computers past present and future essay

Past, Present, and Future of Computers

These tasks would be givin to the machine and could figure out values of almost any algebraic equation. Soon, a silk weaver wanted to make very intricate designs. The designs were stored on punch-cards which could be fed into the loom in order to produce the designs requested. This is an odd beginning for the most powerful invention in the world. In the 1930’s, a man named Konrad Zuse started to make his own type of computer. Out of his works, he made several good advances in the world of computing. First, he developed the binary coding system .

Before this, computers only had a number of valves, none were fully driven by them because of the complexity and difficulty of producing it. Despite the odds, several Cambridge professors accomplished the mammoth task. Once it was built, the computer could decode the encrypted messages in enough time to be of use, and was an important factor in the end of World War II. The war also provided advancements in the United States as well. The trajectory of artillery shells was a complex process that took alot of time to compute on the field. A new, more powerful computer was in dire need.

Working ith the Moore School of Electrical Engineering, the Ballistics Research Laboratory created the Electronic Numerical Integrator and Computer. The ENIAC could compute things a thousand times faster than any machine built before it. Even though it was not completed until 1946 and was not any help during the war, it provided another launching pad for scientists and inventors of the near future. The only problem with the ENIAC was that it was a long a tedious process to program it. What was needed was a computation device that could store simple programs into it’s memory for call later.

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Computer Hardware: Past, Present, and Future Essay

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To a great extent, the development of computer hardware illustrates the technological and scientific progress achieved by people in the course of world history. This paper is aimed at discussing the definition of this term hardware, its history, current development, and prospects.

It seems that the discussion of these questions can throw light on the evolution of information technologies, and the main factors that influence this process. Overall, one can identify several important trends that profoundly affected the development of hardware, and one of them is the need to improve its design, functionality, and capacity. Secondly, it is important to remember about the increasing commercialization of these technologies.

First of all, the term hardware can be defined as physical units of a computer that are supposed to perform the processing, input, storage, transfer, and output of information (Stair & Reynolds, 2011, p. 62). Among these elements, one can distinguish processors, keyboards, hard disk drives, motherboards, graphics adapters, scanners, printers, and so forth (Stair & Reynolds, 2011).

These units have to do separate tasks, and one cannot say that they are equally important; yet, they are closely dependent on one another (Stair & Reynolds, 2011). Overall, this concept of hardware is used to describe the tangible components of a computing system. In contrast, the word software refers to intangible elements of a computing system, and they are used to manipulate the hardware. This is one of the distinctions that should be taken into consideration. On the whole, this term is critical for the understanding of information technologies and their functioning.

The importance of hardware can hardly be underestimated because it ensures that every process is completed successfully. Furthermore, the physical units are important for the effective work of software applications and operational systems. Finally, these components are used to connect different computing systems.

Scientists have long tried to create a machine that can process information or store it; for example, the famous mathematician Blaise Pascal was able to create a mechanical calculator in 1642 (Singh & Nath, 2007, p. 59). Similar attempts were made by other mathematicians, but it is very difficult to call these inventions computers because they consisted only of mechanical elements (Singh & Nath, 2007).

More importantly, their functionality was very limited. One can say that the true development of computing systems began in the first half of the twentieth century and at that time, this hardware had to serve military needs (Singh & Nath, 2007, p. 61). In particular, one should mention the model introduced by John Von Neumann who believed that computers had to include such elements as CPU, input and output devices, and memory storage (Singh & Nath, 2007, p. 61). To a great extent, this model contributed to the future development of hardware.

It should be noted that in the course of history, computer hardware gradually became smaller and more effective. For instance, the Colossus computer occupied an entire floor of the building, while modern computers can be smaller than a notebook (Singh & Nath, 2007). To a great extent, the need to minimize hardware became one of the most important trends, and this requirement is considered by many designers and engineers.

Moreover, the improvement of hardware components led to the greater commercial use of computers or its components. Today, they can be regarded as mass consumer goods that are accessible to people of different incomes. This is one of the issues that should be taken into account.

