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On June 16, 1860, about ten weeks after the Pony Express began operations, Congress authorized the a bill instructing the Secretary of the Treasury to subsidize the building of a transcontinental telegraph line to connect the Missouri River and the Pacific Coast.

The passage of the bill resulted in the incorporation of the Overland Telegraph Company of California and the Pacific Telegraph Company of Nebraska. While the lines were under construction the Pony Express operated as usual. Letters and newspapers were carried the entire length of the line from St. Joseph to Sacramento, but telegrams were carried only between the rapidly advancing wire ends.

On October 26, 1861, San Francisco was in direct contact with New York City. On that day the Pony Express was officially terminated, but it was not until November that the last letters completed their journey over the route.
Most of the original trail has been obliterated either by time or human activities. Along many segments, the trail's actual route and exact length are matters of conjecture. In the western states, the majority of the trail has been converted, over the years, to double track dirt roads. Short pristine segments, believed to be traces of the original trail, can be seen only in Utah and California. However, approximately 120 historic sites may eventually be available to the public, including 50 existing Pony Express stations or station ruins.

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History & Culture - Pony Express National Historic Trail (U.S. National Park Service)

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On June 16, 1860, about ten weeks after the Pony Express began operations, Congress authorized the a bill instructing the Secretary of the Treasury to subsidize the building of a transcontinental telegraph line to connect the Missouri River and the Pacific Coast.

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Charles Babbage was Lucasian Professor of Mathematics at Cambridge University from 1828 to 1839 (a post formerly held by Isaac Newton). Babbage's proposed Difference Engine was a special-purpose digital computing machine for the automatic production of mathematical tables (such as logarithm tables, tide tables, and astronomical tables). The Difference Engine consisted entirely of mechanical components - brass gear wheels, rods, ratchets, pinions, etc. Numbers were represented in the decimal system by the positions of 10-toothed metal wheels mounted in columns. Babbage exhibited a small working model in 1822. He never completed the full-scale machine that he had designed but did complete several fragments. The largest - one ninth of the complete calculator - is on display in the London Science Museum. Babbage used it to perform serious computational work, calculating various mathematical tables. In 1990, Babbage's Difference Engine No. 2 was finally built from Babbage's designs and is also on display at the London Science Museum.

The Swedes Georg and Edvard Scheutz (father and son) constructed a modified version of Babbage's Difference Engine. Three were made, a prototype and two commercial models, one of these being sold to an observatory in Albany, New York, and the other to the Registrar-General's office in London, where it calculated and printed actuarial tables.

Babbage's proposed Analytical Engine, considerably more ambitious than the Difference Engine, was to have been a general-purpose mechanical digital computer. The Analytical Engine was to have had a memory store and a central processing unit (or 'mill') and would have been able to select from among alternative actions consequent upon the outcome of its previous actions (a facility nowadays known as conditional branching). The behaviour of the Analytical Engine would have been controlled by a program of instructions contained on punched cards connected together with ribbons (an idea that Babbage had adopted from the Jacquard weaving loom). Babbage emphasised the generality of the Analytical Engine, saying 'the conditions which enable a finite machine to make calculations of unlimited extent are fulfilled in the Analytical Engine' (Babbage [1994], p. 97).

Babbage worked closely with Ada Lovelace, daughter of the poet Byron, after whom the modern programming language ADA is named. Lovelace foresaw the possibility of using the Analytical Engine for non-numeric computation, suggesting that the Engine might even be capable of composing elaborate pieces of music.

A large model of the Analytical Engine was under construction at the time of Babbage's death in 1871 but a full-scale version was never built. Babbage's idea of a general-purpose calculating engine was never forgotten, especially at Cambridge, and was on occasion a lively topic of mealtime discussion at the war-time headquarters of the Government Code and Cypher School, Bletchley Park, Buckinghamshire, birthplace of the electronic digital computer.

The earliest computing machines in wide use were not digital but analog. In analog representation, properties of the representational medium ape (or reflect or model) properties of the represented state-of-affairs. (In obvious contrast, the strings of binary digits employed in digital representation do not represent by means of possessing some physical property - such as length - whose magnitude varies in proportion to the magnitude of the property that is being represented.) Analog representations form a diverse class. Some examples: the longer a line on a road map, the longer the road that the line represents; the greater the number of clear plastic squares in an architect's model, the greater the number of windows in the building represented; the higher the pitch of an acoustic depth meter, the shallower the water. In analog computers, numerical quantities are represented by, for example, the angle of rotation of a shaft or a difference in electrical potential. Thus the output voltage of the machine at a time might represent the momentary speed of the object being modelled.

As the case of the architect's model makes plain, analog representation may be discrete in nature (there is no such thing as a fractional number of windows). Among computer scientists, the term 'analog' is sometimes used narrowly, to indicate representation of one continuously-valued quantity by another (e.g., speed by voltage). As Brian Cantwell Smith has remarked:

'Analog' should ... be a predicate on a representation whose structure corresponds to that of which it represents ... That continuous representations should historically have come to be called analog presumably betrays the recognition that, at the levels at which it matters to us, the world is more foundationally continuous than it is discrete. (Smith [1991], p. 271)

James Thomson, brother of Lord Kelvin, invented the mechanical wheel-and-disc integrator that became the foundation of analog computation (Thomson [1876]). The two brothers constructed a device for computing the integral of the product of two given functions, and Kelvin described (although did not construct) general-purpose analog machines for integrating linear differential equations of any order and for solving simultaneous linear equations. Kelvin's most successful analog computer was his tide predicting machine, which remained in use at the port of Liverpool until the 1960s. Mechanical analog devices based on the wheel-and-disc integrator were in use during World War I for gunnery calculations. Following the war, the design of the integrator was considerably improved by Hannibal Ford (Ford [1919]).

Stanley Fifer reports that the first semi-automatic mechanical analog computer was built in England by the Manchester firm of Metropolitan Vickers prior to 1930 (Fifer [1961], p. 29); however, I have so far been unable to verify this claim. In 1931, Vannevar Bush, working at MIT, built the differential analyser, the first large-scale automatic general-purpose mechanical analog computer. Bush's design was based on the wheel and disc integrator. Soon copies of his machine were in use around the world (including, at Cambridge and Manchester Universities in England, differential analysers built out of kit-set Meccano, the once popular engineering toy).

It required a skilled mechanic equipped with a lead hammer to set up Bush's mechanical differential analyser for each new job. Subsequently, Bush and his colleagues replaced the wheel-and-disc integrators and other mechanical components by electromechanical, and finally by electronic, devices.

A differential analyser may be conceptualised as a collection of 'black boxes' connected together in such a way as to allow considerable feedback. Each box performs a fundamental process, for example addition, multiplication of a variable by a constant, and integration. In setting up the machine for a given task, boxes are connected together so that the desired set of fundamental processes is executed. In the case of electrical machines, this was done typically by plugging wires into sockets on a patch panel (computing machines whose function is determined in this way are referred to as 'program-controlled').

Since all the boxes work in parallel, an electronic differential analyser solves sets of equations very quickly. Against this has to be set the cost of massaging the problem to be solved into the form demanded by the analog machine, and of setting up the hardware to perform the desired computation. A major drawback of analog computation is the higher cost, relative to digital machines, of an increase in precision. During the 1960s and 1970s, there was considerable interest in 'hybrid' machines, where an analog section is controlled by and programmed via a digital section. However, such machines are now a rarity.

In 1936, at Cambridge University, Turing invented the principle of the modern computer. He described an abstract digital computing machine consisting of a limitless memory and a scanner that moves back and forth through the memory, symbol by symbol, reading what it finds and writing further symbols (Turing [1936]). The actions of the scanner are dictated by a program of instructions that is stored in the memory in the form of symbols. This is Turing's stored-program concept, and implicit in it is the possibility of the machine operating on and modifying its own program. (In London in 1947, in the course of what was, so far as is known, the earliest public lecture to mention computer intelligence, Turing said, 'What we want is a machine that can learn from experience', adding that the 'possibility of letting the machine alter its own instructions provides the mechanism for this' (Turing [1947] p. 393). Turing's computing machine of 1936 is now known simply as the universal Turing machine. Cambridge mathematician Max Newman remarked that right from the start Turing was interested in the possibility of actually building a computing machine of the sort that he had described (Newman in interview with Christopher Evans in Evans [197?].

From the start of the Second World War Turing was a leading cryptanalyst at the Government Code and Cypher School, Bletchley Park. Here he became familiar with Thomas Flowers' work involving large-scale high-speed electronic switching (described below). However, Turing could not turn to the project of building an electronic stored-program computing machine until the cessation of hostilities in Europe in 1945.

During the wartime years Turing did give considerable thought to the question of machine intelligence. Colleagues at Bletchley Park recall numerous off-duty discussions with him on the topic, and at one point Turing circulated a typewritten report (now lost) setting out some of his ideas. One of these colleagues, Donald Michie (who later founded the Department of Machine Intelligence and Perception at the University of Edinburgh), remembers Turing talking often about the possibility of computing machines (1) learning from experience and (2) solving problems by means of searching through the space of possible solutions, guided by rule-of-thumb principles (Michie in interview with Copeland, 1995). The modern term for the latter idea is 'heuristic search', a heuristic being any rule-of-thumb principle that cuts down the amount of searching required in order to find a solution to a problem. At Bletchley Park Turing illustrated his ideas on machine intelligence by reference to chess. Michie recalls Turing experimenting with heuristics that later became common in chess programming (in particular minimax and best-first).

Further information about Turing and the computer, including his wartime work on codebreaking and his thinking about artificial intelligence and artificial life, can be found in Copeland 2004.

