5 ms·

Nice try Veritasium, but I’m afraid that as clever as these pen wiggles are. the defining characteristic of computers[cience] is the calculation of discrete, di

• dredmorbius 5y ago
  I'm fairly certain that there exist logic mechanisms for analogue computers. An alarm clock is a fairly trivial example, but might serve as a minimal case. Various mechanical governors (see Watt's flywheel governor), pressure relief valves, and similar automated controls within hydraulic systems, also come to mind. Similarly, a mechanical thermostat.
  • gorgoiler 5y ago
    Good point, but these are really just switches. Can you think of any examples where one switch controls multiple others? The mechanical equivalent of a clockless processor?
    • dredmorbius 5y ago
      Not offhand. Though I strongly suspect they exist. Physical analogues of all the basic logic functions (AND, OR, NAND, XOR, NOR, NOT) should all be constructable.
      • tagoregrtst 5y ago
      • jerf 5y ago
        "Physical analogues of all the basic logic functions (AND, OR, NAND, XOR, NOR, NOT) should all be constructable." Trivially so. Electronic circuits are not intrinsically digital, nor are integrated chip components. They are intrinsically analog. We get digital behavior out of them by building and driving them in a certain way. The cost/benefits tradeoff on using these components digitally is so nice for us that we do it so often that we can forget this fact, but it is a fact. There are a number of chips that have analog components and behaviors in them; you can find a lot of them in sound synthesis, for instance, especially older ones like the SID chip ("The chip combines analogue and digital circuitry, that cannot be emulated with 100% fidelity even today." https://www.c64-wiki.com/wiki/SID https://www.c64-wiki.com/wiki/SID ). gorgoiler, you are speaking as if you consider analog computing a subset of what digital computing can do, but it's actually the other way around. Digital computing is a subset of analog computing where we deliberately construct a digital computer out of what are still analog parts. Anything a digital computer can do, an analog computer can do, because it can simply function digitally for that portion but then incorporate other analog components. And as is often the case, when you really get down to it the line gets fuzzy... is the Commodore 64 an "analog" computer just because it had an analog part used in a particular set of ways? I think most people would say no... but it certainly wasn't 100% digital. As I mentioned in another comment, I kinda think the video does a disservice by going too deeply into "analogical" vs. "analog". They've separated in meaning now. The people he shows at the end doing modern analog computers are, as far as I know, building things that look a lot more like a modern computer, except the requirement that all the components be driven by a clock signal and that all voltages stabilize before the next clock is loosened and they permit other analog behaviors of electronics to come in, allowing for programmable analog computers. They're not building things out of cogs with direct and obvious connections to underlying processes... modern digital computers are far better for those sorts of things.
        • dredmorbius 5y ago
          Right. I'm thinking specifically of cases in which, say, a water-control system might utilise such elements, and just how complex they might get. This isn't my area, so I'm being conservative in statements. An electronic switch translates to a hydraulic valve. And a hydraulic valve is a direct analogue of a vacuum tube or transistor: an applied input delivers a controlled input. Often but not necessarily amplified --- there are cases where the input effort might be much larger than the output, in control or precision implementations. Mind: a valve itself might be water activated, in the sense of a small flow through one channel translating to a large flow in a controlled channel. One obvious example of this is the fantail of a post windmill, where any orientation of the mill's main sails outside the primary wind flow starts spinning the fantail which reorients the mill into the wind. See: https://upload.wikimedia.org/wikipedia/commons/6/65/Beebe_Windmill_Isometric_of_Machinery_Long_Island_NY.jpg https://upload.wikimedia.org/wikipedia/commons/6/65/Beebe_Wi... There are numerous cases of interlocks, many of which are mechanical. Some of these are through inherently fail-safe designs. A canal's locks, aeroplane doors, and airlocks all open such that they require pressure equalisation, preventing opening whilst the lock and channel are at different levels, or pressurised and depressurised regions are not at equilibrium. Shift-lever and starter interlocks require that brakes be engaged and vehicles in neutral to start, or shift from