Currently, the development of hardware is based on several important principles. First of all, manufacturers attempt to make their devices efficient in terms of energy consumption, size, and compatibility with existing software solutions. These are the most important concerns of engineers and designers who have to work various requirements when developing new products. To some degree, the improvement of CPUs, Random Access Memory (RAM) and other devices reflects the development of existing software solutions that require the excellent performance of technologies. This is one of the most important trends that one can identify.

Additionally, the physical components of a computer are adjusted to different devices that were not previously related to computing systems. For instance, Random Access Memory can be easily incorporated into mobile phones. Apart from that, the physical components of the computer become are improved in terms of their capacity and connectivity. For example, contemporary hard-disk drives can store several terabytes of data. This capacity was hardly imaginable at least a decade ago when users had fewer opportunities.

Moreover, a hard-disk drive can be connected to a variety of devices such as personal computers, mobile phones, and even TV sets. These are some of the issues that one can identify. Overall, it is possible to argue that the development of computer hardware continuously brings new opportunities to people.

At this point, it is very difficult to predict the future of hardware development, because a single discovery or invention can significantly contribute to technological progress. Apart from that, it can make previous hardware completely obsolete. Moreover, it is very difficult to appreciate the value of an invention. For example, very few people could predict that the Internet would turn the world into a global village.

Similarly, the discovery of liquid crystal was not initially connected with computer hardware, but now they are widely used for the production of displays. One can make several conjectures about future trends. First of all, the researchers point out that the speed and efficiency of hardware tend to double every two years (Giaretta, 2011, p. 131). This tendency was observed during the last two decades (Giaretta, 2011, p. 131). This means that in the future, the functionality and performance of hardware will rise dramatically.

Moreover, it is possible to say that the size of different hardware components will significantly diminish (Giaretta, 2011, p. 131). Finally, the hardware, which is now believed to be innovative, may go out-of-date within five years. These are the main arguments that can be put forward. One can say that the development of hardware will offer new opportunities to users.

These examples suggest that in the course of its history, computer hardware has undergone significant transformation due to technological and scientific discoveries. Although at the very beginning, it was used mostly for military purposes, the situation changed dramatically in the second half of the twentieth century when computers began to serve commercial goals and turned into consumer goods.

Moreover, it is very difficult to predict future changes that information technologies will undergo. Additionally, different elements of computer hardware will be improved in terms of functionality, performance, and design. Finally, different components of a computer can become obsolete very quickly. Overall, by looking at the historic development of computer hardware, one can better understand technological progress and its future trends.

Reference List

Giaretta, D. (2011). Advanced Digital Preservation . New York: Springer.

Singh, Y. & Nath, R. (2007). Teaching of Computers . New York: APH Publishing.

Stair, R. & Reynolds, G. (2011). Fundamentals of Information Systems . New York: Cengage Learning.

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Computers: Past, Present, and Future

Many people know what a computer is, but not how one works. The National Institute of Standards and Technology’s Computer Security Resource Center (What a mouthful! It’s usually called NIST CSRC) defines a computer as “a device that accepts digital data and manipulates the information based on a program or sequence of instructions for how data is to be processed.” [ NIST CSRC glossary page, 2010 ] 

This definition is important because computers have changed a lot since the first few were built. Someone could look at what was considered a computer in the late 1900s or early 2000s and not know anything about how the object works or what they could do with it. The basics remained, but what we in tech call the User Interface, whether it was there or not, changed how people interact with computers. 

For example, you may have read in school that the abacus was considered one of the first computers. An abacus is a mathematical tool originating from China used for arithmetic and counting. More information about the abacus and other early number processors can be found here [ Ryerson University, 2015 ]

Modern computing starts with Charles Babbage. Known as the Father of Modern Computing, the device he designed and set to invent was known as the Difference Engine. There were multiple designs and at least two broad attempts at successfully building the machine. The Computer History Museum has more information on how the Analytical and Difference engines work. Both machines give us the blueprints for computers as we know them today. [ Computer History Museum, circa 2008 ] 

According to Khan Academy, computers have four things that are common to every iteration. They must take input, store and process information, and output the results. One example of this would be displaying a sentence on a computer screen. The input comes from the keyboard, which a user presses in order to get the letter they want on screen. Another example would be having a computer do a simple calculation. A calculator is a type of computer but most computers are able to take input like (2+2), translate that into something the computer understands, calculate the answer based on math languages stored in the computer, come up with the right answer, and output that answer into a language that humans understand. [ Khan Academy, circa 2018 ]