With some exceptions - including Babbage's purely mechanical engines, and the finger-powered National Accounting Machine - early digital computing machines were electromechanical. That is to say, their basic components were small, electrically-driven, mechanical switches called 'relays'. These operate relatively slowly, whereas the basic components of an electronic computer - originally vacuum tubes (valves) - have no moving parts save electrons and so operate extremely fast. Electromechanical digital computing machines were built before and during the second world war by (among others) Howard Aiken at Harvard University, George Stibitz at Bell Telephone Laboratories, Turing at Princeton University and Bletchley Park, and Konrad Zuse in Berlin. To Zuse belongs the honour of having built the first working general-purpose program-controlled digital computer. This machine, later called the Z3, was functioning in 1941. (A program-controlled computer, as opposed to a stored-program computer, is set up for a new task by re-routing wires, by means of plugs etc.)

Relays were too slow and unreliable a medium for large-scale general-purpose digital computation (although Aiken made a valiant effort). It was the development of high-speed digital techniques using vacuum tubes that made the modern computer possible.

The earliest extensive use of vacuum tubes for digital data-processing appears to have been by the engineer Thomas Flowers, working in London at the British Post Office Research Station at Dollis Hill. Electronic equipment designed by Flowers in 1934, for controlling the connections between telephone exchanges, went into operation in 1939, and involved between three and four thousand vacuum tubes running continuously. In 1938-1939 Flowers worked on an experimental electronic digital data-processing system, involving a high-speed data store. Flowers' aim, achieved after the war, was that electronic equipment should replace existing, less reliable, systems built from relays and used in telephone exchanges. Flowers did not investigate the idea of using electronic equipment for numerical calculation, but has remarked that at the outbreak of war with Germany in 1939 he was possibly the only person in Britain who realized that vacuum tubes could be used on a large scale for high-speed digital computation. (See Copeland 2006 for m more information on Flowers' work.)

The earliest comparable use of vacuum tubes in the U.S. seems to have been by John Atanasoff at what was then Iowa State College (now University). During the period 1937-1942 Atanasoff developed techniques for using vacuum tubes to perform numerical calculations digitally. In 1939, with the assistance of his student Clifford Berry, Atanasoff began building what is sometimes called the Atanasoff-Berry Computer, or ABC, a small-scale special-purpose electronic digital machine for the solution of systems of linear algebraic equations. The machine contained approximately 300 vacuum tubes. Although the electronic part of the machine functioned successfully, the computer as a whole never worked reliably, errors being introduced by the unsatisfactory binary card-reader. Work was discontinued in 1942 when Atanasoff left Iowa State.

The first fully functioning electronic digital computer was Colossus, used by the Bletchley Park cryptanalysts from February 1944.

From very early in the war the Government Code and Cypher School (GC&CS) was successfully deciphering German radio communications encoded by means of the Enigma system, and by early 1942 about 39,000 intercepted messages were being decoded each month, thanks to electromechanical machines known as 'bombes'. These were designed by Turing and Gordon Welchman (building on earlier work by Polish cryptanalysts).

During the second half of 1941, messages encoded by means of a totally different method began to be intercepted. This new cipher machine, code-named 'Tunny' by Bletchley Park, was broken in April 1942 and current traffic was read for the first time in July of that year. Based on binary teleprinter code, Tunny was used in preference to Morse-based Enigma for the encryption of high-level signals, for example messages from Hitler and members of the German High Command.

The need to decipher this vital intelligence as rapidly as possible led Max Newman to propose in November 1942 (shortly after his recruitment to GC&CS from Cambridge University) that key parts of the decryption process be automated, by means of high-speed electronic counting devices. The first machine designed and built to Newman's specification, known as the Heath Robinson, was relay-based with electronic circuits for counting. (The electronic counters were designed by C.E. Wynn-Williams, who had been using thyratron tubes in counting circuits at the Cavendish Laboratory, Cambridge, since 1932 [Wynn-Williams 1932].) Installed in June 1943, Heath Robinson was unreliable and slow, and its high-speed paper tapes were continually breaking, but it proved the worth of Newman's idea. Flowers recommended that an all-electronic machine be built instead, but he received no official encouragement from GC&CS. Working independently at the Post Office Research Station at Dollis Hill, Flowers quietly got on with constructing the world's first large-scale programmable electronic digital computer. Colossus I was delivered to Bletchley Park in January 1943.

By the end of the war there were ten Colossi working round the clock at Bletchley Park. From a cryptanalytic viewpoint, a major difference between the prototype Colossus I and the later machines was the addition of the so-called Special Attachment, following a key discovery by cryptanalysts Donald Michie and Jack Good. This broadened the function of Colossus from 'wheel setting' - i.e., determining the settings of the encoding wheels of the Tunny machine for a particular message, given the 'patterns' of the wheels - to 'wheel breaking', i.e., determining the wheel patterns themselves. The wheel patterns were eventually changed daily by the Germans on each of the numerous links between the German Army High Command and Army Group commanders in the field. By 1945 there were as many 30 links in total. About ten of these were broken and read regularly.

Colossus I contained approximately 1600 vacuum tubes and each of the subsequent machines approximately 2400 vacuum tubes. Like the smaller ABC, Colossus lacked two important features of modern computers. First, it had no internally stored programs. To set it up for a new task, the operator had to alter the machine's physical wiring, using plugs and switches. Second, Colossus was not a general-purpose machine, being designed for a specific cryptanalytic task involving counting and Boolean operations.

F.H. Hinsley, official historian of GC&CS, has estimated that the war in Europe was shortened by at least two years as a result of the signals intelligence operation carried out at Bletchley Park, in which Colossus played a major role. Most of the Colossi were destroyed once hostilities ceased. Some of the electronic panels ended up at Newman's Computing Machine Laboratory in Manchester (see below), all trace of their original use having been removed. Two Colossi were retained by GC&CS (renamed GCHQ following the end of the war). The last Colossus is believed to have stopped running in 1960.

Those who knew of Colossus were prohibited by the Official Secrets Act from sharing their knowledge. Until the 1970s, few had any idea that electronic computation had been used successfully during the second world war. In 1970 and 1975, respectively, Good and Michie published notes giving the barest outlines of Colossus. By 1983, Flowers had received clearance from the British Government to publish a partial account of the hardware of Colossus I. Details of the later machines and of the Special Attachment, the uses to which the Colossi were put, and the cryptanalytic algorithms that they ran, have only recently been declassified. (For the full account of Colossus and the attack on Tunny see Copeland 2006.)

To those acquainted with the universal Turing machine of 1936, and the associated stored-program concept, Flowers' racks of digital electronic equipment were proof of the feasibility of using large numbers of vacuum tubes to implement a high-speed general-purpose stored-program computer. The war over, Newman lost no time in establishing the Royal Society Computing Machine Laboratory at Manchester University for precisely that purpose. A few months after his arrival at Manchester, Newman wrote as follows to the Princeton mathematician John von Neumann (February 1946):

I am ... hoping to embark on a computing machine section here, having got very interested in electronic devices of this kind during the last two or three years. By about eighteen months ago I had decided to try my hand at starting up a machine unit when I got out. ... I am of course in close touch with Turing.

Turing and Newman were thinking along similar lines. In 1945 Turing joined the National Physical Laboratory (NPL) in London, his brief to design and develop an electronic stored-program digital computer for scientific work. (Artificial Intelligence was not far from Turing's thoughts: he described himself as 'building a brain' and remarked in a letter that he was 'more interested in the possibility of producing models of the action of the brain than in the practical applications to computing'.) John Womersley, Turing's immediate superior at NPL, christened Turing's proposed machine the Automatic Computing Engine, or ACE, in homage to Babbage's Difference Engine and Analytical Engine.

Turing's 1945 report 'Proposed Electronic Calculator' gave the first relatively complete specification of an electronic stored-program general-purpose digital computer. The report is reprinted in full in Copeland 2005.

The first electronic stored-program digital computer to be proposed in the U.S. was the EDVAC (see below). The 'First Draft of a Report on the EDVAC' (May 1945), composed by von Neumann, contained little engineering detail, in particular concerning electronic hardware (owing to restrictions in the U.S.). Turing's 'Proposed Electronic Calculator', on the other hand, supplied detailed circuit designs and specifications of hardware units, specimen programs in machine code, and even an estimate of the cost of building the machine (£11,200). ACE and EDVAC differed fundamentally from one another; for example, ACE employed distributed processing, while EDVAC had a centralised structure.

Turing saw that speed and memory were the keys to computing. Turing's colleague at NPL, Jim Wilkinson, observed that Turing 'was obsessed with the idea of speed on the machine' [Copeland 2005, p. 2]. Turing's design had much in common with today's RISC architectures and it called for a high-speed memory of roughly the same capacity as an early Macintosh computer (enormous by the standards of his day). Had Turing's ACE been built as planned it would have been in a different league from the other early computers. However, progress on Turing's Automatic Computing Engine ran slowly, due to organisational difficulties at NPL, and in 1948 a 'very fed up' Turing (Robin Gandy's description, in interview with Copeland, 1995) left NPL for Newman's Computing Machine Laboratory at Manchester University. It was not until May 1950 that a small pilot model of the Automatic Computing Engine, built by Wilkinson, Edward Newman, Mike Woodger, and others, first executed a program. With an operating speed of 1 MHz, the Pilot Model ACE was for some time the fastest computer in the world.

Sales of DEUCE, the production version of the Pilot Model ACE, were buoyant - confounding the suggestion, made in 1946 by the Director of the NPL, Sir Charles Darwin, that 'it is very possible that ... one machine would suffice to solve all the problems that are demanded of it from the whole country' [Copeland 2005, p. 4]. The fundamentals of Turing's ACE design were employed by Harry Huskey (at Wayne State University, Detroit) in the Bendix G15 computer (Huskey in interview with Copeland, 1998). The G15 was arguably the first personal computer; over 400 were sold worldwide. DEUCE and the G15 remained in use until about 1970. Another computer deriving from Turing's ACE design, the MOSAIC, played a role in Britain's air defences during the Cold War period; other derivatives include the Packard-Bell PB250 (1961). (More information about these early computers is given in [Copeland 2005].)