reverse. https://realpars.com/interlock/ https://realpars.com/interlock/ And I'm finding a few references specifically to mechanical control logic: "Mechanical Logic Devices and Circuits" http://www.nacomm09.ammindia.org/NaCoMM-2009/nacomm09_final_pap/R/RCA18.pdf http://www.nacomm09.ammindia.org/NaCoMM-2009/nacomm09_final_... (PDF) "PDF Pneumatic Logic & Controls - Parker Hannifin" https://www.parker.com/literature/Literature%20Files/pneumatic/Literature/Telepneumatic/PCC-4.pdf https://www.parker.com/literature/Literature%20Files/pneumat... (PDF) The second is a catalogue of available products based on pneumatic logic. One specific domain in which analogue / mechanical controls have been specifically discussed is for future Venus lander missions. Temperature and pressure profiles are too high for virtually all electronic systems (circuit boards, solder, and componentry would thermally degrade or melt). JPL have specifically explored the concept of a "clockwork rover", AREE (advanced rover for extreme environments) for a Venus or similar mission: https://www.jpl.nasa.gov/news/a-clockwork-rover-for-venus https://www.jpl.nasa.gov/news/a-clockwork-rover-for-venus
    • tagoregrtst 5y ago
      You’ve conceded that logic gates can be built with pneumatic (and other) systems. Complex logic systems were built, car automatic transmissions come to mind. I don't know if there were Turing complete since there was no need - these were purposely built for a specific task. Anyway, analog computers are computers in that any realization of a Turing complete machine is necessarily created by analog circuits - albeit very non linear analog circuit. Therefore, Turing complete machines can be emulated on an analog computer but the reverse isn’t obvious - CFD, DFT, ad-initio can never really get the answer of the general case right (try simulating turbulence, crack propagation, the transition state of oxygen adsorbed on Pt under an applied external electric field etc)
    • freemint 5y ago
      One obvious approach is to have a model of the system that runs faster then the system define a fitness function and a fitness gradient depending on the controls and the do model predictive control.
• esarbe 5y ago
  A spring balance is a computer in the same way a lever is a machine.
• DonHopkins 5y ago
  You should real Steven Wolfram's "A New Kind of Science", and you'll get a much deeper and wider appreciation for just what is a computer and how Turing completeness can apply to so many situations. Even the simplest systems can be universal computers! https://en.wikipedia.org/wiki/A_New_Kind_of_Science https://en.wikipedia.org/wiki/A_New_Kind_of_Science >Generally, simple programs tend to have a very simple abstract framework. Simple cellular automata, Turing machines, and combinators are examples of such frameworks, while more complex cellular automata do not necessarily qualify as simple programs. It is also possible to invent new frameworks, particularly to capture the operation of natural systems. The remarkable feature of simple programs is that a significant percentage of them are capable of producing great complexity. Simply enumerating all possible variations of almost any class of programs quickly leads one to examples that do unexpected and interesting things. This leads to the question: if the program is so simple, where does the complexity come from? In a sense, there is not enough room in the program's definition to directly encode all the things the program can do. Therefore, simple programs can be seen as a minimal example of emergence. A logical deduction from this phenomenon is that if the details of the program's rules have little direct relationship to its behavior, then it is very difficult to directly engineer a simple program to perform a specific behavior. An alternative approach is to try to engineer a simple overall computational framework, and then do a brute-force search through all of the possible components for the best match. Even a reservoir of water (or a non-linear mathematical model of one) can be used to piggyback arbitrary computation on the way liquid naturally behaves. Here's a paper about literally using a bucket of water and some legos and sensors to perform pattern recognition with a "Liquid State Machine" (see Figure 1: The Liquid Brain): https://www.semanticscholar.org/paper/Pattern-Recognition-in-a-Bucket-Fernando-Sojakka/af342af4d0e674aef3bced5fd90875c6f2e04abc https://www.semanticscholar.org/paper/Pattern-Recognition-in... >Pattern Recognition in a Bucket. Chrisantha Fernando, Sampsa Sojakka. Published in ECAL 14 September 2003, Computer Science. >This paper demonstrates that the waves produced on the surface of water can be used as the medium for a “Liquid State Machine” that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Interference between waves allows non-linear parallel computation upon simultaneous sensory inputs. Temporal patterns of stimulation are converted to spatial patterns of water waves upon which a linear discrimination can be made. Whereas Wolfgang Maass’ Liquid State Machine requires fine tuning of the spiking neural network parameters, water has inherent self-organising properties such as strong local interactions, time-dependent spread of activation to distant areas, inherent stability to a wide variety of inputs, and high complexity. Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. An analogy is made between water molecules and neurons in a recurrent neural network. This idea can be applied to digital neural networks, using a model of a liquid reservoir as a "black box", and training another neural network layer to interpret its output in response to inputs. Instead of training the water (which is futile, since water will do what it wants: as the apologetics genius Bill O'Reilly proclaims, "Tide go in, tide go out, never a miscommunication."), you just train a water interpreter (a linear output layer)! https://www.youtube.com/watch?v=NUeybwTMeWo https://www.youtube.com/watch?v=NUeybwTMeWo Reservoir Computing https://en.wikipedia.org/wiki/Reservoir_computing https://en.wikipedia.org/wiki/Reservoir_computing >Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir.[1] After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output.[1] The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed.[1] The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be used to reduce the effective computational cost.[2] >History: The concept of reservoir computing stems from the use of recursive connections within neural networks to create a complex dynamical system.[3] It is a generalisation of earlier neural network architectures such as recurrent neural networks, liquid-state machines and echo-state networks. Reservoir computing also extends to physical systems that are not networks in the classical sense, but rather continuous systems in space and/or time: e.g. a literal "bucket of water" can serve as a reservoir that performs computations on inputs given as perturbations of the surface.[4] The resultant complexity of such recurrent neural networks was found to be useful in solving a variety of problems including language processing and dynamic system modeling.[3] However, training of recurrent neural networks is challenging and computationally expensive.[3] Reservoir computing reduces those training-related challenges by fixing the dynamics of the reservoir and only training the linear output layer.[3] >A large variety of nonlinear dynamical systems can serve as a reservoir that performs computations. In recent years semiconductor lasers have attracted considerable interest as computation can be fast and energy efficient compared to electrical components. >Recent advances in both AI and quantum information theory have given rise to the concept of quantum neural networks.[5] These hold promise in quantum information processing, which is challenging to classical networks, but can also find application in solving classical problems.[5][6] In 2018, a physical realization of a quantum reservoir computing architecture was demonstrated in the form of nuclear spins within a molecular solid.[6] However, the nuclear spin experiments in [6] did not demonstrate quantum reservoir computing per se as they did not involve processing of sequential data. Rather the data were vector inputs, which makes this more accurately a demonstration of quantum implementation of a random kitchen sink[7] algorithm (also going by the name of extreme learning machines in some communities). In 2019, another possible implementation of quantum reservoir processors was proposed in the form of two-dimensional fermionic lattices.[6] In 2020, realization of reservoir computing on gate-based quantum computers was proposed and demonstrated on cloud-based IBM superconducting near-term quantum computers.[8] >Reservoir computers have been used for time-series analysis purposes. In particular, some of their usages involve chaotic time-series prediction,[9][10] separation of chaotic signals,[11] and link inference of networks from their dynamics.[12] Liquid State Machine https://en.wikipedia.org/wiki/Liquid_state_machine https://en.wikipedia.org/wiki/Liquid_state_machine >A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes. The spatio-temporal patterns of activation are read out by linear discriminant units. Echo State Network https://en.wikipedia.org/wiki/Echo_state_network https://en.wikipedia.org/wiki/Echo_state_network >The echo state network (ESN)[1][2] is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can produce or reproduce specific temporal patterns. The main interest of this network is that although its behaviour is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system.