Computers can be sorted into generations one through five based on when and how they were created. Devices created from 1945 to 1956 were made with vacuum tubes and are classified as First Generation computers. The next generation were called such because they were made with transistors, from 1956 – 1963. The third generation were made from integrated circuits, from 1964 – 1971. Computers used today are part of the Fourth and Fifth generations. Fourth Gen computers are made using microprocessors, which are made by companies like Intel. You may see a sticker on your desktop or laptop with the name of that company. Fifth Gen computers are made with Artificial Intelligence and Machine Learning, but we are unlikely to see anything beyond perhaps Cortana on computers and Siri on Apple mobile devices. Even still, those are not true artificial intelligences yet. That is currently limited to processes that are being explored heavily by computer scientists. [ KmacIMS, 2019 ]

This article was meant to be a brief history and technical explanation of what computers are and where they came from.

We’d love to hear your opinion on where computers are headed next. Share below in the comments!

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Past, Present, and Future of Computers

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A computer is an electronic device that is used to automate processes in order to enhance the quality of life for all people. This device will be used extensively in the future because of its availability and its ability to simplify many of the actions in our daily lives. Computers are devices that have a rich history, a revolutionary present, and a promising future mainly due to the fact that it’s a device that remains prominent in our everyday lives. The importance of a computer is emphasized by the quote: “I'm a great believer that any tool that enhances communication has profound effects in terms of how people can learn from each other, and how they can achieve the kind of freedoms that they're interested in.

” – Bill Gates.The quotation by Bill Gates elaborates upon the fact that computers make for a more connected society where an idea can be communicated to anybody who wishes to act on the idea. The first computers were made by individuals who were striving to create devices that have the ability to automate processes. Individuals such as Alan Turing innovated and transformed the computer industry by creating the blueprint for the algorithms and computations for computers called the Turing Model. The Turing Model paved the way for the first working programmable computer that is automatic was called the Z3 Computer made by Konrad Zuse in 1936.The next major computer with workable programming that was made was the IBM 701 EDPM Computer.

IBM became one of the first computers to be marketed by a major company. From the creation of the IBM 701, the first successful programming language called FORTRAN was created by IBM and John Backus. The next major innovation was the computer mouse made by Douglas Englebart. The 1970’s was one of the most innovative decades for computers with the invention of the floppy disk, random access memory, microprocessors and the first computers available to consumers. All of these components made way for the marketing and technology involved in modern day computing. The two major computer rivals, Apple and Microsoft, entered the stage with during the late 1970’s all the way though the 1980’s, giving competition to IBM.

Apple released Apple I, Apple II, and the Apple Lisa Computer, which had the first graphic user interface, and the Apple Macintosh, which revolutionized the market by making computers more affordable to the public. During this time, Microsoft developed MS-DOS operating system and the business renowned Microsoft Windows. Smaller companies contributed to the computer industry by creating with the creation of WordStar, which is the first word processing software, and VisiCalc Spreadsheet, which was the first software which had a similar function as Excel.Computers in the present day are part of our everyday lives with its increased availability to everybody, the convenience it provides and the successful marketing that many companies use to promote the idea that their technology will make life better as a whole. The current market is dominated by mobile computing such as smartphones and tablets, with the traditional laptop and desktop losing market share. The shift in mobile computing is largely due to the fact that mobile computers are cheaper, more portable, and able to do most of the actions that the traditional computer can do.

Most smartphones are essentially an ultimate system for entertainment, business and social interaction because they provide features such as cameras and applications that help us communicate, navigate, socialize, work, and play.Tablet computing on the other hand strikes a balance with the traditional computer and the smartphone due to its size, portability and smartphone-like features. Many technology companies today are shifting into the development of new and revolutionary products to improve the processing capability on our smartphones and tablets instead of the traditional personal computer market. The computer industry is an industry that runs and survives on innovation. Every year, new products are released that make a substantial improvement on the previous model, therefore creating a cycle of never ending improvement upon a product in order to stay competitive.The computer market constantly shifts to whoever has the best hardware integrated with the most usable software, therefore giving an advantage to those who have access to what is considered to be good technology.

