The Discussion of Von Neumann Architecture and Artificial Intelligence

The Discussion of Von Neumann Architecture and Artificial IntelligenceXiao MaInstructor: Stephen KaislerCourse: CS6461Department of Computer scienceThe George Washington UniversityTable of contentsTable of contents (2)Introduction (3)Origin of Von Neumann architecture (4)Features of Von Neumann architecture (5)Problems of Von Neumann architecture (7)Origin of Artificial Intelligence (9)Comparison between Von Neumann machines and human (12)How Von Neumann architecture impede the development of AI (15)Current situation and the trend of AI (16)Conclusion (18)IntroductionThe study of computer architecture has experienced almost a century, but in some areas, such as recognizing images and navigating in unfamiliar spaces, the thinking abilities of computers are incredibly inefficient like ba by‟s brain. Such tasks can be performed in some labs, but they are energy-cost and they need specialized programming.Nowadays, almost all computers are based on the Von Neumann architecture. Von Neumann architecture is very efficient and people are familiar with it. However, some arguments show that the development of Artificial Intelligence is tied to the Von Neumann architecture; people are prisoners of Von Neumann architecture.In this paper, I go through the studies and papers published before and discuses the details of the Von Neumann architecture. Then I will talk about some arguments that the current situation trend of development of Artificial Intelligence. First, I will discuss the origin and principles of the Von Neumann architecture. Then, the advantages of the Von Neumann architecture are introduced. After that, I will talk about the relation between the Von Neumann architecture and development of AI.Origin of Von Neumann ArchitectureThe name of Von Neumann architecture (also known as the Princeton architecture) comes from John von Neumann, who was the first to propose the requirements of a general-purpose electronic computer. Von Neumann‟s design eventually results in t he construction of EDVAC computer in 1952. However, the first machine constructed with this architecture is Manchester Mark I (in Manchester University).The Von Neumann architecture derives from a 1945 computer architecture description by the mathematician and physicist John von Neumann and others. It is a design architecture for an electronic digital computer with subdivisions of a processing unit consisting of an arithmetic logic unit and processor registers, a control unit containing an instruction register and program counter, a memory to store both data and instructions, external mass storage, and input and output mechanisms. [1]The meaning of the Von Neumann architecture has been evolved. It also means a stored-program computer that instruction fetch and data operation never occur at the same time (because instruction fetch and data operation need a common bus). This is a shortcoming of the Von Neumann architecture, which can limits computer performance.Nowadays, almost every electronic computer is built in Von Neumann architecture virtually.Features of Von Neumann architecture∙The Von Neumann architecture is comprised of memory, control unit, input-output, arithmetic logic unit, and bus.∙Memory is used to hold both data and the program processing the data. Today‟s RAM means memory.∙Control unit can manage the carrying of data (transferring data into/out of memory) and executing program instructions. But Von Neumann architecture is one-at-a-time operation machine, which only can execute the instructions sequentially. In suchsituation, Von Neumann architecture machine needs to use some registers, likes…accumulator‟, to hold the intermediate values.∙Input-Output is designed for people to interact with the machine.∙Arithmetic Logic Unit only involved with executing calculations upon the data.Arithmetic Logic Unit can do lot kinds of arithmetic and logic work, such as Add, Subtract, Multiply and Divide. It also can do data comparisons work, like Greater Than, Less Than, Equal To.∙Bus allows information flow between various parts of the computer. Information passes back and forth along a …bus‟ in modern Von Neumann architecture computer.Address bus can identify locations in memory. Data bus allows the flow of data and program instructions.Figure 1. Von Neumann architectureThe von Neumann architecture is designed to execute linear sequences of instructions. All today’s computers, from mp3 to supercomputers, have just two main components: a central processing unit, or CPU, to manipulate data, and a block of random access memory, or RAM, to store the data and the instructions on how to manipulate it. The CPU begins by fetching its first instruction from memory, followed by the data needed to execute it; after the instruction is performed, the result is sent back to memory and the cycle repeats. The key of a Von Neumann machines (general purpose device) is the ability to store. In Von Neumann architecture, the memory can not only store data and the intermediate results of computations, but also can store the instructions and program.With all these components and features Von Neumann architecture is incredibly successful with almost all of the modern computers. We can find almost all personal computers have memory (the main memory is in form of RAM sticks), control unit and arithmetic logic unit.Problems of Von Neumann architectureAlthough the Von Neumann architecture is incredibly successful with modern computer, it still has some problems that limit the development of computer. The three main problems are discussed below.Because data memory and program memory share common bus, so the data memory and program memory cannot be accessed at the same time. Each data and instruction has to pass along the data bus, so that they can be moved from main memory into (or back) the CPU. But the transfer rate of the data bus is a lot slower than carrying out the instructions. Instructions and data have to be fetched in sequential order. If nothing were done, the CPU has to spend most of its time waiting for the instructions (throughput is much smaller than the rate that the CPU can work). It limits the operation bandwidth. This problem is a bottleneckof the Von Neumann architecture.Figure 2. Features of Von Neumann architectureData and programs need to share the same memory space. This character will lead to programs being trashed, because