My foremost priority is a world without severe suffering.
Currently, I’m a researcher and administrator at Invincible Wellbeing. Before that, I did earning-to-give as a software engineer at the Wellcome Sanger Institute, Spark Wave, and other organisations.
Feel free to book an (informal) call w/ me if you would like to discuss suffering abolition or effective altruism more broadly: https://calendly.com/nil-ea/call-with-nil.
Views are my own.
My minimum commitment for the upcoming meetup is to 1) keep researching options for my potential full-time volunteering for an EA org that cannot visa-sponsor yet, and 2) share the opportunity to propose a roundtable discussion at the UK’s ARIA w/ relevant orgs (e.g. nonprofits in animal testing alternatives and in cell-ag).
Looking forward to meeting up w/ everyone )
[Updated w/ an expression of interest]
Location: UK
Remote: Yes (NZ, UK, or US visa sponsorship required)
Willing to relocate: within NZ, UK, or US
Skills:
agile software product development
effective learning
copy editing & proof reading; UX; process improvement; documentation; onboarding & mentoring; literature review; office & productivity software; troubleshooting; Linux; software development; Scrum; digital photography; etc.
aspiring EA since 2014 🖤
Résumé/CV/LinkedIn: LinkedIn
Email: see here
Notes:
Expression of interest
[urgent] Looking for a UK org willing to visa sponsor me for a joint project w/ a longterm- and suffering- focused EA org (they think I’m not replaceable for the role)
Otherwise, I’m looking for growing career capital for social entrepreneurship. Thus I’m most interested in operations, grant writing, research, and other roles relevant for founding & operating an EA nonprofit.
I’m not interested in (purely) software development roles.
part-time preferred (I’d volunteer the rest of my work time for one particular EA org.)
Cause areas that I’m most interested in:
non-human animal suffering; eradication of (extreme) suffering; meta EA incl. cause prioritization and improving research quality
Available from: 3-4 weeks after accepting a job offer
I’m glad to see such research is being done. I especially appreciate the section on proposed resilience measures. Thanks, Matt & Nick!
(I’m currently being torn between staying in the UK or relocating to NZ long-term (I need to decide ASAP, as I have a job offer). So this post is quite timely for me ATM, FWIW.)
Faunalytics may want to add the symposium to the forum’s events page so that e.g. it stays discoverable as the date approaches. Thanks :)
Brave New World? A Defence of Paradise-Engineering (David Pearce, 1998, last updated in 2022)
Thanks for linking to GFI’s posts (and for your and Linch’s article in the first place!). GFI’s concerns w/ some of the points made in the TEAs and the Counter article seem sound to me (FWIW, as I have only some basic knowledge of the field at best). I couldn’t find any responses to GFI, unfortunately.
For those who might be interested in checking GFI’s posts, below are some excerpts.
From the overview of “Preliminary review of technical assumptions within the Humbird analysis”:
… [Humbird’s] analysis is valuable for identifying and prioritizing areas where additional innovation is needed to overcome the current limitations. However, this analysis incorporates several assumptions and interpretations and that we feel merit further discussion or fail to acknowledge the current state of the science (e.g., where researchers or companies are already developing promising technologies that address said issue). We have enumerated several of these points below, in the hopes of providing clarity on some of the nuanced reasons that different TEAs reach different conclusions at this speculative stage of development of this emerging technology.
From “Statement addressing TEA analyses”:
… the Humbird analysis, relying only on publicly available data, necessarily draws insights from the existing literature on parallel fields such as biopharma and cell therapy. The CE Delft analysis [commissioned by GFI], on the other hand, is the only publicly available techno-economic analysis we are aware of that uses actual data submitted by companies producing cultivated meat and its inputs, shared under NDA directly with the CE Delft team (not shared with GFI). Furthermore, all of the techno-economic analyses published to date only reflect challenges of pursuing scale-up of cultivated meat in fed-batch and perfusion-based stirred-tank reactor systems, which by themselves represent some of the methods that cultivated meat manufacturers are currently pursuing but do not necessarily represent the full range of [potential future] scale-up (e.g., here) or biomass-generating methodologies … (e.g., here and here).
Modeling a process on public data from parallel but distinct cell culture processes that are also necessarily out of date (biopharma companies are typically not publishing their most recent innovations on process or cell line improvements, preferring to retain them as intellectual property) will lead to a muddled understanding of the state of the art, which can be particularly misleading in such a rapidly-progressing field.
As just one example relevant to the all-important questions of density and sterility, … process intensification via innovations in bioreactor design, media formulation, and a shift toward continuous perfusion processes is now enabling densities 10- to 100-fold greater [than ~ 3×106 cells/ml] and with sufficient contamination control measures to allow for production runs that last weeks or months rather than days. These advances are only starting to be rolled out commercially in the last few years, and they are still mostly kept close to the vest within biopharma companies … because of the enormous competitive advantage they confer.
Acknowledging areas where novel solutions may arise is not the same as naïve or wishful thinking. There are some areas where true constraints, dictated by the laws of physics or thermodynamics, forbid workarounds. But for many of the core challenges facing the cultivated meat field, the search for alternate solutions is still in its infancy — we have far from exhausted the creativity of researchers in this field.