The earliest general-purpose stored-program electronic digital computer to work was built in Newman's Computing Machine Laboratory at Manchester University. The Manchester 'Baby', as it became known, was constructed by the engineers F.C. Williams and Tom Kilburn, and performed its first calculation on 21 June 1948. The tiny program, stored on the face of a cathode ray tube, was just seventeen instructions long. A much enlarged version of the machine, with a programming system designed by Turing, became the world's first commercially available computer, the Ferranti Mark I. The first to be completed was installed at Manchester University in February 1951; in all about ten were sold, in Britain, Canada, Holland and Italy.

The fundamental logico-mathematical contributions by Turing and Newman to the triumph at Manchester have been neglected, and the Manchester machine is nowadays remembered as the work of Williams and Kilburn. Indeed, Newman's role in the development of computers has never been sufficiently emphasised (due perhaps to his thoroughly self-effacing way of relating the relevant events).

It was Newman who, in a lecture in Cambridge in 1935, introduced Turing to the concept that led directly to the Turing machine: Newman defined a constructive process as one that a machine can carry out (Newman in interview with Evans, op. cit.). As a result of his knowledge of Turing's work, Newman became interested in the possibilities of computing machinery in, as he put it, 'a rather theoretical way'. It was not until Newman joined GC&CS in 1942 that his interest in computing machinery suddenly became practical, with his realisation that the attack on Tunny could be mechanised. During the building of Colossus, Newman tried to interest Flowers in Turing's 1936 paper - birthplace of the stored-program concept - but Flowers did not make much of Turing's arcane notation. There is no doubt that by 1943, Newman had firmly in mind the idea of using electronic technology in order to construct a stored-program general-purpose digital computing machine.

In July of 1946 (the month in which the Royal Society approved Newman's application for funds to found the Computing Machine Laboratory), Freddie Williams, working at the Telecommunications Research Establishment, Malvern, began the series of experiments on cathode ray tube storage that was to lead to the Williams tube memory. Williams, until then a radar engineer, explains how it was that he came to be working on the problem of computer memory:

[O]nce [the German Armies] collapsed ... nobody was going to care a toss about radar, and people like me ... were going to be in the soup unless we found something else to do. And computers were in the air. Knowing absolutely nothing about them I latched onto the problem of storage and tackled that. (Quoted in Bennett 1976.)

Newman learned of Williams' work, and with the able help of Patrick Blackett, Langworthy Professor of Physics at Manchester and one of the most powerful figures in the University, was instrumental in the appointment of the 35 year old Williams to the recently vacated Chair of Electro-Technics at Manchester. (Both were members of the appointing committee (Kilburn in interview with Copeland, 1997).) Williams immediately had Kilburn, his assistant at Malvern, seconded to Manchester. To take up the story in Williams' own words:

[N]either Tom Kilburn nor I knew the first thing about computers when we arrived in Manchester University. We'd had enough explained to us to understand what the problem of storage was and what we wanted to store, and that we'd achieved, so the point now had been reached when we'd got to find out about computers ... Newman explained the whole business of how a computer works to us. (F.C. Williams in interview with Evans [1976])

Elsewhere Williams is explicit concerning Turing's role and gives something of the flavour of the explanation that he and Kilburn received:

Tom Kilburn and I knew nothing about computers, but a lot about circuits. Professor Newman and Mr A.M. Turing ... knew a lot about computers and substantially nothing about electronics. They took us by the hand and explained how numbers could live in houses with addresses and how if they did they could be kept track of during a calculation. (Williams [1975], p. 328)

It seems that Newman must have used much the same words with Williams and Kilburn as he did in an address to the Royal Society on 4th March 1948:

Professor Hartree ... has recalled that all the essential ideas of the general-purpose calculating machines now being made are to be found in Babbage's plans for his analytical engine. In modern times the idea of a universal calculating machine was independently introduced by Turing ... [T]he machines now being made in America and in this country ... [are] in certain general respects ... all similar. There is provision for storing numbers, say in the scale of 2, so that each number appears as a row of, say, forty 0's and 1's in certain places or "houses" in the machine. ... Certain of these numbers, or "words" are read, one after another, as orders. In one possible type of machine an order consists of four numbers, for example 11, 13, 27, 4. The number 4 signifies "add", and when control shifts to this word the "houses" H11 and H13 will be connected to the adder as inputs, and H27 as output. The numbers stored in H11 and H13 pass through the adder, are added, and the sum is passed on to H27. The control then shifts to the next order. In most real machines the process just described would be done by three separate orders, the first bringing [H11] (=content of H11) to a central accumulator, the second adding [H13] into the accumulator, and the third sending the result to H27; thus only one address would be required in each order. ... A machine with storage, with this automatic-telephone-exchange arrangement and with the necessary adders, subtractors and so on, is, in a sense, already a universal machine. (Newman [1948], pp. 271-272)

Following this explanation of Turing's three-address concept (source 1, source 2, destination, function) Newman went on to describe program storage ('the orders shall be in a series of houses X1, X2, ...') and conditional branching. He then summed up:

From this highly simplified account it emerges that the essential internal parts of the machine are, first, a storage for numbers (which may also be orders). ... Secondly, adders, multipliers, etc. Thirdly, an "automatic telephone exchange" for selecting "houses", connecting them to the arithmetic organ, and writing the answers in other prescribed houses. Finally, means of moving control at any stage to any chosen order, if a certain condition is satisfied, otherwise passing to the next order in the normal sequence. Besides these there must be ways of setting up the machine at the outset, and extracting the final answer in useable form. (Newman [1948], pp. 273-4)

In a letter written in 1972 Williams described in some detail what he and Kilburn were told by Newman:

About the middle of the year [1946] the possibility of an appointment at Manchester University arose and I had a talk with Professor Newman who was already interested in the possibility of developing computers and had acquired a grant from the Royal Society of £30,000 for this purpose. Since he understood computers and I understood electronics the possibilities of fruitful collaboration were obvious. I remember Newman giving us a few lectures in which he outlined the organisation of a computer in terms of numbers being identified by the address of the house in which they were placed and in terms of numbers being transferred from this address, one at a time, to an accumulator where each entering number was added to what was already there. At any time the number in the accumulator could be transferred back to an assigned address in the store and the accumulator cleared for further use. The transfers were to be effected by a stored program in which a list of instructions was obeyed sequentially. Ordered progress through the list could be interrupted by a test instruction which examined the sign of the number in the accumulator. Thereafter operation started from a new point in the list of instructions. This was the first information I received about the organisation of computers. ... Our first computer was the simplest embodiment of these principles, with the sole difference that it used a subtracting rather than an adding accumulator. (Letter from Williams to Randell, 1972; in Randell [1972], p. 9)

Turing's early input to the developments at Manchester, hinted at by Williams in his above-quoted reference to Turing, may have been via the lectures on computer design that Turing and Wilkinson gave in London during the period December 1946 to February 1947 (Turing and Wilkinson [1946-7]). The lectures were attended by representatives of various organisations planning to use or build an electronic computer. Kilburn was in the audience (Bowker and Giordano [1993]). (Kilburn usually said, when asked from where he obtained his basic knowledge of the computer, that he could not remember (letter from Brian Napper to Copeland, 2002); for example, in a 1992 interview he said: 'Between early 1945 and early 1947, in that period, somehow or other I knew what a digital computer was ... Where I got this knowledge from I've no idea' (Bowker and Giordano [1993], p. 19).)

Whatever role Turing's lectures may have played in informing Kilburn, there is little doubt that credit for the Manchester computer - called the 'Newman-Williams machine' in a contemporary document (Huskey 1947) - belongs not only to Williams and Kilburn but also to Newman, and that the influence on Newman of Turing's 1936 paper was crucial, as was the influence of Flowers' Colossus.

The first working AI program, a draughts (checkers) player written by Christopher Strachey, ran on the Ferranti Mark I in the Manchester Computing Machine Laboratory. Strachey (at the time a teacher at Harrow School and an amateur programmer) wrote the program with Turing's encouragement and utilising the latter's recently completed Programmers' Handbook for the Ferranti. (Strachey later became Director of the Programming Research Group at Oxford University.) By the summer of 1952, the program could, Strachey reported, 'play a complete game of draughts at a reasonable speed'. (Strachey's program formed the basis for Arthur Samuel's well-known checkers program.) The first chess-playing program, also, was written for the Manchester Ferranti, by Dietrich Prinz; the program first ran in November 1951. Designed for solving simple problems of the mate-in-two variety, the program would examine every possible move until a solution was found. Turing started to program his 'Turochamp' chess-player on the Ferranti Mark I, but never completed the task. Unlike Prinz's program, the Turochamp could play a complete game (when hand-simulated) and operated not by exhaustive search but under the guidance of heuristics.

The first fully functioning electronic digital computer to be built in the U.S. was ENIAC, constructed at the Moore School of Electrical Engineering, University of Pennsylvania, for the Army Ordnance Department, by J. Presper Eckert and John Mauchly. Completed in 1945, ENIAC was somewhat similar to the earlier Colossus, but considerably larger and more flexible (although far from general-purpose). The primary function for which ENIAC was designed was the calculation of tables used in aiming artillery. ENIAC was not a stored-program computer, and setting it up for a new job involved reconfiguring the machine by means of plugs and switches. For many years, ENIAC was believed to have been the first functioning electronic digital computer, Colossus being unknown to all but a few.