faulty piece of code can write data into areas holding other instructions quite easily.The rate that data needs to be fetched and the rate that instructions need to be fetched are always so different. And as mentioned above, they share the same bottlenecked data bus. This is the third problem of Von Neumann architecture.The von Neumann architecture is designed to execute linear sequences of instructions. All tod ay‟s computers, from smartmp3 to supercomputers, have just two main components: a central processing unit, or CPU, to manipulate data, and a block of random access memory, or RAM, to store the data and the instructions on how to manipulate it. The CPU begins byfetching its first instruction from memory, followed by the data needed to execute it; after the instruction is performed, the result is sent back to memory and the cycle repeats. Even multicore chips that handle data in parallel are limited to just a few simultaneous linear processes.The main problem of the Von Neumann architecture is the limited data transfer rate between CPU and memory compare to the amount of memory. Since CPU speed and memory size have increased much faster than the throughput between them, the bottleneck problem has become a challenge problem with every new generation of CPU. Even multicore chips that handle data in parallel are limited to just a few simultaneous linear processes.Origin of Artificial IntelligenceArtificial Intelligence (AI) is spelled out by John McCarthy in 1955, defined as “the science and engineering of making intelligent machines.”[2] It means developing or designing machines or software with human-like intelligence.At first, AI researchers just study the algorithms by imitating humans step-by-step and want to solve some logical problems. By the late 1980s and 1990s, AI researchers used some statistical methods to solve some problems with uncertain or incomplete information.Nowadays, neural net approaches and statistical methods are highly used to attempt to mimic the structure of human‟s brain and the way how human thinks.The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. [3] In today‟s research, there are many approaches to study them, include statistical methods, computational intelligence, cybernetics and brain simulation, traditional symbolic AI and integrating the approaches. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability, neural networks, control theory, economics, and many others. [4]In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. In this test, a human judge engages in natural language conversations with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test. [5]It‟s very challenging, till today, all computers (Von Neumann machines) are failed in this test.Artificial Intelligence (AI) can also be evaluated in some other standards, the following standards are one classification for outcomes of an AI test: [6]∙Optimal: it is not possible to perform better.∙Strong super-human: performs better than all humans.∙Super-human: performs better than most humans.∙Sub-human: performs worse than most humans.Nowadays, Artificial Intelligence (AI) has become an important field in development of society and a forefront research field in computer science.Figure 3. Parent disciplines and Application areas of AI Comparison between Von Neumann machines and humanVon Neumann architecture has experienced for decades and has proven its value. Although Von Neumann architecture machines cannot think as human, it can do a lot of things that human is unable to do.Von Neumann machines do better than human in the following areas:∙Von Neumann machines can do arithmetic computations work much faster than human do.∙Von Neumann machines rarely make mistakes in computation.∙Von Neumann machines never forget or confuse data unless using instructions to do so.∙Von Neumann machines can memorize (store) a large amount of data and can recall it quickly at anytime.∙Von Neumann machines can memorize data at any time (they don‟t need to learn how to memorize).∙Von Neumann machines‟ ability to process data (information) is little affected by hardware failures.Although Von Neumann machines have so many advantages than human, they still cannot pass the Turing test for intelligence. Because Von Neumann machines can only deal with specific problems, while human have a global view when address some complex problems.The following properties that human have that Von Neumann machines do not [7]:∙We excel at pattern recognition tasks.∙We confuse, associate, and mix data.∙We forget data without having to be explicitly instructed to do so. (In fact, we have more difficulty forgetting when we are instructed to do so. Try not thinking about elephants for the next 60 seconds.)∙With the exception of those few claiming eidetic memory, we do not rapidly memorize data with the perfect recall characteristic of a Von Neumann machine.∙We are terrible at arithmetic computation. Even a hand calculator can beat any of us if data can be fed to it as fast as to us.∙Neurons certainly die from time to time, but that makes little difference in our ability to process data.Figure 4. Brain Computing HistoryAs a conclusion, Von Neumann machines cannot think as human and are not good at pattern recognition, but they excel at arithmetic computation and information memorization.How Von Neumann architecture impede the development of AIThe best minds in the world have been studying on Artificial Intelligence for around 60 years, but the results are still dissatisfactory. Although the technologies of Artificial Intelligence are used in many areas now, almost all of these machines cannot do the work as well as best human do. Moreover, none of them passed Turing‟s test for intelligence. It is possible that the Von Neumann architecture do not support the machines to think in human‟s way.Under Von Neumann architecture, AI researchers simply make an assumption that language is equal to thinking. Moreover, people become prisoners of Von Neumann architecture. We think the Von Neumann machine is the only available vehicle and it is more difficult to design a new machine than design a new language.Modern Von Neumann machines are built in silicon. Human‟s brain is based on electrochemical process that is much slower than silicon-based process. So we should have enough speed to mimic human though processes with the Von Neumann machines. The human cortex is estimated to contain some 10 billion neurons, each