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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”

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How artificial intelligence is transforming the world

Subscribe to the center for technology innovation newsletter, darrell m. west and darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies john r. allen john r. allen.

April 24, 2018

Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making—and already it is transforming every walk of life. In this report, Darrell West and John Allen discuss AI’s application across a variety of sectors, address issues in its development, and offer recommendations for getting the most out of AI while still protecting important human values.

Table of Contents I. Qualities of artificial intelligence II. Applications in diverse sectors III. Policy, regulatory, and ethical issues IV. Recommendations V. Conclusion

• 49 min read

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. 1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. 2

In order to maximize AI benefits, we recommend nine steps for going forward:

• Encourage greater data access for researchers without compromising users’ personal privacy,
• invest more government funding in unclassified AI research,
• promote new models of digital education and AI workforce development so employees have the skills needed in the 21 st -century economy,
• create a federal AI advisory committee to make policy recommendations,
• engage with state and local officials so they enact effective policies,
• regulate broad AI principles rather than specific algorithms,
• take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,
• maintain mechanisms for human oversight and control, and
• penalize malicious AI behavior and promote cybersecurity.

Qualities of artificial intelligence

Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.” 3  According to researchers Shubhendu and Vijay, these software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up. 4 As such, they operate in an intentional, intelligent, and adaptive manner.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. 5 Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

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Applications in diverse sectors

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. 6

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.” 7 That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.” 8 Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion. 9 According to observers in that sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.” 10 In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.” 11 These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decisionmaking. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions. 12 Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. 13 That dramatically increases storage capacity and decreases processing times.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. 14

National security

AI plays a substantial role in national defense. Through its Project Maven, the American military is deploying AI “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.” 15 According to Deputy Secretary of Defense Patrick Shanahan, the goal of emerging technologies in this area is “to meet our warfighters’ needs and to increase [the] speed and agility [of] technology development and procurement.” 16

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time—if not eventually in real time—thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position “humans in the loop” in a hyperwar scenario will ultimately dictate the West’s capacity to be competitive in this new form of conflict. 17

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a “thinking” defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating “WannaCry” and “Petya” viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China’s State Council issued a plan for the country to “build a domestic industry worth almost $150 billion” by 2030. 18 As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs. 19 The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well. 20

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that “detects lymph nodes in the human body in Computer Tomography (CT) images.” 21 According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they “predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital.” 22

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven “Strategic Subject List” that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. 23

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. 24

However, critics worry that AI algorithms represent “a secret system to punish citizens for crimes they haven’t yet committed. The risk scores have been used numerous times to guide large-scale roundups.” 25 The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Despite these concerns, other countries are moving ahead with rapid deployment in this area. In China, for example, companies already have “considerable resources and access to voices, faces and other biometric data in vast quantities, which would help them develop their technologies.” 26 New technologies make it possible to match images and voices with other types of information, and to use AI on these combined data sets to improve law enforcement and national security. Through its “Sharp Eyes” program, Chinese law enforcement is matching video images, social media activity, online purchases, travel records, and personal identity into a “police cloud.” This integrated database enables authorities to keep track of criminals, potential law-breakers, and terrorists. 27 Put differently, China has become the world’s leading AI-powered surveillance state.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. 28

Autonomous vehicles—cars, trucks, buses, and drone delivery systems—use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. 29

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key—not the physical car or truck itself.

Since these cameras and sensors compile a huge amount of information and need to process it instantly to avoid the car in the next lane, autonomous vehicles require high-performance computing, advanced algorithms, and deep learning systems to adapt to new scenarios. This means that software is the key, not the physical car or truck itself. 30 Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. 31

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services—such as Uber and Lyft in the United States, Daimler’s Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China—demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. 32

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. 33 Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call—whether a patient can be treated on-site or needs to be taken to the hospital—by taking into account several factors, such as the type of call, location, weather, and similar calls. 34

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a “City Hall To Go” that makes sure underserved communities receive needed public services. It also has deployed “cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots.” San Francisco has certified 203 buildings as meeting LEED sustainability standards. 35

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are “smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement.” 36

Policy, regulatory, and ethical issues

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm? 37

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a “data-friendly ecosystem with unified standards and cross-platform sharing.” AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are “accessible for exploration” in the research community is a prerequisite for successful AI development. 38

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China. 39

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

In some instances, certain AI systems are thought to have enabled discriminatory or biased practices. 40 For example, Airbnb has been accused of having homeowners on its platform who discriminate against racial minorities. A research project undertaken by the Harvard Business School found that “Airbnb users with distinctly African American names were roughly 16 percent less likely to be accepted as guests than those with distinctly white names.” 41

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person’s face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, “If your facial recognition data contains mostly Caucasian faces, that’s what your program will learn to recognize.” 42 Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody’s risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have. We don’t have to bring the structural inequalities of the past into the future we create. 43

