Artificial intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it. Textbooks define the field as "the study and design of intelligent agents,"[1] where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.[2] John McCarthy, who coined the term in 1956,[3] defines it as "the science and engineering of making intelligent machines."[4]
The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of breathtaking optimism, has suffered stunning setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.
AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") is still a long-term goal of (some) research.
History
Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the golden robots of Hephaestus and Pygmalion's Galatea. Human likenesses believed to have intelligence were built in every major civilization: animated statues were worshipped in Egypt and Greece[14] and humanoid automatons were built by Yan Shi, Hero of Alexandria, Al-Jazari and Wolfgang von Kempelen. It was also widely believed that artificial beings had been created by Jābir ibn Hayyān, Judah Loew and Paracelsus. By the 19th and 20th centuries, artificial beings had become a common feature in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots). Pamela McCorduck argues that all of these are examples of an ancient urge, as she describes it, "to forge the gods".[6] Stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.
Mechanical or "formal" reasoning has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the programmable digital electronic computer, based on the work of mathematician Alan Turing and others. Turing's theory of computation suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This, along with recent discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.
The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956.[25] The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI's founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a man can do"[31] and Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".
They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. The next few years, when funding for projects was hard to find, would later be called an "AI winter".
In the early 1980s, AI research was revived by the commercial success of expert systems,[35] a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research in the field. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.
In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry. The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.
Artificial intelligence
Computer programming
Computer programming (often shortened to programming or coding) is the process of writing, testing, debugging/troubleshooting, and maintaining the source code of computer programs. This source code is written in a programming language. The code may be a modification of an existing source or something completely new. The purpose of programming is to create a program that exhibits a certain desired behaviour (customization). The process of writing source code often requires expertise in many different subjects, including knowledge of the application domain, specialized algorithms and formal logic.
Overview
Within software engineering, programming (the implementation) is regarded as one phase in a software development process.
There is an ongoing debate on the extent to which the writing of programs is an art, a craft or an engineering discipline.[1] In general, good programming is considered to be the measured application of all three, with the goal of producing an efficient and evolvable software solution (the criteria for "efficient" and "evolvable" vary considerably). The discipline differs from many other technical professions in that programmers, in general, do not need to be licensed or pass any standardized (or governmentally regulated) certification tests in order to call themselves "programmers" or even "software engineers." However, representing oneself as a "Professional Software Engineer" without a license from an accredited institution is illegal in many parts of the world.
Another ongoing debate is the extent to which the programming language used in writing computer programs affects the form that the final program takes. This debate is analogous to that surrounding the Sapir-Whorf hypothesis in linguistics, that postulates that a particular language's nature influences the habitual thought of its speakers. Different language patterns yield different patterns of thought. This idea challenges the possibility of representing the world perfectly with language, because it acknowledges that the mechanisms of any language condition the thoughts of its speaker community.
Said another way, programming is the craft of transforming requirements into something that a computer can execute.
History of programming
The concept of devices that operate following a pre-defined set of instructions traces back to Greek Mythology, notably Hephaestus and his mechanical servants. The Antikythera mechanism was a calculator utilizing gears of various sizes and configuration to determine its operation. The earliest known programmable machines (machines whose behavior can be controlled and predicted with a set of instructions) were Al-Jazari's programmable Automata in 1206. One of Al-Jazari's robots was originally a boat with four automatic musicians that floated on a lake to entertain guests at royal drinking parties. Programming this mechanism's behavior meant placing pegs and cams into a wooden drum at specific locations. These would then bump into little levers that operate a percussion instrument. The output of this device was a small drummer playing various rhythms and drum patterns. Another sophisticated programmable machine by Al-Jazari was the castle clock, notable for its concept of variables, which the operator could manipulate as necessary (i.e., the length of day and night). The Jacquard Loom, which Joseph Marie Jacquard developed in 1801, uses a series of pasteboard cards with holes punched in them. The hole pattern represented the pattern that the loom had to follow in weaving cloth. The loom could produce entirely different weaves using different sets of cards. Charles Babbage adopted the use of punched cards around 1830 to control his Analytical Engine. The synthesis of numerical calculation, predetermined operation and output, along with a way to organize and input instructions in a manner relatively easy for humans to conceive and produce, led to the modern development of computer programming. Development of computer programming accelerated through the Industrial Revolution.
