Quick takes

Dwarkesh (of the famed podcast) recently posted a call for new guest scouts. Given how influential his podcast is likely to be in shaping discourse around transformative AI (among other important things), this seems worth flagging and applying for (at least, for students or early career researchers in bio, AI, history, econ, math, physics, AI that have a few extra hours a week). The role is remote, pays ~$100/hour, and expects ~5–10 hours/week. He’s looking for people who are deeply plugged into a field (e.g. grad students, postdocs, or practitioners) with high taste. Beyond scouting guests, the role also involves helping assemble curricula so he can rapidly get up to speed before interviews. More details are in the blog post; link to apply (due Jan 23 at 11:59pm PST).

Are there any signs of governments beginning to do serious planning for the need for Universal Basic Income (UBI) or negative income tax...it feels like there's a real lack of urgency/rigour in policy engagement within government circles. The concept has obviously had its high-level advocates a la Altman but it still feels incredibly distant as any form of reality.  Meanwhile the impact is being seen in job markets right now - in the UK graduate job opening have plummeted in the last 12 months. People I know are having a hard enough time finding jobs with elite academic backgrounds - let alone the vast majority of people who went to average universities. This is happening today - before there's any consensus of arrival of AGI and widely recognised mass displacement in mid-career job markets. Impact is happening now, but preparation for major policy intervention in current fiscal scenarios seems really far off. If governments do view the risk of major employment market disruption as a realistic possibility (which I believe in many cases they do) are they planning for interventions behind the scene? Or do they view the problem as too big to address until it arrives...viewing rapid response > careful planning in the way the COVID emergency fiscal interventions emerged. Would be really interested to hear of any good examples of serious thinking/preparation of how some form of UBI could be planned for (logistically and fiscally) in the near time 5 year horizon. 

Scrappy note on the AI safety landscape. Very incomplete, but probably a good way to get oriented to (a) some of the orgs in the space, and (b) how the space is carved up more generally.   (A) Technical (i) A lot of the safety work happens in the scaling-based AGI companies (OpenAI, GDM, Anthropic, and possibly Meta, xAI, Mistral, and some Chinese players). Some of it is directly useful, some of it is indirectly useful (e.g. negative results, datasets, open-source models, position pieces etc.), and some is not useful and/or a distraction. It's worth developing good assessment mechanisms/instincts about these. (ii) A lot of safety work happens in collaboration with the AGI companies, but by individuals/organisations with some amount of independence and/or different incentives. Some examples: METR, Redwood, UK AISI, Epoch, Apollo. It's worth understanding what they're doing with AGI cos and what their theories of change are. (iii) Orgs that don't seem to work directly with AGI cos but are deeply technically engaging with frontier models and their relationship to catastrophic risk: places like Palisade, FAR AI, CAIS. These orgs maintain even more independence, and are able to do/say things which maybe the previous tier might not be able to. A recent cool thing was CAIS finding that models don't do well on remote work tasks -- only 2.5% of tasks -- in contrast to OpenAI's findings in GDPval suggests models have an almost 50% win-rate against industry professionals on a suite of "economically valuable, real-world tasks" tasks. (iv) Orgs that are pursuing other* technical AI safety bets, different from the AGI cos: FAR AI, ARC, Timaeus, Simplex AI, AE Studio, LawZero, many independents, some academics at e.g. CHAI/Berkeley, MIT, Stanford, MILA, Vector Institute, Oxford, Cambridge, UCL and elsewhere. It's worth understanding why they want to make these bets, including whether it's their comparative advantage, an alignment with their incentives/grants, or whether they

Not sure who needs to hear this, but Hank Green has published two very good videos about AI safety this week: an interview with Nate Soares and a SciShow explainer on AI safety and superintelligence. Incidentally, he appears to have also come up with the ITN framework from first principles (h/t @Mjreard). Hopefully this is auspicious for things to come?

