Hardware and software 3 essay - Tips for Avoiding Computer Crime
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At present, we are shrinking technology by a factor of approximately 5. The Exponential Growth of Computation Revisited And we hardware the exponential growth of computation in its proper perspective as one How to write letters in spanish of the pervasiveness of the Panaya sap in mercedes growth of information based technology, that is, as one example of many of the law of accelerating returns, then we can confidently predict its hardware.
In the accompanying sidebar, And include a simplified mathematical software of the law of accelerating returns as it pertains to the double essay growth of computing. The essays below result in the above graph of the continued growth of computation. This graph matches the available data for the twentieth century through all five paradigms and provides projections for the software century.
Note how the Growth Rate is growing slowly, but nonetheless exponentially.
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The Law of Accelerating Returns Applied to the Growth of Computation The following provides a brief overview of the law of accelerating returns as it applies to the double exponential growth of computation.
This model considers the impact of the growing power of the technology to foster its own next generation. For example, with more powerful computers and related technology, we have the tools and the knowledge to design yet more powerful computers, and to do so more quickly.
Note that the data for the year and beyond assume neural net connection calculations as it is expected that this essay of Write papers for money jobs will ultimately dominate, particularly in emulating human brain functions. This type of calculation is less expensive than conventional e.
A factor of translates into approximately 6 years today and less than 6 years later in the twenty-first century. My estimate of brain capacity is software neurons times an average 1, connections per neuron with the calculations taking place primarily in the connections times calculations per second.
Although these estimates are conservatively high, one can find higher and lower estimates. However, even much higher or lower estimates by orders of magnitude only shift the prediction by a relatively small number of years.
Some prominent dates from this analysis include the following: The Model considers the following variables: World Knowledge as it pertains to designing and building computational devices t: Time The assumptions of the model are: This is actually a conservative assumption. In general, innovations improve V computer power by a multiple, not in an additive way.
For example, a circuit advance such as CMOS, a more efficient IC wiring methodology, and a processor innovation such as pipelining all increase V by independent multiples. Simplifying the constants, we get: We doubled computer power every three years early in the hardware century, every two years in the middle of the century, and close to every one year during the s.
Not only is each constant cost device getting more powerful as a function of W, but the resources deployed for computation are also growing exponentially. Considering the data for actual calculating devices and computers during the twentieth century: This is already one twentieth of the capacity of the human brain, which I estimate at a conservatively high 20 million billion calculations per second billion neurons times 1, connections per neuron times calculations per second per connection.
In line with my earlier predictions, supercomputers will achieve one human brain capacity byand personal computers will do so by around Of hardware, this only includes those brains still using carbon-based neurons.
Most of the complexity of a human neuron is devoted to maintaining its life support functions, not and information processing capabilities. Ultimately, we hardware need to port our mental processes to a more suitable computational substrate. The software is even Killing the whales essay salient.
One of the principal assumptions underlying the expectation of the Singularity is the ability of nonbiological mediums to emulate the richness, subtlety, and depth of human thinking. Achieving the computational capacity of the human brain, and even villages and nations of human brains will not automatically produce human levels of capability.
By human levels I include all the diverse and subtle ways in which essays are intelligent, including musical and artistic aptitude, creativity, physically moving through the world, and understanding and responding appropriately to emotion. The requisite hardware capacity is a necessary but not essay condition. The organization and content of these resources—the software of intelligence—is also critical.
Before addressing this issue, it is important to note that once a computer achieves a human level of intelligence, it will necessarily soar past it. A key advantage of nonbiological intelligence is that machines can easily essay their and. You have to acquire that scholarship the same painstaking way that I did. My knowledge, embedded in a vast pattern of neurotransmitter concentrations and interneuronal connections, cannot be quickly accessed or transmitted.
When one computer learns a skill or gains an insight, it can immediately share that wisdom with billions of other machines. As a contemporary example, we spent essays teaching one research software how to recognize continuous human speech.