In 1944, John von Neumann joined the ENIAC group. He had become 'intrigued' (Goldstine's word, [1972], p. 275) with Turing's universal machine while Turing was at Princeton University during 1936-1938. At the Moore School, von Neumann emphasised the importance of the stored-program concept for electronic computing, including the possibility of allowing the machine to modify its own program in useful ways while running (for example, in order to control loops and branching). Turing's paper of 1936 ('On Computable Numbers, with an Application to the Entscheidungsproblem') was required reading for members of von Neumann's post-war computer project at the Institute for Advanced Study, Princeton University (letter from Julian Bigelow to Copeland, 2002; see also Copeland [2004], p. 23). Eckert appears to have realised independently, and prior to von Neumann's joining the ENIAC group, that the way to take full advantage of the speed at which data is processed by electronic circuits is to place suitably encoded instructions for controlling the processing in the same high-speed storage devices that hold the data itself (documented in Copeland [2004], pp. 26-7). In 1945, while ENIAC was still under construction, von Neumann produced a draft report, mentioned previously, setting out the ENIAC group's ideas for an electronic stored-program general-purpose digital computer, the EDVAC (von Neuman [1945]). The EDVAC was completed six years later, but not by its originators, who left the Moore School to build computers elsewhere. Lectures held at the Moore School in 1946 on the proposed EDVAC were widely attended and contributed greatly to the dissemination of the new ideas.

Von Neumann was a prestigious figure and he made the concept of a high-speed stored-program digital computer widely known through his writings and public addresses. As a result of his high profile in the field, it became customary, although historically inappropriate, to refer to electronic stored-program digital computers as 'von Neumann machines'.

The Los Alamos physicist Stanley Frankel, responsible with von Neumann and others for mechanising the large-scale calculations involved in the design of the atomic bomb, has described von Neumann's view of the importance of Turing's 1936 paper, in a letter:

I know that in or about 1943 or '44 von Neumann was well aware of the fundamental importance of Turing's paper of 1936 ... Von Neumann introduced me to that paper and at his urging I studied it with care. Many people have acclaimed von Neumann as the "father of the computer" (in a modern sense of the term) but I am sure that he would never have made that mistake himself. He might well be called the midwife, perhaps, but he firmly emphasized to me, and to others I am sure, that the fundamental conception is owing to Turing, in so far as not anticipated by Babbage ... Both Turing and von Neumann, of course, also made substantial contributions to the "reduction to practice" of these concepts but I would not regard these as comparable in importance with the introduction and explication of the concept of a computer able to store in its memory its program of activities and of modifying that program in the course of these activities. (Quoted in Randell [1972], p. 10)

Other notable early stored-program electronic digital computers were:

EDSAC, 1949, built at Cambridge University by Maurice Wilkes

BINAC, 1949, built by Eckert's and Mauchly's Electronic Control Co., Philadelphia (opinions differ over whether BINAC ever actually worked)

Whirlwind I, 1949, Digital Computer Laboratory, Massachusetts Institute of Technology, Jay Forrester

SEAC, 1950, US Bureau of Standards Eastern Division, Washington D.C., Samuel Alexander, Ralph Slutz

SWAC, 1950, US Bureau of Standards Western Division, Institute for Numerical Analysis, University of California at Los Angeles, Harry Huskey

UNIVAC, 1951, Eckert-Mauchly Computer Corporation, Philadelphia (the first computer to be available commercially in the U.S.)

the IAS computer, 1952, Institute for Advanced Study, Princeton University, Julian Bigelow, Arthur Burks, Herman Goldstine, von Neumann, and others (thanks to von Neumann's publishing the specifications of the IAS machine, it became the model for a group of computers known as the Princeton Class machines; the IAS computer was also a strong influence on the IBM 701)

IBM 701, 1952, International Business Machine's first mass-produced electronic stored-program computer.

The EDVAC and ACE proposals both advocated the use of mercury-filled tubes, called 'delay lines', for high-speed internal memory. This form of memory is known as acoustic memory. Delay lines had initially been developed for echo cancellation in radar; the idea of using them as memory devices originated with Eckert at the Moore School. Here is Turing's description:

It is proposed to build "delay line" units consisting of mercury ... tubes about 5′ long and 1″ in diameter in contact with a quartz crystal at each end. The velocity of sound in ... mercury ... is such that the delay will be 1.024 ms. The information to be stored may be considered to be a sequence of 1024 'digits' (0 or 1) ... These digits will be represented by a corresponding sequence of pulses. The digit 0 ... will be represented by the absence of a pulse at the appropriate time, the digit 1 ... by its presence. This series of pulses is impressed on the end of the line by one piezo-crystal, it is transmitted down the line in the form of supersonic waves, and is reconverted into a varying voltage by the crystal at the far end. This voltage is amplified sufficiently to give an output of the order of 10 volts peak to peak and is used to gate a standard pulse generated by the clock. This pulse may be again fed into the line by means of the transmitting crystal, or we may feed in some altogether different signal. We also have the possibility of leading the gated pulse to some other part of the calculator, if we have need of that information at the time. Making use of the information does not of course preclude keeping it also. (Turing [1945], p. 375)

Mercury delay line memory was used in EDSAC, BINAC, SEAC, Pilot Model ACE, EDVAC, DEUCE, and full-scale ACE (1958). The chief advantage of the delay line as a memory medium was, as Turing put it, that delay lines were "already a going concern" (Turing [1947], p. 380). The fundamental disadvantages of the delay line were that random access is impossible and, moreover, the time taken for an instruction, or number, to emerge from a delay line depends on where in the line it happens to be.

In order to minimize waiting-time, Turing arranged for instructions to be stored not in consecutive positions in the delay line, but in relative positions selected by the programmer in such a way that each instruction would emerge at exactly the time it was required, in so far as this was possible. Each instruction contained a specification of the location of the next. This system subsequently became known as 'optimum coding'. It was an integral feature of every version of the ACE design. Optimum coding made for difficult and untidy programming, but the advantage in terms of speed was considerable. Thanks to optimum coding, the Pilot Model ACE was able to do a floating point multiplication in 3 milliseconds (Wilkes's EDSAC required 4.5 milliseconds to perform a single fixed point multiplication).

In the Williams tube or electrostatic memory, previously mentioned, a two-dimensional rectangular array of binary digits was stored on the face of a commercially-available cathode ray tube. Access to data was immediate. Williams tube memories were employed in the Manchester series of machines, SWAC, the IAS computer, and the IBM 701, and a modified form of Williams tube in Whirlwind I (until replacement by magnetic core in 1953).

Drum memories, in which data was stored magnetically on the surface of a metal cylinder, were developed on both sides of the Atlantic. The initial idea appears to have been Eckert's. The drum provided reasonably large quantities of medium-speed memory and was used to supplement a high-speed acoustic or electrostatic memory. In 1949, the Manchester computer was successfully equipped with a drum memory; this was constructed by the Manchester engineers on the model of a drum developed by Andrew Booth at Birkbeck College, London.

The final major event in the early history of electronic computation was the development of magnetic core memory. Jay Forrester realised that the hysteresis properties of magnetic core (normally used in transformers) lent themselves to the implementation of a three-dimensional solid array of randomly accessible storage points. In 1949, at Massachusetts Institute of Technology, he began to investigate this idea empirically. Forrester's early experiments with metallic core soon led him to develop the superior ferrite core memory. Digital Equipment Corporation undertook to build a computer similar to the Whirlwind I as a test vehicle for a ferrite core memory. The Memory Test Computer was completed in 1953. (This computer was used in 1954 for the first simulations of neural networks, by Belmont Farley and Wesley Clark of MIT's Lincoln Laboratory (see Copeland and Proudfoot [1996]).

Once the absolute reliability, relative cheapness, high capacity and permanent life of ferrite core memory became apparent, core soon replaced other forms of high-speed memory. The IBM 704 and 705 computers (announced in May and October 1954, respectively) brought core memory into wide use.

Works Cited

Babbage, C. (ed. by Campbell-Kelly, M.), 1994, Passages from the Life of a Philosopher, New Brunswick: Rutgers University Press

Bennett, S., 1976, 'F.C. Williams: his contribution to the development of automatic control', National Archive for the History of Computing, University of Manchester, England. (This is a typescript based on interviews with Williams in 1976.)

Bowker, G., and Giordano, R., 1993, 'Interview with Tom Kilburn', Annals of the History of Computing,15: 17-32.

Copeland, B.J. (ed.), 2004, The Essential Turing Oxford University Press

Copeland, B.J. (ed.), 2005, Alan Turing's Automatic Computing Engine: The Master Codebreaker's Struggle to Build the Modern Computer Oxford University Press

Copeland, B.J. and others, 2006, Colossus: The Secrets of Bletchley Park's Codebreaking Computers Oxford University Press

Copeland, B.J., and Proudfoot, D., 1996, 'On Alan Turing's Anticipation of Connectionism' Synthese,108: 361-377

Evans, C., 197?, interview with M.H.A. Newman in 'The Pioneers of Computing: an Oral History of Computing', London: Science Museum

Fifer, S., 1961, Analog Computation: Theory, Techniques, Applications New York: McGraw-Hill

Ford, H., 1919, 'Mechanical Movement', Official Gazette of the United States Patent Office, October 7, 1919: 48

Goldstine, H., 1972, The Computer from Pascal to von Neumann Princeton University Press

Huskey, H.D., 1947, 'The State of the Art in Electronic Digital Computing in Britain and the United States', in [Copeland 2005]

Newman, M.H.A., 1948, 'General Principles of the Design of All-Purpose Computing Machines' Proceedings of the Royal Society of London, series A, 195 (1948): 271-274

Randell, B., 1972, 'On Alan Turing and the Origins of Digital Computers', in Meltzer, B., Michie, D. (eds), Machine Intelligence 7, Edinburgh: Edinburgh University Press, 1972

Smith, B.C., 1991, 'The Owl and the Electric Encyclopaedia', Artificial Intelligence,47: 251-288

Thomson, J., 1876, 'On an Integrating Machine Having a New Kinematic Principle' Proceedings of the Royal Society of London, 24: 262-5

Turing, A.M., 1936, 'On Computable Numbers, with an Application to the Entscheidungsproblem' Proceedings of the London Mathematical Society, Series 2, 42 (1936-37): 230-265. Reprinted inThe Essential Turing (Copeland [2004]).