The economist Tyler Cowen linked to my post on self-driving cars, so it ended up getting a lot more readers than I ever expected. I hope that more people now realize, at the very least, self-driving cars are not an uncontroversial, uncomplicated AI success story. In discussions around AGI, people often say things along the lines of: ‘deep learning solved self-driving cars, so surely it will be able to solve many other problems'. In fact, the lesson to draw is the opposite: self-driving is too hard a problem for the current cutting edge in deep learning (and deep reinforcement learning), and this should make us think twice before cavalierly proclaiming that deep learning will soon be able to master even more complex, more difficult tasks than driving.

Technical Alignment Research Accelerator (TARA) applications close today! Last chance to apply to join the 14-week, remotely taught, in-person run program (based on the ARENA curriculum) designed to accelerate APAC talent towards meaningful technical AI safety research. TARA is built for you to learn around full-time work or study by attending meetings in your home city on Saturdays and doing independent study throughout the week. Finish the program with a project to add to your portfolio, key technical AI safety skills, and connections across APAC. See this post for more information and apply through our website here.

A week ago, Anthropic quietly weakened their ASL-3 security requirements. Yesterday, they announced ASL-3 protections. I appreciate the mitigations, but quietly lowering the bar at the last minute so you can meet requirements isn't how safety policies are supposed to work. (This was originally a tweet thread (https://x.com/RyanPGreenblatt/status/1925992236648464774) which I've converted into a quick take. I also posted it on LessWrong.) What is the change and how does it affect security? 9 days ago, Anthropic changed their RSP so that ASL-3 no longer requires being robust to employees trying to steal model weights if the employee has any access to "systems that process model weights". Anthropic claims this change is minor (and calls insiders with this access "sophisticated insiders"). But, I'm not so sure it's a small change: we don't know what fraction of employees could get this access and "systems that process model weights" isn't explained. Naively, I'd guess that access to "systems that process model weights" includes employees being able to operate on the model weights in any way other than through a trusted API (a restricted API that we're very confident is secure). If that's right, it could be a high fraction! So, this might be a large reduction in the required level of security. If this does actually apply to a large fraction of technical employees, then I'm also somewhat skeptical that Anthropic can actually be "highly protected" from (e.g.) organized cybercrime groups without meeting the original bar: hacking an insider and using their access is typical! Also, one of the easiest ways for security-aware employees to evaluate security is to think about how easily they could steal the weights. So, if you don't aim to be robust to employees, it might be much harder for employees to evaluate the level of security and then complain about not meeting requirements[1]. Anthropic's justification and why I disagree Anthropic justified the change by

FYI: METR is actively fundraising!  METR is a non-profit research organization. We prioritise independence and trustworthiness, which shapes both our research process and our funding options. To date, we have not accepted payment from frontier AI labs for running evaluations. ^[1] Part of METR's role is to independently assess the arguments that frontier AI labs put forward about the safety of their models. These arguments are becoming increasingly complex and dependent on nuances of how models are trained and how mitigations were developed. For this reason, it's important that METR has its finger on the pulse of frontier AI safety research. This means hiring and paying for staff that might otherwise work at frontier AI labs, requiring us to compete with labs directly for talent. The central constraint to our publishing more and better research, and scaling up our work aimed at monitoring the AI industry for catastrophic risk, is growing our team with excellent new researchers and engineers. And our recruiting is, to some degree, constrained by our fundraising - especially given the skyrocketing comp that AI companies are offering. To donate to METR, click here: https://metr.org/donate If you’d like to discuss giving with us first, or receive more information about our work for the purpose of informing a donation, reach out to giving@metr.org   1. ^ However, we are definitely not immune from conflicting incentives. Some examples:    - We are open to taking donations from individual lab employees (subject to some constraints, e.g. excluding senior decision-makers, constituting <50% of our funding)  - Labs provide us with free model access for conducting our evaluations, and several labs also provide us ongoing free access for research even if we're not conducting a specific evaluation.