Aryeh Englander

I work on applied mathematics and AI at the Johns Hopkins University Applied Physics Laboratory. I also do AI safety related work for the Johns Hopkins Institute for Assured Autonomy. I am currently doing a CS PhD focused on AI safety at the University of Maryland, Baltimore County.

Wiki Contributions

Comments

Paths To High-Level Machine Intelligence

Thanks Daniel for that strong vote of confidence!

The full graph is in fact expandable / collapsible, and it does have the ability to display the relevant paragraphs when you hover over a node (although the descriptions are not all filled in yet). It also allows people to enter in their own numbers and spit out updated calculations, exactly as you described. We actually built a nice dashboard for that - we haven't shown it yet in this sequence because this sequence is mostly focused on phase 1 and that's for phase 2.

Analytica does have a web version, but it's a bit clunky and buggy so we haven't used it so far. However, I was just informed that they are coming out with a major update soon that will include a significantly better web version, so hopefully we can do all this then.

I certainly don't think we'd say no to additional funding or interns! We could certainly use them - there are quite a few areas that we have not looked into sufficiently because all of our team members were focused on other parts of the model. And we haven't gotten yet to much of the quantitative part (phase 2 as you called it), or the formal elicitation part.

[AN #156]: The scaling hypothesis: a plan for building AGI

I'd like to hear more thoughts, from Rohin or anybody else, about how the scaling hypothesis might affect safety work.

[Event] Weekly Alignment Research Coffee Time (05/10)

Thanks Adam for setting this up! I have no idea if my experience is representative, but that was definitely one of the highest-quality discussion sessions I've had at events of this type.

[Linkpost] Treacherous turns in the wild

I don't think this is quite an example of a treacherous turn, but this still looks relevant:

Lewis et al., Deal or no deal? end-to-end learning for negotiation dialogues (2017):

Analysing the performance of our agents, we find evidence of sophisticated negotiation strategies. For example, we find instances of the model feigning interest in a valueless issue, so that it can later ‘compromise’ by conceding it. Deceit is a complex skill that requires hypothesising the other agent’s beliefs, and is learnt relatively late in child development (Talwar and Lee, 2002). Our agents have learnt to deceive without any explicit human design, simply by trying to achieve their goals.

(I found this reference cited in Kenton et al., Alignment of Language Agents (2021).)

Timeline of AI safety

That's later in the linked wiki page: https://timelines.issarice.com/wiki/Timeline_of_AI_safety#Full_timeline

Timeline of AI safety

Excellent, thanks! Now I just need a similar timeline for near-term safety engineering / assured autonomy as they relate to AI, and then a good part of a paper I'm working on will have just written itself.

The ethics of AI for the Routledge Encyclopedia of Philosophy

Also - particular papers that you think are important, especially if you think they might be harder to find in a quick literature search. I'm part of an AI Ethics team at work, and I would like to find out about these as well.

The ground of optimization

This was actually part of a conversation I was having with this colleague regarding whether or not evolution can be viewed as an optimization process. Here are some follow-up comments to what she wrote above related to the evolution angle:

We could define the natural selection system as:

All configurations = all arrangements of matter on a planet (both arrangements that are living and those that are non-living)

Basis of attraction = all arrangements of matter on a planet that meet the definition of a living thing

Target configuration set = all arrangements of living things where the type and number of living things remains approximately stable.

I think that this system meets the definition of an optimizing system given in the Ground for Optimization essay. For example, predator and prey co-evolve to be about “equal” in survival ability. If a predator become so much better than its prey that it eats them all, the predator will die out along with its prey; the remaining animals will be in balance. I think this works for climate perturbations, etc. too.

HOWEVER, it should be clear that there are numerous ways in which this can happen – like the ball on bumpy surface with a lot of convex “valleys” (local minima), there is not just one way that living things can be in balance. So, to say that “natural selection optimized for intelligence” is quite not right – it just fell into a “valley” where intelligence happened. FURTHER, it’s not clear that we have reached the local minimum! Humans may be that predator that is going to fall “prey” to its own success. If that happened (and any intelligent animals remain at all), I guess we could say that natural selection optimized for less-than-human intelligence!

Further, this definition of optimization has no connotation of “best” or even better – just equal to a defined set. The word “optimize” is loaded. And its use in connection with natural selection has led to a lot of trouble in terms of human races, and humans v. animal rights.

Finally, in the essay’s definition, there is no imperative that the target set be reached. As long as the set of living things is “tending” toward intelligence, then the system is optimizing. So even if natural selection was optimizing for intelligence there is no guarantee that it will be achieved (in its highest manifestation). Like a billiards system where the table is slick (but not frictionless) and the collisions are close to elastic, the balls may come to rest with some of the balls outside the pockets. The reason I think this is important for AI research, especially AGI and ASI, is perhaps we should be looking for those perturbations to prevent us from ever reaching what we may think of as the target configuration, despite our best efforts.

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