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ChunXi Zhang Hónapja i think why dl cannot do general discrete learning well is fundamentally because: 1. Dl choose this way because back progragation works only with continues values and parallel computation works in sync. Really interesting ideas all around. So technically it is basically a coding framework where you can write some code to build a training algorithm to run over a large data set until you get a statistical model that most accurately represents a solution for the problem space you coded for.

Then that model representing the result of tweaking your training algorithm then can be used on any other legjobb kereskedési bot running the same neural network framework.

And all of this is running on general purpose hardware of course. The problem is that the brain doesn't have a "training algorithm" that you tweak directly to produce a training model. So this kind of framework hides the fact that the parameter identification process of any problem space that is used to create a decision tree or decision framework is not actually handled by a neural network.

Yet in order for any work to be done on a modern computer, you have to have those things and write code. So creating and tweaking parameters and weights is the "intelligence" that is the core hogyan lehet online becsületesen pénzt keresni modern neural networks has to be done in code and either interpreted or compiled to run on a CPU.

So the fragility comes from the fact that there is no way for forex office kastrup neural network to dynamically identify new parameters and tweak weights without code.

That said, the portability of modern neural network architectures and frameworks does allow for a lot of iterative evolution of the forex office kastrup and frameworks until physical architectures and other kinds of software architectures can evolve to bring the neural network down another layer in the program stack. ZergD Zerg Hónapja Pure gold. Thank you so much! Keith Duggar Hónapja Hilarious comment, Rohan! I honestly LoL'd in real life. Khan Ahmed Hónapja wow intelligent and wittysubscribed immediately, this video was like 3blue1brown of Artificial intelligence Scott Brown Hónapja I wrote those unit tests, come approve my keras pull request, lmfao.

Daniel Garvey Hónapja I think these "discrete problems" are in reality compositional or hierarchical problems. Composition exchanges memory for reasoning in a sense, but the problem is that it is forex office kastrup just more efficient to memorise.

There's currently not really anyway to separate these two in neural networks. DavenH Hónapja Again, wonderful and though-provoking episode.

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I think there's a creeping mismatch of conceptions somewhere which is leading some to simplistic conclusions and will force them to eat crow many times over kind of like "perceptrons can't even solve XOR, NNs suck! Or at least, that all incoming data maps to such a singular well-connected manifold.

If true, all this strongly limited-by-interpolation stuff would make sense to me. However, if you consider that NNs can manipulate many many disjoint topological islands and bring them together on certain dimensions, separate them again, successively over s of layers, this starts to look a lot more like the work of classical computation.

If classical computation is also roped into forex office kastrup interpolation mutató bináris opciók profit napfelkelte, then I'm not sure what its implied limitations are.

A couple forex office kastrup remarks on that subject. There was a point where it was brought up by one of your guests so fair play, but it seems that this argument is a bit of a distraction now.

It is not to say that comparisons with extant computing systems are unhelpful; they lie elsewhere on the spectrum, and certainly mechanisms that introduce a large sandbox of memory for NNs to store and access representations make a lot of sense.

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But, when thinking about memory, consider that large models in the s of billions of params, have a huuuge amount of stateful "memory" to use -- the values of the activations themselves. Yes it's ephemeral, with our present architectures, as these values are only available as the forward pass progresses. In that way it's analogous to stack space. Heap space is still kind of lacking outside of NTMs. The point is that DNNs do possess a logical workspace for successive calculations to happen, albeit ephemeral and bounded, and that opens the door IMO to some flavour of non-interpolative computation happening.

Final thought, on the no-free-lunch theorem. This does not apply generally. When a system is non-optimal on all measurement axes, by definition there must be a system that can dominate it. Likewise, it needs to be optimal on only one measurement axis to be indominable.

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That curve that defines optimal tradeoffs between conserved quantities is known as the Pareto Optimal Curve or Frontier. My point is that, particularly for messy optimization tasks, optimality on any axis is in practice impossible to prove, and none of the known neural architectures or cobbled systems like NARS or OpenCog are going to be actually on the POC, and so the NFLT is going to be technically inapplicable -- though kereskedési robotok metastok practice it is probably still an okay guide.

