Computing has undergone an Evolution
Web 0.0: Nodular Computing
When there is no network, you have either a server or a PC on which either a standard program (e.g., an accounting package, image editor, or word processor) or a custom program (e.g., company-specific inventory management, actuarial table calculation, mortgage calculator) takes input on that computer, does some calculations, stores intermediate data, and produces output.
Organizations and individuals liked this configuration for doing local things that could be confined to one location. It opened up the prepackaged software industry, because there were many applications that were general enough that you could sell to many locations. The ability to choose locally which software to run (either on a managed machine or a personal machine) was a great source of empowerment and led to a surge in the purchase of first managed corporate machines in the 1960’s and 1970’s, and then the PCs in the 1980’s. Every system could be different.
Nodular Computer: Mainframe: Priesthoods Operate
Nodular Network: ARPANET: Priesthoods Connect
Nodular Data: Variable: Noun: Priesthoods Query
Nodular Language: Variable PL: Assembler: Priesthoods Manipulate
Nodular Communication: Variable Packet: MULTIVALUABLEML: Priesthoods Communicate
Nodular Schedule: Multi-Stating: DedicatedOn
Nodular Objective: Active
Web 1.0: Linear Computing
Transaction processing systems have a single server with custom software doing computation and data storage. Each client machine is usually a standard “smart” terminal or a personal computer running a standard program acting as one. The input and output occurs on the PC. The PC has no storage and is only connected directly to the single server through a dedicated communications system. Part of the input sometimes comes from other sources.
This configuration let organizations do applications that involved people at diverse locations involved with a single database. It was best for applications that were worth dedicating the communications system as well as the dedicated client machines. Since the client machines were standard, their cost and maintenance could be commodities. The applications were often mission-critical to the company employing them. Interacting with computers started to be integral to the operation of an enterprise, for example at banks or airlines.
Client-server systems have servers similar to the transaction processing systems, with computation, data storage, and maybe some input, and run custom software. The PC is connected by a communications system (a LAN or WAN) that is usable for more than one application. The PCs run custom software that does computation as well as input and output.
As corporations started learning the value of using computers as part of their operation, client-server configurations let them take advantage of the power of the PCs on people’s desks and the sharing afforded by the LAN. The standalone applications showed them the value of user-friendly, usable user interfaces. By running custom applications communicating with the servers they could make more efficient applications. The LAN let them share expensive hardware like large disks and expensive printers. Because they used standard equipment on the client side, they could take advantage of commodity pricing.
Linear Computer: Minicomputer: Scribes Operate
Linear Network: Ethernet: Scribes Connect
Linear Data: String dbms: Verb: Scribes Query
Linear Language: String PL: 3GL: Scribes Manipulate
Linear Communication: String Packet: DIMENSIONALML: Scribes Communicate
Linear Schedule: Multi-Tasking: BatchOn
Linear Objective: Reactive
Web 2.0: Tabular Computing
Web applications is a term for custom software on web servers.This has the same characteristics as the web sites, except that there is a much higher cost to construct the web sites and much more of the data comes from the PCs connecting to the web application.
Rather than hooking sales people to transaction terminals that were connected to a purchasing system you could directly hook purchasers to the purchasing system. Cloud Computing is similar to transaction processing, except that the client machines are usually shared with many applications from many servers. This would let them have those applications managed centrally by specialists, and let them share the application beyond the confines of a single LAN. It would also enable business models where the use of the server could be charged for like a service, bringing in recurring revenue without doing additional development.
Tabular Computer: Microcomputer: Educated Operate
Tabular Network: Internet: Educated Connect
Tabular Data: Relational dbms: Noun Set: Educated Query
Tabular Language: Relational PL: SQL: Educated Manipulate
Tabular Communication: Relation Packet: MULTIDIMENSIONALML: Educated Communicate
Tabular Schedule: Multi-Threading: DemandOn
Tabular Objective: Interactive
All of the preceding systems use single main systems (either a standalone PC or a server) to do the main computation and store the main data. The only change is where the I/O is done. The view of the world is always through this shared main data or computation. With the growth of the Internet because of email and the web, the communications system to connect dispersed, independent PCs together became available. PCs also became more powerful and data storage costs dropped so much that most PCs had huge capacities. New configurations came about that didn’t depend on the server so much. These configurations became known as Peer-to-Peer.