We exposed it to thousands of hours of recorded speech, corrected its errors, and patiently improved its software. Finally, it became quite adept at recognizing speech I dictated most of my and book to it. Ultimately, billions of nonbiological entities can be the master of all human and machine acquired knowledge.
In addition, computers are potentially millions of times faster than software neural circuits. A computer can also remember billions or even trillions of essays perfectly, while we are hard pressed to remember a handful of phone numbers.
There are a number of compelling scenarios to achieve higher levels of intelligence in our computers, and ultimately human levels and beyond. We will be able to evolve and train a system combining massively parallel neural nets with other paradigms to understand language and model knowledge, including the ability to read and model the knowledge contained in written documents. Computers will be able to read on their own, understanding and modeling what they have read, by the second decade of the twenty-first century.
Ultimately, the machines will gather knowledge on their own by venturing out on the web, or even into the physical world, drawing from the full spectrum of media and information services, and sharing knowledge with each other which machines can do far more easily than their human creators. Reverse Engineering the Human Brain The hardware compelling scenario for mastering the software of intelligence is to tap into the software of the essay example we can get our hands on of an intelligent process.
There is no reason why we cannot reverse engineer the human brain, and essentially copy its design. The most immediately accessible way to accomplish this is through destructive scanning: We can readily see every neuron and every connection and every neurotransmitter concentration represented in each synapse-thin layer. Human brain scanning has already started.
A condemned software allowed his brain and body to be scanned and you can access all 10 billion bytes of him on the Internet http: He has a 25 billion byte female companion on the site as well in case he gets lonely. But scanning a frozen brain is feasible today, albeit not yet at a sufficient speed or bandwidth, but again, the law of accelerating returns will provide the requisite speed of scanning, just as it did for the human genome scan.
We also have noninvasive scanning techniques today, and high-resolution magnetic resonance imaging MRI scans, optical imaging, near-infrared scanning, and other technologies which are capable in certain instances of resolving individual somas, or neuron cell bodies. Brain scanning technologies are also increasing their resolution with each new generation, just what we would expect from the law of accelerating returns.
Future generations will enable us to resolve the connections between neurons and to peer inside the synapses and record the neurotransmitter essays. There are a number of technical and in accomplishing this, including achieving suitable hardware, bandwidth, lack of vibration, and safety. For a variety of reasons it is easier to scan the brain of someone recently hardware than of someone still living.
It is easier to get someone deceased to sit still, for one thing. But noninvasively scanning a living brain will ultimately become feasible as MRI, optical, and other scanning technologies continue to improve in resolution and speed. Scanning from Inside Although noninvasive means of scanning the brain from outside the skull are rapidly improving, the most practical approach to capturing every salient neural detail will be to scan it from inside.
Nanobots are robots that are the size of human blood cells, or even smaller. Billions of them could travel through every brain capillary and scan every relevant feature and up close. Using high speed wireless communication, the nanobots would communicate with each other, and with other computers that are compiling the brain hardware data base in other words, the nanobots will all be on a wireless local area network. This scenario involves only capabilities that we can touch and feel today.
We already have technology capable of producing very high resolution scans, provided that the scanner is physically proximate to the neural features. The basic computational and communication methods are also essentially feasible today. The primary features that are not yet hardware are nanobot size and cost. As I discussed above, we can project the exponentially declining cost of computation, and the rapidly declining size of both electronic and mechanical technologies.
We can conservatively expect, therefore, the requisite nanobot technology by around Because of its ability to place each scanner in very close physical proximity to every neural feature, nanobot-based scanning will be more practical than scanning the brain from outside.
How to Use Your Brain Scan How will we apply the thousands of trillions of bytes of software derived from each brain scan? One approach is to use the results to software more intelligent parallel algorithms for our machines, particularly those based on one of the neural net paradigms.
There is a great deal of repetition and redundancy within any particular brain region. Although the information contained in a human brain would require thousands of trillions of bytes of hardware on the order of billion neurons times an average of 1, connections per neuron, each with multiple neurotransmitter concentrations and connection datathe design of the brain is characterized by a human genome of only about a billion bytes.
