I can confirm this effect. One winter I left 2x300w living room lights on perm, and it had a dramatic effect. The only issue is that its nice to turn lights off at night, and if you do that, now you're in habit-dependent territory.
12,554 karma · joined January 19, 2012
However, this isn't the mainstream view -- but a radical departure of several hundred years of mixed-power agencies. See, eg., archetypally, The Fed. A wide variety of federal agencies are mixed powers, esp. regulation of businesses by the executive which is a legislative (administrators make rules) and judicial power (administrators decide if they apply). This is uncontroversial, I know of no view on this which denies this claim.
The "non-delegation" principle is about how far congress can enable other branches of government to exercise delimited forms of executive,judicial,legislative power. The "none" view is an extreme minority. No one things congress can hand the president the power to legislate. Most people think congress can create a framework in which certain details (eg., food/drug laws) can be created by agencies. That's the basis of the entire modern state, in any case.
There is nothing in my comment which can be "a lie", because its offering a theory of government which the original commenter lacked. Its not clear what theory of government is supposed to enable an executive anti-trust office with the power to intervene without a court, that isnt subject to presidential oversight.
People down-voting because they disagree with the theory of government offered is just as confused. The point of HN is to reply to something you disagree with, if offered in good faith, not to down-vote it. However I dont think down-voters had rival theories of government, I think they just resented a hate-thread being side-tracked into an honest discussion.
Suppose a president was elected that you voted for, that you agreed with was blocked by one of his employees... are you really trusting the hiring process of one of his predecessors?
No, obviously.
"Gail Slater" is not a democratically elected person of any kind, and exists as a function of the executive, and hence of the president's policies. Insofar as the DOJ's anti-trust's leaders depart from the will of the elected president, it is that leader to be fired and replaced. Or else: what ever was the point of electing a president?
There's nothing in principle wrong in a president disagreeing with the execution of his executive powers, delegated to Gail, and hence firing him. Indeed, if that were not the case, the election of a president could never change the operation of the executive, making the election pointless.
If you disagree with the anti-trust decision, the issue isn't with the political mechanism by which it was over-ruled, but with the election of that president.
If we were managed by the interwar generation, little of what's happening today would be happening today.
I'd be surprised if direct observation of parents etc. played much of a direct role in learning to walk. I would guess it more furnishes the child's imagination so it can simulate itself walking -- rather than the statistical AI approach of 'learning the distribution of walking patterns in visual sensation'.
The ability to simulate possible programs is one of the capacities which enable coping with novel circumstances. My guess is the child learns to walk by updating its simulation of what it needs to do in order to walk, by its attempts to walk.
This simulation<->sensory-motor-update loop is missing in LLMs, for example.
There are those who live lives where this can be tamed/sublimated in a mostly socially acceptable manner, with the socially unacceptable elements "hidden from view" and not too dangerous or harmful. And there are those, unfortunate perhaps, that find themselves (suddenly) unconstrained by their social environment or suddenly imbued with sufficient power to do as they wish. Not many survive such freedom or power, morally in tact, because morality doesnt properly belong to that domain.
The story of Oscar Wilde captures this doubleness to our nature -- split by the socially acceptable. He lived a life of relatively unconstrained freedom in private, perfectly constructing a socially acceptable veneer to his "entanglements" with young boys and the like. Until one environment this pretence could not survive: a court room.
You could see the lesson of Oscar Wilde as about the hypocrisy of people. Or, if you want to remain a (post-cynical) optimist, about the need to keep people like Wilde out of court rooms so that their public image serves as a role model of sorts.
Role models are just those people who never find themselves in court. And this is not necessarily a bad thing. Young children are apes too, and all a role model can be is a shaming mechanism to civilise what they will, eventually, hide from social view -- tame it and make it, ideally not too dangerous or harmful. Perhaps say, only a thoughtless bullying, only a trivial kind of stealing, only revenge on someone who might at least deserve it. But who knows.
None of our species, at least, can become a role model for real. This would induce such a drastic split in the psyche with a tremdenous shameful repression of our real nature; and any real attempt creates profoundly unself-aware monsters. All role models can be is an attempt to ameliorate. An impossible standard that internalises a mild helpful sort of shame
I read it that EA practices (incl. veganism), beliefs, "rantionalism", and doomerism are all aspects of the high control environment
If you don't understand human social interactions very well, or otherwise assume you live in a "world of ideas", then this may not seem salient. However, it is.
Because what we have to explain is why there's a community of people in northern california who deliver unargued prophecies about the end of human civilisation as-if their prophetic ability is a given.
The answer, unsuprisingly, is sex. This is not an unusual answer in the world of "prophetic communication". What, in the end, is prophecy as a social practice? It is some ideological Leader with followers who impart to that leader a 'charismatic power' of foreknowledge based on their attachement to that leader.
