[HN Gopher] DeepMind's AI helps untangle the mathematics of knots ___________________________________________________________________ DeepMind's AI helps untangle the mathematics of knots Author : bryan0 Score : 78 points Date : 2021-12-10 19:48 UTC (3 hours ago) (HTM) web link (www.nature.com) (TXT) w3m dump (www.nature.com) | vanusa wrote: | _One technique in particular, called saliency maps, turned out to | be especially helpful. It is often used in computer vision to | identify which parts of an image carry the most-relevant | information. Saliency maps pointed to knot properties that were | likely to be linked to each other, and generated a formula that | seemed to be correct in all cases that could be tested. Lackenby | and Juhasz then provided a rigorous proof that the formula | applied to a very large class of knots2._ | | Kewl, and not to discount the result but sounds like ... pattern | matching, right? | | So why do the editors of all these journals keep feeding off the | ML == AI meme? | | Surely they know better by now. | Jensson wrote: | Because pattern matching is the only significant new thing in | AI research the past few decades. Every time they apply pattern | matching to something new we will get articles like this and AI | optimists will come and say that soon AI will take our jobs. | Pattern matched images, pattern matched texts, reverse pattern | matching to generate uncanny images and articles, pattern | matched gameplay states etc. | version_five wrote: | Deep learning is pattern finding, not pattern matching. That | is the difference. There is a business narrative that's | hyping up ML/AI, there's lots of retrospective tracing of | modern ML's roots back to something older, but that doesn't | change the fact that there has been a big explosion of | demonstrated advances opened up by modern approaches over the | past 10 years. | | Edit: just to expand a bit, the "intelligence" in modern AI | is in the training, not inference. I think people see | inference in a neural network as pattern matching or some | kind of filter, and that's basically true, and "dumb" if you | like. But learning the weights is a whole different thing. | Stochastic Gradient Descent et al are using often only a | comparative few examples to learn a pattern and embed it in a | set of weights in a way that can generalize despite being | highly underdetermined. It's not general intelligence, but | it's a much different thing than the casual dismissals people | like to post, usually directed at the inference part as if | the weights just magically appeared | vanusa wrote: | _Deep learning is pattern finding, not pattern matching._ | | "Pattern matching" obviously includes "pattern finding" | already, in common parlance. | | By "deep learning" one means, in effect, "hierarchical | pattern finding" (which is basically what "feature" or | "representation" learning means to a lay person). | | But still, at the end of the day ... pattern finding. | | Or that is to say: "just ML", not AI. | jonas21 wrote: | I think most people would take "pattern matching" to mean | matching against fixed patterns or engineered features, | as opposed to learned features which you might call | "pattern finding". | vanusa wrote: | Although the distinctions you're drawing are valid -- | from the bigger picture point of view, this is basically | hair splitting. | | The more basic and important point is: technology that | works on the basis of "pattern finding" (however you wish | to call it) -- even if it performs exponentially better | and faster than humans, in certain applications -- is | still far different from, and falls far short of | technology that actually mimics full-scale sentient | (never mind if it needs to be human) cognition. | | Or that is to say; of any technology that can | (meaningfully) be called AI. | fault1 wrote: | I think what you are describing is the AI effect: | https://en.wikipedia.org/wiki/AI_effect | | 'AI' has always almost been a marketing term, anyways. | | I would call all of this pattern detection btw. | | But so would i call the kernel perceptron (1964): | https://en.wikipedia.org/wiki/Kernel_perceptron | Jensson wrote: | I think the AI effect is reversed. It should be "any time | a program can compete with humans at a new task it will | get marketed as AI". | | I don't really get the original argument. I never thought | "todays AI isn't smart, but if it could play Go, that | would be an intelligent agent!". So "the AI effect" is | just a strawman, I have seen no evidence that anyone | actually made such a change of heart. AI research is | important, but nothing so far has been anywhere remotely | intelligent and I never thought "if it could do X it | would be intelligent" for any of the X AI can do today. | When an AI can hold a remote job and get paid without | getting fired for like a year, that is roughly the point | I'd agree we have an intelligent agent. | YeGoblynQueenne wrote: | >> "Pattern matching" obviously includes "pattern | finding" already, in common parlance. | | So, the terminology I recognise is "pattern matching" | when referring to matching an object against a pattern, | as in matching strings to regular expressions or in | unification (where arbitrary programs can be matched); | and "pattern _recognition_ " when referring specifically | to machine vision tasks, as an older term that as far as | I can tell has fallen out of fashion. The latter term has | a long history that I don't know very well and that goes | back to the 1950's. You can also find it as "statistical | pattern recognition". | | To be honest, I haven't heard of "pattern finding" | before. Can you say what "common parlance" is that? What | I'm asking is, whom is it common to, in what circles, | etc? Note that a quick google for "pattern finding" gives | me only "pattern recognition" results. | | To clarify, deep learning is not "pattern matching", or | in any case you will not find anyone saying that it is. | rackjack wrote: | I'm pretty sure they meant "pattern finding" as | "discovering new patterns that can be used for matching", | not "finding a pattern that matches an existing known | one". | Jensson wrote: | But currently humans do that "pattern finding". If you | want it to learn to recognize animals you give it | thousands of images of different animals in all sorts of | poses, angles and environments and tells it what those | animals are, basically the "pattern" is created by the | humans who compiles the dataset with enough examples to | catch every situation, and then the program uses that | pattern to find matches in other images. However if you | want a human to recognize animals it is enough to show | this picture and then they will be able to recognize most | of these animals in other photos and poses, humans | creates the pattern from the image and don't just rely on | having tons of data: | | https://i.pinimg.com/originals/35/78/47/35784708f8cc9ef23 | 45c... | | Edit: In some cases you can write a program to explore a | space. For example, you can write a program that plays | board games randomly and notes the outcome, that is a | data generator. Then you hook that to a pattern | recognizer powered by a data centre, and you now have a | state of the art gameplay AI. It isn't that simple, since | writing the driver and feedback loop is extremely hard | work that an AI can't do, humans has to do it. | visarga wrote: | > But still, at the end of the day ... pattern finding. | | And programming at the end of the day ... if's and for's. | See how ridiculous it is? A fuzzy high level concept | explaining away the complexity of getting there. | space_fountain wrote: | In addition to what others are saying I want to highlight | that there's decent evidence that "all" we're doing is | pattern finding and pattern matching. Definitely ML isn't | at a human level yet, but I see no reason to think that | we're missing some secrete sauce that's needed to make a | true AI. When someday we do achieve human level | intelligences in computers, it will be at least in large | part with ML. | Jensson wrote: | > In addition to what others are saying I want to | highlight that there's decent evidence that "all" we're | doing is pattern finding and pattern matching. | | Is there? Link? It is possible, I just don't think there | is enough evidence to say anything about it. What | evidence would show that humans are just pattern finding | and matching machines? | vanusa wrote: | We're getting into the territory of some external debates | -- that is to say, into areas that have already been | debate by others, so there isn't much knew I could tell | you here. | | But basically I'm in the camp of Gary Marcus and others, | who would probably respond by saying something along the | lines of the following: | | "No, there's not particularly good evidence that all | we're doing is pattern matching, and a lot of evidence to | the contrary. For one thing, a lot of these ML algorithms | (touted as near- or superhuman) are easily fooled, | especially when you jiggle the environmental context by | even just a little bit." | | "For another, and on a higher level, what algorithms lack | -- but which sentient mammals have in spades -- is an | ability to 'catch' themselves, an ability to look at the | whole situation and say 'Woah, something just isn't right | here'. (This is referred to as the 'elephant in the room' | problem with modern AI)." | | "And then there's the lack of a genuine survival drive -- | not to mention the fact that we don't see any inkling of | evidence of some capacity for self-awareness in any | currently existing system." | | Just as a starting point. But these are some huge | differences between currently existing AI systems | (however you want to define "AI here) and actual sentient | cognition. | | Huge, huge differences... such that I don't see one can | get the idea that "all" we're doing is pattern | recognition. Even if it may cover roughly 90 percent of | what our neural tissues do - that other 10 percent is | absolutely crucial, and completely elusive to any | current, working technology as such. | | _When someday we do achieve human level intelligences in | computers, it will be at least in large part with ML._ | | No doubt it will, but still there's that ... remaining 10 | percent. Which you won't get to with accelerated pattern | finding any more than a faster airplane will get you to | the moon. | vlovich123 wrote: | We're quite far from human-level intelligence and require | drastically more power and data storage. It wouldn't be | surprising to see that the ML powering general AI would | look the same as today's AI to 90s ML. Still useful but | qualitatively a meaningfully different approach. | DrNuke wrote: | Some combination (sequential, parallel, meta, whatever | convenient shape) of single tasks executed at super-human | ability (computer vision + path learning are there | already) would inevitably lead to some sort of | singularity when coupled with just SoA motion, handling | and decision making / optimization. Generalizing | occasional singularities, though, still seems a long way | ahead. | gmadsen wrote: | sounds more like pattern finding. and if you break down human | cognition, it isn't much different.. | vanusa wrote: | Yep - that's the whole point, the only point. | | And again, I thought it was pretty simple and generally | known. | atty wrote: | I get that when you boil it down to pattern matching it sounds | less impressive, but we are starting to see superhuman pattern | matching algorithms, and algorithms that display logic through | advanced "pattern matching". And it's quite obvious now that | pattern matching is far more useful than pure rule based | systems for any problem of sufficient complexity. | | And AI is a very broad field, including pattern matching | (supervised ML or otherwise), logic/rule based systems, etc. Is | your complaint that they use AI/ML interchangeably? Because in | this case, while AI is a more term for the technology than ML, | it is not a strictly incorrect title. | vanusa wrote: | _Is your complaint that they use AI /ML interchangeably?_ | | Yes, that's all I'm saying. And it seems like such a known | point that they're not the same and not interchangeable, or | so I thought (that I'm kind of astonished at the downvotes at | my original comment). | | _Because in this case, while AI is a more term for the | technology than ML, it is not a strictly incorrect title._ | | I get what you're saying at the product level -- and the fact | that the vast bulk of the public subjected to these | technologies couldn't tell you the difference, nor could they | begin to care. | | But to practitioners, the basic facts remain: ML [?] AI, it's | a proper subset, and we're doing the public a genuine | disservice (and arguably causing substantial harm) by | pretending to tell them that we're making good progress | developing AI _as distinct from hypercharged ML_ , that any | day now they'll have self driving cars ... and all that crap | the industry has basically been telling people. | aerodog wrote: | surprised Erik Demaine wasn't part of this | [deleted] ___________________________________________________________________ (page generated 2021-12-10 23:00 UTC)