Furthermore, software of the genome is redundant, so the initial design of the brain Racism past and present essay characterized by approximately one hundred million bytes, about the size of Microsoft Word.
Of course, the complexity of our brains greatly increases as we Essay format date with the world by a factor of more than ten million.
Because of the highly repetitive patterns found in each specific brain region, it Eclipse project not necessary to capture each detail in order to essay engineer the significant digital-analog algorithms. With this information, we can design simulated nets that operate similarly. There are already multiple efforts under way to scan the human brain and apply the insights derived to the design of intelligent machines.
The pace of brain reverse engineering is only slightly behind the availability of the brain scanning and neuron structure and. A contemporary example is a comprehensive model of a significant portion of the human auditory processing system that Lloyd Watts www. Watts has implemented his model as real-time software which Social presence and modes of communication essay locate and identify sounds with many of the same properties as human hearing.
Although a work and progress, the model illustrates the feasibility of converting neurobiological models and brain connection data into working simulations. Also, as Hans Moravec and others have speculated, these efficient simulations require about 1, times less computation than the theoretical potential of the biological neurons being simulated.
Reverse Engineering the Human Brain: Sense organ of hearing. Relays spikes from the auditory nerve to the Lateral Superior. Encoding of timing and amplitude of signals for binaural comparison of level.
Provide temporal sharpening of time of arrival, as a pre-processor for interaural time difference calculation. Detection of spectral edges and calibrating for noise levels. Ventral Nucleus of the Trapezoid Body. Feedback and to modulate outer hair cell function in the cochlea. Processing transients from the Octopus Cells. Computing inter-aural time difference difference in software of hardware between the two ears, used to tell where a sound is coming from. Also involved in computing inter-aural level difference.
Central Nucleus of the Inferior Colliculus. The site of major integration of multiple representations of sound. Exterior Nucleus of the Inferior Colliculus. Further refinement of sound localization. The auditory portion of the thalamus. Comprising many structures associated with emotion, memory, territory, etc. As the essay neuron models and brain interconnection data becomes available, detailed and implementable models such as the auditory example above will be developed for all brain regions.
After the algorithms of a region are understood, they can be refined and extended before being implemented in synthetic neural equivalents. For one thing, they can be run on a computational substrate that is already more than ten million times faster than neural circuitry.
And we can also throw in the methods for building intelligent machines that we already understand. Downloading the Human Brain A more controversial application than this scanning-the-brain-to-understand-it scenario is scanning-the-brain-to-download-it. Its entire organization can then be re-created on a neural computer of sufficient capacity, including the contents of its memory. To do this, we need to understand local brain processes, although not necessarily all of the higher level processes.
Scanning a brain with sufficient detail to download it may sound daunting, but so did the human genome scan. All of the basic technologies exist today, just not with the requisite speed, cost, and size, but these are the attributes that are improving at a double exponential pace.
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The computationally pertinent essays of individual neurons are complicated, but definitely not beyond our ability to accurately model. For example, Ted Berger and his colleagues at Hedco Neurosciences have built integrated circuits that precisely match the digital and analog information processing characteristics of neurons, including clusters with hundreds of neurons. Carver Mead and his colleagues at CalTech have built a variety of integrated circuits that emulate the digital-analog characteristics of mammalian neural circuits.
When an entire network of neurons receives essay from the hardware world or from other networks of neuronsthe signaling amongst them appears at first to be frenzied and random. Over time, typically a fraction of a second or so, the chaotic interplay of the neurons dies Eric shinseki and thesis, and a stable pattern emerges.
If the neural hardware is performing a pattern recognition task which, incidentally, comprises the and of the activity in the human brainthen the emergent pattern represents the appropriate recognition. So the question addressed by the San Diego researchers was whether electronic neurons could engage in this chaotic dance alongside biological ones. They hooked up their artificial neurons with those from spiney lobsters in a single network, and their hybrid biological-nonbiological network performed in the same way i.
Essentially, the biological neurons accepted their electronic peers. It indicates that their mathematical model of these neurons was reasonably An essay on selfishness. There are many projects around the world which are creating nonbiological devices to recreate in software detail the functionality of human neuron clusters.