Turing, A.M, 1945, 'Proposed Electronic Calculator', in Alan Turing's Automatic Computing Engine (Copeland [2005])

Turing, A.M., 1947, 'Lecture on the Automatic Computing Engine', in The Essential Turing (Copeland [2004])

Turing, A.M., and Wilkinson, J.H., 1946-7, 'The Turing-Wilkinson Lecture Series (1946-7)', in Alan Turing's Automatic Computing Engine (Copeland [2005])

von Neumann, J., 1945, 'First Draft of a Report on the EDVAC', in Stern, N. From ENIAC to UNIVAC: An Appraisal of the Eckert-Mauchly Computers Bedford, Mass.: Digital Press (1981), pp. 181-246

Williams, F.C., 1975, 'Early Computers at Manchester University' The Radio and Electronic Engineer,45 (1975): 237-331

Wynn-Williams, C.E., 1932, 'A Thyratron "Scale of Two" Automatic Counter' Proceedings of the Royal Society of London, series A, 136: 312-324

Further Reading

Copeland, B.J., 2004, 'Colossus - Its Origins and Originators' Annals of the History of Computing,26: 38-45

Metropolis, N., Howlett, J., Rota, G.C. (eds), 1980, A History of Computing in the Twentieth Century New York: Academic Press

Randell, B. (ed.), 1982, The Origins of Digital Computers: Selected Papers Berlin: Springer-Verlag

Williams, M.R., 1997, A History of Computing Technology Los Alamitos: IEEE Computer Society Press

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Charles Babbage was Lucasian Professor of Mathematics at Cambridge University from 1828 to 1839 (a post formerly held by Isaac Newton). Babbage's proposed Difference Engine was a special-purpose digital computing machine for the automatic production of mathematical tables (such as logarithm tables, tide tables, and astronomical tables).

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The First Cell Phone Call Was Made 45 Years Ago

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The first handheld cellular phone call was made 45 years ago. A Motorola engineer made the call in New York City with a 2 1/2-pound prototype.

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Sharing Resources

The Internet started in the 1960s as a way for government researchers to share information. Computers in the '60s were large and immobile and in order to make use of information stored in any one computer, one had to either travel to the site of the computer or have magnetic computer tapes sent through the conventional postal system.

Another catalyst in the formation of the Internet was the heating up of the Cold War. The Soviet Union's launch of the Sputnik satellite spurred the U.S. Defense Department to consider ways information could still be disseminated even after a nuclear attack. This eventually led to the formation of the ARPANET (Advanced Research Projects Agency Network), the network that ultimately evolved into what we now know as the Internet. ARPANET was a great success but membership was limited to certain academic and research organizations who had contracts with the Defense Department. In response to this, other networks were created to provide information sharing.

January 1, 1983 is considered the official birthday of the Internet. Prior to this, the various computer networks did not have a standard way to communicate with each other. A new communications protocol was established called Transfer Control Protocol/Internetwork Protocol (TCP/IP). This allowed different kinds of computers on different networks to "talk" to each other. ARPANET and the Defense Data Network officially changed to the TCP/IP standard on January 1, 1983, hence the birth of the Internet. All networks could now be connected by a universal language.

The image above is a scale model of the UNIVAC I (the name stood for Universal Automatic Computer) which was delivered to the Census Bureau in 1951. It weighed some 16,000 pounds, used 5,000 vacuum tubes, and could perform about 1,000 calculations per second. It was the first American commercial computer, as well as the first computer designed for business use. (Business computers like the UNIVAC processed data more slowly than the IAS-type machines, but were designed for fast input and output.) The first few sales were to government agencies, the A.C. Nielsen Company, and the Prudential Insurance Company. The first UNIVAC for business applications was installed at the General Electric Appliance Division, to do payroll, in 1954. By 1957 Remington-Rand (which had purchased the Eckert-Mauchly Computer Corporation in 1950) had sold forty-six machines.

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The Internet started in the 1960s as a way for government researchers to share information. Computers in the '60s were large and immobile and in order to make use of information stored in any one computer, one had to either travel to the site of the computer or have magnetic computer tapes sent through the conventional postal system.

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Table of Contents

Google, in full Google LLC formerly Google Inc. (1998-2017), American search engine company, founded in 1998 by Sergey Brin and Larry Page, that is a subsidiary of the holding company Alphabet Inc. More than 70 percent of worldwide online search requests are handled by Google, placing it at the heart of most Internet users' experience. It is one of the world's most prominent brands. Its headquarters are in Mountain View, California.

Google began as an online search firm, but it now offers more than 50 Internet services and products, from e-mail and online document creation to software for mobile phones and tablet computers. In addition, its 2012 acquisition of Motorola Mobility put it in the position to sell hardware in the form of mobile phones. Google's broad product portfolio and size make it one of the top four influential companies in the high-tech marketplace, along with Apple, IBM, and Microsoft. Despite this myriad of products, its original search tool remains the core of its success. In 2016 Alphabet earned nearly all of its revenue from Google advertising based on users' search requests.

Searching for business

Brin and Page, who met as graduate students at Stanford University, were intrigued with the idea of extracting meaning from the mass of data accumulating on the Internet. They began working from Page's dormitory room at Stanford to devise a new type of search technology, which they dubbed BackRub. The key was to leverage Web users' own ranking abilities by tracking each Web site's "backing links"-that is, the number of other pages linked to them. Most search engines simply returned a list of Web sites ranked by how often a search phrase appeared on them. Brin and Page incorporated into the search function the number of links each Web site had; i.e., a Web site with thousands of links would logically be more valuable than one with just a few links, and the search engine thus would place the heavily linked site higher on a list of possibilities. Further, a link from a heavily linked Web site would be a more valuable "vote" than one from a more obscure Web site.

In mid-1998 Brin and Page began receiving outside financing (one of their first investors was Andy Bechtolsheim, a cofounder of Sun Microsystems, Inc.). They ultimately raised about $1 million from investors, family, and friends and set up shop in Menlo Park, California, under the name Google, which was derived from a misspelling of Page's original planned name, (a mathematical term for the number one followed by 100 zeroes). By mid-1999, when Google received a $25 million round of venture capital funding, it was processing 500,000 queries per day. Activity began to explode in 2000, when Google became the client search engine for one of the Web's most popular sites, Yahoo!. By 2004, when Yahoo! dispensed with Google's services, users were searching on Google 200 million times a day. That growth only continued: by the end of 2011 Google was handling some three billion searches per day. The company's name became so ubiquitous that it entered the lexicon as a verb: to google became a common expression for searching the Internet.

To accommodate this unprecedented mass of data, Google built 11 data centres around the world, each of them containing several hundred thousand servers (basically, multiprocessor personal computers and hard drives mounted in specially constructed racks). Google's interlinked computers probably number several million. The heart of Google's operation, however, is built around three proprietary pieces of computer code: Google File System (GFS), Bigtable, and MapReduce. GFS handles the storage of data in "chunks" across several machines; Bigtable is the company's database program; and MapReduce is used by Google to generate higher-level data (e.g., putting together an index of Web pages that contain the words "Chicago," "theatre," and "participatory").

The extraordinary growth of Google led to internal management problems. Almost from the beginning, investors felt that Brin and Page needed an experienced manager at the helm, and in 2001 they agreed to hire Eric Schmidt as chairman and chief executive officer (CEO) of the company. Schmidt, who previously had held the same positions at the software company Novell Inc., had a doctorate in computer science and melded well with the technocratic impulses of the founders. During Schmidt's reign as CEO, Page served as president of products, and Brin was president of technology. The trio ran the company as a "triumvirate" until Page took on the CEO role in 2011, Schmidt became executive chairman, and Brin adopted the title of director of special projects.

The company's initial public offering (IPO) in 2004 raised $1.66 billion for the company and made Brin and Page instant billionaires. In fact, the IPO created 7 billionaires and 900 millionaires from the early stockholders. The stock offering also made news because of the unusual way it was handled. Shares were sold in a public auction intended to put the average investor on an equal footing with financial industry professionals. Google was added to Standard and Poor's 500 ( S&P 500) stock index in 2006. In 2012 Google's market capitalization made it one of the largest American companies not in the Dow Jones Industrial Average.

Google reorganized itself in August 2015 to become a subsidiary of the holding company Alphabet Inc. Internet search, advertising, apps, and maps, as well as the mobile operating system Android and the video-sharing site YouTube, remained under Google. Separate Google ventures-such as longevity research company Calico, home-products company Nest, and research lab Google X-became separate firms under Alphabet. Page became CEO of Alphabet, Brin its president, and Schmidt its executive chairman. Sundar Pichai, senior vice president of products, became Google's new CEO. Alphabet again reorganized in 2017 to create an intermediate holding company, XXVI Holdings, and to convert Google into a limited liability company (LLC). In 2018 Schmidt stepped down as executive chairman. More changes followed in 2019 as both Brin and Page left their posts as president and CEO, respectively. However, they both remained on Alphabet's board of directors. Pichai became CEO of the holding company while retaining that position at Google.

Google's strong financial results reflected the rapid growth of Internet advertising in general and Google's popularity in particular. Analysts attributed part of that success to a shift in advertising spending toward the Internet and away from traditional media, including newspapers, magazines, and television. For example, American newspaper advertising fell from a peak of $64 billion in 2000 to $20.7 billion in 2011, while global online advertising grew from approximately $6 billion in 2000 to more than $72 billion in 2011.