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. 44

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans–based Bricolage Academy “gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school’s broad geographic area.” 45 Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don’t like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. 46

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have “the right to opt out of personally tailored ads” and “can contest ‘legal or similarly significant’ decisions made by algorithms and appeal for human intervention” in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the “black box” operates. 47

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation’s facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. 48 The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm “requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service.” By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. 49 But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

Recommendations

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. 50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. 51 As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its “Trends” site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. 52 That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. As noted by Ian Buck, the vice president of NVIDIA, “Data is the fuel that drives the AI engine. The federal government has access to vast sources of information. Opening access to that data will help us get insights that will transform the U.S. economy.” 53 Through its Data.gov portal, the federal government already has put over 230,000 data sets into the public domain, and this has propelled innovation and aided improvements in AI and data analytic technologies. 54 The private sector also needs to facilitate research data access so that society can achieve the full benefits of artificial intelligence.

Increase government investment in AI

According to Greg Brockman, the co-founder of OpenAI, the U.S. federal government invests only $1.1 billion in non-classified AI technology. 55 That is far lower than the amount being spent by China or other leading nations in this area of research. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics. Higher investment is likely to pay for itself many times over in economic and social benefits. 56

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. 57 The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be “big picture” thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. 58 As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the “Future of Artificial Intelligence Act,” a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a “climate of investment and innovation to ensure the global competitiveness of the United States,” “optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce,” “support the unbiased development and application of artificial intelligence,” and “protect the privacy rights of individuals.” 59

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

States and localities also are taking action on AI. For example, the New York City Council unanimously passed a bill that directed the mayor to form a taskforce that would “monitor the fairness and validity of algorithms used by municipal agencies.” 60 The city employs algorithms to “determine if a lower bail will be assigned to an indigent defendant, where firehouses are established, student placement for public schools, assessing teacher performance, identifying Medicaid fraud and determine where crime will happen next.” 61

According to the legislation’s developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won’t go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. 62 It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The European Union has taken a restrictive stance on these issues of data collection and analysis. 63 It has rules limiting the ability of companies from collecting data on road conditions and mapping street views. Because many of these countries worry that people’s personal information in unencrypted Wi-Fi networks are swept up in overall data collection, the EU has fined technology firms, demanded copies of data, and placed limits on the material collected. 64 This has made it more difficult for technology companies operating there to develop the high-definition maps required for autonomous vehicles.

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, “Regulations prohibit any automated decision that ‘significantly affects’ EU citizens. This includes techniques that evaluates a person’s ‘performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.’” 65 In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the “black boxes” and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, “The key problem confronting predictive analytics is really transparency. We’re in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing.” 66

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning “cyberbullying, stock manipulation or terrorist threats,” as well as “entrap[ping] people into committing crimes.” Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, “An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.” 67 His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for “nondeception” and “honesty,” according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. 68

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a “voting-based system” that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. 69 This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

As with any emerging technology, it is important to discourage malicious treatment designed to trick software or use it for undesirable ends. 70 This is especially important given the dual-use aspects of AI, where the same tool can be used for beneficial or malicious purposes. The malevolent use of AI exposes individuals and organizations to unnecessary risks and undermines the virtues of the emerging technology. This includes behaviors such as hacking, manipulating algorithms, compromising privacy and confidentiality, or stealing identities. Efforts to hijack AI in order to solicit confidential information should be seriously penalized as a way to deter such actions. 71

In a rapidly changing world with many entities having advanced computing capabilities, there needs to be serious attention devoted to cybersecurity. Countries have to be careful to safeguard their own systems and keep other nations from damaging their security. 72 According to the U.S. Department of Homeland Security, a major American bank receives around 11 million calls a week at its service center. In order to protect its telephony from denial of service attacks, it uses a “machine learning-based policy engine [that] blocks more than 120,000 calls per month based on voice firewall policies including harassing callers, robocalls and potential fraudulent calls.” 73 This represents a way in which machine learning can help defend technology systems from malevolent attacks.

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. 74 Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.

Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. 

