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Ask HN: What happened to fuzzy logic?

• pestatije 3y ago
  yes, washing machines...you put your dirty clothes in and just press start...the fuzzy logic works out the best program for it
  • tjohns 3y ago
    Also, higher end rice cookers.
  • carrolldunham 3y ago
    the washing machine with it was made in the 1990s
  • psychphysic 3y ago
    I'm not convinced they use fuzzy logic. Is it fuzzy logic because there is presumably hysteresis in the water heating phase? Or is it fuzzy in the way to converts turbidity of water to wash times?
    • p_l 3y ago
      They generally use fuzzy logic to better drive various components with minimal BOM. You can easily encode a bunch of rules from expert on best decisions given certain conditions (for example for motor control for best efficiency, or heating, etc) and use fuzzy logic to switch between modes smoothly. I remember reading about dedicated fuzzy logic circuits which essentially gave you simple fuzzy state machine programmed with cheap ROM, and it was all you needed instead of MCU for many tasks. In a way cheap powerful MCU are biggest killers of the technique.
    • mlry 3y ago
      When I used FL during uni mid-90s it was used to optimize revolutions and torque for shifting loads. Another often described use case was a loaded cat on the arm of a crane, that moves along the arm without getting the load to swing.
• h2odragon 3y ago
  Pratchett's "Wooly Thinking" was the 2nd version of the idea, much more popular, took over the world immediately after he coined the phrase. Thruly, tho look at things like QAM encoding schemes and the tricks stepper motor controllers are doing. We have found ways to analog-ize a lot of things that were formerly viwed as totally digital; which i think is what the "fuzzy logic" hype boiled down to anyway.
  • stargrazer 3y ago
    Would you be able to supply a reference to some of the tricks of stepper motor controllers?
    • h2odragon 3y ago
      See "microstepping controllers" where they're presenting a digital control (steps) to varying levels of holding current in the actual motor. AFAIK its the trick that enabled 3d printing without position sensing.
      • friendzis 3y ago
        Microstepping is adding virtual half-steps (or even further) to stepper motors. It "merely" enhances resolution and vibration characteristics of the motor. Microstepping itself is not strictly necessary for 3D printing without position feedback
• segmondy 3y ago
  It's took the form of neural networks and deep learning.
  • fedeb95 3y ago
    humans like to invent many names for the same things
    • eloisant 3y ago
      "Deep learning" as used today isn't fuzzy logic, it's actually multi-layered neural networks. (hence the "deep": because it has multiple layers of neural networks instead of a single one). The reason why researchers on neural networks started to talk about "deep learning" in the 2000's is because neural networks had a bad press and articles were being rejected. Machine learning on the other hand was hot so it was a way to get their papers accepted.
      • segmondy 3y ago
        logic is binary. yes or no. classic AI used pure logic via means of rules engine. you could tell exactly which path to take to solve a problem. fuzzy logic is not binary, but multimodal/range. what maps unto that today? probability on top of layered NN, aka deep learning. It's no coincidence that no one knows how the deep learning models "think." They are by design very fuzzy.
      • im3w1l 3y ago
        There was a popular conception (like taught in textbooks even) that one hidden layer was enough since a neural network with one hidden layer could approximate any function. Deep learning became a thing when it was found to work great in practice.
  • regularfry 3y ago
    Not really? Relu and softmax look a bit like the components of a fuzzy associative map if you squint a bit, but the lineage is distinct.
  • 3y ago
• revskill 3y ago
  Fuzziness means illogic ?
  • gisborne 3y ago
    No. There is a particular logic (“Classical Logic”) that people are usually thinking of when they mention logic. Many folks don’t know that there are many, many logics that have been invented by mathematicians and philosophers, and an infinity of possible logics. All are as mathematically sound as each other. They vary in how truth behaves (SQL’s true-false-null is a poor attempt at a 3-valued logic, for example; or you might include notions like necessity and possibility or time). Fuzzy logic is another logic. It’s quite precise about how truth behaves, and truth is a real number on the range [0, 1]. And it does a nice job of providing a notion of partial truth.
    • eloisant 3y ago
      To give an example: you can define "tall" for an adult human as being 0 if 150cm, 1 if 210cm, 0.7 if 190cm, etc. Unlike boolean logic where you're either "tall" or "not tall".
      • thrdbndndn 3y ago
        I've learned fuzzy logic during my graduate control theory study, but never get what the point of it, at least in practice. Using your example, if the control system can utilize non-binary, numerical input and do something with it in actuators, then just give it the actual height (in cm). If it can't, then introducing the concept of "semi-tall" isn't going to be helpful. The example we learned in class is the classic thermostat of an air conditioner. Sure, it would be nice if it can tell between cold, warm and hot instead of a binary 0 and 1, but why not just use the actual numeral value (0.1, 0.22, etc.) then?