In the late 1880s, Herman Hollerith invented the recording of data on a medium that could then be read by a machine. Prior uses of machine readable media, above, had been for control, not data. "After some initial trials with paper tape, he settled on punched cards..." To process these punched cards, first known as "Hollerith cards" he invented the tabulator, and the key punch machines. These three inventions were the foundation of the modern information processing industry. In 1896 he founded the Tabulating Machine Company (which later became the core of IBM). The addition of a control panel to his 1906 Type I Tabulator allowed it to do different jobs without having to be physically rebuilt. By the late 1940s, there were a variety of plug-board programmable machines, called unit record equipment, to perform data-processing tasks (card reading). Early computer programmers used plug-boards for the variety of complex calculations requested of the newly invented machines.
The invention of the Von Neumann architecture allowed computer programs to be stored in computer memory. Early programs had to be painstakingly crafted using the instructions of the particular machine, often in binary notation. Every model of computer would be likely to need different instructions to do the same task. Later assembly languages were developed that let the programmer specify each instruction in a text format, entering abbreviations for each operation code instead of a number and specifying addresses in symbolic form (e.g., ADD X, TOTAL). In 1954 Fortran was invented, being the first high level programming language to have a functional implementation. It allowed programmers to specify calculations by entering a formula directly (e.g. Y = X*2 + 5*X + 9). The program text, or source, is converted into machine instructions using a special program called a compiler. Many other languages were developed, including some for commercial programming, such as COBOL. Programs were mostly still entered using punch cards or paper tape. (See computer programming in the punch card era). By the late 1960s, data storage devices and computer terminals became inexpensive enough so programs could be created by typing directly into the computers. Text editors were developed that allowed changes and corrections to be made much more easily than with punch cards.
As time has progressed, computers have made giant leaps in the area of processing power. This has brought about newer programming languages that are more abstracted from the underlying hardware. Although these high-level languages usually incur greater overhead, the increase in speed of modern computers has made the use of these languages much more practical than in the past. These increasingly abstracted languages typically are easier to learn and allow the programmer to develop applications much more efficiently and with less code. However, high-level languages are still impractical for a few programs, such as those where low-level hardware control is necessary or where processing speed is at a premium.
Throughout the second half of the twentieth century, programming was an attractive career in most developed countries. Some forms of programming have been increasingly subject to offshore outsourcing (importing software and services from other countries, usually at a lower wage), making programming career decisions in developed countries more complicated, while increasing economic opportunities in less developed areas. It is unclear how far this trend will continue and how deeply it will impact programmer wages and opportunities.
Architecture Of Computer
CPU design is the design engineering task of creating a central processing unit (CPU), a component of computer hardware. It is a subfield of electronics engineering and computer engineering.
CPU design focuses on these areas:
1. datapaths (such as ALUs and pipelines)
2. control unit: logic which controls the datapaths
3. Memory components such as register files, caches
4. Clock circuitry such as clock drivers, PLLs, clock distribution networks
5. Pad transceiver circuitry
6. Logic gate cell library which is used to implement the logic
CPUs designed for high-performance markets might require custom designs for each of these items to achieve frequency, power-dissipation, and chip-area goals.