John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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• Congress.gov, “H.R. 4625 FUTURE of Artificial Intelligence Act of 2017,” December 12, 2017.
• Elizabeth Zima, “Could New York City’s AI Transparency Bill Be a Model for the Country?” Government Technology , January 4, 2018.
• Julia Powles, “New York City’s Bold, Flawed Attempt to Make Algorithms Accountable,” New Yorker , December 20, 2017.
• Sheera Frenkel, “Tech Giants Brace for Europe’s New Data Privacy Rules,” New York Times , January 28, 2018.
• Claire Miller and Kevin O’Brien, “Germany’s Complicated Relationship with Google Street View,” New York Times , April 23, 2013.
• Cade Metz, “Artificial Intelligence is Setting Up the Internet for a Huge Clash with Europe,” Wired , July 11, 2016.
• Eric Siegel, “Predictive Analytics Interview Series: Andrew Burt,” Predictive Analytics Times , June 14, 2017.
• Oren Etzioni, “How to Regulate Artificial Intelligence,” New York Times , September 1, 2017.
• “Ethical Considerations in Artificial Intelligence and Autonomous Systems,” unpublished paper. IEEE Global Initiative, 2018.
• Ritesh Noothigattu, Snehalkumar Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, and Ariel Procaccia, “A Voting-Based System for Ethical Decision Making,” Computers and Society , September 20, 2017 (www.media.mit.edu/publications/a-voting-based-system-for-ethical-decision-making/).
• Miles Brundage, et al., “The Malicious Use of Artificial Intelligence,” University of Oxford unpublished paper, February 2018.
• John Markoff, “As Artificial Intelligence Evolves, So Does Its Criminal Potential,” New York Times, October 24, 2016, p. B3.
• Economist , “The Challenger: Technopolitics,” March 17, 2018.
• Douglas Maughan, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” March 7, 2018.
• Levi Tillemann and Colin McCormick, “Roadmapping a U.S.-German Agenda for Artificial Intelligence Policy,” New American Foundation, March 2017.

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How Technology Has Changed Our Lives Essay | Technology Has Changed Our Life Positively, Impact of Technology in Day To Day Life

December 3, 2021 by Prasanna

How Technology Has Changed Our Lives Essay: Technological innovations, applications, and advancements have impacted human civilization through ages that gradually transformed our lives. Technology has taken a key role for societies to thrive and evolve, while at the same time the structure and aspirations of human societies have been modified based on how they are being influenced by technology. As technological systems reflect the very essence of a population’s needs, human societies and their technology has become inseparable from one another. Our lives move around technology that results in the development of further innovations and applications to meet the needs of society.

You can also find more  Essay Writing  articles on events, persons, sports, technology and many more.

Long Essay on How Technology Has Changed Our Lives

Technology affecting the way of life

We all know that necessity is the mother of invention; so all invented technology came into place to meet the needs of people. Once developed, it changed our lives and behaviors in society, which may result in new ways of life. The people may simply use the technology to survive, or it may help the society to evolve and attain progress by creating a greater level of efficiency. At the same time, technological developments may even change the lifestyle and habits of people to the point of affecting human adaptive mechanisms and thus facilitating further technological evolution. Throughout the years, technology has kept providing us with amazing resources that can bring a vast difference in our everyday lives.

Every human society in the modern world has experienced technology as a utility and means of living more efficient lives. The infiltration of technology into our lives has been gradual and sometimes we may not even realize the extent to which technology has become part of our every waking moment. From the tiny to the enormous, every application of modern technology is opening a new world to us.

Technology is everywhere

The field of communication has seen very quick and significant changes in technology. Communication is immediate regardless of if a person is right there or across the globe. The education system has adapted new technology where students have the freedom to learn at any time and location of their choice through online facilities. The need for comfort and convenience has always been a strong motivator for the emergence of new technologies.

Access to any information is just a matter of a few clicks on the devices. The definition of entertainment has taken a new form with the latest technology. There has been a drastic change in our personal lives as we are open to numerous choices but we need to keep pace with their rapidly changing profiles. The most noticeable change in our lives has been the introduction of social media. This culture of getting involved in social networking through online mode has developed too fast. It allows a virtual entry into the lives of others in real-time whether they’re friends, followers, or celebrities.

Technology controlling us

Technology has made our lives faster and convenient by changing the way we do everything. As we move forward, technology accompanies us. We are surrounded by technology and become dependent on it. As we look around us and realize how technology has positively changed our lives, we must also remember how technology is controlling our lives by influencing our thought processes, ideas, and preferences. The big question is, whether we are using technology or being used by technology?

Too much dependence on technology has restricted the scope to flourish our creative and intellectual abilities. We are shifting to quantity from quality in terms of time, emotions, and relations. Our lives are getting trapped within technology and we feel helpless without the support. Technology is like an elevator that can take us to new heights as we desire, but we have to be ready to use the staircase as well in case it fails.

Short Essay on How Technology Has Changed Our Lives

Introduction

Technology has changed our lives and has made the world smaller with faster communication, instant information access, and online interactions. Technological advancements have brought everything to our fingertips, making life more enjoyable and convenient. Today, if you want to find something out, it only requires a couple of clicks on the internet. There is literally an app for anything, which renders instant and relevant information. From learning, traveling, dining to almost anything that you can think of is accessible through app technology.