A distinguishing characteristic between various forms of P2P is the role of the other PCs to a given user. In the standalone or central server world, there is one computer that is “special”. In the P2P world, there are configurations where the mass of other PCs serve as a “super-server” for the purposes of redundancy, cost reduction, parallel processing, privacy, or whatever. Which person, if any, uses that PC doesn’t matter. At most, the anonymous properties of the data make a difference, or which other computers it is connected to. I call these “anonymous distributed serving” configurations. Other configurations are for connecting particular PCs because of the individuals using, or location of, each PC. I call these “person to person” configurations.
Netular Computer: Scale-free CPUs: Laypeople Operate
Netular Network: Wireless Network: Laypeople Connect
Netular Data: Associational DBMS: Verb Set: Laypeople Query
Netular Language: Assocational PL: Iconic Language: Laypeople Manipulate
Netular Communication: Association Packet: VECTORALML: Laypeople Communicate
Netular Schedule: Multi-Weaving: PresentOn
Netular Objective: Proactive
And what comes next?
Tensors are geometrical entities introduced into mathematics and physics to extend the notion of scalars, (geometric) vectors, and matrices. Many physical quantities are naturally regarded, not as vectors themselves, but as correspondences between one set of vectors and another. An example is the stress, that takes one vector as input and produces another vector as output and so expresses a relationship between the input and output vectors. Tensors were first conceived by Bernhard Riemann and Elwin Bruno Christoffel, and later developed by Tullio Levi-Civita and Gregorio Ricci-Curbastro, in order to formulate the intrinsic differential geometry of a manifold in the form of the Riemann curvature tensor.
Because they express a relationship between vectors, tensors themselves are independent of a particular choice of coordinate system. It is possible to represent a tensor by examining what it does to a coordinate basis or frame of reference; the resulting quantity is then an organized multi-dimensional array of numerical values. The coordinate-independence of a tensor then takes the form of a “covariant” transformation law that relates the array computed in one coordinate system to that computed in another one. The order (or degree) of a tensor is the dimensionality of the array needed to represent it. Thus a scalar is a zeroth-order tensor: its magnitude is its sole component, so it can be represented as a 0-dimensional array. A vector is a first-order tensor, being representable in coordinates as a 1-dimensional array of components. A matrix is a second-order tensor, being representable as a 2-dimensional array. And so on: an order-k tensor can be represented as a k-dimensional array of components. The order is the number of numerical indices necessary to specify an individual component of a tensor
Tensular Computer: Multiple CPUs: Sensory Operation
Tensular Network: High Frequency Gravity Wave Network: Sensory Connection
Tensular Data: Vector DBMS: Sentence Set: Sensory Question
Tensular Language: Vector PL: Mathematical Language: Sensory Manipulation
Tensular Communication: Vector Packet: MULTIVECTORALML: Sensory Communcation
Tensular Schedule: Multi-Layering: PastOn
Tensular Objective: Retroactive
Web 5.0: Quantular Computing
A quantum computer is a device for computation that makes direct use of quantum mechanical phenomena, such as superposition and entanglement, to perform operations on data. The basic principle behind quantum computation is that quantum properties can be used to represent data and perform operations on these data. A theoretical model is the quantum Turing machine, also known as the universal quantum computer.
Although quantum computing is still in its infancy, experiments have been carried out in which quantum computational operations were executed on a very small number of qubits (quantum bit). Both practical and theoretical research continues with interest, and many national government and military funding agencies support quantum computing research to develop quantum computers for both civilian and national security purposes, such as cryptanalysis.