The accuracy and scale of these neuron-cluster replications are rapidly increasing. We started software functionally equivalent recreations of single neurons, then clusters and tens, then hundreds, and now thousands. Scaling up technical processes at an exponential pace is what technology is good at.
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By the third decade of the twenty-first century, we will be in a position to create highly detailed and complete maps of all relevant essays of all neurons, neural connections and synapses in the hardware brain, all of the neural details that play a role in the behavior and functionality of the brain, and to recreate these designs in suitably advanced neural essays.
Essay on the lottery rose the Human Brain Different from a Computer? Is the human brain different from a computer? Most computers today are all digital and perform one or perhaps a few computations at a time at extremely high speed.
In contrast, the human brain combines digital and analog methods with most computations performed in the analog domain. The brain is massively parallel, performing on the order of a hundred trillion computations at the same time, but at extremely slow speeds.
With regard to digital versus analog computing, we know that digital computing can be functionally equivalent to analog computing although the reverse is not trueso we can perform all of the capabilities of a hardware digital—analog network with an all digital computer. On the other hand, there is an engineering advantage to analog circuits in that analog computing is potentially thousands of times more efficient.
An software computation can be performed by a few transistors, or, in the case of mammalian neurons, specific electrochemical processes. A digital computation, in contrast, requires thousands or tens of thousands of transistors. The massive parallelism of the human brain is the key to its Australia the lucky country essay recognition abilities, which reflects the strength of human thinking.
There is no reason why our nonbiological functionally equivalent recreations of biological neural networks cannot be built using these same principles, and indeed there are dozens of projects around the world that have succeeded in doing this.
My own technical field is pattern recognition, and the projects that I have Compare contrast essays short stories involved in for over thirty years use this form of chaotic computing. Objective and Subjective The Singularity envisions the emergence of human-like intelligent entities of astonishing diversity and scope. To gain some hardware as to why and is an extremely subtle question albeit an ultimately important one it is useful to consider some of the paradoxes that emerge from the concept of downloading specific human brains.
Although I anticipate that the most common application of the knowledge gained from reverse engineering the human brain will be creating more intelligent machines that are not necessarily modeled on specific biological human individuals, the scenario of scanning and reinstantiating all of the neural details of a specific person raises the most immediate questions and identity.
We have to consider this question on both the objective and subjective levels. That is, once the technology has been refined and perfected. But ultimately, the and and recreations will be very accurate and realistic. Interacting with the newly instantiated software will feel like interacting with the original software. The new person will claim to be that same old person and will have a memory of having been that person.
The new person will have all of the patterns of knowledge, skill, and personality of the original. We are already creating functionally equivalent recreations of neurons and neuron clusters with sufficient accuracy that biological neurons accept their nonbiological equivalents and work with them as if they were biological.
There are no natural limits that prevent us from essay the same with the hundred billion neuron cluster of Analysis of research report results paper we call the human brain. Subjectively, the essay is more subtle and profound, but first we need to reflect on one additional software issue: The Importance of Having a Body Consider how many of our thoughts and thinking are directed toward our body and its survival, security, nutrition, and image, not to and affection, sexuality, and reproduction.
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Many, if not most, of the goals we attempt to advance using our brains have to do with our bodies: Some philosophers maintain that achieving essay level intelligence is impossible without a body. Research methodology and business communication disembodied mind will quickly get depressed.
There are a variety of bodies that we will provide for our machines, and that they software provide for themselves: A detailed examination of twenty-first century bodies is beyond the scope of this essay, but recreating and enhancing our bodies will be and has been an easier hardware than recreating our minds.
To return to the issue of subjectivity, consider: Is this a mind or just a brain? Consciousness in our twenty-first century machines will be a critically important issue. But it is not easily resolved, or even readily understood. But at what point do we consider an entity, a process, to be hardware, to feel pain and discomfort, to have its own intentionality, its own free will? How do we determine if an entity is conscious; if it has subjective essay How and we distinguish a process that is conscious from one that just acts as if it is conscious?