Technology and Future

Technology has revolutionized our daily lives by giving us access to amazing tools and resources. Modern technology has paved the way for multi-functional devices which are faster, more portable, high-powered, and user-friendly. All these revolutions of technology have made our lives better, easier, faster, and more fulfilling. Technology has changed how we can entertain ourselves, interact with each other, and consume all types of information. There are so many new technologies evolving day by day that it seems overwhelming to adapt and keep track of. There is no doubt that the future of technology will continue to revolutionize our lives. In the coming days, driverless cars may be the new trend and robots will replace humans in factories.

The Online World

The latest technology trend has driven our daily lives centered on online activities more than ever before. Almost every aspect of our daily routines can be catered to online today, so it seems inevitable that our time spent online will only increase. Online accessibility to anything of our choice gives us a satisfactory level of convenience. It has changed our habits and preferences as well. But it has also made us vulnerable. Every digital footprint we make online is recorded and can be used by cybercriminals in unethical ways again by using some latest technology. So we have to be careful and updated while getting adapted to new technology.

FAQ’s on How Technology Has Changed Our Lives Essay

Question 1. Which app has helped us reach out somewhere more conveniently?

Answer: If we want to know how to reach somewhere, an app like Google Maps helps us get thereby giving the best route complete with directions, as well as satellite imaging.

Question 2. What is the impact of technology in the communication arena?

Answer: There are various online social networking sites that give us a chance to meet the rest of the world and make communication direct on this platform. It not only has changed the process of communication but also the way to build relationships.

Question 3 . How does the online mode of learning and education help us?

Answer: Education has now migrated from the classroom to the online platform and become accessible from any part of the world.

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The Impact of Digital Technologies

Technologies can help make our world fairer, more peaceful, and more just. Digital advances can support and accelerate achievement of each of the 17 Sustainable Development Goals – from ending extreme poverty to reducing maternal and infant mortality, promoting sustainable farming and decent work, and achieving universal literacy. But technologies can also threaten privacy, erode security and fuel inequality. They have implications for human rights and human agency. Like generations before, we – governments, businesses and individuals – have a choice to make in how we harness and manage new technologies.

A DIGITAL FUTURE FOR ALL?

Digital technologies have advanced more rapidly than any innovation in our history – reaching around 50 per cent of the developing world’s population in only two decades and transforming societies. By enhancing connectivity, financial inclusion, access to trade and public services, technology can be a great equaliser.

In the health sector, for instance, AI-enabled frontier technologies are helping to save lives, diagnose diseases and extend life expectancy. In education, virtual learning environments and distance learning have opened up programmes to students who would otherwise be excluded. Public services are also becoming more accessible and accountable through blockchain-powered systems, and less bureaucratically burdensome as a result of AI assistance.Big data can also support more responsive and accurate policies and programmes.

However, those yet to be connected remain cut off from the benefits of this new era and remain further behind. Many of the people left behind are women, the elderly, persons with disabilities or from ethnic or linguistic minorities, indigenous groups and residents of poor or remote areas. The pace of connectivity is slowing, even reversing, among some constituencies. For example, globally, the proportion of women using the internet is 12 per cent lower than that of men. While this gap narrowed in most regions between 2013 and 2017, it widened in the least developed countries from 30 per cent to 33 per cent.

The use of algorithms can replicate and even amplify human and systemic bias where they function on the basis of data which is not adequately diverse. Lack of diversity in the technology sector can mean that this challenge is not adequately addressed.

THE FUTURE OF WORK

Throughout history, technological revolutions have changed the labour force: creating new forms and patterns of work, making others obsolete, and leading to wider societal changes. This current wave of change is likely to have profound impacts. For example, the International Labour Organization estimates that the shift to a greener economy could create 24 million new jobs globally by 2030 through the adoption of sustainable practices in the energy sector, the use of electric vehicles and increasing energy efficiency in existing and future buildings.

Meanwhile, reports by groups such as McKinsey suggest that 800 million people could lose their jobs to automation by 2030 , while polls reveal that the majority of all employees worry that they do not have the necessary training or skills to get a well-paid job.

There is broad agreement that managing these trends will require changes in our approach to education, for instance, by placing more emphasis on science, technology, engineering, and maths; by teaching soft skills, and resilience; and by ensuring that people can re-skill and up-skill throughout their lifetimes. Unpaid work, for example childcare and elderly care in the home, will need to be better supported, especially as with the shifting age profile of global populations, the demands on these tasks are likely to increase.

THE FUTURE OF DATA

Today, digital technologies such as data pooling and AI are used to track and diagnose issues in agriculture, health, and the environment, or to perform daily tasks such as navigating traffic or paying a bill. They can be used to defend and exercise human rights – but they can also be used to violate them, for example, by monitoring our movements, purchases, conversations and behaviours. Governments and businesses increasingly have the tools to mine and exploit data for financial and other purposes.