For example, the smartphone market always goes in the favor the phones with the best cameras, best processors and adaptable software, with a few exceptions. The most prominent way that a product gains market share is through marketing. Apple is the most recent technology company to make a breakthrough in technology with the creation of devices such as the iPhone and iPad. The iDevices don’t have the best hardware, nor do they necessarily have the best software, but through the marketing campaigns that emphasized where the device excels, Apple has converted the flaws of the devices into something that can be accepted as high-end. Many other companies are also gaining market share with the creation of a multitude of products that have the ability to compete with all of the Apple products.

Computers are products that dominate our daily lives. We use computers for everything ranging from manufacturing and research, to entertainment and productivity. Computers have made many jobs obsolete such as assembly line manufacturing and aspects of marketing such as keeping track of sales and costs. Despite the loss of jobs, computers also make a lot of fields such as healthcare and the government easier by storing a lot of important and confidential data on secure databases. The effects that computers have on the field of jobs are tremendous, but it’s important to look at its effects on our daily lives.

Computers help us get started in the day by acting as our planner, entertain us by providing rich a lot of content, and helping us do work. Schools are also a huge market for computing because a multitude of students require computers to do research and write and edit papers.Computers for students are also one of the best tools for learning because it provides a new interactive way of learning with content that is up to date. Teachers also use computers in order to keep track of students, communicate with other teachers and plan lessons accordingly. Major industries such as the health industry, tech industry, and banking industry will continue to benefit from computers because computers help the growth of business and improve management of large swaths of data needed to function.

The computer has and will continue to revolutionize the way we work by acting as a tool that makes a work force more connected, therefore increasing productivity.The future of the computer industry is a promising one because of the numerous advances that can be made in the industry. The key component in the advancement of computer technology is based on Moore’s Law, which states that the number of transistors that can fit in a chip will double every two years. Moore’s law also applies to the processing speed of computers and the amount of RAM that can be inserted to the computer. However, slowdown in the chip development industry has made Moore’s law a bit inaccurate by making the doubling in transistors occur every 3 years instead of two years. The increase in processing speed and the transistors in the chip will all lead to the development of Artificial Intelligence that is capable of learning.

Artificial Intelligence, or AI, that can learn will revolutionize modern and future society by providing machines that are able to act human without some of the flaws that are natural to all humans. In accordance to current technology, Artificial intelligence will take the form of a hardware robot due to its ability to do physical activity while being able to process a lot of information at the same time.Artificial Intelligence is a technology that already exists in the form of software in our phones, cars and many other electronic devices. The most marketed version of Artificial Intelligence is Apple’s Siri, which is a voice assistant that makes searching the internet and keeping track of dates easier. Siri is a stepping stone in the development of artificial technology due to the fact that it demonstrates the capability of computer hardware and the many advances that can be made through the software that will be integrated to the hardware.

The only way to understand how the computer has integrated into our present and will continue to the future is through the understanding of the internet, more specifically the cloud. The cloud is in reference that all our data can and most likely will be stored wirelessly in the near future. The perfection and infinity of the cloud will mean that our computer dependency will continue to increase over time. The cloud provides a way of storing all our digital content on the internet, ready for access and open to sharing. All the major tech companies such as Apple and Google have made cloud computing exclusive to only devices that are made by the company or the backers of the company.

The internet is also a web of things that continually grows as time passes due to the addition of new content. Major advancements in cloud technology can lead to the futuristic problem of dependency on the internet ranging from leisure to work. The cloud revolutionizes the way that information is stored and shared.Computers are the devices that enlighten us through intercommunication and the sharing of ideas, but at the same time dehumanizes us through the dependency in the technology. Aside from the negative effects of the computer, a computer will be the one device that will make the overall quality of life better by making digital media accessible and for the most part mobile.

In accordance to the rich history of computer, it is clear that the future of computing will be a largely positive one. Future concepts such as artificial intelligence is clear evidence that there are still many undiscovered artifacts in the world of computers. Advances in computer technology will be the defining factor of humanity due to its importance in defining the innovative spirit.

