As someone who does both hardware design (mostly high precision analog electronics) and software engineering, the difference between both fields in AI capability is striking. Models like Fable and Astra can provide lots of interesting insights into the design of specialty electronics when prompted correctly. Yet they can hardly put that knowledge to work if instructed to generate a schematic or lay out a PCB.
Of course, for readers here with a solid grasp on how LLM's operate it's not exactly surprising. Still, I feel this is exactly the kind of thing that drives home that the way we reason about (general) intelligence does not map cleanly to LLM operation, no matter how advanced the current models are.
If anything, it is surprising that the LLM is so bad at stealing a ready-made RP2350 design! There are plenty of easy-to-find tutorials like [0] and [1] that handhold you through the entire design process, and a simple search will give you dozens of design files for open-source boards.
At this level of board complexity you almost have to try to make it completely non-functional, so although it is nice to see it in practice I am not exactly surprised that a LLM with a decent bunch of assistance managed to do it. But there's quite a gap between the equivalent of poorly copying a "Hello World" from StackOverflow and making actual production-ready boards.
Here’s one crucial difference: there’s overwhelming evidence that human emissions have an effect on our climate. The mechanisms are generally well-understood and the research is widely disseminated and easily available to anyone that’s interested.
With the ‘dangers’ touted by these insiders, it’s all “trust me bro”, hyperbole, and very little hard evidence. As such, a skeptical mind would question their motives.
You're simultaneously overestimating what was observable in the 70s climate research, and ignoring all the actual research and evaluation test results for AI today.
I don't expect people to be familiar with more than "trust me bro", but it's all right there for you to find with a search engine of choice.
And, indeed, available for the LLMs themselves to explain to you in interrogative conversation.
Who should I be more scared of? China, which has been doubling down on open transparent research, or the secretive US companies who are in bed with the most unhinged administration we've ever had and has been actively starting wars?
They're not proposing anything concrete, and when they do, what do you think the proposal will be? Will OpenAI and Anthropic open themselves for inspection so we can verify they really have stopped developing these "world ending" technologies? Or are their proposals going to be aimed at everyone running open Chinese models?
And if a threat to the human race does come from AI, it's going to come from OpenAI/Anthropic. Hypercapitalist, secretive, in bed with the government, plus multiple real documented hackings of open source infrastructure already.
We're a hell of a lot safer with China doing the same research out in the open and making it available to anyone. The choice might well be: one or two superintelligent autonomous AIs at OpenAI/Anthropic - or a lot of smaller ones, unable to be controlled but also coming out of a diverse set of environments.
One of those leads to a stable ecosystem where we can all coexist, the other is genuinely terrifying. But make no mistake, from OpenAI/Anthropic this is all motivated by their stock price - when you're in the silicon valley mindset, it distorts your reality. They've convinced themselves that everyone's safer if they stay on top and in control, conveniently ignoring how that benefits them, and I don't believe them for a minute.
Boredom may cause teenagers to commit acts of vandalism; it’s not a typical cause for revolution. (That would be things like famine, gross inequality, severe injustices and so on.)
From the article: “For well over a decade, the reliability data that exists (and I think this is backed up by the anecdotal experience that mechanics who work on Volvos have) is that Volvo reliability is mediocre to poor, but of course Volvo forums are full of people who insist that Volvos are among the most reliable cars and that the data are all wrong.”
The author mentions one explanation, which is that most people driving a Volvo won’t experience a breakdown (and then generalize from that). Another one is of a very different kind known as post-purchase cognitive dissonance (https://en.wikipedia.org/wiki/Cognitive_dissonance#Consumer_...).
A mistake that both Dan and the forum users made, was to discuss averages instead of reliability for certain platforms and/or engine types. Most manufacturers have models with wildly different reliability.
There is a confounding variable, however -- the person is most likely a good dev. It stands to reason that they've had a decent career at least since then.
It is not necessarily untrue (source: am currently involved in battery R&D). However, the degree to which it is true will vary strongly based on the particular choice of chemistry, what sort of additives/agents are used (e.g. scavengers/getters), cell/pack engineering choices (protection against ingress of outside atmosphere and such), and so on and so forth.
It's always a good idea, and it's rather an obvious point - not to mention, something schools try to drill into kids since they're single-digit years old.
At this point I treat lack of dating articles as being done intentionally, and as evidence of malicious intent - i.e. that the entity publishing it has a reason they don't want readers to know the creation date, and there's really no reason other than trying to get away with lies or otherwise screw the reader over.
(Above is a heuristic I apply by default; this post is an example of a case-by-case exception, because of the type of site, my impression of articles read in the past, and of the author themselves.)
As a side-gig I taught within the doctoral education program for several high-ranking universities in Europe for about a decade (over a thousand PhD candidates). My impression is that nearly all funding for PhD projects flows to fields like medicine, physics, chemistry, computer science, electronics, and so on. Spending on humanities is absolutely minuscule compared to those.
I don't know, I did grad school in psychology and our grants would have been classified as "Medicine" or "Health" but a huge % of it is fundamentally ideological work rather than basic research. Academia really is a complicated mess, it's not an easy problem.
My first guess was that they used an external brick for the power supply with a relatively low output voltage--that would eliminate a lot of the CE test load. However, a cursory glance at the product photos suggests the power supply sits within the base of the lamp. Maybe the product developer can shed some more light on this. ;-)
That would certainly make certification easier, but as I suspect you understand, wouldnt achieve it alone.
Even if every component was CE qualified, the combination would have to pass its own testing, plus there are a lot more to the standards than just not electrocuting you immediately upon contact.
I can't see any of the energy efficiency labelling that would be required in the UK or EU for example...
Of course, for readers here with a solid grasp on how LLM's operate it's not exactly surprising. Still, I feel this is exactly the kind of thing that drives home that the way we reason about (general) intelligence does not map cleanly to LLM operation, no matter how advanced the current models are.
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