        • regularfry 3y ago
          The most important thing it lets you do is combine and compare factors in nonlinear but intuitive ways. The aircon example isn't really that great, to be honest, because there's only one rule and only one action you can take. If you add in "how full is the room" (and letting that be tuned for room size), an automated window vent, the outside temperature, and some sort of air quality reading, you can see how the job of making a room tolerable to be in starts to combine factors in interesting ways. https://en.wikipedia.org/wiki/Fuzzy_associative_matrix https://en.wikipedia.org/wiki/Fuzzy_associative_matrix and https://en.wikipedia.org/wiki/Defuzzification https://en.wikipedia.org/wiki/Defuzzification are things I'm sure your course would have covered, but that's where I'd point at as a start.
• dollo_7 3y ago
  Academic silo with little to no real transfer to business. ML eventually enabled building better continuous and discrete models for inference, control, and prediction.
  • a2800276 3y ago
    I would have though the other way around: marketing buzzword for a non-problem solved by engineers since Maxwell's times by variations of applying the concept of hysteresis. At least my recollection of fuzzy logic are from around the late eighties / early nineties and always involved the example of a thermostat that can only turn fully on or fully off. :)
    • HPsquared 3y ago
      It was also popular in automatic transmissions in the 1990s. These are always asking "Should I change gear? Up or down?"
      • MrMan 3y ago
        and rice cookers. fuzzy logic was not speculative it founds its way into a fair number of real world control applications
    • kalleboo 3y ago
      The only place I've seen Fuzzy Logic advertised is on washing machines
      • charlieyu1 3y ago
        True, this is the first thing I’m reminded when I hear this term. Then I wonder what Fuzzy logic has anything to do with washing machines. Modern washing machines has everything routine programmed and are deterministic
        • giraffe_lady 3y ago
• currymj 3y ago
  I don't endorse the following as an accurate history, but it is a narrative that is taught within machine learning and probably has some elements of truth to it. If you want to take a system that's working on Boolean logic, and introduce uncertainty, replacing true/false with probabilities does a great job of this. However in the 1960s/1970s, people believed it was hopeless to have AI systems use probability to deal with uncertainty. This is because probability requires you to use Bayes' Theorem, and computing the denominator in Bayes' Theorem requires summing over an exponentially large number of different outcomes. Thus people came up with all kinds of alternative systems to avoid dealing with probability. Among these would have been fuzzy logic. However people came up with ways to cope with the computational intractability (belief propagation on Bayesian networks, better Markov-chain Monte Carlo algorithms, etc.), so probability became practically viable. And on the merits, if you can deal with the computational issues, probability seems to be much nicer than these other formal systems. So since the 1980s/1990s the probabilistic approach to AI has become dominant (even if deep learning has displaced the actual models).
  • usernew 3y ago
    I'll first admit you likely know way more than I about this. So this is not an attack on your statements, but an ask for clarification. >replacing true/false with probabilities does a great job of this ... people came up with all kinds of alternative systems to avoid dealing with probability a probability is a number between 0 and 1, instead of either 0 or 1. a number between 0 and 1, instead of 0 and 1, is called fuzzy logic. so I'm really lost about what you're saying here.
    • foldr 3y ago
      I believe fuzzy logic had its own axioms which aren't the axioms of propositional logic extended to probabilities. So the representations look similar but are manipulated differently.
      • dragonwriter 3y ago
        For example, in Bayesian logic: x AND y is xy, x OR y is x + y - xy Whereas in (Zadeh) fuzzy logic: x AND y is min(x, y), x OR y is max(x, y) IIRC, “fuzzy logic” is actually a class that includes all generalizations of crisp binary logic to continuous values over [0,1] with operators meeting a set of definitions which basically boil down to “reduces to crisp logic when the input values are constrained to 0 and 1”, so that Bayesian logic is a fuzzy logic. The Zadeh operators in particular I remember being constructed, or at keast rationalized, as ways to combine the degree of truth of propositions as distinct from the probability of truth of uncertain proposition. But I think interest in the kind of epistemic differences in alternative extensions to propositional logic faded with the lack of a practical need in terms of computational efficiency to avoid Bayesian probability (and I think there was also a separate philosophical battle and the side in favor of “Bayesianism is the only meaningful extension of propositional logic” was winning that battle when the computational problems were resolved, which helped sweep aside the alternatives.)
• nabla9 3y ago
  (positives:) Fuzzy Logic has some utility in engineering applications, because it can be intuitive for humans interpret and to create rules using it. Interpretability is also the strong point of neuro-fuzzy logic (hybrid system). Applications are usually control systems, business logic, or embedded systems with tight resource constraints. (negatives:) In academic research it's not that interesting, because it's just one multi-valued logic among others, subset of infinite-valued logic. It's not very useful in machine learning because there are no efficient way to learn fuzzy rules automatically.