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In this chapter, we follow up on the call by Borba et al. ( 2014 ) for historical research to understand technology trends in education. We conduct a historical scan of the origin of Computing in Education. We start with the Logo movement led by Papert and present an overview of the implementation of Computing in Education, including a survey of the main technological resources used and talk about robotics in educational contexts. We continue by recognizing the growth of research on Computational Thinking worldwide and present a brief history of this type of thinking, as well as its characteristics. We also describe some current research that deals with this theme in Basic Education, from Early Childhood Education to High School. With this, we seek to indicate possible trends for research on Computational Thinking in Education and Mathematical Education, enabling a reflection on the future. Therefore, in the conclusion in section 5, we provide a synthesis of what may be learned from the past and the present, so that we can look to the future of Mathematics Education with regard to the use of Computing in Basic Education.

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Gadanidis, G., Javaroni, S.L., Santos, S.C., Silva, E.C. (2021). Computing in Mathematics Education: Past, Present, and Future. In: Danesi, M. (eds) Handbook of Cognitive Mathematics. Springer, Cham. https://doi.org/10.1007/978-3-030-44982-7_35-1

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How AI could change computing, culture and the course of history

Expect changes in the way people access knowledge, relate to knowledge and think about themselves.

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A mong the more sombre gifts brought by the Enlightenment was the realisation that humans might one day become extinct. The astronomical revolution of the 17th century had shown that the solar system both operated according to the highest principles of reason and contained comets which might conceivably hit the Earth. The geological record, as interpreted by the Comte de Buffon, showed massive extinctions in which species vanished for ever. That set the scene for Charles Darwin to recognise such extinctions as the motor of evolution, and thus as both the force which had fashioned humans and, by implication, their possible destiny. The nascent science of thermodynamics added a cosmic dimension to the certainty of an ending; Sun, Earth and the whole shebang would eventually run down into a lifeless “heat death”.

The 20th century added the idea that extinction might not come about naturally, but through artifice. The spur for this was the discovery, and later exploitation, of the power locked up in atomic nuclei. Celebrated by some of its discoverers as a way of indefinitely deferring heat death, nuclear energy was soon developed into a far more proximate danger. And the tangible threat of imminent catastrophe which it posed rubbed off on other technologies.

None was more tainted than the computer. It may have been guilt by association: the computer played a vital role in the development of the nuclear arsenal. It may have been foreordained. The Enlightenment belief in rationality as humankind’s highest achievement and Darwin’s theory of evolution made the promise of superhuman rationality the possibility of evolutionary progress at humankind’s expense.

Artificial intelligence has come to loom large in the thought of the small but fascinating, and much written about, coterie of academics which has devoted itself to the consideration of existential risk over the past couple of decades. Indeed, it often appeared to be at the core of their concerns. A world which contained entities which think better and act quicker than humans and their institutions, and which had interests that were not aligned with those of humankind, would be a dangerous place.

It became common for people within and around the field to say that there was a “non-zero” chance of the development of superhuman AI s leading to human extinction. The remarkable boom in the capabilities of large language models ( LLM s), “foundational” models and related forms of “generative” AI has propelled these discussions of existential risk into the public imagination and the inboxes of ministers.

As the special Science section in this issue makes clear, the field’s progress is precipitate and its promise immense. That brings clear and present dangers which need addressing. But in the specific context of GPT-4 , the LLM du jour , and its generative ilk, talk of existential risks seems rather absurd. They produce prose, poetry and code; they generate images, sound and video; they make predictions based on patterns. It is easy to see that those capabilities bring with them a huge capacity for mischief. It is hard to imagine them underpinning “the power to control civilisation”, or to “replace us”, as hyperbolic critics warn.

But the lack of any “Minds that are to our minds as ours are to those of the beasts that perish, intellects vast and cool and unsympathetic [drawing] their plans against us”, to quote H.G. Wells, does not mean that the scale of the changes that AI may bring with it can be ignored or should be minimised. There is much more to life than the avoidance of extinction. A technology need not be world-ending to be world-changing.

The transition into a world filled with computer programs capable of human levels of conversation and language comprehension and superhuman powers of data assimilation and pattern recognition has just begun. The coming of ubiquitous pseudocognition along these lines could be a turning point in history even if the current pace of AI progress slackens (which it might) or fundamental developments have been tapped out (which feels unlikely). It can be expected to have implications not just for how people earn their livings and organise their lives, but also for how they think about their humanity.