However, personal data would become an asset to a person, if there were a formula for better regulation of personal data ownership. Data-powered technology has the potential to empower individuals, improve human welfare, and promote universal rights, depending on the type of protections put in place.

THE FUTURE OF SOCIAL MEDIA

Social media connects almost half of the entire global population . It enables people to make their voices heard and to talk to people across the world in real time. However, it can also reinforce prejudices and sow discord, by giving hate speech and misinformation a platform, or by amplifying echo chambers.

In this way, social media algorithms can fuel the fragmentation of societies around the world. And yet they also have the potential to do the opposite.

THE FUTURE OF CYBERSPACE

How to manage these developments is the subject of much discussion – nationally and internationally – at a time when geopolitical tensions are on the rise. The UN Secretary-General has warned of a ‘great fracture’ between world powers, each with their own internet and AI strategy, as well as dominant currency, trade and financial rules and contradictory geopolitical and military views. Such a divide could establish a digital Berlin Wall. Increasingly, digital cooperation between states – and a universal cyberspace that reflects global standards for peace and security, human rights and sustainable development – is seen as crucial to ensuring a united world. A ‘global commitment for digital cooperation’ is a key recommendation by the Secretary-General’s High-level Panel on Digital Cooperation .

FOR MORE INFORMATION

The Sustainable Development Goals

The Age of Digital Interdependence: Report of the UN Secretary-General’s High-level Panel on Digital Cooperation

ILO | Global Commission on the Future of Work

Secretary General’s Address to the 74th Session of the UN General Assembly

Secretary General’s Strategy on New Technology

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Essay on How Technology Changed Our Lives

Students are often asked to write an essay on How Technology Changed Our Lives in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on How Technology Changed Our Lives

The advent of technology.

Technology has revolutionized our lives in many ways. It has made tasks easier, faster, and more efficient. We use technology in our daily activities, from cooking to communicating.

Communication and Technology

Technology has drastically changed the way we communicate. With the advent of smartphones and the internet, we can now connect with anyone, anywhere, anytime.

Education and Technology

Technology has also transformed education. It has made learning more interactive and accessible. With online classes, students can learn from home.

Healthcare and Technology

In healthcare, technology has improved diagnosis and treatment. It has made healthcare more effective and convenient.

250 Words Essay on How Technology Changed Our Lives

Technology has revolutionized our world, transforming every aspect of our lives. It has brought about a digital revolution, making tasks easier, faster, and more efficient. From communication to transportation, health to education, technology has permeated every sphere of human life.

Impact on Communication

The advent of smartphones and the internet has revolutionized communication. We can now connect with anyone, anywhere, anytime, breaking geographical boundaries. Social media platforms, video conferencing, and instant messaging apps have not only made communication instantaneous but also fostered global connections and collaborations.

Transformation in Transportation

Technology has also drastically changed transportation. With GPS technology, navigation has become easier and more precise. Electric cars and autonomous vehicles are on the rise, promising a future of sustainable and self-driving transportation.

Healthcare Advancements

In healthcare, technology has brought about advancements like telemedicine, wearable devices, and AI-driven diagnostics. These innovations have improved patient care, made health monitoring easier, and increased the accuracy of diagnoses.

Educational Innovations

The education sector has also seen significant changes with e-learning platforms, virtual classrooms, and digital resources. This has made education more accessible, interactive, and personalized.

500 Words Essay on How Technology Changed Our Lives

The advent of technology has revolutionized human life, transforming the world into a global village. It has impacted every facet of our existence, from communication to transportation, health to education, and entertainment to business.

Revolutionizing Communication

One of the most profound changes brought about by technology is in the field of communication. The invention of the internet and smartphones has made it possible to connect with anyone, anywhere, at any time. Social media platforms, emails, and video calls have removed geographical barriers, fostering global collaboration and understanding.

Transforming Transportation

Advancements in health and medicine.

In the field of health and medicine, technology has been a game-changer. Advanced diagnostic tools, telemedicine, robotic surgeries, and personalized medicine have improved patient care and outcomes. Additionally, wearable technology and health apps have empowered individuals to take charge of their health.

Revamping Education

Education is another sector where technology has left an indelible mark. Online learning platforms, digital classrooms, and educational apps have democratized education, making it accessible to all. The recent pandemic has underscored the importance of technology in education, with schools and universities worldwide transitioning to remote learning.

Entertainment and Leisure

Impacting business and economy.

Lastly, technology has significantly influenced business operations and the global economy. E-commerce, digital marketing, and remote work have redefined traditional business models, promoting efficiency and inclusivity.

In conclusion, technology has dramatically altered our lives, reshaping the way we communicate, travel, learn, stay healthy, entertain ourselves, and conduct business. While it presents challenges, such as privacy concerns and digital divide, the benefits it offers are immense. As we move forward, it is essential to harness technology responsibly and ethically, ensuring it serves as a tool for progress and inclusivity.