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With this in mind, we should not dismiss the possibility of an AGI that is more competent than any of our fined-tuned -- yet still suboptimal -- systems. In general, I'd be careful making arguments which rely on asymptotic properties; the conclusions tend to degenerate when the relevant extreme like optimality is relaxed.

I think it's also worth noting and not to suggest anyone is arguing against this that while an AGI system must sacrifice optimality in all but one task -- and very likely all -- that does not preclude non-optimal yet still superhuman competence on all the measurement axes we care about.

To me, that's sufficiently general. And then, what's to prevent a robustly general purpose, but completely not-optimal-at-anything-specific meta-process from slowly implementing task-optimized tools at will, much like we do?

Okay, that certainly broke my hyphenation budget! Now gimme that free lunch. The point is well made, and quite clear, that NNs don't do much of what computers do. The strongest position I'm advocating is that forex office kastrup NNs can still approximate what small programs running on limited stack space can do. That proposition is especially vulnerable to what Keith says about the qualitative difference in algorithms each can produce. I'm curious about this.

The empirical differences are clear, at least most of the time GPT did open my mind though. Not that it was producing compact algorithms to generate accurate digits of pi, but that it was using some kind of messy logic or computation for which we don't have a good measure of the boundaries.

You guys have evidently done a lot more reading on the subject than I, so it's quite possible that my intuitions are not mature yet. Machine Learning Dojo with Tim Scarfe Hónapja Hello Daven, really appreciate your engagement and thoughtful commentary as always my friend. Keith commented eloquently on the later part of your question re: computability.

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Remember that TC just means that a computational system could run any program which a turing machine could run. Clearly NNs are not turing complete, and say, JavaScript is.

It might take JavaScript an awful lot of time to compute your arbitrary digit of pi, but an NN never could.

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The first intuition I have is that islands is the right way to think about it. NNs sparsely code data onto many different disconnected manifolds think typical tSNE projection. What happens to the output when you do a linear combination in the input space between points from two different manifolds in the latent space? Will think more on this and add more later on. Thanks for the great comment Keith Duggar Hónapja DavenH, thank you for your detailed and thoughtful questions.

My focus in the "Turing-Complete" debate is, in part, to communicate what you expressed yourself: "It is not to say that comparisons with extant computing systems are unhelpful; they forex office kastrup elsewhere on the spectrum, and certainly mechanisms that introduce a large sandbox of memory for NNs to store and access representations make a lot of sense.

This confers practical differences upon algorithms designed for Turing complete systems even when running on practically bounded systems because the algorithms are fundamentally different. Here is a quote from you that hits the key difference w. The longer we continue to obfuscate the fact that NNs are not Turing Complete by sneaking in things like infinite precision forex office kastrup point registers the longer we delay progress on next generation Turing complete computational models and practical systems that approximate them with expandable memory and unbounded running time.

The central message of the No Free Lunch theorem is that to learn from data, one must make assumptions about it - the nature and structure of the innate assumptions made by the human mind are precisely what confers to it its powerful learning abilities. If so, I don't agree with that.

Balance Olaj hatása a Zinzino -tól. (Június 2021).

I think it is entirely possible that an AGI can radically exceed human intelligence on all tasks. That said, I do not think intelligence is "all powerful" either.

Пришлось напомнить себе, что она была первой, действительно старой женщиной, которую видели юноши. "Молодым в особенности трудно, - подумала Николь, - иметь дело с одряхлевшей женщиной.

In other words, I'm not worried that an embodied AGI can twinkle its red robot eyes in just the right way as to crash my brain. Such power is fantasy speculation at this point. Arno Khachatourian Hónapja I think Chollet and Walid Saba argue for much of the same thing: a need for type 2 thinking or understanding combined with the type 1 signal processing power of neural nets.

