Perth, Australia too: https://www.education.wa.edu.au/mobile-phones - phones are banned from the start to the end of school. Primary students cannot have them on them, secondary students can but they must be off and out of sight (even at break times).
That seems impossible (unless maybe you meant to write "predates", and even then it seems to only be about 3 years).
The comic was published on 9 February 2007 [0].
The PS3 was first released in November 2006. I haven't watched the video yet, but its description says "2010 saw the first hacks for the Playstation 3".
Almost went past looking at this option, it has a far bigger ecosystem of both storage and auth plugins and otherwise looks to be more mature than I might have guessed from the passing comment.
I think for phone calls this only works on Facetime and for calls maybe only on Wifi.
SMS syncing is true. But if you have a phone and you're out and about you also need mobile data, so would otherwise need a second mobile plan. Or to swap the SIM over, which was easier with a physical SIM, more difficult with eSIMs.
But is it really what we want, machines with the same defects as humans? I don't want a pocket calculator that make mistakes "sometimes" so I have to double-check the results, I want a pocket calculator that works (to those who want to argue that pocket calculators don't give the correct result for (1/3)*3: STFU).
>But is it really what we want, machines with the same defects as humans?
Sort of, actually. I think we humans actually have some intuition that we'd be more effective if our cognition were augmented more directly by machine strengths: the ability to run precise calculations, more memory, ability to look facts in some sort of knowledge graph.
I think we're on the right track, but instead of augmenting humans with machine strengths, we're building intelligence in hardware in a way where it can access that augmentation. Plus, then we can quickly distribute updates, run parallel instances, etc.
If intelligence is compression, and hallucinations are essentially loss, then as the models grow in size performance (at least as far as hallucinations) should reduce. Or we'll get things fast enough that we can afford to stop relying on model weights for memory and check an increasingly larger set of discrete facts as part of reasoning.
Right now, the models are making trade-offs. As compute grows, and inference gets faster, we can make fewer of those trade-offs and start to use the unique strengths of machines to fill the gaps we're seeing, I suspect.
One of the biggest strengths of a computer is reproducibility. The worst software bugs are inconsistent or non reproducible. The least useful calculators apply rules inconsistently, to your example.
the inconsistency of LLMs is by far one of the biggest gripes I have with them. Closely related to their apparently deep desire to avoid following instructions.
I know these are both a byproduct of noise (which is somewhat tunable) and noise is inherent to these systems in a lode bearing way.
I still hate it. it’s holding the technology back. I don’t honestly see how we can safely or even successfully approach the idealized realm of AI without bypassing this problem, which to my understanding, probably means not using language models at all and trying a totally different approach. But I really don’t know much about machine learning, I’m a super novice compared to a lot on this website.
When we are really thinking about something we do it forwards, backwards and middle out, and regenerate and distill many times.
When we do meta thinking about that process after the fact, two things happen. 1, we change our total “thought” by adding that meta thinking to it. And 2: it’s a very lossy process, because we don’t have very good data about what our brain or mind was actually doing during that first think and emotional factors are nearly always at play and even more complex.
Now for the more complex AI, the fragmented process of multiple agents and loops and reruns are pretty similar to that first think we do. At least structurally. But the meta think is where they differ. They have no emotion, but they also have even worse data about its own function. They constantly degenerate so I would argue their “changing the thought by thinking about it” factor is also generally way higher than ours.
Getting better at consistent/reproducible thinking, with many ‘steps’, that leaves good documentation of that thinking behind for future analysis, has to be one of the more important areas for the big flagships going forward. I’m certain that “what is this fucker doing and why” is the biggest pain point for AI researchers. Or the math, it’s usually the math.
But you’re correct in the general structure; they generally do the same post hoc analysis we do, just noticeably worse because of their opaque nature(even to themselves) and general degenerative instability.
> Please don't use HN primarily for promotion. It's ok to post your own stuff part of the time, but the primary use of the site should be for curiosity.
potentially overly-aggressive spam filtering? the first comment by that account happens to start off with "The new $39.99 USD Anker Nano 45W(A121D) charger" verbatim.
I think he means that, when Lucas sends himself the link to HN, it's autodead. If you click the domain, you see all submissions from LucasLabs are dead.
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