If large-scale quantum computers can be built, they will be able to solve certain problems much faster than any of our current classical computers (for example Shor’s algorithm). Quantum computers are different from other computers such as DNA computers and traditional computers based on transistors. Some computing architectures such as optical computers may use classical superposition of electromagnetic waves. Without some specifically quantum mechanical resources such as entanglement, it is conjectured that an exponential advantage over classical computers is not possible. Quantum computers however do not allow one to compute functions that are not theoretically computable by classical computers, i.e. they do not alter the Church–Turing thesis. The gain is only in efficiency.
Quantular Computer: Autonomous CPUs: Computers Operate
Quantular Network: Quantum Entanglement Network: Computers Connect
Quantular Data: Tensor DBMS: Autonomous Computers Query
Quantular Language: Vector PL: Mathematical Language: Computers Manipulate
Quantular Communication: Vector Packet: TENSORALML: Computers Communcate
Quantular Schedule: Multi-Bundling: FutureOn
Quantular Objective: Creative
Web 6.0: Qualular Computing
Consider your visual experience as you stare at a bright turquoise color patch in a paint store. There is something it is like for you subjectively to undergo that experience. What it is like to undergo the experience is very different from what it is like for you to experience a dull brown color patch. This difference is a difference in what is often called “phenomenal character.” The phenomenal character of an experience is what it is like subjectively to undergo the experience. If you are told to focus your attention upon the phenomenal character of your experience, you will find that in doing so you are aware of certain qualities. These qualities — ones that are accessible to you introspectively and that together make up the phenomenal character of the experience are standardly called ‘qualia’.
There are more restricted uses of the term ‘qualia’. Consider a painting of a dalmatian. Viewers of the painting can apprehend not only its content (i.e., its representing a dalmatian) but also the colors, shapes, and spatial relations obtaining among the blobs of paint on the canvas. It has sometimes been supposed that being aware or conscious of a visual experience is like viewing an inner, non-physical picture or sense-datum. So, for example, on this conception, if I see a dalmatian, I am subject to a mental picture-like representation of a dalmatian (a sense-datum), introspection of which reveals to me both its content and its intrinsic, non-representationational features (counterparts to the visual features of the blobs of paint on the canvas). These intrinsic, non-representational features have been taken by advocates of the sense-datum theory to be the sole determinants of what it is like for me to have the experience. In a second, more restricted sense of the term ‘qualia’, then, qualia are intrinsic, consciously accessible, non-representational features of sense-data and other non-physical phenomenal objects that are responsible for their phenomenal character.
There is another established sense of the term ‘qualia’, which is similar to the one just given but which does not demand of qualia advocates that they endorse the sense-datum theory. However sensory experiences are ultimately analyzed — whether, for example, they are taken to involve relations to sensory objects or they are identified with neural events or they are held to be physically irreducible events — many philosophers suppose that they have intrinsic, consciously accessible features that are non-representational and that are solely responsible for their phenomenal character. These features, whatever their ultimate nature, physical or non-physical, are often dubbed ‘qualia’.
In the case of visual experiences, for example, it is frequently supposed that there is a range of visual qualia, where these are taken to be intrinsic features of visual experiences that (a) are accessible to introspection, (b) can vary without any variation in the representational contents of the experiences, (c) are mental counterparts to some directly visible properties of objects (e.g., color), and (d) are the sole determinants of the phenomenal character of the experiences. Philosophers who hold or have held this view include, for example, Nagel (1974), Peacocke (1983) and Block (1990).
Qualular Computer: Unified CPU: Plurality Operates
Qualular Network: Quantum Entanglement Network: Plurality Connects
Qualular Data: Extensor DBMS: Plurality Queries
Qualular Language: Tensor PL: Unified Language: Plurality Manipulates
Qualular Communication: Tensor Packet: MULTITENSORALML: Plurality Communcate
Qualular Schedule: Multi-Versioning: EvolutiOn
Qualular Objective: Procreative