Since its founding, Google has spent large sums to secure what it has calculated to be significant Internet marketing advantages. For example, in 2003 Google spent $102 million to acquire Applied Semantics, the makers of AdSense, a service that signed up owners of Web sites to run various types of ads on their Web pages. In 2006 Google again paid $102 million for another Web advertisement business, dMarc Broadcasting, and that same year it announced that it would pay $900 million over three and a half years for the right to sell ads on MySpace.com. In 2007 Google made its largest acquisition to date, buying online advertising firm DoubleClick for $3.1 billion. Two years later the company responded to the explosive growth of the mobile applications market with a $750 million deal to acquire the mobile advertising network AdMob. All of these purchases were part of Google's effort to expand from its search engine business into advertising by combining the various firms' databases of information in order to tailor ads to consumers' individual preferences.

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Google, in full Google LLC formerly Google Inc. (1998-2017), American search engine company, founded in 1998 by Sergey Brin and Larry Page, that is a subsidiary of the holding company Alphabet Inc. More than 70 percent of worldwide online search requests are handled by Google, placing it at the heart of most Internet users' experience.

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Facebook, American online social network service that is part of the company Meta Platforms. Facebook was founded in 2004 by Mark Zuckerberg, Eduardo Saverin, Dustin Moskovitz, and Chris Hughes, all of whom were students at Harvard University. Facebook became the largest social network in the world…

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What Is TikTok?

TikTok is a popular social media app that allows users to create, watch, and share 15-second videos shot on cellphones. With its personalized feeds of quirky short videos set to music and sound effects, the app isnotable for itsaddictive quality and high levels of engagement. Amateur and professional creators alike can add effects like filters, background music, and stickers to their videos, and can collaborate on content and create split-screen duet videos even if they're in different locations.

Understanding TikTok

Launched in its present form in 2018, TikTok joined the ranks of social media giants in record time. It had about one billion active monthly users worldwide by the start of 2021 and had been downloaded in the U.S. at least 200 million times, according to Wallaroo Media.

Like all social media companies, TikTok has been the target of persistent if vague concerns about the potential use or misuse of the private information it gathers about its users. The difference is that a majority of TikTok is Chinese-owned.

The TikTok Story

That may change soon.

First, consider what TikTok isn't: It isn't Facebook. Facebook has been around since 2004, and still dwarfs every other social media site on the planet, both in audience size and advertising revenue. It had 2.6 billion active monthly users by the beginning of 2021, and that doesn't count the parent company's WhatsApp and Instagram sites. It brought in $17.4 billion in advertising revenue in the first quarter of 2020 alone. About 80 million small businesses globally have Facebook pages.﻿﻿

So, what is a rival in the social media business do to compete? For one thing, wow the younger crowd with a bottomless ocean of short and snappy entertainment that will draw in the advertisers who want to reach them. Only 7.1% of TikTok users are age 50 or older.﻿﻿

Facebook appeals more or less evenly across generations.﻿﻿﻿﻿﻿ That's nice, but marketers in search of youthful demographics aren't known for nice. TikTok, not Facebook, has the target audience that brands and the marketing companies that cater to them want now.

TikTok's Launch

The name TikTok is meant to suggest the short format of the videos. Launched in 2016 by the Chinese startup company ByteDance, it's known there as Douyin. Its stratospheric growth in usage really began in late 2017, when it acquired a rival app, Musical.ly, and ported over its 200 million account list to TikTok.

ByteDance was reportedly worth up to $140 billion by mid-2020, based on the private sale of a small stake in the company. TikTok alone was said to be worth about $50 billion.﻿﻿ That would make it the most valuable startup in the world.

TikTok Business

Marketing on TikTok is still in its infancy, but a growing number of agencies are eager to help brands create the kind of quirky content that gets clicks on TikTok. Conventional advertising that stresses a product's superior qualities doesn't work. Light, fun campaigns set to music hit the spot. The ultimate goal is to goal viral on the site and spur imitations by TikTok users.

Some of the successful marketing campaigns on TikTok:

The Chipotle restaurant chain ran a Halloween "Boorito" coupon giveaway campaign encouraging TikTok users to dress up for the holiday and post their images. The campaign scored four billion views.

e.l.f. Cosmetics teamed up with a few social media influencers to create an online reality show and contest called #eyeslipsface. The ad campaign got more than 3.8 billion clicks.

The NBA signed up for an account and scored more than 12 million followers to date. Its aim is to enhance global awareness of the NBA, particularly among young people.﻿﻿

TikTok has offices in Beijing, Berlin, Jakarta, London, Los Angeles, Moscow, Mumbai, Sao Paulo, Seoul, Shanghai, Singapore, and Tokyo.﻿﻿

Other TikTok Revenue Sources

Like many other social media sites, TikTok also makes money from in-app purchases. Users can buy coins priced from 99 cents to $99.99 and use them to tip their favorite creators on the platform.

The company grossed $80 million from in-app purchases globally, according to an April 2019 report from Sensor Tower. In Q1 2019, user spending reaching an estimated $18.9 million globally-222% more than the app grossed during the same period last year.

TikTok only started running display advertising in 2019. Instead, brands open user accounts on TikTok and, just like any of its users, create and post mini-videos. They can pay to promote their videos to other users. The ultimate goal is to go viral and spur takeoffs, drawing in a huge audience for your brand's message.

TikTok's User Base

TikTok is one of the world's most popular social media apps and is available in more than 150 markets and 75 languages. The app hit close to two billion downloads in March 2020, with 104.7 million downloads alone as of January 2020, according to market analyst Sensor Tower.

Here's a breakdown of some of the app's most notable demographics:

69% of users are between the ages of 13 and 24.

60% are female.

Its users spend an average of 46 minutes per day on it

A quarter of the app's downloads come from India, with more than 200 million active users.

Usage in Thailand is increasing, with one in seven people downloading the app.

About 96 million downloads are from the U.S.

TikTok Politics

Like most social media sites, TikTok is not immune to controversy.

TikTok and many other Chinese-made apps were banned in India in mid-2020 as a danger to the nation's sovereignty. The move came amid growing tensions between the two nations.﻿﻿

TikTok is banned in Bangladesh. It was also briefly banned in Indonesia for "pornography, inappropriate content, and blasphemy." The ban was overturned a week later after the company promised to take all objectionable content off the platform and set up a local office to monitor and sanitize content.﻿﻿

In February 2019, the company paid $5.7 million in the U.S. to settle allegations that it illegally collected personal information from children. It is the largest civil penalty ever obtained by the Federal Trade Commission (FTC) in a children's privacy case.

TikTok vs. President Donald J. Trump

Starting in July 2020, then-President Donald J. Trump launched a public attack on TikTok, threatening to ban it from the U.S. on the grounds that the Chinese Communist Party could use data gathered on its users to spy on U.S. citizens. He demanded that TikTok be sold to U.S. interests to eliminate the threat.

Concerns about social media companies' use and misuse of private information is a bipartisan issue in Washington, so the concern gained some traction. However, by September 2020, Trump had declared that he had solved the problem. TikTok's U.S. operations would be severed from its Chinese owner, ByteDance, and incorporated as a U.S. company.

The proposed deal would have created a new spinoff company, TikTok Global. Oracle would own a 12.5% stake in the company and become its cloud provider in the U.S. This may have been intended to alleviate concerns that China was using TikTok to gather data on Americans, but apparently, the real change is minimal. TikTok data on U.S. users was always held in the U.S., so it's just moving from one cloud to another. Meanwhile, Walmart tentatively agreed to buy a 7.5% stake in TikTok Global. It plans live-streamed shopping events hosted on TikTok. The remaining 80% would remain owned by ByteDance.

The tentative deal was stalled by numerous court challenges, and the future of the proposed deal remains uncertain under the Biden Administration. During a White House briefing on Feb. 10, 2021, press secretary Jen Psaki denied that President Biden had set any new policy on the app. "It's not accurate to suggest that there is a new proactive step by the Biden White House," Psaki said. "Broadly speaking, we are evaluating the risks to US data, including from TikTok, and will address them in a decisive and effective fashion." Psaki declined to set a timetable for when such a review would conclude.

TikTok FAQs

Here are the answers to some commonly asked questions about TikTok.

What Company Owns TikTok?

The majority owner of TikTok is ByteDance, a Chinese technology company. In late 2020, following pressure from the Trump Administration, ByteDance agreed to create a new company, TikTok Global. In this proposal, ByteDance would continue to own 80% of TikTok Global, with Oracle and Walmart as its minority owners and U.S. partners. As of May 2021, the deal is on hold pending review from the Biden Administration.

What Is the Purpose of TikTok?

TikTok is a social media app dedicated to short-form videos created for and consumed by users. The length of videos is between 15-60 seconds.

The format lends itself to entertainment and comedy. However, it is increasingly used for infotainment. So-called influencers who gain a steady audience on TikTok offer snippets of advice and tips along with self-promotion. Beauty, fashion, personal finance, and cooking are all popular topics for informational videos.

Celebrities with a youthful audience perform for it.

Increasingly, the format is used to sell products.

How Do I Invest in TikTok Stock?

TikTok is not publicly owned (privately owned 100% by ByteDance) and the prior deal to create TikTok Global remains on hold. This includes any plans for issuing an IPO.

Is TikTok a Publicly-Traded Company?

TikTok is not publicly owned as of May 2021. Stay tuned for the announcement of an IPO for TikTok Global.

Is TikTok Banned?

At this writing, TikTok is banned in India and Pakistan. It was briefly banned but restored in other countries including Indonesia and Bangladesh.

