site banner

Culture War Roundup for the week of September 5, 2022

This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.

Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.

We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:

  • Shaming.

  • Attempting to 'build consensus' or enforce ideological conformity.

  • Making sweeping generalizations to vilify a group you dislike.

  • Recruiting for a cause.

  • Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.

In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:

  • Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.

  • Be as precise and charitable as you can. Don't paraphrase unflatteringly.

  • Don't imply that someone said something they did not say, even if you think it follows from what they said.

  • Write like everyone is reading and you want them to be included in the discussion.

On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.

106
Jump in the discussion.

No email address required.

Red tribe, to the extent much of their jobs include manipulating the physical world directly, may turn out to be relatively robust against AI replacement.

Perhaps, but look at DayDreamer:

The Dreamer algorithm has recently shown great promise for learning from small amounts of interaction by planning within a learned world model, outperforming pure reinforcement learning in video games. Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment. [...] Dreamer trains a quadruped robot to roll off its back, stand up, and walk from scratch and without resets in only 1 hour. We then push the robot and find that Dreamer adapts within 10 minutes to withstand perturbations or quickly roll over and stand back up. On two different robotic arms, Dreamer learns to pick and place multiple objects directly from camera images and sparse rewards, approaching human performance. On a wheeled robot, Dreamer learns to navigate to a goal position purely from camera images, automatically resolving ambiguity about the robot orientation.

Stable Diffusion and GPT-3 are impressive, but most problems, physical or non-physical, don't have that much training data available. Algorithms are going to need to get more sample-efficient to achieve competence on most non-physical tasks, and as they do they'll be better at learning physical tasks too.

Yes, I'll freely admit that I was startled by how quickly machine learning produced superhuman competence in very specific areas, so am NOT predicting that AI will stall out or only see marginal progress on any given 'real world' task. Especially once they start networking different specialized AIs together in ways that leverage their respective advantages.

Just observing that the complexities of the real world are something that humans are good at navigating whilst AIs have had trouble dealing with the various edge cases and exceptions that will inevitably arise.

Tasks that already involve manipulating digital data are inherently legible to the machine brain, whilst tasks that involve navigating an inherently complex external world are not (yet).

It is entirely possible that we might eventually have an AI that is absurdly good at manipulating digital data and producing profits which it can then spend on other pursuits, but finds unbounded physical tasks so difficult to model that it just pays humans to do that stuff rather than waste efforts developing robots that can match human capability.