For a sense of what may be on the way, consider three possible analogues, or precursors: the browser, the printing press and practice of psychoanalysis. One changed computers and the economy, one changed how people gained access and related to knowledge, and one changed how people understood themselves.

The humble web browser, introduced in the early 1990s as a way to share files across networks, changed the ways in which computers are used, the way in which the computer industry works and the way information is organised. Combined with the ability to link computers into networks, the browser became a window through which first files and then applications could be accessed wherever they might be located. The interface through which a user interacted with an application was separated from the application itself.

The power of the browser was immediately obvious. Fights over how hard users could be pushed towards a particular browser became a matter of high commercial drama. Almost any business with a web address could get funding, no matter what absurdity it promised. When boom turned to bust at the turn of the century there was a predictable backlash. But the fundamental separation of interface and application continued. Amazon, Meta ( née Facebook) and Alphabet ( née Google) rose to giddy heights by making the browser a conduit for goods, information and human connections. Who made the browsers became incidental; their role as a platform became fundamental.

The months since the release of Open AI ’s Chat GPT , a conversational interface now powered by GPT-4 , have seen an entrepreneurial explosion that makes the dotcom boom look sedate. For users, apps based on LLM s and similar software can be ludicrously easy to use; type a prompt and see a result. For developers it is not that much harder. “You can just open your laptop and write a few lines of code that interact with the model,” explains Ben Tossell, a British entrepreneur who publishes a newsletter about AI services.

And the LLM s are increasingly capable of helping with that coding, too. Having been “trained” not just on reams of text, but lots of code, they contain the building blocks of many possible programs; that lets them act as “co-pilots” for coders. Programmers on GitHub, an open-source coding site, are now using a GPT-4 -based co-pilot to produce nearly half their code.

There is no reason why this ability should not eventually allow LLM s to put code together on the fly, explains Kevin Scott, Microsoft’s chief technology officer. The capacity to translate from one language to another includes, in principle and increasingly in practice, the ability to translate from language to code. A prompt written in English can in principle spur the production of a program that fulfils its requirements. Where browsers detached the user interface from the software application, LLM s are likely to dissolve both categories. This could mark a fundamental shift in both the way people use computers and the business models within which they do so.

Every day I write the book

Code-as-a-service sounds like a game-changing plus. A similarly creative approach to accounts of the world is a minus. While browsers mainly provided a window on content and code produced by humans, LLM s generate their content themselves. When doing so they “hallucinate” (or as some prefer “confabulate”) in various ways. Some hallucinations are simply nonsense. Some, such as the incorporation of fictitious misdeeds to biographical sketches of living people, are both plausible and harmful. The hallucinations can be generated by contradictions in training sets and by LLM s being designed to produce coherence rather than truth. They create things which look like things in their training sets; they have no sense of a world beyond the texts and images on which they are trained.

In many applications a tendency to spout plausible lies is a bug. For some it may prove a feature. Deep fakes and fabricated videos which traduce politicians are only the beginning. Expect the models to be used to set up malicious influence networks on demand, complete with fake websites, Twitter bots, Facebook pages, TikTok feeds and much more. The supply of disinformation, Renée DiResta of the Stanford Internet Observatory has warned, “will soon be infinite”.

This threat to the very possibility of public debate may not be an existential one; but it is deeply troubling. It brings to mind the “Library of Babel”, a short story by Jorge Luis Borges. The library contains all the books that have ever been written, but also all the books which were never written, books that are wrong, books that are nonsense. Everything that matters is there, but it cannot be found because of everything else; the librarians are driven to madness and despair.

This fantasy has an obvious technological substrate. It takes the printing press’s ability to recombine a fixed set of symbols in an unlimited number of ways to its ultimate limit. And that provides another way of thinking about LLM s.