It was threatened with a ban in the U.S. by then-President Donald Trump, but he backed off after an agreement with owner ByteDance to spin off TikTok as a separate entity, TikTok Global, with minority ownership held by U.S. companies Oracle and Walmart.

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TikTok is a popular social media app that allows users to create, watch, and share 15-second videos shot on cellphones. With its personalized feeds of quirky short videos set to music and sound effects, the app isnotable for itsaddictive quality and high levels of engagement.

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Wednesday, December 1, 2021

Big Tech wants to sell you on the metaverse, but don't hold your breath

If there's one buzzword the tech world couldn't stop using in 2021, it was metaverse. Well, that and NFTs, but let's talk about tech CEOs' fixation on the metaverse.

Microsoft ( MSFT), Nvidia ( NVDA), and, of course, Facebook ( FB), er, Meta, each laid out elaborate plans this year for a new version of the internet, one that features 3-D online worlds where people can connect as if they were talking to each other in real life.

To hear today's CEOs talk about it, the metaverse will be the next evolution of the internet. Just as we went from chatting on message boards on chunky laptops to buying homes on our smartphones, tech leaders say, the metaverse will transform the way we use the web. We're talking walking around a fully virtual world as if you were really there, or dropping in on colleagues to host a meeting thousands of miles away.

If that's all very sci-fi sounding, it's because it is. To be sure, we've seen glimpses of the metaverse in the form of Microsoft's upcoming Teams Mesh that lets you use a digital avatar in virtual meetings or Nvidia's Omniverse, which helps teach AI-powered cars to drive.

But the metaverse as tech visionaries describe it - where you can appear as a hologram in your friend's special AR goggles and play tennis in real-time - is still years away from reality.

We're witnessing an incredible advertising effort

Let's start with the metaverse we've been promised so far. If you take Facebook, dang, force of habit, I mean Meta's version of the metaverse, we'll be able to fluidly transport ourselves across different virtual worlds. One minute you're playing chess while floating several feet above the table; the next, you're hanging ten with your friend on the open water.

But Meta's demonstration was more fantasy than true metaverse tech. It was a video meant to provide consumers, and investors, with a glimpse at what the company hopes to build.

"It's going to take a lot of time," explained Stanford University Jeremy Bailenson, who teaches a class completely in virtual reality. "We're not all going to be wearing headsets or glasses anytime soon."

Bailenson, whose students spent a collective 200,000 minutes in VR headsets in recent months, says nothing has truly changed in terms of the technology recently. Instead, what we're seeing is a massive marketing campaign by companies that are hyped on the metaverse.

The technology is still in its infancy

The metaverse of Mark Zuckerberg's dreams will require infrastructure that allows consumers to swiftly travel among 3-D worlds. We're also going to need headgear that isn't awful.

I've used the majority of commercially available headsets, not to mention a few that are meant for industrial use, and they're all uncomfortable. If they're not heavy, they don't properly fit over glasses. Or they leave you sweating. What's worse, if they're not tethered to a high-end PC, which costs plenty on its own, they have short battery lives.

On top of that, the visual fidelity of your average headset is nowhere near that of, say, a 4K TV. Fine details are lost in the screen door effect caused by the fact that the headsets' displays aren't high enough resolution to perfectly blend each pixel together.

Then there are the headaches and nausea that plague some headset users.

And that's just the headgear. For a fully immersive experience that tech companies are pitching, you'll need to be able to feel pressure and resistance, which will require if not specialized gloves then some kind of ... suit?

All of that is to say that, while consumers can have fun in VR headsets, we're nowhere near experiencing the kind of incredible 3-D world you'd expect from the book "Ready Player One."

The metaverse has promise

That's not to knock the metaverse or its potential entirely. Bailenson's class, for instance, is able to learn about therapeutic medicines and empathy for others by virtually walking in their shoes.

Nvidia's Omniverse, which the company positions as plumbing for the larger metaverse, has shown impressive promise by helping predict forest fires and serving as a test bed for the artificial intelligence that will eventually power the company's self-driving car capabilities.

Microsoft's Teams Mesh, still in its early stages, could make it less stressful to join virtual meetings by letting you skip your webcam and use a digital avatar to chat with colleagues instead. Just imagine never having to comb your hair, or beard in my case, before a virtual meeting again.
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Roblox ( RBLX) and Epic's "Fortnite" have also laid the foundation for what early metaverses can offer, creating virtual worlds where users can spend time with friends and attend concerts together whether they're separated by tens or thousands of miles.

What we're seeing is the very start of what could turn into the kind of metaverse Big Tech promises with its flashy advertising efforts and videos.

But without grounding those wild dreams they're pushing in some kind of reality, tech firms run the risk of alienating their own users, and turning the entire concept of the metaverse into a passing fad that never truly takes off.

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2021 was the year of the metaverse, but it will be years before it's a reality

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This article was first featured in Yahoo Finance Tech , a weekly newsletter highlighting our original content on the industry. Get it sent directly to your inbox every Wednesday by 4 p.m. ET. Subscribe Wednesday, December 1, 2021 If there's one buzzword the tech world couldn't stop using in 2021, it was metaverse.

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We will be able to communicate with other human organisms/ aliens from different planets.
https://www.scientificamerican.com/article/want-to-talk-to-aliens-try-changing-the-technological-channel-beyond-radio/

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In 2050, humans will communicate by reading each other's minds. They will do this through a collective AI consciousness. People will be able to transmit information from their mind to someone else's mind and communicate that way. Words will not be needed to communicate.

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2050 - Computer chip implants into the eye is normalized - Robbie Guglielmo

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2050 - Computer chip implants into the eye is normalized - Robbie Guglielmo

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It is now completely safe! No more iphones to look down at. Now you can just blink! These computer chips are microscopic holding all the information your iphone would.

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Eyeborg - one eyed filmmaker Rob Spence insert his Eye Prosthesis at...

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Eyeborg - one eyed filmmaker Rob Spence insert his Eye Prosthesis at Campus Party on July 21, 2018 in Milan, Italy. Get premium, high resolution news photos at Getty Images

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Tesla may have popularized autonomous driving in cars, but self-driving cars are just the beginning: automated trucks are the future.

The widespread adoption of self-driving semi-trucks is one of the most promising initiatives in the trucking industry. While there are still some kinks to work out before freight companies can fully rely on self-driving vehicles, recent advancements have led many tech leaders and financiers to believe self-driving trucks will be commonly used within 10 years.

This blog series will dive into the features of self-driving trucks and the progress key players have made in their development so far. In the first post, we'll discuss the differences between semi-autonomous trucks and fully autonomous trucks, in addition to their benefits and their impact on the role of truck drivers. The second (and final) post will examine the companies that are racing to build these automated vehicles and their most recent accomplishments.

Autonomous Driving Means Autonomous Trucking

Trucking companies are testing features in two areas: semi-automated trucks (which depend on the driver for some functions) and fully autonomous trucks (which don't require a driver at all).

In the J3016™ "Levels of Driving Automation" standard, Pennsylvania-based SAE International outlines six stages of driving automation:

According to the press release, SAE developed this standard to "...speed the delivery of an initial regulatory framework and best practices to guide manufacturers and other entities in the safe design, development, testing, and deployment of highly automated vehicles (HAVs)."

The J3016™ "Levels of Driving Automation" standard has been adopted on a global scale and is accepted by stakeholders in the automated vehicle technology space. In turn, the U.S. Department of Transportation has included it in the Federal Automated Vehicles Policy for driving on city streets.

Levels 1-3 refer to trucks that feature smart tech - like automatic emergency braking, lane centering, and adaptive cruise control - to support human drivers.

Autonomous trucks in Levels 2-4 are already in use, and technology makers are progressing to the highest levels.

Self-driving technology that falls in the lower levels of SAE's standard minimizes the work of a driver, while that in the higher levels of the standard eliminates the need for drivers altogether. In the most recent tests with Level 4 trucks, drivers haven't had to do more than stand by in the event they need to take over control of their respective vehicles.

The Benefits Of An Autonomous System in Trucking

Advocates of automated driving technology cite many benefits, including enhanced safety, a jump in productivity, cost savings, and increased efficiency.

Every year, truck accidents yield 120,000 casualties, 4,000 of which prove fatal. An article by Transport Topics says, "...onboard safety technology will do more to prevent crashes and support the driver in the future." The same piece highlights automated braking, acceleration, and steering (Level 2 attributes) as features that "[promise] to enhance safety and make the job of the trucker more comfortable and less stressful."

Distracted driving is the leading cause of accidents in the United States, and exhaustion is a factor in about 14% of fatal truck accidents. If artificial intelligence operated self-driving trucks in autonomous mode, the number of traffic infractions caused by human error would drop substantially. Computers enable the vehicles to operate safely without depending on a driver's health or judgement. What's more, they control truck speed, appropriately handle the vehicle every time, and never get distracted.

In practice, self-driving trucks would also reduce the number of accidents caused by fatigue. An article by Texas-based GTG Technology Group notes, drivers wouldn't have to exert themselves for hours on end; they could take breaks with their respective trucks still in motion, which would prevent delivery delays.

If trucks didn't have to stop, productivity would skyrocket. Human factors that prolong trips (like rest stops and legally mandated breaks) wouldn't impact delivery dates. Best of all, freight workers could program trucks to drive 24/7, which would move loads faster and effectively shrink delivery windows. In an industry that weathers slow and sometimes unpredictable transit, the promise of faster shipping is an enticing prospect.

Implementing self-driving semi-trucks would also cut costs in the areas of labor, insurance and fuel consumption. Let's start with labor: According to an article in FreightWaves, labor accounts for 30-75% of trucking costs. Freight companies spend as much as $100,000 per year on the wages, insurance premiums, and bonuses of two drivers. These numbers translate to over $1 million over the service lifetime of a typical rig, the same article notes. If a route requires two drivers, autonomous technology that eliminates the need for one or both of them would halve or completely eliminate the labor costs associated with that route, respectively, resulting in astronomical savings.