In the [pharmaceutical] … industries, cell biomass is essentially a waste product. … For production of cultivated meat, the goal is to increase biomass while reducing the production of secreted proteins and metabolites. Optimization of cell lines for this purpose will therefore have opposite goals compared to all commercial animal cell processes in the past.
One option is to assume that animal cell culture is not feasible for large-volume, low-cost applications simply because it has historically not been used as such, but retrospectives tend to serve as poor barometers of the prospects for future innovation. A more forward-looking interpretation is that there is a tremendous amount of low-hanging fruit for relatively low-effort, high-impact process improvements guided by the new incentives and considerations under which the cultivated meat field operates.
… one could argue that the cell culture industry until now has actually been pushed toward higher costs than are necessary even at the current scale at which it operates, simply because of the “arbitrarily high margin[s]” it can capture. … current prices reflect the realities of the current business model, market incentives, and risk tolerances more than they reflect true manufacturing or technology costs.
Even at small volumes and high price points, many cultivated meat companies will be able to find a scaling foothold that simply doesn’t exist within commodity fuels and chemicals. … A better comparative industry would be the plant-based meat industry, which is theoretically also competing with mostly-commoditized, massively-scaled incumbents but has proven capable of commanding several-fold higher price points … .
Hybrid [ie plant-based X cultured meat] products could offset the high production costs of animal cell cultivation with more affordable plant materials while competing on other important consumer metrics such as taste and sustainability.
In general, techno-economic analyses and the critical perspectives of scientific voices well versed in other fields add great value to the cultivated meat industry by highlighting several key areas for further empirical and modeling research to validate assumptions and improve upon the limitations identified.
The article concludes:
It is far too early to write off cultivated meat based on early analyses that do not examine the full range of future possibilities. To the contrary: given what is at stake, we cannot afford not to invest significant resources in affording cultivated meat the greatest chance of success.
I just got a notification that the livestream starts in an hour. One can still register for it here: https://hopin.com/events/new-harvest-2022. Similar to the past conferences, the livestream may be recorded.
Thanks for the post and the interview, Gaetan!
For any one interested, David Pearce’s own written response on EA’s longtermism can be found on his website.
Does enhancing one’s mood / increasing one’s hedonic set-point and making one more resistant to suffering fall within your definition of mind enhancement? I think a case can be made that wellbeing can be hugely empowering (an intuition pump: imagine waking up in an extremely good mood, w/ a sense of things to be done…). David Pearce may be the most prominent EA writing on (e.g. one, two) and promoting (and defending) this type of mind enhancement. And then there is one EA-aligned organization working in this area as well, called Invincible Wellbeing.
I’d be glad to hear your thoughts on this, especially if you disagree that wellbeing should be a significant part of mind enhancement. Thanks :)
EA author Magnus Vinding has a blog post on such not-immediately-obvious reasons for avoiding consuming animal “products”.
Pearce calls it “full-spectrum” to emphasise the difference w/ Bostrom’s “Super-Watson” (using Pearce’s words).
… a blind optimisation process that has no subjective internal experience, and no inclination to gain one, given it’s likely initial goals …
Given how apparently useful cross-modal world simulations (ie consciousness) have been for evolution, I, again, doubt that such a dumb (in a sense of not knowing what it is doing) process can pose an immediate existential danger to humanity that we won’t notice or won’t be able to stop.
Regarding feasibility of conscious AGI / Pearce’s full-spectrum superintelligence, maybe it would be possible with biology involved somewhere. But the getting from here to there seems very fraught ethically (e.g. the already-terrifying experiments with mini-brains).
Actually, if I remember correctly, Pearce thinks that if “full-spectrum superintelligence” is going to emerge, it’s most likely to be biological, and even post-human (ie it is human descendants who will poses such super minds, not (purely?) silicon-based machines). Pearce sometimes calls this “biotechnological singularity”, or “BioSingularity” for short, analogously to Kurzweil’s “technological singularity”. One can read more about this in Pearce’s The Biointelligence Explosion (or in this “extended abstract”).
Magnus Vinding, the author of Reflections on Intelligence, recently appeared on the Utilitarian podcast, where he shared his views on intelligence as well (this topic starts at 35:30).
Thanks for the guide, Alex!
You say from the start that most of the advice is applicable to similar tools, but I’d still note that one limitation of (the free version of) Slack is that message history is limited to 10,000 messages (incl. private messages). So one cannot search and view messages made 10,000 messages before.
Discord (as well as Mattermost and self-hosted Zulip), in contrast, have unlimited message histories (paid versions of Slack or Zulip don’t have this limitation as well, but the pricing (x$ per user per month) isn’t suitable for a public group). That said, these platforms must have their own downsides, which may still make one choose Slack in the end.
> … perhaps they should be deliberately aimed for?