Dreams never end

The degree to which the modern world is unimaginable without printing makes any guidance its history might provide for speculation about LLM s at best partial, at worst misleading. Johannes Gutenberg’s development of movable type has been awarded responsibility, at some time or other, for almost every facet of life that grew up in the centuries which followed. It changed relations between God and man, man and woman, past and present. It allowed the mass distribution of opinions, the systematisation of bureaucracy, the accumulation of knowledge. It brought into being the notion of intellectual property and the possibility of its piracy. But that very breadth makes comparison almost unavoidable. As Bradford DeLong, an economic historian at the University of California, Berkeley puts it, “It’s the one real thing we have in which the price of creating information falls by an order of magnitude.”

Printed books made it possible for scholars to roam larger fields of knowledge than had ever before been possible. In that there is an obvious analogy for LLM s, which trained on a given corpus of knowledge can derive all manner of things from it. But there was more to the acquisition of books than mere knowledge.

Just over a century after Gutenberg’s press began its clattering Michel de Montaigne, a French aristocrat, had been able to amass a personal library of some 1,500 books—something unimaginable for an individual of any earlier European generation. The library gave him more than knowledge. It gave him friends. “When I am attacked by gloomy thoughts,” he wrote, “nothing helps me so much as running to my books. They quickly absorb me and banish the clouds from my mind.”

And the idea of the book gave him a way of being himself no one had previously explored: to put himself between covers. “Reader,” he warned in the preface to his Essays , “I myself am the matter of my book.” The mass production of books allowed them to become peculiarly personal; it was possible to write a book about nothing more, or less, than yourself, and the person that your reading of other books had made you. Books produced authors.

As a way of presenting knowledge, LLM s promise to take both the practical and personal side of books further, in some cases abolishing them altogether. An obvious application of the technology is to turn bodies of knowledge into subject matter for chatbots. Rather than reading a corpus of text, you will question an entity trained on it and get responses based on what the text says. Why turn pages when you can interrogate a work as a whole?

Everyone and everything now seems to be pursuing such fine-tuned models as ways of providing access to knowledge. Bloomberg, a media company, is working on Bloomberg GPT , a model for financial information. There are early versions of a Quran GPT and a Bible GPT ; can a puffer-jacketed Pontiff GPT be far behind? Meanwhile several startups are offering services that turn all the documents on a user’s hard disk, or in their bit of the cloud, into a resource for conversational consultation. Many early adopters are already using chatbots as sounding boards. “It’s like a knowledgeable colleague you can always talk to,” explains Jack Clark of Anthropic, an LLM- making startup.

It is easy to imagine such intermediaries having what would seem like personalities—not just generic ones, such as “avuncular tutor”, but specific ones which grow with time. They might come to be like their users: an externalised version of their inner voice. Or they might be like any other person whose online output is sufficient for a model to train on (intellectual-property concerns permitting). Researchers at the Australian Institute for Machine Learning have built an early version of such an assistant for Laurie Anderson, a composer and musician. It is trained in part on her work, and in part on that of her late husband Lou Reed.

Without you

Ms Anderson says she does not consider using the system as a way of collaborating with her dead partner. Others might succumb more readily to such an illusion. If some chatbots do become, to some extent, their user’s inner voice, then that voice will persist after death, should others wish to converse with it. That some people will leave chatbots of themselves behind when they die seems all but certain.

Such applications and implications call to mind Sigmund Freud’s classic essay on the Unheimliche , or uncanny. Freud takes as his starting point the idea that uncanniness stems from “doubts [as to] whether an apparently animate being is really alive; or conversely, whether a lifeless object might not be in fact animate”. They are the sort of doubts that those thinking about LLM s are hard put to avoid.

Though AI researchers can explain the mechanics of their creations, they are persistently unable to say what actually happens within them. “There’s no ‘ultimate theoretical reason’ why anything like this should work,” Stephen Wolfram, a computer scientist and the creator of Wolfram Alpha, a mathematical search engine, recently concluded in a remarkable (and lengthy) blog post trying to explain the models’ inner workings.

This raises two linked but mutually exclusive concerns: that AI ’s have some sort of internal working which scientists cannot yet perceive; or that it is possible to pass as human in the social world without any sort of inner understanding.

