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auraithx

@ auraithx @lemmy.dbzer0.com

Posts
56
Comments
289
Joined
2 yr. ago

  • Who remembers this?

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  • fMRI studies show that white-and-gold perceivers exhibit more activity in frontal and parietal brain regions, suggesting that their interpretation involves more top-down processing. This means they are more, not less, engaged in contextual interpretation.

    Some differences may relate to physiological traits like macular pigment density, which affects how much blue light is absorbed before reaching the retina. People with higher density tend to see white and gold

    Color perception is not only about the visual cortex’s function but about the image’s properties and the brain’s inferential processes. You’d know this if you weren’t a dumb blue-n-black’er

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  • Yes a very light blue, nobody is seeing brilliant white. But on a colour slider it’s much closer to white than the ‘true’ dark blue of the dress. If you sample the sleeve or whatever that is hanging over it’ll be even closer to pure white.

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  • Well the pixels themselves are white and gold so…

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  • It’s subconscious it’s not something you can learn. If that were the case people would have no issue understanding how others weren’t ‘decrypting’ the photo.

    Also the majority see it as blue and black. 30% as white and gold.

    The Journal of Vision, a scientific journal about vision research, announced in March 2015 that a special issue about the dress would be published with the title A Dress Rehearsal for Vision Science.

    The first large-scale scientific study on the dress was published in Current Biology three months after the image went viral. The study, which involved 1,400 respondents, found that 57 per cent saw the dress as blue and black, 30 per cent saw it as white and gold, 11 per cent saw it as blue and brown, and two per cent reported it as "other". Women and older people disproportionately saw the dress as white and gold. The researchers further found that, if the dress was shown in artificial yellow-coloured lighting, almost all respondents saw the dress as black and blue, while they saw it as white and gold if the simulated lighting had a blue bias.

    Another study in the Journal of Vision, by Pascal Wallisch, found that people who were early risers were more likely to think the dress was lit by natural light, perceiving it as white and gold, and that "night owls" saw the dress as blue and black.

    A study carried out by Schlaffke et al. reported that individuals who saw the dress as white and gold showed increased activity in the frontal and parietal regions of the brain. These areas are thought to be critical in higher cognition activities such as top-down modulation in visual perception

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  • Zoom in or sample the colours. They’re not blue and black.

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  • Optical illusion innit

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  • That we’re curious problem solvers?

    Anyway, science has determined that my way is most based

    A study carried out by Schlaffke et al. reported that individuals who saw the dress as white and gold showed increased activity in the frontal and parietal regions of the brain. These areas are thought to be critical in higher cognition activities such as top-down modulation in visual perception

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  • You can sample the colours and see it’s white with a very light blue tinge and gold.

    People who see it as blue and black are (correctly in this case) auto-correcting for the yellow light as the dress itself is black and blue.

    Whereas people who see it as white and gold are (subconsciously) assuming a blue shadow and seeing the pixels as they’re displayed.

  • Newsom asks judge for emergency intervention in Trump troop deployment in LA

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  • In a world of headlines there is no difference. Opinion pieces are just a way to publish something and deny association to it.

  • Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.

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  • You’re absolutely right that inference in an LLM is a fixed, deterministic function after training, and that the input space is finite due to the discrete token vocabulary and finite context length. So yes, in theory, you could precompute every possible input-output mapping and store them in a giant table. That much is mathematically valid. But where your argument breaks down is in claiming that this makes an LLM equivalent to a conventional Markov chain in function or behavior.

    A Markov chain is not simply defined as “a function from finite context to next-token distribution.” It is defined by a specific type of process where the next state depends on the current state via fixed transition probabilities between discrete states. The model operates over symbolic states with no internal computation. LLMs, even during inference, compute outputs via multi-layered continuous transformations, with attention mixing, learned positional embeddings, and non-linear activations. These mechanisms mean that while the function is fixed, its structure does not resemble a state machine—it resembles a hierarchical pattern recognizer and function approximator.

    Your claim is essentially that “any deterministic function over a finite input space is equivalent to a table.” This is true in a computational sense but misleading in a representational and behavioral sense. If I gave you a function that maps 4096-bit inputs to 50257-dimensional probability vectors and said, “This is equivalent to a transition table,” you could technically agree, but the structure and generative capacity of that function is not Markovian. That function may simulate reasoning, abstraction, and composition. A Markov chain never does.

    You are collapsing implementation equivalence (yes, the function could be stored in a table) with model equivalence (no, it does not behave like a Markov chain). The fact that you could freeze the output behavior into a lookup structure doesn’t change that the lookup structure is derived from a fundamentally different class of computation.

    The training process doesn’t “build a Markov chain.” It builds a function that estimates conditional token probabilities via optimization over a non-Markov architecture. The inference process then applies that function. That makes it a stateless function, yes—but not a Markov chain. Determinism plus finiteness does not imply Markovian behavior.

  • Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.

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  • Yes, LLM inference consists of deterministic matrix multiplications applied to the current context. But that simplicity in operations does not make it equivalent to a Markov chain. The definition of a Markov process requires that the next output depends only on the current state. You’re assuming that the LLM’s “state” is its current context window. But in an LLM, this “state” is not discrete. It is a structured, deeply encoded set of vectors shaped by non-linear transformations across layers. The state is not just the visible tokens—it is the full set of learned representations computed from them.

    A Markov chain transitions between discrete, enumerable states with fixed transition probabilities. LLMs instead apply a learned function over a high-dimensional, continuous input space, producing outputs by computing context-sensitive interactions. These interactions allow generalization and compositionality, not just selection among known paths.

    The fact that inference uses fixed weights does not mean it reduces to a transition table. The output is computed by composing multiple learned projections, attention mechanisms, and feedforward layers that operate in ways no Markov chain ever has. You can’t describe an attention head with a transition matrix. You can’t reduce positional encoding or attention-weighted context mixing into state transitions. These are structured transformations, not symbolic transitions.

    You can describe any deterministic process as a function, but not all deterministic functions are Markovian. What makes a process Markov is not just forgetting prior history. It is having a fixed, memoryless probabilistic structure where transitions depend only on a defined discrete state. LLMs don’t transition between states in this sense. They recompute probability distributions from scratch each step, based on context-rich, continuous-valued encodings. That is not a Markov process. It’s a stateless function approximator conditioned on a window, built to generalize across unseen input patterns.

  • Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.

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  • You can say that the whole system is deterministic and finite, so you could record every input-output pair. But you could do that for any program. That doesn't make every deterministic function a Markov process. It just means it is representable in a finite way. The question is not whether the function can be stored. The question is whether its behavior matches the structure and assumptions of a Markov model. In the case of LLMs, it does not.

    Inference does not become a Markov chain simply because it returns a distribution based on current input. It becomes a sequence of deep functional computations where attention mechanisms simulate hierarchical, relational, and positional understanding of language. That does not align with the definition or behavior of a Markov model, even if both map a state to a probability distribution. The structure of the computation, not just the input-output determinism, is what matters.

  • Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.

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  • You're correct that the formal definition of a Markov process does not exclude internal computation, and that it only requires the next state to depend solely on the current state. But what defines a classical Markov chain in practice is not just the formal dependency structure but how the transition function is structured and used. A traditional Markov chain has a discrete and enumerable state space with explicit, often simple transition probabilities between those states. LLMs do not operate this way.

    The claim that an LLM is "just" a large compressed Markov chain assumes that its function is equivalent to a giant mapping of input sequences to output distributions. But this interpretation fails to account for the fundamental difference in how those distributions are generated. An LLM is not indexing a symbolic structure. It is computing results using recursive transformations across learned embeddings, where those embeddings reflect complex relationships between tokens, concepts, and tasks. That is not reducible to discrete symbolic transitions without losing the model’s generalization capabilities. You could record outputs for every sequence, but the moment you present a sequence that wasn't explicitly in that set, the Markov table breaks. The LLM does not.

    Yes, you can say a table is just one implementation of a function, and from a purely mathematical perspective, any function can be implemented as a table given enough space. But the LLM’s function is general-purpose. It extrapolates. A precomputed table cannot do this unless those extrapolations are already baked in, in which case you are no longer talking about a classical Markov system. You are describing a model that encodes relationships far beyond discrete transitions.

    The pi analogy applies to deterministic functions with fixed outputs, not to learned probabilistic functions that approximate conditional distributions over language. If you give an LLM a new input, it will return a meaningful distribution even if it has never seen anything like it. That behavior depends on internal structure, not retrieval. Just because a function is deterministic at temperature 0 does not mean it is a transition table. The fact that the same input yields the same output is true for any deterministic function. That does not collapse the distinction between generalization and enumeration.

    So while yes, you can implement any deterministic function as a lookup table, the nature of LLMs lies in how they model relationships and extrapolate from partial information. That ability is not captured by any classical Markov model, no matter how large.

  • Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.

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  • This is an elegant metaphor, but it fails to capture the essential difference between symbolic enumeration and neural computation. Representing an LLM as a decompression function that reconstructs a giant transition table assumes that the model is approximating a complete, enumerable mapping of inputs to outputs. That’s not what is happening. LLMs are not trained to reproduce every possible sequence. They are trained to generalize over an effectively infinite space of token combinations, including many never seen during training.

