Lucas Teixeira

Program Lead at PIBBSS
Previously: Applied epistemologist and Research Engineer at Conjecture.

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Re "big science": I'm not familiar with the term, so I'm not sure what the exact question being asked is. I am much more optimistic in the worlds where we have large scale coordination amongst expert communities. If the question is around what the relationship between governments, firms and academia, I'm still developing my gears around this. Jade Leung's thesis seems to have an interesting model but I have yet to dig very deep into it.

Hey Ryan, thank you for your support for the thoughtful write-up! It’s very useful for us to see what the alignment community at large, and our supporters specifically think of our work. I’ll respond to the point on “pivoting away from blue sky research” here and let Dušan address the other reservations in a separate comment.

As Nora has already mentioned, different people hold different notions on what it means to “keep it weird” and conduct “blue sky” and/or “non-paradigmatic” research. But in as far as this cluster of terms is pointing at research which is (a) aimed at innovating novel conceptual frames and (b) free from compromising pressures of short-term applications, then I would say that this is still the central focus of PIBBSS and that recent developments should be seen as updates to the founding vision, as opposed to full on departures.

The main technical bet in my reading of the PIBBSS founding mission (which people are free to disagree with, I’m curious in the ways in which they do), is that one can overcome the problem of epistemic access by leveraging insights from present day physically instantiated proxies. Current day deep learning systems are impressive, and arguably stronger approximations to the kinds of AGI/ASI which we are concerned with, but they’re still proxies nonetheless and failing to treat them as such tends towards a set of associated failure cases.

Given both my personal experience with LLMs and my reading of the role that empirical engagement has historically played in non-paradigmatic research, I tend to advocate for a methodology which incorporates immediate feedback loops with present day deep learning systems over the classical "philosophy -> math -> engineering" deconfusion/agent foundations paradigm. This was most strongly reflected in the first iteration of the affiliateship cohort and is present in the language of the Manifund funding memo.

With that being said, given that PIBBSS, especially the fellowship, is largely a talent intervention aiming at providing a service to the field, I don’t believe its total portfolio should be confined to the limits of my research taste and experience. Especially after MIRI’s recent pivot, I think there’s a case to be made for PIBBSS to host research which doesn’t meet my personal preferences towards quick empirical engagement.

For clarity, how do you distinguish between P1 & P4?

It's unclear to me what:
(1) You consider the Yudowskian argument for FOOM to be

(2) Which of the premises in the argument you find questionable

I would like to say that there's a study group being formed in the AI Alignment Slack server with similar intentions! If you are not a part of that server and would like to join, feel free to email me at melembroucarlitos@gmail.com telling me a bit about yourself and your hopes and intentions and I'll send you an invite.