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Tech at the edge: Trends reshaping the future of IT and business

It is easy to become numb to the onslaught of new technologies hitting the market, each with its own promise of changing (more often “revolutionizing”) the business world. But our analysis of some of the more meaningful tech trends lays out a convincing case that something significant is happening. 1 Michael Chui, Roger Roberts, and Lareina Yee, “ McKinsey Technology Trends Outlook 2022 ,” McKinsey, August 24, 2022.

These tech trends are generally accelerating the primary characteristics that have defined the digital era: granularity, speed, and scale. But it’s the magnitude of these changes—in computing power, bandwidth, and analytical sophistication—that is opening the door to new innovations, businesses, and business models.

The emergence of cloud and 5G , for example, exponentially increases compute power and network speeds that can enable greater innovation. Developments in the metaverse of augmented and virtual reality open the doors to virtual R&D via digital twins , for example, and immersive learning. Advances in AI, machine learning, and software 2.0 (machine-written code) bring a range of new services and products, from autonomous vehicles to connected homes, well within reach.

Much ink has been spilled on identifying tech trends, but less attention has been paid to the implications of those changes. To help understand how management will need to adapt in the face of these technology trends in the next three to five years, we spoke to business leaders and leading thinkers on the topic. We weren’t looking for prognostications; we wanted to explore realistic scenarios, their implications, and what senior executives might do to get ready.

The discussions pinpointed some broad, interrelated shifts, such as how technology’s radically increasing power is exerting a centrifugal force on the organization, pushing innovation to expert networks at the edges of the company; how the pace and proliferation of these innovations calls for radical new approaches to continuous learning built around skills deployed at points of need; how these democratizing forces mean that IT can no longer act as a centralized controller of technology deployment and operations but instead needs to become a master enabler and influencer; and how these new technologies are creating more data about, and touchpoints with, customers, which is reshaping the boundaries of trust and requiring a much broader understanding of a company’s security responsibilities.

1. Innovation at the edge

Key tech trends.

We estimate that 70 percent of companies will employ hybrid or multicloud management technologies, tools, and processes . 2 “ The top trends in tech ,” McKinsey, June 15, 2021. At the same time, 5G will deliver network speeds that are about ten times faster than current speeds on 4G LTE networks, 3 Irina Ivanova, “What consumers need to know about this week’s AT&T–Verizon 5G rollout,” CBS News, January 20, 2022. with expectations of speeds that are up to 100 times faster with 40 times faster latency. 4 “5G speed: 5G vs. 4G performance compared,” Tom’s Guide, June 1, 2021. By 2024, more than 50 percent of user touches will be augmented by AI-driven speech, written word, or computer-vision algorithms , 5 “ The top trends in tech ,” June 15, 2021. while global data creation is projected to grow to more than 180 zettabytes by 2025, up from 64.2 zettabytes in 2020. 6 “Amount of data created, consumed, and stored 2010–2025,” Statista Research Department, May 23, 2022. The low-code development platform market‘s compound annual growth rate (CAGR) is projected at about 30 percent through 2030. 7 “Global $187 billion low-code development platform market to 2030,” GlobeNewswire, November 10, 2020.

Shift: Innovation develops around personal networks of experts at the porous edge of the organization and is supported by capabilities that scale the benefits across the business.

These technologies promise access to virtually unlimited compute power and massive data sets, as well as a huge leap in bandwidth at low cost, making it cheaper and easier to test, launch, and scale innovations quickly. The resulting acceleration in innovation will mean that companies can expect more disruptions from more sources. Centralized strategic and innovation functions cannot hope to keep pace on their own. Companies will need to be much more involved in networks outside their organizations to spot, invest in, and even acquire promising opportunities.

Corporate venture-capital (VC) funds with centralized teams have looked to find and fund innovation, but their track record has been spotty, often because the teams lack the requisite skills and are simply too far removed from the constantly evolving needs of individual business units. Instead, companies will need to figure out how to tap their front lines, particularly business domain experts and technologists, to enable them to act, in effect, as the business’s VC arm. That’s because the people who are writing code and building solutions are often well plugged into strong external networks in their fields and have the expertise to evaluate new developments. One pharma company, for example, taps its own expert researchers in various fields, such as gene expression, who know well the people outside the company who are leaders in the field.

While companies will need to create incentives and opportunities for engineers to build up and engage with their networks, the key focus must be on empowering teams so they can spend their allocated budget as they see fit—for example, experimenting and failing without penalty (within boundaries) and deciding on technologies to meet their goals (within prescribed guidelines).