    Your thought experiment—recording the output for every possible input at temperature 0—would indeed give you a deterministic function that could be stored. But this imagined table is not a Markov chain. It is a cached output of a deep contextual function, not a probabilistic state machine. A Markov model, by definition, uses transition probabilities based on fixed state history and lacks internal computation. An LLM generates the distribution through recursive transformation of continuous embeddings with positional and attention-based conditioning. That is not equivalent to symbolically defining state transitions, even if you could record the output for every input.

    The analogy to a spigot algorithm for pi misses the point. That algorithm computes digits of a predefined number. An LLM doesn't compute a predetermined output. It computes a probability distribution conditioned on a context it was never explicitly trained on, using representations learned across many dimensions. The model encodes distributed knowledge and compositional patterns. A Markov table does not. Even a giant table with manually filled hypothetical entries lacks the inductive bias, generalization, and emergent capabilities that arise from the structure of a trained network.

    Equivalence in output does not imply equivalence in function. Replacing a rich model with an exhaustively recorded output set may yield the same result, but it loses what makes the model powerful: the reasoning behavior from structure, not just output recall. The function is not a shortcut to a table. It is the intelligence.

  • Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.

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  • This argument collapses the entire distinction between parametric modeling and symbolic lookup. Yes, the weights are fixed after training, but the key point is that an LLM does not store or retrieve a state transition table. It learns to approximate the probability of the next token given a sequence through function approximation, not by memorizing discrete transitions. What appears to be a "table" is actually a deep, distributed representation compressed into continuous weight matrices. It is not indexing state transitions, it is computing probabilities from patterns in the input space.

    A true Markov chain defines transition probabilities over explicit states. An LLM embeds tokens into high-dimensional vectors, then transforms them repeatedly using self-attention and feedforward layers that can capture subtle syntactic, semantic, and structural features. These features interact in nonlinear ways that go far beyond what any finite transition table could express. You cannot meaningfully represent an LLM’s behavior as a finite Markov model, even in principle, because its representations are not enumerable states but regions of a continuous latent space.

    Saying “you just need all token combinations in a table” ignores the fact that the model generalizes to combinations never seen during training. That is the core of its power. It doesn’t look up learned transitions-it constructs responses by interpolating through an embedding space guided by attention and weight structure. No Markov chain does this. A lossy compressor of a transition table still implies a symbolic map; a neural network is a differentiable function trained to fit a distribution, not to encode it explicitly.

  • Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.

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  • Your conflating surface-level architectural limits with core functional behaviour. Yes, an LLM is deterministic at temperature 0 and produces the same output for the same input, but that does not make it equivalent to a Markov chain. A Markov chain defines transitions based on fixed-order memory and static probabilities. An LLM generates output by applying a series of matrix multiplications, activations, and attention-weighted context aggregations across multiple layers, where the representation of each token is conditioned on the entire input sequence, not just on recent tokens.

    While the model has a maximum token limit, it does not receive a fixed-length input filled with nulls. It processes variable-length input sequences up to the context limit, and attention masks control which positions are used. These are not hardcoded state transitions; they are dynamically computed weightings over continuous embeddings, where meaning arises from the interaction of tokens, not from simple position or order alone.

    Saying that output diversity is just randomness misunderstands why random sampling exists: to explore the rich distribution the model has learned from data, not to fake intelligence. The depth of its output space comes from how it models relationships, hierarchies, syntax, and semantics through training. Markov chains do not do any of this. They map sequences to likely next symbols without modeling internal structure. An LLM’s output reflects high-dimensional reasoning over the prompt. That behavior cannot be reduced to fixed transition logic.

  • Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.

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  • Because transformer architecture is not equivalent to a probabilistic lookup. A Markov chain assigns probabilities based on a fixed-order state transition, without regard to deeper structure or token relationships. An LLM processes the full context through many layers of non-linear functions and attention heads, each layer dynamically weighting how each token influences every other token.

    Although weights do not change during inference, the behavior of the model is not fixed in the way a Markov chain’s state table is. The same model can respond differently to very similar prompts, not just because the inputs differ, but because the model interprets structure, syntax, and intent in ways that are contextually dependent. That is not just longer context-it is fundamentally more expressive computation.

    The process is stateless across calls, yes, but it is not blind. All relevant information lives inside the prompt, and the model uses the attention mechanism to extract meaning from relationships across the sequence. Each new input changes the internal representation, so the output reflects contextual reasoning, not a static response to a matching pattern. Markov chains cannot replicate this kind of behavior no matter how many states they include.

  • Is moderately severe back pain really normal at 30?

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  • I think hEDS is just when mutations in the TNXB gene are extreme enough to warrant a diagnosis, yes.

    The 20% of the pop who is hypermobile have milder mutations, but can suffer all the same from the downsides of hypermobility, and benefit from targeted interventions.

  • Is moderately severe back pain really normal at 30?

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  • Do you have any hypermobility? Even in your fingers?