“These models are just representations of the distributions of words in texts that can be used to produce more words,” says Emily Bender, a professor at the University of Washington in Seattle. She is one of the authors of “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” a critique of LLM triumphalism. The models, she argues, have no real understanding. With no experience of real life or human communication they offer nothing more than the ability to parrot things they have heard in training, an ability which huge amounts of number crunching makes frequently appropriate and sometimes surprising, but which is nothing like thought. It is a view which is often pronounced in those who have come into the field through linguistics, as Dr Bender has.

For some in the LLM -building trade things are not that simple. Their models are hard to dismiss as “mere babblers”, in the words of Blaise Agüera y Arcas, the leader of a group at Alphabet which works on AI -powered products. He thinks the models have attributes which cannot really be distinguished from an ability to know what things actually mean. It can be seen, he suggests, in their ability reliably to choose the right meaning when translating phrases which are grammatically ambiguous, or to explain jokes.

If Dr Bender is right, then it can be argued that a broad range of behaviour that humans have come to think of as essentially human is not necessarily so. Uncanny “doubts [as to] whether an apparently animate being is really alive” are fully justified.

To accept that human-seeming LLM s are calculation, statistics and nothing more could influence how people think about themselves. Freud portrayed himself as continuing the trend begun by Copernicus—who removed humans from the centre of the universe—and Darwin—who removed them from a special and God-given status among the animals. Psychology’s contribution, as Freud saw it, lay in “endeavouring to prove to the ‘ego’ of each one of us that he is not even master in his own house”. LLM s could be argued to take the idea further still. At least one wing of Freud’s house becomes an unoccupied “smart home”; the lights go on and off automatically, the smart thermostat opens windows and lowers blinds, the roomba roombas around. No master needed at all.

Uncanny as that may all be, though, it would be wrong to think that many people will take this latest decentring to heart. As far as everyday life is concerned, humankind has proved pretty resilient to Copernicus, Darwin and Freud. People still believe in gods and souls and specialness with little obvious concern for countervailing science. They could well adapt quite easily to the pseudocognitive world, at least as far as philosophical qualms are concerned.

You do not have to buy Freud’s explanation of the unsettling effect of the uncanny in terms of the effort the mind expends on repressing childish animism to think that not worrying and going with the animistic flow will make a world populated with communicative pseudo-people a surprisingly comfortable one. People may simultaneously recognise that something is not alive and treat it as if it were. Some will take this too far, forming problematic attachments that Freud would have dubbed fetishistic. But only a few sensitive souls will find themselves left behind staring into an existential—but personal—abyss opened up by the possibility that their seeming thought is all for naught.

New gold dream

What if Mr Agüera y Arcas is right, though, and that which science deems lifeless is, in some cryptic, partial and emergent way, effectively animate? Then it will be time to do for AI some of what Freud thought he was doing for humans. Having realised that the conscious mind was not the whole show, Freud looked elsewhere for sources of desire that for good or ill drove behaviour. Very few people now subscribe to the specific Freudian explanations of human behaviour which followed. But the idea that there are reasons why people do things of which they are not conscious is part of the world’s mental furniture. The unconscious is probably not a great model for whatever it is that provides LLM s with an apparent sense of meaning or an approximation of agency. But the sense that there might be something below the AI surface which needs understanding may prove powerful.

Dr Bender and those who agree with her may take issue with such notions. But they might find that they lead to useful actions in the field of “ AI ethics”. Winkling out non-conscious biases acquired in the pre-verbal infancy of training; dealing with the contradictions behind hallucinations; regularising rogue desires: ideas from psychotherapy might be seen as helpful analogies for dealing with the pseudocognitive AI transition even by those who reject all notion of an AI mind. A concentration on the relationship between parents, or programmers, and their children could be welcome, too. What is it to bring up an AI well? What sort of upbringing should be forbidden? To what extent should the creators of AI s be held responsible for the harms done by their creation?

And human desires may need some inspection, too. Why are so many people eager for the sort of intimacy an LLM might provide? Why do many influential humans seem to think that, because evolution shows species can go extinct, theirs is quite likely to do so at its own hand, or that of its successor? And where is the determination to turn a superhuman rationality into something which does not merely stir up the economy, but changes history for the better? ■

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This article appeared in the Essay section of the print edition under the headline “THE AGE OF PSEUDOCOGNITION”

From the April 22nd 2023 edition

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