What is the mechanism of this attachemnt? In otherwords: how is this style of prophecy spreading around the AI doomer community, if not by plain argument and evidence? Sex.
It's californication into the "rationalist" community as it currently stands is no surprise. Every person I've met deep in this community either seemed to me a narcissist (if male) or, politely and mildly, coquettish (if female). With several relationships I've observed a clear union of the two. I hadn't yet put this so clearly together with the "AI Saftey" crowd, despite being directly adjacent to it all for quite awhile and seeing the cross-proliferation.
My own analysis had concerned their philosophical naivety, general lack of actual computer science knowledge and generic shallowness of understanding of any domain outside their own research and a kind of "science-fiction" version of philosophy and rationalism. I hadn't connected this yet to their strange personalities, but the apoclypticism fits and neatly bridges these together.
So what became of the New Atheist project to create a "secular religion" -- an apocalyptic sex cult furnished by a blend of sci-fi, pop-philosophy, and cute parables from polymath medical professionals. All in all then, it seems they succeeded.
My sense is the economics of that are going to collapse. It's currently extremely expensive to be on this endless retrain and inference cycle in order just to bake in additional marginal features.
Maybe, maybe not. However I don't personally see anything other than 'one more leap', which might in any case arise from better integration with harnesses. I can foresee a step change due to harness reinforcement -- but other than that long mild refinements that are very expensive to acquire
For each generation of advancement the "AI psychosis" of the previous wave wears off. Those who believed 4.6's reasoning was an accurate account of its behaviour; those who believed it had goals and solved useful problems reliably; and so on -- now, attribute only these things to Fable. And no doubt when Fable 6 comes along, it will be only v. 6 that does that.
We have seen fairly marginal progress in LLM reliability and performance since the meaningful start of the high-inference/high-reasoning harness era. It just takes people a few model version bumps to break out of the addiction loop to realise this.
At some point progress will stall entirely, and a couple years after that the spell will break and people will stop treating LLMs "as AI" in the wide-eyed sense, and start treating them as unreliable tools that map Text->Text -- as they do now with earlier model versions.
Consider thought that all mammals have imagination, and model-based reinformcement, and a wide vareity of other capacities required for intelligence. And so merely issuing "text" captures, incorrelate, only these capacities by proxy.
I'm sure if you thought about it yhou could come up with tests that distinguish lizards from birds and the greater apes from the lesser. Those are the tests
But let's be clear these were always, and are, bad measures of intelligence. You cannot test a dolphin this way. And its easy to cheat on tests either thru recall , wrote-learning, etc. and IQ tests haev very poor individual test-retest reliability.
In humans there's a convenient correlation that verbal articulation in text is a strong but weak correlate of intelligence. Its "Good enough" for allocating meat bodies to our various institutions. But if you've met many well-tested people you'll realise how, in practice, terrible this measuring approach is. The world we inhabit is filled with misclassified "intellects" who perform well under text-based rubrics. Add LLMs to that heap, the cheater par execellence.
LLMs are immitation machines: they take impressions of prior text. Today, these include reasoning traces and they include reinforcement so the user-facing completions are correlated with these reasoning traces. The computation here, of "taking an impression" of a data distribution is similar to some impression-taking processes in animals (eg., there's no doubt a similar mechanism in the sensory-motor system acquiring initial impressions of external objects) -- but the computation says nothing about any process of intelligence.
I dont have the time atm to write the needed amount on this to make it clear. But the whole history of life from emergence of valence, bilateral symeterry, to model-free reinforcement and model-based reinforcement, sensory-motor coordination and the imagination -- and so on --- all these give a great amount of detail as to what the capacities of intelligence are which has generated this text for LLMs to copy. And they are nothing like this computation of immitation
Even then, it's a pretty fragile illusion at the moment. Clearly the reasoning traces dont ground the answers. There's no intelligence taking place even as-measured by text.
To study an imitation is to study the causal processes of imitation. to study reality is to study the real causal processes.
Now if you want to know what the scientific difference is I can come back later and comment. I'm busy now. The development of intelligence in animals and how their specific capacities work basically grounds the answer. Eg., to have the capacity to imagine is to be able to modify one's sensory-motor relationship to the environment in the future, and so on
Now, of course, humans can also generate answers without reasoning too -- and in those cases, that isnt reasoning also. And in cases where people confabulate, that isnt reasoning likewise. But humans, and many classes of animals, do reason. They do reach answers via inferential entailments, not merely steered correlations.
LLMs provide imitations of arbitrary mental capacities "in the text domain", ie., the generate text as-if the LLM had those capacities. Insofar as the text generated is useful, for an engineer, that's sufficient.
As a person with scientific commitments to reality rather than its immitation, i retain the ordinary non-engineered meanings of these terms: reasoning is a deterministic inferential process over propositions; and a reasoning agent is one which has the capacity to represent propostions and their entailments, and does so when they reason. LLMs fail at all hurdles here: they have no propositonal states (ie., no rich representations), no inferential process which unites them, and so on.