  • regularfry 3y ago
    This is pretty much my recollection of learning it at university in the early 2000's. Learning rules automatically wasn't so much of a thing; people were far more interested in being able to efficiently get rules from expert humans into the systems without too many intermediate steps.
  • iExploder 3y ago
    could you have LLMs generate LFLMs (large fuzzy logic models)? :D
• ly3xqhl8g9 3y ago
  Contrary to popular belief, perhaps we are not yet ready for fuzzy logic-based mathematics†. Like asking that does Boolean logic do in the 13th century, before Leibniz had any glimpse of characteristica universalis [1] (software, as opposed to calculus ratiocinator, hardware). If cryptocurrencies are solutions in search of a problem, fuzzy logic, and especially fuzzy dual numbers logic [2] [3] [4], doesn't even know it is a solution. † one speculative use would be to assign degrees(?) of agency, some kind of a future unit of measurement: a person can be 100% evolved, or maybe 49% evolved and 51% designed (prosthetics, neural implants, and so on). [1] https://en.wikipedia.org/wiki/Characteristica_universalis https://en.wikipedia.org/wiki/Characteristica_universalis [2] 2017, Fuzzy Dual Numbers: Theory and Applications, https://dl.acm.org/doi/book/10.5555/3172931 https://dl.acm.org/doi/book/10.5555/3172931 [3] "Fuzzy Dual Dynamic Programming", https://hal-enac.archives-ouvertes.fr/hal-01372543/document https://hal-enac.archives-ouvertes.fr/hal-01372543/document [4] "Dual Numbers & Automatic Differentiation", https://blog.demofox.org/2014/12/30/dual-numbers-automatic-differentiation https://blog.demofox.org/2014/12/30/dual-numbers-automatic-d...
• regularfry 3y ago
  It always struck me as an interesting way to get out of having to do a bunch of horrible control theory calculus and still get credit for it.
• vivegi 3y ago
  IEEE Spectrum's profile on Lotfi Zadeh and the Birth of Fuzzy Logic. https://spectrum.ieee.org/lotfi-zadeh https://spectrum.ieee.org/lotfi-zadeh
• ndsipa_pomu 3y ago
  Reminds me that I played around a tiny bit with Fril back in the day. Prolog with probabilities/uncertainties https://en.wikipedia.org/wiki/Fril https://en.wikipedia.org/wiki/Fril
• captainmuon 3y ago
  Fuzzy logic always seemed misguided to me. You're just calculating values with real arithmetic instead of booleans. It's not a whole different kind of mathematics. When I'm drawing on screen and placing a widget not on the left or the right, but at x=f*width, is that already fuzzy logic? The classical examples of Japanese washing machines determining detergent dose are not much more complicated.
• wrp 3y ago
  Although fuzzy logic was developed by Lotfi Zadeh in the USA, it never gained the level of interest and respectability here that it did in Japan and Eastern Europe. I remember American engineers saying that fuzzy techniques were things they already understood and were doing under different names. They saw no benefit in adopting a new conceptual system. Among the more philosophically inclined, there was controversy other whether fuzzy logic was really something new or just a repackaging of old ideas with a misleading vocabulary. There was a book on how this debate played out in journal articles, but I can't think of the title now.
  • bigattichouse 3y ago
    I believe Bart Kosko, in his book on Fuzzy logic, said to expect the division of use of FL techniques to break down on cultural lines - that western thought would resist using it when "more granular binary logic" and cpu speed would prevail.
• deleted 3y ago
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• PinguTS 3y ago
  Coming not from the background of IT but from electrical engineering and industrial control systems, like programmable logic controllers (PLC) and embedded control, I learned that 1990ish. Fuzzy logic was in favor to the relative new field of neural nets (ML as we know it today). Because from the limitations in industrial control neural nets were too costly. Yes, we had already things like the first i386embedded. But that was way too expansive. Most of the PLCs were based on 16-bit microcontrollers and even those were more on the expansive side. So if you think commercially in terms of industrial control systems, then Fuzzy Logic was implemented and used. I assume old systems can be found anywhere even today. Remember that in industrial control 20 years is no time. Today, everybody in industrial control is also about ML. But here again, that is still on the expansive side. Such Nvidia Jetsons do not come for cheap.
  • joezydeco 3y ago
    I spent some time in small industrial controls and we never touched fuzzy logic, but we did a ton of stuff with PID algorithms. Maybe PID was just easier to implement?