The IT organization of the future can play an important role in building up a scaling capability to make that innovation work for the business, something that has traditionally been a challenge. Individual developers or small teams working fast don’t tend to naturally think about how to scale an application. That issue is likely to be exacerbated as nontechnical users working in pockets across organizations use low-code/no-code (LC/NC) applications to design and build programs with point-and-click or pull-down-menu interfaces.

One pharma company has taken this idea to heart by giving local business units the flexibility to run with a nonstandard idea when it has proven to be better than what the company is already doing. In return for that flexibility, the business unit must commit to helping the rest of the organization use the new idea, and IT builds it into the company’s standards.

In considering how this scaling capability might work, companies could, for example, assign advanced developers to “productize” applications by refactoring code so they can scale. IT leadership can provide tools and platforms, reusable-code libraries that are easily accessible, and flexible, standards-based architecture so that innovations can be scaled across the business more easily.

Questions for leadership

• What incentives will best encourage engineers and domain experts to develop, maintain, and tap into their networks?
• What processes are in place for tracking and managing VC activity at the edge?
• What capabilities do you need to identify innovation opportunities and “industrialize” the best ones so they can be shared across the organization?

For more on how to empower workers at the edge, see “ Tech companies innovate at the edge. Legacy companies can too ,” in Harvard Business Review.

Would you like to learn more about McKinsey Digital ?

2. a perpetual-learning culture.

Advances in AI, machine learning, robotics, and other technologies have increased the pace of change tenfold . By 2025, we estimate that 50 billion devices will be connected to the Industrial Internet of Things (IIoT), while 70 percent of manufacturers are expected to be using digital twins regularly (by 2022). 8 “ The top trends in tech ,” June 15, 2021. Some 70 percent of new applications will use LC/NC technologies by 2025, up from less than 25 percent in 2020. 9 “Gartner says cloud will be the centerpiece of new digital experiences,” Gartner, November 10, 2021. The global metaverse revenue opportunity could approach $800 billion in 2024, up from about $500 billion in 2020. 10 Bloomberg Intelligence, “Metaverse may be $800 billion market, next tech platform,” Bloomberg, December 1, 2021. This proliferation of technological innovations means we can expect to experience more progress in the next decade than in the past 100 years combined, according to entrepreneur and futurist Peter Diamandis. 11 Peter Diamandis and Steven Kotler, The Future Is Faster than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives , New York: Simon & Schuster, 2020.

Shift: Tech literacy becomes core to every role, requiring learning to be continuous and built at the level of individual skills that are deployed at the point of need.

With the pace and proliferation of technologies pushing innovation to the edge of the organization, businesses need to be ready to incorporate the most promising options from across the front lines. This will create huge opportunities, but only for those companies that develop true tech intelligence through a perpetual-learning culture. The cornerstone of this effort includes training all levels of personnel, from “citizen developers” working with easy-to-use LC/NC tools or in entirely new environments such as the metaverse, to full-stack developers and engineers, who will need to continually evolve their skills to keep up with changing technologies. We’re already seeing situations where poorly trained employees use LC/NC to churn out suboptimal products.

While there will always be a need for more formalized paths for foundational learning, we anticipate an acceleration in the shift from teaching curricula periodically to continuous learning that can deliver varying technical skills across the entire organization. In practice, that will mean orienting employee development around delivering skills. This requires breaking down a capability into its smallest sets of composite skills. One large tech company, for example, created 146,000 skills data points for the 1,200 technical skills it was assessing.

The key point is that these skills “snippets”—such as a block of code or a video of a specific negotiating tactic—need to be integrated into the workflow so that they’re delivered when needed. This might be called a “LearnOps” approach, where learning is built into the operations. This integration mentality is established at Netflix, where data scientists partner directly with product managers, engineering teams, and other business units to design, execute, and learn from experiments. 12 Netflix Technology Blog , “Experimentation is a major focus of data science across Netflix,” blog entry by Martin Tingley et al., January 11, 2022.

As important as being able to deploy learning is building a learning culture by making continuous learning expected and easy to do. The way top engineers learn can be instructive. This is a community that is highly aware of the need to keep their skills up to date. They have ingrained habits of sharing code, and they gravitate to projects where they can learn. One advantage of using open source, for example, is the built-in community that constantly updates and reviews code. In the same spirit, we’re seeing companies budget extra time to allow people to try new tools or technologies when they’re building a product. Other companies are budgeting for “learning buffers” to allow for setbacks in product development that teams can learn from. 13 “ The big boost: How incumbents successfully scale their new businesses ,” McKinsey, August 27, 2020.

Netflix, which makes broad, open, and deliberate information sharing a core value, built the Netflix experimentation platform as an internal product that acts as a repository of solutions for future teams to reuse. It has a product manager and innovation road map, with the goal of making experimentation a simple and integrated part of the product life cycle. 14 Netflix Technology Blog , “Netflix: A culture of learning,” blog entry by Martin Tingley et al., January 25, 2022.