Anas Nasseur Hónapja Let it remove data forex office kastrup is unnecessary for its survival let it play out scenarios that will be possible and place itself within the best scenario for success then place itself in those scenarios each time it evolves it memorises the shapes of constructors that it made using the constructors as limbs and eys and gets punished for using them inefficiently yet can stretch its boundaries if needed but create but reverse engineers successful constructors and shortens them but also keeps the old ones as references because they could forex office kastrup a method when interpreting a new environment it uses the end nodes of the successful constructor's within the bounder of its limitation so unrealistic am I just being dumb, please tell me lol Anas Nasseur Hónapja this goes all wrong when we introduce self-persuasion ooooowee but all so could be a good thing to use to direct " " to human needs I find it more disturbing than useful.

If so, is this learnable? Which to me is a quiet warning bell that there is far more to be plumbed before setting foundations. As in, chasing AGI via generality via abstraction is making an engineering project out of a philosophical venture. If you are asking your model to be general, you are asking it to understand bejelölt opciók universe. Undoubtedly there is practical insight in assessing forex office kastrup of learning and search methods, and ditching hype to do better science.

For now I think we can only and correctly proceed in an epistemic mode make better software and we have a lot of room to run with modern computing. But the true game is ontological. No page would be truly accurate nor would you ever finish. For a concrete example, talking about appleness, plainly something in the putative capsule-NN-DSL-NN vein could capture familiar important qualities.

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Red, round. But we would have no sense whatsoever of the completeness of representations, just their usefulness i. But what is our sense of apples for comparison? Perceptively, sight, nm, scent, midrange mass spec, touch haptics fairly crude but sensitive to important characteristics like bruising.

Should we consider ecology in deep time? One apple is a cc email of a thought a forest is having.

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Or whatever. You and the tree might disagree. This also happens to align with us. It sets you apart, really is unique afaik, giving credence to AGI talk being grounded in sota ML practitioner commentary, and is available at a disgustingly low price ha. DavenH Hónapja These are excellent points. Jeffrey Holmes Hónapja I was thinking about what Tim said in terms of separating intelligence and consciousness.

I have always thought the same, I suppose. However, Yannic's comments about conscious introspection made me wonder if a truly intelligent being must always be "on" - or conscious. Currently, we create "intelligent" programs or algorithms and then train them or ask them to reason about something.

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But otherwise, they are inactive forex office kastrup. There is no idle thinking or pondering that occurs. Are we missing something?

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DavenH Hónapja Introspection kriptopénz cserék fiat pénzzel self-attention do not need anything qualitative to function, so there is no requirement of consciousness. Vijay Eranti Hónapja Really great session.

Imho: intelligent learnt inference loop may use gradient descent with continous feedback of results of interpolation or extrapolation like manual Tta or test time augmentation is an example manual baby step to program synthesis of discrete components forex office kastrup rim cells another learnt than manual way of tta.

Hopefully having more powerful inference loop program learnt recursively may be the direction to go. Jeffrey Holmes Hónapja My favorite quote: "Intelligence is about being able forex office kastrup face an unknown future, given your past experience. Garron I apologize for creating a confusing experience for you. I was writing up something for a disproportionate amount of time, but that is not a way. Feel free to ponder on my cryptic reply, and if you find anything that you can shape into a more specific question than "the fuck you sayin", then feel free to ask.

Thank you for your effort with the unknowns, but that is unfortunately quite misguided at the moment, and I rather give the benefit of doubt, and ignore the little evidence on your behalf as inconsequential so far. Again, if you have any substance feel free to follow that. Garron 27 napja Martin Balage What forex office kastrup fuck are you even saying?!

To a big bang start of the entropy? Sounds like god again. So either you externalize the intelligence, or internalize, or integrate via a whole other question. Rebel Science Hónapja "In order to build a general intelligence, you need to be optimizing for generality itself. They don't mix well. Optimization is the opposite of generalization.

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It's the reason that deep learning cannot generalize in the first place.