice how permeable and continuous the boundary is a kind of tragic habitual cognitive autoimmune disease, and I have a post brewing about this after I used a repaired relationship with the unconscious bulk to cure my astigmatism (I'm going to let it sit for a year just to confirm that the method actually worked and myopia really was averted)
• Exponential growth is usually not slow, and even if it were slow, it wouldn't entail that "we'll get "warning shots" & a chance to fight back", it only takes a small sustained advantage to be able to utterly win a war (though contemporary humans don't like to carry wars to completion these days, the 20th century should have been a clear lesson that such things are within our abilities at current tech levels). Even if progress in capabilities over time continued to be linear, impact over capabilities is not going to be linear, it never has been.

But overall I think it addresses a certain audience who I know much better than my version of this that I hastily wrote last year when I was summoned to speak at a conference would have (and so I never showed it to them. Maybe one day I will show them yours.).