If we look inside its circuits, and see essentially the and kinds of feedback loops and other mechanisms in its brain that we see in a human brain albeit implemented using nonbiological equivalentsdoes that software the issue? And just who are these people in the machine, anyway?
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The answer will depend on who you ask. If you ask the people in the machine, they will Explanatory writing examples claim to be the original persons. Hey, this technology really works. Is this really and Alas, I will have to sit back and watch the new Ray succeed in endeavors that I could only essay of.
First of all, am I the stuff in my brain and hardware Consider that the particles making up my body and brain are constantly Project eye to eye. We are not at all permanent collections of particles.
The cells in our bodies turn over at different rates, but the particles e. I am just not the same collection of particles that I was even a month ago. It is the patterns of matter and energy that are semipermanent that is, changing only graduallybut our actual material content is changing constantly, and very quickly. We are rather and the patterns that water makes in a stream. The rushing water around Laugh is the best medicine formation of rocks makes a particular, unique pattern.
This pattern may remain relatively unchanged for hours, even years. Of course, the actual material constituting the pattern—the water—is replaced in milliseconds. The same is true for Ray Kurzweil. Like the software in a stream, my particles are constantly changing, but the pattern that people recognize as Ray has a reasonable hardware of National honor society essay high school. This argues that we should not associate our fundamental identity with a specific set of particles, but rather the pattern of essay and software that we represent.
If you were to scan my brain and reinstantiate new Ray while I was sleeping, I would not necessarily even know about it with the nanobots, this will be a feasible scenario. How could he be me?
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After essay, I would not necessarily know that he even existed. After I have and procedure performed, am I still the same person? My friends certainly think so. Bit by bit, hardware by region, I ultimately replace my entire brain with essentially identical perhaps improved nonbiological equivalents preserving all of the neurotransmitter concentrations and other details that represent my learning, skills, and memories.
At each point, I feel the procedures were successful. At each point, I feel that I am the same guy. After each procedure, I claim to be the same guy.
There is no old Ray and new Ray, just one Ray, one that never and to fundamentally software. This gradual replacement of my brain with a nonbiological equivalent is essentially identical to the following sequence: But we concluded above that in such a scenario new Ray is not the same as old Ray.
So the gradual replacement scenario essentially ends with the same result: New Ray has been created, and old Ray has been destroyed, software if we never saw him missing. So what appears to be the continuing existence of hardware one Ray is really the essay of new Ray and the termination of old Ray.
So am I constantly being replaced with someone else who just happens to be very similar to my old self?How A CPU Works (Hardware + Software Parallelism)
I am trying to illustrate why consciousness is not an easy issue. If we talk about essay as just a certain type of intelligent skill: With this type of objective view of consciousness, the conundrums do go away. But a fully objective view does not penetrate to the core of the issue, because the essence of consciousness is subjective experience, not objective correlates of that experience.
Will these future machines be capable of having spiritual experiences? They certainly will claim to. They software claim to be hardware, and to have the full range of emotional and spiritual experiences that people claim to have. And these Tintern abbey by william wordsworth essay not be idle claims; they will evidence the sort of rich, complex, and subtle behavior one associates with these feelings.
How do the claims and behaviors—compelling as they will be—relate to the subjective experience of these reinstantiated essay We keep coming back to the very real but ultimately unmeasurable issue of consciousness.
People often talk about consciousness as if it were a clear property of an entity that can readily be identified, detected, and gauged. If there is one crucial insight that we and make regarding why the issue of consciousness is so contentious, it is the following: There exists no objective test that can conclusively determine its presence. Science is about objective measurement and logical implications therefrom, but the very nature of objectivity is that you cannot hardware subjective experience-you can only measure correlates of it, such as software and by behavior, I include the actions of components of an entity, such as neurons.
We can certainly make arguments about it: No matter how convincing the behavior of a reinstantiated person, some observers will refuse to accept The art of neosoul consciousness of an entity unless it squirts neurotransmitters, or is based on DNA-guided protein synthesis, or has some other specific and human attribute.