    • p_l 3y ago
      It depends on whether your problem maps well to PID, or whether you need to adapt a lot of expert knowledge on a more complex control curve.
      • quickthrower2 3y ago
        PID is alive and well in espresso machines. PID for temperature control is a marketed feature (of cheaper machines: expensive ones you just trust they know what they are doing and have designed it to work at the right temperature)
        • p_l 3y ago
          From what I recall from Fuzzy Logic in University, the main difference is that with PID you give it a setpoint and it reaches and maintains it - while with fuzzy logic you can encode essentially different strategies for different input ranges, something that quite possibly would in turn provide setpoints to PID controllers.
• born-jre 3y ago
  lol, i did not know fuzzylogic was seperate field, thats what i call ml/ai . I also named one of my repo fuzzylogic [0]. [0]: https://github.com/temphia/fuzzylogic https://github.com/temphia/fuzzylogic
• ojosilva 3y ago
  I've built digital systems based on Fuzzy Logic in the US sometime in the mid 1990s. Fuzzy was good because it allowed us to implement very complex decision making (outcomes, classification...) on very, very basic hardware. It was, in all forms, a simplification of a neural net which was much harder to implement hardware-wise. Fuzzy didn't go very far because it could already be implemented back then with cheap processors (we had Moore's law!) and some slap-on programming. Or domain-specific logic hardwired by engineers who knew what the outcome should be given a bunch of inputs: think "balancing robot" or washing machine torque control. Modern ML and powerful Arduinos make fuzzy logic irrelevant today, except that Markov chains should also be irrelevant but they aren't, so who knows, maybe fuzzy could stage a come back. Despite its huge limitations, FL certainly is better than current neural nets when it comes to transparency so that humans can better visualize what it's doing. Disclaimer: back in the day I built a "FL tracker" in Visual Basic so that you could visualize the full system trickle-down a decision in realtime and it was very enlightening. Today I'd love to build the same visualizaer but for LLM generation!
  • truculent 3y ago
    Why do you say Markov chains should be irrelevant? Outclassed by NN?
• re36081240 3y ago
  Should ask Bart Kosko https://www.edge.org/3rd_culture/serpentine07/Kosko.html https://www.edge.org/3rd_culture/serpentine07/Kosko.html
• tpoacher 3y ago
  There are two things that are referred to as "fuzzy logic". The first, is the true fuzzy logic, which consists of Fuzzy Set theory and Fuzzy Measure theory. The two are distinct but interrelated, and heavily mathematical in nature. In particular, Fuzzy Measure theory completely subsumes Probability theory (i.e. probability theory is a strict subset of fuzzy measure theory, and therefore anything that can be expressed in probabilistic terms can be expressed in equivalent fuzzy measure theory terms, with leeway for added generality). The second, is a class of techniques, based on Fuzzy Set / Fuzzy Measure theory, which implement some very simple behaviours which allow you to solve a particular class of problems. This is the standard Sugeno/Mamdani logic, fuzzy inference as the application of a few straightforward rules for conducting conjunction/disjunction among fuzzy rules. The first is still thriving, but largely in the theoretical realm. The second is shunned, on the basis that it feels like a bunch of interpolation heuristics, and a Bayesian approach is usually "better", even if not as intuitive of straightforward. Unfortunately this has had a self-prophetic impact, in the sense that any literature which would have been better described as fuzzy, is labelled as probabilistic, even if strictly speaking the concepts involved are not strictly-speaking probabilities, but should more correctly have been addressed as fuzzy measures. Personally I have published papers where I had to "disguise" fuzziness as probability just to get it considered for publication, or where I had to "apologize" for the term, or making it explicit that fuzziness in this context is unrelated to this set of tools that is typically thought of as "the fuzzy approach". Having said that, one area that is thriving is the use of Fuzzy methods in the context of more general AI / NeuralNetwork methods, particularly in the context of explainability. Type 2 Fuzzy methods in particular (which introduce one or more layers/dimensions of fuzziness/uncertainty over the membership function itself) are quite an active area of research. Finally, there are some frameworks that borrow heavily from fuzzy theory, but strictly speaking are independent. The one that interested me the most in recent years was "Subjective Logic", proposed by a guy called Audun Jøsang; this sounded like a very interesting logic framework and I was very keen to use it in my work, but in the end I just didn't have the time to justify that investment ... It's worth looking up though. The basic idea is that you have a framework where you have rules that have a built-in way of carrying uncertainty with them, with a one-to-one mapping with beta probabilities over your fuzzy-like rules, and a set of logical operations (which map to the usual logical operators) that are adapted to carry the uncertainty with them. This means that you can reduce your problem into a set of simple logical statements, and the framework would then take care of the uncertainty calculations for you seamlessly.
• deleted 3y ago
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