of which has on the order of 10,000 synapses or interconnections with other neurons3. The active electronic network of the brain, then, consists of some 100 trillion active components. Semiconductor memory chips containing two million active components within a one-square-centimeter area currently exist4. We could then have as many active elements as a human brain contains in a collection of silicon chips in a square measuring 71 X 71 meters. If we were to stack them with a separation of one centimeter, we could fit our silicon-based brain into a cubemeasuring 17 meters on a side. [8] This calculation even has not considered the space need for the situation of mounting the chips and cooling the cube. From this perspective, we can infer that maybe we don‟t have the proper machine architecture that is necessary to perform human thought.The human system has at least 5 kinds of input ways (sound, taste, sight, touch, smell), while Von Neumann machines have only ones and zeros as input. Human receives most information through visual imagery. Languages are used to express it abstractly. AI researchers are not successful in language-understanding project, it is because languages are just abstraction of visual data. Also it is no surprise that computers cannot understand us, because we use abstracted medium (language) to communicate with it.Current situation and the trend of AIToday, the biggest technology companies in the world, such as Google, Apple, I.B.M., Facebook, and Microsoft, all have made large investments in Artificial Intelligence, which make Artificial Intelligence in a better shape than before. There are some exciting projects, such as driverless cars, which is believed will be realized soon. Neuromorphic engineering and deep learning are extremely amazing, but whether these investments can really result in producing human-level A.I. is still unclear.Researchers are wondering for decades that if the Von Neumann architecture is not an appropriate architecture for building artificially intelligent machines, then what kind of architecture can achieve this?New machines with appropriate architecture may have been developed. The neural net machines begin to show many similarities with human brain. It excels at learning and self-organization of memory. When machines can learn from mistakes (or experience) and keep growing up, we can say that AI research has stepped into a new age. Such kind of architecture is designed base on the principle of how human’s brain works and it seems to be on the right track to realize Artificial Intelligence. Researchers begin to design central process unit via studying and mimicking the way our brains work. Some visual memory systems based on neural associative memory simulations are designed and it seems to bring research of AI into a new age.The research of brain-inspired chips began in the early 1980s at the California Institute of Technology. A professor named Mead is famous for helping develop a method of designing computer chips called Very Large Scale Integration (VLSI). This outcome of research enabled manufacturers to create much more complex microprocessors. This triggered explosive growth in computation power: computers looked set to become mainstream, even ubiquitous.Eventually, A new kind of computer chips has been designed, which can process information much more like human’s brain may be about to narrow the gulf between artificial and natural computation. With the help of neuroscience and chip technology, researchers have built devices (should be on a small scale that can be managed) thatprocess information the way a mammalian brain does. These chips with “neuromorphic” may be the solution of many hopeful but unachieved projects in artificial intelligence, such as cars that can drive themselves automatically and securely in all kinds of roads, and smartphones or smart watches that can work as powerful assistants. This kind of CUP has already been produced in labs. Its powerful intelligence is beyond traditional chips (Von Neumann machines). It can process images incredible efficiently and get new skills through learning (like the way mammalian learn). Some of famous company (like IBM) let their software engineers program these brain-inspired chips. Also the other chips, at HRL Laboratories in Malibu, California, will soon be installed on robotic aircraft, researchers want it to learn to recognize its surroundings and can navigation or drive itself automatically.It's not the difficulty of developing such AI machines limits the outcome of our research, we just being imprisoned to Von Neumann architecture because of its incredible success. We still cannot say we have solved the AI problems till now. If we had, we can talk to machines as if we chat with another person. Machines can not only understand, but also think.ConclusionThe human brain is the most complicated organ in the known universe, and we still have almost no idea about how it works. Duplicating its awesome powerful abilityis not an easy task.Although Von Neumann machines have given us many surprises and they are incredibly beyond human’s abilities in many areas, we sti ll cannot say our machines have achieved Artificial Intelligence. There is a huge gulf between Von Neumann architecture and human brains.The primary barrier to us is stubbornly working on the current Von Neumann architecture. I believe that the AI problem will be solved if we deeply study our brain and design some other machine architectures. I believe neural networks are close to the how the brains work and that further work in this area may eventually result in inventing the appropriate machine architecture to support AI. We will have machines can think as human one day.Reference[1]. Von Neumann, John (1945), First Draft of a Report on the EDVAC, retrieved August 24, 2011[2]. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2[3]. Luger, George; Stubblefield, William (2004). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). The Benjamin/Cummings Publishing Company, Inc. ISBN 0-8053-4780-1.[4]. /wiki/Artificial_intelligence[5]. /wiki/Turing_test[6]. Rajani, Sandeep (2011). "Artificial Intelligence - Man or Machine". International Journal of Information Technology and Knowlede Management 4 (1): 173–176. Retrieved 24 September 2012.[7]. Howard C.Anderson (1987). “Why Artificial Intelligence isn‟t (Yet)”.[8]. Howard C.Anderson (1987). “Why Artificial Intelligence isn‟t (Yet)”.21。