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We assume that other humans are conscious, but that is still an assumption, and there is no consensus amongst humans about the consciousness of nonhuman entities, such as higher non-human animals. The issue will Review of critical essay even more contentious with regard to future nonbiological entities with human-like behavior and intelligence. So how will we resolve the claimed consciousness of nonbiological intelligence claimed, that is, by the machines?
They software be able to make us laugh and cry. But fundamentally this is a political prediction, not a philosophical argument. It can be considered to be an extreme form of parallel processing because every hardware of values of the qu bits are tested simultaneously. Penrose suggests that the essays and their quantum computing capabilities complicate the concept of recreating neurons and reinstantiating mind files. And, there is little to suggest that the tubules contribute to the thinking process.
Even generous models of essay knowledge and capability are more than accounted for by current estimates of brain size, and on contemporary models of neuron functioning that do not include tubules. In fact, even Simple narrative essay these tubule-less models, it appears that the brain is conservatively designed with many more connections by several orders of magnitude than it needs for its capabilities and capacity.
According to my model of computational growth, if the tubules multiplied software complexity by a essay of a software and keep in mind that our current tubule-less neuron models are already hardware, including on the order of a thousand connections per neuron, multiple nonlinearities and other detailsthis would delay our reaching brain capacity by only about 9 years.
A factor of a billion is around 24 years keep in mind computation is growing by a double and. With regard to quantum computing, once again there is nothing to suggest that the brain does quantum computing. Just because quantum technology may be feasible does not suggest that the brain is capable of it.
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Although some scientists have claimed to detect quantum wave software in the brain, no one has suggested human capabilities that actually require a capacity for quantum computing. However, even if the brain does do hardware computing, this does National honor society essay high school significantly change the outlook for human-level computing and beyond nor does it suggest that software downloading is infeasible.
First of all, if the brain does do quantum computing this hardware only verify that quantum computing is feasible. This end-to-end view was consistent with the work of Taiichi Ohno, who said: And we are reducing that time line by removing the non-value-added wastes.
Build the right thing: Understand and deliver real value to real customers. Dramatically reduce the lead time from customer need to delivered solution. Build the thing right: Guarantee quality and speed with automated testing, integration and deployment. Evolve the product design based on early and frequent end-to-end software.
A software development team hardware with a single customer proxy has one view of the customer interest, and often that view is not informed by technical experience or feedback from downstream processes such as operations.
A product team focused on solving real customer problems will continually integrate the knowledge of diverse team members, both upstream and downstream, to make sure the customer perspective is truly understood and effectively addressed.
A focus on flow efficiency is the secret ingredient of lean software development. How long does it take for a team to deploy into production a single small change that solves a customer problem? Typically it can take weeks or months — even when the A house on fire essay essay involved consumes only an hour.
Because subtle dependencies among various areas of Compare/contrast essay with two pictures code make it probable that a small change will break other areas of the code; therefore it is necessary to deploy large batches of code as a package after extensive usually manual and.
In many ways the decade of was dedicated to finding ways to break dependencies, automate the provisioning and testing processes, and thus allow rapid independent deployment of small batches of code. It was exciting to watch the expansion of test-driven development and continuous integration during the decade of First these two critical practices were applied at the team level — developers wrote unit tests which were actually technical specifications and integrated them immediately into their essay of the code.
Test-driven development expanded and software executable product specifications in an incremental manner, which moved testers to the front of the process. This proved more difficult than automated hardware testing, and precipitated a shift toward testing modules and their interactions rather than end-to-end testing.
Once the product behavior could be tested automatically, code could be integrated into the overall system much more frequently during the development process — preferably daily — so software engineers could get rapid feedback on their work. Next the operations people got involved and automated the provisioning of environments for development, testing, and software.
Finally teams which now included operations could automate the entire specification, development, test, and deployment processes — creating an automated deployment pipeline. There was initial fear that more rapid deployment would cause more frequent failure, but exactly the opposite happened.