David Pearce might argue for this if he thought that a “superintelligent” unconscious AGI (implemented on a classical digital computer) were feasible. E.g. from his The Biointelligence Explosion:
Full-spectrum superintelligence, if equipped with the posthuman cognitive generalisation of mirror-touch synaesthesia, understands your thoughts, your feelings and your egocentric perspective better than you do yourself.
Could there arise “evil” mirror-touch synaesthetes? In one sense, no. You can’t go around wantonly hurting other sentient beings if you feel their pain as your own. Full-spectrum intelligence is friendly intelligence. But in another sense yes, insofar as primitive mirror-touch synaesthetes are prey to species-specific cognitive limitations that prevent them acting rationally to maximise the well-being of all sentience. Full-spectrum superintelligences would lack those computational limitations in virtue of their full cognitive competence in understanding both the subjective and the formal properties of mind. Perhaps full-spectrum superintelligences might optimise your matter and energy into a blissful smart angel; but they couldn’t wantonly hurt you, whether by neglect or design.
Hi, Greg :)
Thanks for taking your time to read that excerpt and to respond.
First of all, the author’s scepticism in a “superintelligent” AGI (as discussed by Bostrom at least) doesn’t rely on consciousness being required for an AGI: i.e. one may think that consciousness is fully orthogonal to intelligence (both in theory and practice) but still on the whole updating away from the AGI risk based on the author’s other arguments from the book.
Then, while I do share your scepticism about social skills requiring consciousness (once you have data from conscious people, that is), I do find the author’s points about “general wisdom” (esp. about having phenomenological knowledge) and the science of mind much more convincing (although they are probably much less relevant to the AGI risk). (I won’t repeat the author’s point here: the two corresponding subsections from the piece are really short to read directly.)
In GPT-3 we already have a (narrow) AI that can convincingly past the Turing Test in writing. Including writing displaying “social skills” and “general wisdom”.
Correct me if I’m wrong, but these “social skills” and “general wisdom” are just generalisations (impressive and accurate as they may be) from actual people’s social skills and knowledge. GPT-3 and other ML systems are inherently probabilistic: when they are ~right, they are ~right by accident. They don’t know, esp. about what-it-is-likeness of any sentient experience (although, once again, this may be orthogonal to the risk, at least in theory with unlimited computational power).
What’s to say that a sufficiently large pile of linear algebra, seeded with a sufficiently large amount of data, and executed on a sufficiently fast computer, could not build an accurate world model, recursively rewrite more efficient versions of itself, reverse engineer human psychology, hide it’s intentions from us, create nanotech in secret, etc etc, on the way to turning the future lightcone into computronium in pursuit of the original goal programmed into it at its instantiation (making paperclips, making a better language model, making money on the stock market, or whatever), all without a single conscious subjective internal experience?
“Sufficiently” does a lot of work here IMO. Even if something is possible in theory, doesn’t mean it’s going to happen in reality, especially by accident. Also, ”… reverse engineer human psychology, hide it’s intentions from us …” arguably does require a conscious mind, for I don’t think (FWIW) that there could be a computationally-feasible substitute (at least one implemented on a classical digital computer) for being conscious in the first place to understand other people (or at least to be accurate enough to mislead all of us into a paperclip “hell”).
(Sorry for a shorthand reply: I’m just afraid of mentioning things that have been discussed to death in arguments about the AGI risk, as I don’t have any enthusiasm in perpetuating similar (often unproductive IMO) threads. (This isn’t to say though that it necessarily wouldn’t be useful if, for example, someone who were deeply engaged in the topic of “superintelligent” AGI read the book and had a recorded discussion w/ the author for everyone’s benefit…))
The book that made me significantly update away from “superintelligent” AGI being a realistic threat is Reflections on Intelligence (2016/2020) (free version) by Magnus Vinding. The book criticises several lines of arguments of Nick Bostrom’s Superintelligence and the notion of “intelligence explosion”, talks about an individual’s and a collective’s goal-achieving abilities, whether consciousness is orthogonal or crucial for intelligence, and the (unpredictability of) future intelligence/goal-seeking systems. (Re. “intelligence explosion” see also the author’s “A Contra AI FOOM Reading List”.)
(I’m mentioning the book because my impression, FWIW, is that “anti-AGI” or just AGI-deprioritisation arguments are rarely even mentioned on the Forum. I recommend the book if one wants to learn more about such consideration. (The book is pretty short BTW − 53 pages in the PDF version I have—in part because it is focused on the arguments w/ few digression .))
Have you considered asking for this thesis advice the Effective Thesis? They probably can connect you w/ someone w/ a background in this area.
[Below are my (totally optional) thoughts on this (and I’m only a software engineer working in a genomic research institute w/ no formal biomed background):]
I would think that (ethically approved) studies on humans are more useful in general, as they translate better to other humans. Also, the more we try to reduce and substitute non-human animal experimentations w/ alternatives, the more there is incentive to develop these alternatives. I must admit though that I’m “biased”, as I consider non-human animal suffering no less urgent than equivalent suffering of humans, other things being equal.
I’d recommend Reflections on Intelligence (the revised edition) by Magnus Vinding. It’s a short e-book/long essay which is rather critical about the notion of “superintelligent” AI specifically.