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Let's say you have a computer that simply adds two large numbers. You can scale it indefinitely to add even larger numbers, but it will never do anything interesting beyond that because it's not a complex system. Computation in itself does not necessarily lead to emergent properties, it is the structure of the information processing that dictates this.

Nonsense analogy. Fermentation is a natural phenomenon that was discovered, researched and found to have useful applications; it was never designed. Computer programs and their functions, on the other hand, are the creations of human inventors, but somehow these inventors don’t understand how they actually function?

I'm not sure what anyone is expecting to happen in terms of widespread EV adoption until solid state batteries are viable. And that's at least 8-10 years away.

In the meantime, hybrids and PHV's are much more effective at reducing emission than full EV's.

> Knowing that neural networks are theoretically Turing complete does not imply that the networks we train (ie the sets of weights we in fact encounter) have created Turing complete solutions.

• A computer algorithm is anything that runs over an automaton and taking some input encoding a question, gives us the answer.
• ML are systems where we create a model and adjust it through some configuration, until it will, given some input encoding a question, give us the answer.
• ML can only solve the problems its own system can solve. A turing complete ML system can solve anything a turing machine can.
• It stands to reason that some problems can only be truly solved through an algorithm (e.j. if the possible inputs are uncountable infinite).
• If we assume that an ML model can solve these problems, we have to assume that it can encode in its configuration algorithms, including some that we know. Otherwise we assume there's a limit.

Now I wouldn't take this to say that it would learn to be optimal. Say we trained an AI to sort lists, I could see it encoding a sorting algorithm within its network eventually, but I can't say if it'd ever discover an O(NlogN) algorithm, even if pressure was put to optimize the solution as well as being correct. But something that we can say is that neural networks may be able to do Markov Chain models internally, as its own sub-algorithm, if that's the way to solve the problem. But the assumption of this is why we think so much about neural networks nowadays.

That said the problem of sufficiently good learning is not trivial at all. And we certainly could discover its impossible to do. But at the moment, AFAIK, there's no reason not to think it can't happen.

The fact that we observed this happening is good, it basically validates the assumptions and models that we've had up to know, and implies that "sufficiently good learning" is attainable. There may still be limits (like finding the optimal algorithm, vs just an algorithm). So there's a lot of value in seeing it.

But to day-to-day applied ML research I am not sure if it really has that much of an impact, this lays ground work though.

The really interesting discovery here. More than the conclusion the interesting thing is how they reach it, the ability to reach it. As ML starts being used in more areas, we'd want to be able to audit an ML model and verify that it effectively has found a useful solution, and isn't just over-fitted beyond what we understand. Being able to identify algorithms within the system, and be able to split the AI model into simpler "steps" that do all the things, we'd be able to validate that it has found a good solution.

Again not something we need to solve now, but being able to know how to do it is a good thing to start doing already.

And on a more complex theme. This sets a better understanding of how ML models work, and in the process they can give us a hint of how intelligent systems in general work themselves, and we could then revisit that. This is like a longer-vision here. Being able to deconstruct models we may start seeing patterns and start forming more interesting math to describe intelligent systems in general. Which is where mapping it to organic models could allow proving strong AI, for example.

Why not? I’m not trying to be argumentative, rather I’m genuinely curious why this is impossible

It needed it.

"No shit, Sherlock." has echoed quite strongly in my head soon as I read it.

Many people and politicians often prioritize short-term goals, which made it difficult to implement changes that are beneficial for society in the long-term.

Guess we really need to start to threaten ourselves with a hot branding iron to move us along ahead. 🤦‍‍♂️

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I said that you can't have theory of mind appear from scaling just any compute system, not that you can't scale it.

If only a government would give us a bunch of money to retain our stock buyback program while we invest in our business like a business with money is supposed to do….

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Youre in the dunning kruger valley rn, better climb out soon

They’ve only had like…..50 years since that smog shit started in the 70s

Ah, so we’ve found d the novel discovery by the sound of this confusion then… yes?

It's based on Firefox in that there is code overlap, and the same web engine is used to render emails, but this doesn't make it slow. It just means that the performance is similar to Firefox, which is plenty fast since you need some web engine to render the emails anyway.

With electron-based programs though, it goes one layer deeper. The whole program, not just the email content, is basically running inside another separate program that is the electron javascript engine. That's why it's slower. It would be like if Thunderbird was a website that ran inside Firefox.

I've been very happy with bitwarden.

What do cars do ~95% of their time? Parking. Build only 1/20th of them and use them efficiently.

Of course it needs radical change, but those changes will always be vigorously opposed by the fossil fuel industry and all the politicians they own.

Greed is the main factor blocking necessary change

Dude I'm telling you, every time we talk about AI...

It's like you say, "AI is basically just orange"

And someone says "Uhh no, you clearly don't know how AI works, it's actually an orange inside an orange inside an orange"

And you're like "Yeah, so it's a fucking orange"

I wasn't basing that on statements from Lastpass. I just presented on this at work and as part of preparing the presentation, we analyzed Lastpass Bitwarden and 1Password vaults as they are synced to their respective services. Palent's blog was certainly one of the sources we used in putting together the analysis.

Some interesting takeaways are that Shared Folders and Federated authentication offered some additional security. 2FA is completely meaningless in this situation as nothing from 2FA is used as part of the encryption key derivation.

As you also mentioned, age of the account made some differences (though not in username encrypted or not). Default iterations being a big one, and AES-CBC vs AES-ECB, which would certainly make usernames more easily determined.

“Matryoshka-esque-computer-inside-a-computer” bruh. Terry Davis was right when he said that it is the idiots who admire complexity while the smart people admire simplicity.

Incorrect about scaling computers, which is what computers do — scale. They are universal. As for consciousness, we’re not sure it’s not embedded in everything (panpsychism). I accept this may not be accepted, and perhaps impossible to prove, at least in our lifetimes, but brains are computers, and as such, are scaleable

We could start by downvoting this stuff whenever we see it. It's mad that this has 600 upvotes and the top comments are about how it's misleading.

Love thst I got a downvote for not knowing an obscure term..... but thank you for the explanation.