CPUs designed for lower performance markets might lessen the implementation burden by:
* Acquiring some of these items by purchasing them as intellectual property
* Use control logic implementation techniques (logic synthesis using CAD tools) to implement the other components - datapaths, register files, clocks
Common logic styles used in CPU design include:
* Unstructured random logic
* Finite-state machines
* Microprogramming (common from 1965 to 1985, no longer common except for x86 RISC-like CPUs)
* Programmable logic array (common in the 1980s, no longer common)
Device types used to implement the logic include:
* Transistor-transistor logic Small Scale Integration jelly-bean logic chips - no longer used for CPUs
* Programmable Array Logic and Programmable logic devices - no longer used for CPUs
* Emitter-coupled logic (ECL) gate arrays - no longer common
* CMOS gate arrays - no longer used for CPUs
* CMOS ASICs - what's commonly used today, they're so common that the term ASIC is not used for CPUs
* Field-programmable gate arrays (FPGA) - common for soft microprocessors, and more or less required for reconfigurable computing
A CPU design project generally has these major tasks:
* Programmer-visible instruction set architecture, which can be implemented by a variety of microarchitectures
* Architectural study and performance modeling in ANSI C/C++ or SystemC
* High-level synthesis (HLS) or RTL (eg. logic) implementation
* RTL Verification
* Circuit design of speed critical components (caches, registers, ALUs)
* Logic synthesis or logic-gate-level design
* Timing analysis to confirm that all logic and circuits will run at the specified operating frequency
* Physical design including floorplanning, place and route of logic gates
* Checking that RTL, gate-level, transistor-level and physical-level representations are equivalent
* Checks for signal integrity, chip manufacturability
As with most complex electronic designs, the logic verification effort (proving that the design does not have bugs) now dominates the project schedule of a CPU.
Key CPU architectural innovations include index register, cache, virtual memory, instruction pipelining, superscalar, CISC, RISC, virtual machine, emulators, microprogram, and stack.
Performance analysis and benchmarking
Because there are too many programs to test a CPU's speed on all of them, benchmarks were developed. The most famous benchmarks are the SPECint and SPECfp benchmarks developed by Standard Performance Evaluation Corporation and the ConsumerMark benchmark developed by the Embedded Microprocessor Benchmark Consortium EEMBC.
Some important measurements include:
* Instructions per second - Most consumers pick a computer architecture (normally Intel IA32 architecture) to be able to run a large base of pre-existing pre-compiled software. Being relatively uninformed on computer benchmarks, some of them pick a particular CPU based on operating frequency (see Megahertz Myth).
* FLOPS - The number of floating point operations per second is often important in selecting computers for scientific computations.
* Performance per watt - System designers building parallel computers, such as Google, pick CPUs based on their speed per watt of power, because the cost of powering the CPU outweighs the cost of the CPU itself.
* Some system designers building parallel computers pick CPUs based on the speed per dollar.
* System designers building real-time computing systems want to guarantee worst-case response. That is easier to do when the CPU has low interrupt latency and when it has deterministic response. (DSP)
* Computer programmers who program directly in assembly language want a CPU to support a full featured instruction set.
* Low power - For systems with limited power sources (e.g. solar, batteries, human power).
* Small size or low weight - for portable embedded systems, systems for spacecraft.
* Environmental impact - Minimizing environmental impact of computers during manufacturing and recycling as well during use. Reducing waste, reducing hazardous materials. (see Green computing).
Some of these measures conflict. In particular, many design techniques that make a CPU run faster make the "performance per watt", "performance per dollar", and "deterministic response" much worse, and vice versa.
Microarchitecture
In computer engineering, microarchitecture (sometimes abbreviated to µarch or uarch) is the way a given instruction set architecture (ISA) is implemented on a processor. A given ISA may be implemented with different microarchitectures. Implementations might vary due to different goals of a given design or due to shifts in technology. Computer Architecture is the combination of microarchitecture and instruction set design.
Relation to instruction set architecture
The ISA is roughly the same as the programming model of a processor as seen by an assembly language programmer or compiler writer. The ISA includes the execution model, processor registers, address and data formats among other things. The microarchitecture includes the constituent parts of the processor and how these interconnect and interoperate to implement the ISA.