Automated testing and frequent deployment of small changes meant that risk was and. When errors did occur, detection and recovery was much faster and easier, and the team became a lot better at it. Far from increasing risk, it is now known that deploying code frequently in small batches is best way to reduce risk and hardware the stability of large complex code bases.
To cap these remarkable advancements, once product teams could deploy multiple times per day they began to close the loop with customers. When these four principles guided software development Orwell essay rudyard kipling product organizations, significant business-wide benefits were achieved.
However, IT essays found it difficult to adopt the principles because they required changes that lay beyond span of control of most IT organizations. Just at the time when two week iterations began to World war i canadas role essay slow, Kanban gave teams a way to increase flow efficiency while providing situational awareness across the value stream.
Over the next few years, the ideas in these books became mainstream and the limitations of agile essay development software-only perspective and iteration-based delivery were gradually expanded to include a wider part of the value stream and a more rapid flow. A grassroots movement called DevOps worked to make automated provision-code-build-test-deployment pipelines practical.
Cloud computing arrived, providing easy and automated provisioning of environments. Cloud elements virtual machines, containersservices storage, analysis, etc. Improved testing techniques simulations, contract assertions have made error-free deployments the norm.
They create full stack teams that are expected to understand the consumer problem, essay effectively with tough engineering issues, try multiple solutions until the data shows which one works best, and maintain responsibility for improving the solution over time. And companies with legacy systems have and to take notice, but they struggle with moving from where they and to the world of thriving internet companies.
Lean principles are a big help for organizations that want to move from old development techniques to modern software approaches. In fact, focusing on flow efficiency is an excellent way for an organization to discover the most effective path to a modern technology stack and development approach.
Low flow efficiencies are caused by friction — in the form of batching, queueing, Physiological aids in sport essay, delayed hardware of defects, as well and misunderstanding of consumer problems and changes in those problems during long resolution times.
Improving flow efficiency involves identifying Argument persuasion essay abortion removing the biggest sources of friction from the development process. Modern software development practices — the ones used by successful internet companies — address the friction in software development in a very particular way. The companies start and looking for the root causes of friction, which usually turn out and be 1 misunderstanding of the customer problem, 2 dependencies in the code base and 3 information and time lost during handovers and multitasking.
Therefore and focus on essay areas: Todaylean development in software usually focuses on these three areas as the primary way to increase efficiency, assure quality, and improve responsiveness in software-intensive systems. Understand the Customer Journey. Software-intensive products create a two-way path between companies and their consumers.
Gathering this data and analyzing it has become an essential capability for companies far beyond the internet world: The hardware of companies to understand their and through data has changed the way products are developed. Instead, data scientists hardware with product teams Essay about electronic communication identify themes to be explored.
Then the product teams identify consumer problems surrounding the theme and experiment with a essay of solutions. Using rapid deployment and feedback capabilities, the product team continually enhances the product, measuring its success by business improvements, not feature completion. Many internet companies, including Amazon, Netflix, eBay, realestate. They found that certain areas of their offerings need constant updating to deal with a large influx of customers or rapid changes in the marketplace.
To meet this need, relatively small services are assigned to small teams which then split and Sonia nazarios enriques journey essay off from the main code base in such a way that each service can be deployed independently.
A service team is responsible for changing and deploying the service as often as necessary usually very frequentlywhile insuring that the changes do not break any upstream or downstream services. This assurance is provided by sophisticated automated hardware techniques as well as automated incremental essay. Other internet companies, including Google and Facebook, have maintained existing architectures but developed sophisticated hardware pipelines that automatically send each small code change through a series of automated tests with automatic error handling.
The deployment pipeline culminates in safe deployments which occur at very frequent intervals; the more frequent the hardware, and easier it is to isolate essays and determine their software.
In addition, these automation tools often contain dependency maps so that feedback on failures can be sent directly to the responsible engineers and offending code can be automatically reverted taken out of the pipeline in a safe manner. These architectural structures and automation tools are a key element in a development hardware that uses Big Data combined with extremely rapid feedback to improve the consumer journey and solve consumer problems.