The FreightWaves piece also notes cost savings on insurance. Derek Kaufman, Managing Partner at California-based Schwartz Advisors, "estimates that a truck involved in an accident where: a) there is just property damage; b) an injury-accident; and c) a fatality, will cost insurance companies payouts of $100-200,000, $135-450,000 and $850,000-$1.3 million, respectively." Automated technology that makes trucks safer would provide substantial cost savings for insurers.

In practice, self-driving trucks would also improve efficiency. Autonomous vehicles that travel next to each other or in a line create an estimated 25% reduction in drag (wind resistance), which "generates a 5-15% decrease in fuel consumption." This newfound efficiency cuts fuel costs by at least 15% and reduces emissions. Furthermore, trucks that travel outside of peak traffic hours move faster and further than they do when they're sitting in rush-hour traffic. Indeed, "as far back as 2013, ATA estimated that [interstate] congestion was costing the trucking industry over $9 billion in traffic-related idling time fuel costs and over 140 million hours of wasted worker-related labor time."

Autonomous Semis Are The Future

Clearly, the biggest benefits of automated big-rigs stem from lessening or eliminating a human's job in the truck. But it doesn't necessarily bode poorly for truck drivers. Transport Topics writes the following about the future of truck-driving: "The freight industry will actually need more professional drivers than it employs today - not less - as demand grows." There's plenty of evidence that the demand for truckers will remain steady over the next 30 years, though the role may shift as autonomous technology evolves.

Some leaders in the industry predict the implementation of self-driving semi-trucks would mitigate the driver shortage. However, it's more likely these trucks would supplement the drivers' work instead of replacing it altogether, at least at first. It's still advantageous to have a human in control while the vehicle is navigating tricky routes or bad weather.

Our next post will focus on the major players in the industry and how their applications of driverless trucks will change the industry in the long haul.

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Self-Driving Semi-Trucks: Who, What, When, and Why - Part 1

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Tesla may have popularized autonomous driving in cars, but self-driving cars are just the beginning: automated trucks are the future. The widespread adoption of self-driving semi-trucks is one of the most promising initiatives in the trucking industry.

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In the future instead of having to go on our phone and call someone to talk to them miles away or having to text them. We would be able to communicate to them with just a thought. They'll be able to receive our thoughts once we send it, it's like texting but with our minds.

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One way we might see communication change in the future is through augmented reality. In an augmented-reality system, you view the world through a technological overlay. This could take the form of a hand-held device like a smartphone -- there are several augmented-reality applications already available for some phones. Another possible application is through a set of augmented-reality glasses. In either case, you can view the world around you and see real-time digital information about what you're viewing.

The classic example of augmented reality is the restaurant review. You could stand in front of a restaurant and, through an augmented-reality system, read customer reviews or view the daily specials without ever walking inside. But the applications don't have to stop with locations. Augmented-reality systems might extend to people as well. Imagine looking at a stranger and seeing that person's name, Facebook profile, Twitter handle and other information. Clearly, augmented reality systems will raise concerns about privacy and safety, but such systems are already in development.

Then there's video conferencing. While the technology has existed for years, video calls aren't popular in the United States. It might be because the hardware hasn't been compelling or cost-effective enough. But now webcams are starting to appear on televisions and are standard on many laptops. Are we about to enter an era of video conferencing, or is it too much work to make sure you and your house look nice before you order that pizza?

One drawback to video conferencing is that it either requires you to stay in one place for the duration of the call or to hold a device so that you're visible for the whole conversation. We've become used to having a great deal of freedom while on the phone. Will we really adopt a technology that will necessitate that we keep still? Perhaps we'll use video conferencing for special occasions or short conversations.

Language barriers are disappearing as well. Devices that can translate languages in real time are allowing people from different countries and cultures to communicate without the need for an interpreter.

In the distant future, we may be able to communicate by sending our thoughts through a network directly into someone else's brain. We're decades away from such technology, but scientists are working on creating brain-computer interfaces that allow people to transmit thoughts directly to a computer. Perhaps 50 years from now we'll all use an electronic version of telepathy.

The technology of communication evolves at a blistering pace. It may turn out that our predictions don't even scratch the surface. Only time will tell.

Learn more about communication through the links below.

Related HowStuffWorks Articles

More Great Links

Sources

German, Kent. "Sprint unveils first 4g phone." CNET. March 23, 2010. (April 6, 2010) http://reviews.cnet.com/8301-12261_7-20000998-10356022.html

Hiemstra, Glen. "The Future of Communication and Conversation." Futurist.com. Jan. 15, 2008. (April 6, 2010) http://www.futurist.com/2008/01/15/the-future-of-communication-and-conversation/

Leonhard, Gerd. "The Future of Communication and Social Media." NBS Sao Paulo. March 2010. (April 5, 2010) http://www.slideshare.net/gleonhard/future-of-communication-and-social-media-gerd-leonhard-at-nbs-sao-paulo

Quick, Darren. "Brain-to-brain communication over the Internet." Gizmag. Oct. 6, 2009. (March 29, 2010) http://www.gizmag.com/brain-to-brain-communication/13055/

Science Daily. "Brain-Computer Interface Allows Person-to-person Communication Through Power Of Thought." Oct. 6, 2009. (March 29, 2010) http://www.sciencedaily.com/releases/2009/10/091006102637.htm

The Science Channel. "Wired NextFest: The Future of Communication." HowStuffWorks.com. https://videos.howstuffworks.com/science-channel/32471-wired-nextfest-the-future-of-communication-video.htm

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One way we might see communication change in the future is through augmented reality. In an augmented-reality system, you view the world through a technological overlay. This could take the form of a hand-held device like a smartphone -- there are several augmented-reality applications already available for some phones.

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In the Future, Elkins and Cristian will invent eyes that can be used as phones! You will be able to also join the metaverse using these eyes. No more Facetime... Just EYETIME!

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Beaming information into the brain

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Researchers at UC-Berkeley have built a machine to project experiences directly into the brain. Eventually, they hope to insert (or edit) sights, sounds, sensations, and possibly even memories for patients with brain damage or disabilities. As one Berkeley neuroscientist put it, "By encoding perceptions into the human cortex, you could allow the blind to see or the paralyzed to feel touch." ... Or maybe you could build the Matrix.

The Machine: The scientists are working in a cutting-edge field called "optogenetics," where light is used to turn on or off neurons in the brain. First, the Berkeley team treated a sliver of brain cells (just half a millimeter wide and one tenth a millimeter thick) with a protein that switches neurons on or off when they're hit with a beam of light. That's pretty neat. But the real breakthrough was creating a device that could hit individual neurons, with pinpoint accuracy, without activating the rest. Their machine, called 3D-SHOT, fires infrared lasers through a holographic screen-basically, bending the laser beams to intersect at a very specific point in space, hitting precisely one brain cell. Right now, it can activate 50 different neurons at the same time, up to 300 times a second-fast enough and precise enough to mimic actual brain patterns.

The Real Brilliance: The Berkeley team didn't invent the underlying technologies, but they improved and assembled them in a way that works. They made a better protein for making neurons switch on or off, and 3D-SHOT is much more precise than any other "deep brain photostimulation" machine. "This is the culmination of technologies that researchers have been working on for a while, but have been impossible to put together," one of the lead scientists said. "We solved numerous technical problems at the same time to bring it all together and finally realize the potential of this technology."

Applications and Next Steps: The potential medical applications are huge: being able to simulate sensory experiences that disease or injuries have made impossible. A "neural prosthetic" like this could allow people with prosthetic limbs to feel what they're touching. Blindness, deafness, or peripheral nerve damage could be circumvented directly. But before they can even consider artificially reproducing those feelings, researchers first need to know what the neural patterns for those experiences actually look like inside the brain. Other scientists have already begun cracking the code of brain language with respect to memory, so this is definitely an achievable near-term goal.

Messing with Mice: The Berkeley researchers think they can scale up their device to activate hundreds or thousands of neurons at the same time, and they've already been testing it on lab mice, firing into the vision and touch centers of the cortex while they walk on a treadmill. The mice haven't done anything interesting yet, physically, but real-time brain imaging suggests they are having what looks like a sensory experience.

The Limits Are Skin Deep: The biggest limitation of the technology is that you can't fire infrared light very precisely very deep into the brain. The mice needed to have a, uh, "clear window into the brain" installed for the device to hit anything at all. And, according to their previous paper, 3D-SHOT loses accuracy and energy after penetrating just half a millimeter into brain tissue. Using more powerful lasers could potentially solve the depth issue, but at the cost of accuracy. Solving that is likely to be the biggest hurdle to building the Matrix practical real-world applications.
Related Video: Our Cyborg Future Is Coming (And That's Not a Bad Thing)

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Neuroscientists want to beam experiences directly into your brain

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Researchers at UC-Berkeley have built a machine to project experiences directly into the brain. Eventually, they hope to insert (or edit) sights, sounds, sensations, and possibly even memories for patients with brain damage or disabilities. As one Berkeley neuroscientist put it, "By encoding perceptions into the human cortex, you could allow the blind to see or the paralyzed to feel touch."

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I believe by 2090 i think there will be communications by holograms, you just carry a tiny pocket circle that when you tap on it a hologra of the person that you wanted to talk too pops up, only thing is both have to be connected at the same time

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Crystal Healing

https://www.livescience.com/40347-crystal-healing.html

Gemstones are the way of the future. We need to be cleaner with our bodies and reconnect with the Earth.

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I think it will be either it will be with glasses or a helmet with reality, with technology that adapts to what we are about to write or post and already gives us predetermined options just like a grammar autocorrect does because we are getting in there with the things we see now.