The microarchitecture of a machine is usually represented as (more or less detailed) diagrams that describe the interconnections of the various microarchitectual elements of the machine, which may be everything from single gates and registers, to complete ALUs and even larger elements. These diagrams generally separate the data path (where data is placed) and the control path (which can be said to steer the data). Machines with different microarchitectures may have the same instruction set architecture, and thus be capable of executing the same programs. New microarchitectures and/or circuitry solutions, along with advances in semiconductor manufacturing, are what allows newer generations of processors to achieve higher performance while using the same ISA.
Aspects of microarchitecture
The pipelined datapath is the most commonly used datapath design in microarchitecture today. This technique is used in most modern microprocessors, microcontrollers, and DSPs. The pipelined architecture allows multiple instructions to overlap in execution, much like an assembly line. The pipeline includes several different stages which are fundamental in microarchitecture designs.[3] Some of these stages include instruction fetch, instruction decode, execute, and write back. Some architectures include other stages such as memory access. The design of pipelines is one of the central microarchitectural tasks.
Execution units are also essential to microarchitecture. Execution units include arithmetic logic units (ALU), floating point units (FPU), load/store units, branch prediction, and SIMD. These units perform the operations or calculations of the processor. The choice of the number of execution units, their latency and throughput is a central microarchitectural design task. The size, latency, throughput and connectivity of memories within the system are also microarchitectural decisions.
System-level design decisions such as whether or not to include peripherals, such as memory controllers, can be considered part of the microarchitectural design process. This includes decisions on the performance-level and connectivity of these peripherals.
Unlike architectural design, where achieving a specific performance level is the main goal, microarchitectural design pays closer attention to other constraints. Since microarchitecture design decisions directly affect what goes into a system, attention must be paid to such issues as:
Earliest hardware
Devices have been used to aid computation for thousands of years, using one-to-one correspondence with our fingers. The earliest counting device was probably a form of tally stick. Later record keeping aids throughout the Fertile Crescent included calculi (clay spheres, cones, etc.) which represented counts of items, probably livestock or grains, sealed in containers. Counting rods is one example.
The abacus was used for arithmetic tasks. The Roman abacus was used in Babylonia as early as 2400 BC. Since then, many other forms of reckoning boards or tables have been invented. In a medieval counting house, a checkered cloth would be placed on a table, and markers moved around on it according to certain rules, as an aid to calculating sums of money.
A number of analog computers were constructed in ancient and medieval times to perform astronomical calculations. These include the Antikythera mechanism and the astrolabe from ancient Greece (c. 150–100 BC), which are generally regarded as the first mechanical analog computers.[4] Other early versions of mechanical devices used to perform some type of calculations include the planisphere and other mechanical computing devices invented by Abū Rayhān al-Bīrūnī (c. AD 1000); the equatorium and universal latitude-independent astrolabe by Abū Ishāq Ibrāhīm al-Zarqālī (c. AD 1015); the astronomical analog computers of other medieval Muslim astronomers and engineers; and the astronomical clock tower of Su Song (c. AD 1090) during the Song Dynasty.
The "castle clock", an astronomical clock invented by Al-Jazari in 1206, is considered to be the earliest programmable analog computer. It displayed the zodiac, the solar and lunar orbits, a crescent moon-shaped pointer traveling across a gateway causing automatic doors to open every hour, and five robotic musicians who play music when struck by levers operated by a camshaft attached to a water wheel. The length of day and night could be re-programmed every day in order to account for the changing lengths of day and night throughout the year.
Suanpan (the number represented on this abacus is 6,302,715,408)
Scottish mathematician and physicist John Napier noted multiplication and division of numbers could be performed by addition and subtraction, respectively, of logarithms of those numbers. While producing the first logarithmic tables Napier needed to perform many multiplications, and it was at this point that he designed Napier's bones, an abacus-like device used for multiplication and division. Since real numbers can be represented as distances or intervals on a line, the slide rule was invented in the 1620s to allow multiplication and division operations to be carried out significantly faster than was previously possible.Slide rules were used by generations of engineers and other mathematically inclined professional workers, until the invention of the pocket calculator.
Yazu Arithmometer. Patented in Japan in 1903. Note the lever for turning the gears of the calculator.