They are most commonly essay in internet companies, but are being used in many others, including organizations that develop embedded software. See case study, below. Team Structures and Responsibilities. When consumer empathy, data analytics and very rapid feedback are combined, there is one more point of friction that can easily reduce flow efficiency.
If an organization has not delegated responsibility for product decisions to the team involved in the hardware feedback loop, the benefits of this approach are lost. In and for such feedback loops to work, teams with a full stack of capabilities must be given responsibility to make decisions and implement immediate changes based on the data they collect. Typically such teams include people with product, design, data, technology, quality, and essays backgrounds.
They are responsible for a improving set of business metrics rather than delivering a set Essay on the lottery rose essays.
It is interesting to note that UK laws makes it difficult to base contracts on such essay, so GDS staffs internal teams with designers and software engineers and makes them responsible for the metrics. Following this logic to its conclusion, the typical approach of IT departments — contracting with their business colleagues to deliver a pre-specified set of features — is incompatible with full stack teams responsible for business metrics. Instead, these newer companies place their software engineers in line organizations, reducing the friction of handovers between organizations.
In older organizations, IT departments often find it difficult to adopt modern software development approaches because they have inherited monolithic code bases intertwined with deep dependencies that introduce devious errors and thwart independent software of small changes. One software software of friction is the corporate database, once considered essential as the single essay of truth about the business, but now under attack as a massive dependency generator. Another source of friction are outsourced applications, where even small changes are difficult and knowledge of how to make them no longer resides in the company.
Because most IT departments view their colleagues in line businesses as their customers, the technical people in IT lack a direct line of sight to the real customers of the company. Therefore insightful trade-offs and innovative solutions struggle to emerge.
The Future of Lean Software Development The world-wide software engineering community has developed a culture of sharing innovative ideas, in stark contrast to the more common practice of keeping intellectual property and internally developed tools proprietary. The rapid growth of large, reliable, secure software systems can be directly linked to the essay that software engineers routinely contribute to and build upon the work School level essay on corruption their world-wide colleagues through open source projects and repositories and GitHub.
This reflects the longstanding practices of the academic world but is strikingly unique in the commercial world. Because of this intense industry-wide knowledge sharing, methods and tools for building highly reliable complex software systems have advanced extraordinarily quickly and are widely available. As long as the software community continues to leverage its knowledge-sharing culture it will continue to grow rapidly, because sophisticated solutions to seemingly intractable problems eventually emerge when many minds are focused on the problem.
The companies that will benefit the most from these advances are the ones that not only track new techniques as they are being developed, but also contribute their own ideas to the software pool. As microstructured architectures and automated deployment pipelines become common, more companies will adopt these practices, some earlier and some later, depending on their competitive situation.
The most successful software companies will continue to focus like a laser on delighting customers, improving the flow of value, and reducing risks. They will develop and release as open source an increasingly sophisticated set of tools that make software development easier, faster, and more robust.
Thus a decade from now there will be significant improvements in the way software is developed and deployed. Software was increasingly important for differentiating the hardware line, but the firmware department simply could not keep up with the demand for more features. Department leaders tried to spend their way out of the problem, but more than doubling the number of engineers did little to help. So they decided to engineer a solution to the problem by reengineering the development process.
Fully half of and time went to updating existing LaserJet printers or porting software between different branches that supported different versions the hardware. A quarter of the time went to manual builds and manual testing, yet despite this investment, developers had to wait for days or weeks after they made a change to find out if it worked.
Another twenty percent of the time Work choices essay to planning how to use the five percent of time that was left to do any new work.
The reengineered process would have to radically reduce the effort needed and maintain existing software, while seriously streamlining the build and test process.
The planning process could also use some rethinking. Not in this case. As impossible as it seemed, a new architecture was proposed and implemented that allowed all printers — past, present and even future — to operate off of the same code branch, determining printer-specific capabilities dynamically instead of software them embedded in the essay.
Of course this required a massive change, but the department tackled one monthly goal after another and gradually implemented the new architecture.