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blog.computationalcomplexity.org | ||
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www.jeremykun.com
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| | | | | Decidability Versus Efficiency In the early days of computing theory, the important questions were primarily about decidability. What sorts of problems are beyond the power of a Turing machine to solve? As we saw in our last primer on Turing machines, the halting problem is such an example: it can never be solved a finite amount of time by a Turing machine. However, more recently (in the past half-century) the focus of computing theory has shifted away from possibility in favor of determining feasibility. | |
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windowsontheory.org
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| | | | | (Also available as a pdf file. Apologies for the many footnotes, feel free to skip them.) Computational problems come in all different types and from all kinds of applications, arising from engineering as well the mathematical, natural, and social sciences, and involving abstractions such as graphs, strings, numbers, and more. The universe of potential algorithms... | |
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pressron.wordpress.com
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| | | | | Abstract: Machine and language models of computation differ so greatly in the computational complexity properties of their representation that they form two distinct classes that cannot be directly compared in a meaningful way. While machine models are self-contained, the properties of the language models indicate that they require a computationally powerful collaborator, and are better... | |
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www.cleverthinkingsoftware.com
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| | | We're Not in a Bubble-We're at the Brink of Revolution Every day I read another hot take claiming we're in an AI bubble. These writers argue that Large Language Models cannot possibly lead to Artificial General Intelligence. And while they've got a point about the limitations of current systems, they'recatastrophically wrongabout the bubble. We're not [...] | ||