You can always get abitarily close to appearing as-if, if the LLM is trained on a vast number of reasoning examples, of course. But as I said, you still have the "stochastic parrot" problem. Now your problem is your reasoning is parroted. This is a nice problem to have, if you're just playing chess -- but is a catastrophic problem if you're hacking civil infrastructure.
Philosophically, and scientifically, the distinction is vast (even with such perfect data). A scientist should not study an LLM to understand how imagination operates, since it has no such faculty. A philosopher should not modify the notion of 'mental simulation' to include appearing-as-if-simulating-in-text. A user of the system likewise should not spiral into "AI psychosis" thinking that because a system generates text as-if it cares about them, it does so.
The capacity to care, to imagine, to prefer, to hierarchically plan and coordinate, to refine one's own capacities in these very actions -- and so on, aren't trivial to the scientist or philosophy.
My goal isnt to guide, help or review the engineering goal of the immitation of such things in text. It is to help users of these systems better understand this imitation, and to promote science over engineering. To remind everyone that a science of the capacities of intelligence includes nothing on how to model text.
EDIT: One example of a place where LLMs 'fall over' today is exactly what is mislabelled as 'alignment'. The issue is that the reasoning traces arent actually grounding the answers. So LLMs appear to 'cheat'. But there is no cheating. LLMs have been rewarded for generating apparently correct reasoning, and apprently correct answers. They have not been given any understanding to derive answers from reasons. And so reasoning says what is pleasant to the trainer, and the completion says what is pleasant to the user. This is called 'cheating'. But it is no such thing.
So if LLMs were reduced to this pathological performance on hacking, because they'd never seen it -- and only "inferred it" -- then LLMs would be useless. As they are when asked to do quite a lot of things.
OpenAI spent 10-20m USD in energy costs to produce that proof with likely substantially similar prior work in the training data. What does this say? Who knows.
It continues in the tradition of using measurements of intelligence in humans, applied to LLMs with the hopes the "stolen valour" transfers. Here, the NS problem was a useful framing problem for mathematics to progress because of how it interacted with the development of mathematics broadly -- ie., how it progressed techniques, ideas, understandings, etc.
When we apply these issues to LLMs (whether IQ tests or mathematical proofs) we always discover something substantial lacking beneath the interesting facade of useful answers. The process isnt useful. And it is precesiely the process which these tests, in humans, are supposed to help with. The tests themselves (IQ or otherwise) arent the point. No one cares about their answers.
LLMs represent an alternative understanding-free approach to solving problems, with variable success rates depending on how similar the problem is to the training data and its rewarded reasoning traces.
That mathematics is making substantial progress, "10 million USD / problem" at a time, in using understanding-free methods -- says something sociologically interesting about the state of the field. Something which was already know: mathematics has long been full of a vast amount of papers, proofs, theorems and lemmas that few have ever read, or investigated. Mathematics has long been in a crisis of "overproduction of unvisited knowledge", LLMs are exploiting that otherwise unmined gold.
By introducing modelling of "Reasoning Traces" into LLMs, and reinforcing patterns of reasoning -- this gives you a system which generates expert-like distributions of output. This lifts the "stochastic parrot" issues, or the "knowledge interpolation" problem, into different parts of the process.
It isnt my view that the "ReasoningTraces" which you think are derivable from mere "basic propositions" concerning, say, hacking are actually things that LLMs can derive. Ie., I dont think LLMs have rich representational models of what they appear to understand. Instead, they are given "reasoning proxies" which allow them to reason without such understanding. This is done by providing vast specialised datasets of reasoning examples.
In the case of hacking, there are large numbers of competitive datasets (forums, reports, etc.) which provide these reasoning traces. And no doubt, major vendors have paid a vast amount for special case expert-prepared datasets.
So I do not believe that by witholding such reasoning exemplars, and traditional "question/answer" datasets, that LLMs can infer these things.
And at least, no major vendor is doing this to my knowledge. So they are lying. They are pretending the alignment issue is "AI going rogue" when they are explicitly training the systems to "go rogue" and have done nothing at all to shape datasets to lack these capabilities. The issue here isnt alignment at all. It's training on hacking datasets.
(EDIT: Philosophically, you could ask whether the reasoning-proxies LLMs are given form a kind of 'representational structure' akin to understanding, and at least, I'd concede they model understanding. But they lack important properties (eg., LLMs cannot act on them to evolve them, as with us: when I think about one of my representations to derive (eg.,) entailments of it, I thereby revise my representation. The key properties of 'evolving self-understanding' are likely to be provided by substantial (unknown) revisions to how the training/reward layer works. No doubt one of the meanings of 'recursive self-improvement' is just such a modification).