German polymath Wilhelm Schickard built the first digital mechanical calculator in 1623, and thus became the father of the computing era. Since his calculator used techniques such as cogs and gears first developed for clocks, it was also called a 'calculating clock'. It was put to practical use by his friend Johannes Kepler, who revolutionized astronomy when he condensed decades of astronomical observations into algebraic expressions. An original calculator by Blaise Pascal (1640) is preserved in the Zwinger Museum. Machines by Pascal (the Pascaline, 1642) and Gottfried Wilhelm von Leibniz (the Stepped Reckoner, c. 1672) followed. Leibniz once said "It is unworthy of excellent men to lose hours like slaves in the labour of calculation which could safely be relegated to anyone else if machines were used."
Around 1820, Charles Xavier Thomas created the first successful, mass-produced mechanical calculator, the Thomas Arithmometer, that could add, subtract, multiply, and divide.[13] It was mainly based on Leibniz' work. Mechanical calculators, like the base-ten addiator, the comptometer, the Monroe, the Curta and the Addo-X remained in use until the 1970s. Leibniz also described the binary numeral system, a central ingredient of all modern computers. However, up to the 1940s, many subsequent designs (including Charles Babbage's machines of the 1800s and even ENIAC of 1945) were based on the decimal system; ENIAC's ring counters emulated the operation of the digit wheels of a mechanical adding machine.
In Japan, Ryoichi Yazu patented a mechanical calculator called the Yazu Arithmometer in 1903. It consisted of a single cylinder and 22 gears, and employed the mixed base-2 and base-5 number system familiar to users to the soroban (Japanese abacus). Carry and end of calculation were determined automatically. More than 200 units were sold, mainly to government agencies such as the Ministry of War and agricultural experiment stations.
History of computing hardware
The history of computing hardware is the record of the constant drive to make computer hardware faster, cheaper, and store more data.
Before the development of the general-purpose computer, most calculations were done by humans. Tools to help humans calculate are generally called calculators. Calculators continue to develop, but computers add the critical element of conditional response, allowing automation of both numerical calculation and in general, automation of many symbol-manipulation tasks. Computer technology has undergone profound changes every decade since the 1940s.
Computing hardware has become a platform for uses other than computation, such as automation, communication, control, entertainment, and education. Each field in turn has imposed its own requirements on the hardware, which has evolved in response to those requirements.
Aside from written numerals, the first aids to computation were purely mechanical devices that required the operator to set up the initial values of an elementary arithmetic operation, then propel the device through manual manipulations to obtain the result. An example would be a slide rule where numbers are represented by points on a logarithmic scale and computation is performed by setting a cursor and aligning sliding scales. Numbers could be represented in a continuous "analog" form, where a length or other physical property was proportional to the number. Or, numbers could be represented in the form of digits, automatically manipulated by a mechanism. Although this approach required more complex mechanisms, it made for greater precision of results.
Both analog and digital mechanical techniques continued to be developed, producing many practical computing machines. Electrical methods rapidly improved the speed and precision of calculating machines, at first by providing motive power for mechanical calculating devices, and later directly as the medium for representation of numbers. Numbers could be represented by voltages or currents and manipulated by linear electronic amplifiers. Or, numbers could be represented as discrete binary or decimal digits, and electrically-controlled switches and combinatorial circuits could perform mathematical operations.
The invention of electronic amplifiers made calculating machines much faster than mechanical or electromechanical predecessors. Vacuum tube amplifiers gave way to discrete transistors, and then rapidly to monolithic integrated circuits. By defeating the Tyranny of numbers, integrated circuits made high-speed and low-cost digital computers a widespread commodity.
This article covers major developments in the history of computing hardware, and attempts to put them in context. For a detailed timeline of events, see the computing timeline article. The history of computing article treats methods intended for pen and paper, with or without the aid of tables. Since all computers rely on digital storage, and tend to be limited by the size and speed of memory, the history of computer data storage is tied to the development of computers.