To support this kind of continuous learning and experimentation, companies will need to accept mistakes. The art will be in limiting the impact of potentially costly mistakes, such as the loss or misuse of customer data. IT will need to architect protocols, incentives, and systems to encourage good behaviors and reduce bad ones. Many companies are beginning to adopt practices such as automated testing to keep mistakes from happening in the first place ; creating spaces where mistakes won’t affect other applications or systems, such as isolation zones in cloud environments ; and building in resiliency protocols.

• Do you have a list of the most important skills your business needs?
• What is the minimum level of learning needed for advanced users of analytics and manipulators of data?
• How do you track what people are learning and whether that learning is effective and translating into better performance?

3. IT as a service

It is estimated that the global cloud microservices platform market will generate $4.2 billion in revenue by 2028, up from $952 million in 2020. 15 Cloud microservice platform market report , Research Dive, November 2021. GitHub has more than 200 million code repositories and expects 100 million software developers by 2025. 16 Paul Krill, “GitHub expects more than 100 million software developers by 2025,” InfoWorld, December 3, 2020. Nearly 90 percent of developers already use APIs. 17 Christina Voskoglou, “APIs have taken over software development,” Nordic APIs, October 27, 2020. Software 2.0 creates new ways of writing software and reduces complexity. Software sourced by companies from cloud-service platforms, open repositories, and software as a service (SaaS) is growing at a CAGR of 27.5 percent from 2021 to 2028. 18 Software as a service (SaaS) market, 2021–2028 , Fortune Business Insights, January 2022.

Shift: IT becomes the enabler of product innovation by serving small, interoperable blocks of code.

When innovation is pushed to the edge and a perpetual-learning culture permeates an organization, the role of IT shifts dramatically. IT can’t support this dynamic environment by sticking to its traditional role as a controlling entity managing technology at the center. The premium will now be on IT’s ability to enable innovation, requiring a shift in its traditional role as protector of big tech assets to a purveyor of small blocks of code. The gold standard of IT effectiveness will be its ability to help people stitch together snippets of code into a useful product.

We are already seeing what that might look like. Employees at G&J Pepsi-Cola Bottlers with little to no experience at software development created an app that examines images of a store shelf to identify the number and type of bottles on it, then automatically restocks it based on historic trends. 19 Adam Burden, “Low code/no code could reshape business innovation,” VentureBeat, February 5, 2022. One pharmaceutical company grew its low-code platform base from eight users to 1,400 in just one year . Business users outside of IT are now building applications with thousands of monthly sessions. 20 Shivam Srivastava, Kartik Trehan, Dilip Wagle, and Jane Wang, “ Developer Velocity: How software excellence fuels business performance ,” McKinsey, April 20, 2020. Companies that empower “citizen developers” score 33 percent higher on innovation compared with bottom-quartile companies that don’t provide that level of support, according to a McKinsey survey. 21 Shivam Srivastava, Kartik Trehan, Dilip Wagle, and Jane Wang, “ Developer Velocity: How software excellence fuels business performance ,” McKinsey, April 20, 2020.

These developments point toward much more of a “buffet” approach to technology, where IT builds useful blocks of reusable code, sometimes assembles them into specific products, and makes them available through a user-friendly cataloging system for the business to use to create the products it needs. IT provides guiderails, such as API standards and directives on the environments in which the code might be most useful; protects the most sensitive information, such as customer data and financial records; and tracks their adoption. This tracking capability will become particularly crucial as bots, AI, algorithms, and APIs proliferate. Transparency isn’t sufficient. IT will need to make sense of all the activity through advanced tech performance and management capabilities and the development of new roles, such as data diagnosticians and bot managers.

This IT-as-a-service approach puts the product at the center of the operating model, requiring a commitment to organizing IT around product management . Some companies have been moving in this direction. But reaching the scale needed to support fast-paced and more diffuse innovation will require a deeper commitment to product owners, working with leaders in the business side of the house, to run teams with real P&L responsibility. Many organizations, from traditional enterprises to digital natives, have found that putting in place product leaders who set overall product and portfolio strategy, drive execution, and empower product owners to drive innovation aligned with business outcomes and P&L metrics can increase the return on the funding that flows to technology delivery and quicken the pace of innovation.

• Do you have a vision for how the role of the IT organization will change to enable democratization of technology?
• How will you elevate the role of the technology product manager, and do you have a road map for developing that role?
• What systems will you need to put in place to manage and track the use, reuse, and performance of code?

McKinsey Technology Trends Outlook 2022

4. expanded trust boundaries.

It was estimated that almost 100 percent of biometrics-capable devices (such as smartphones) will be using biometrics for transactions by 2022. 22 “Usage of biometric technology in transactions with mobile devices worldwide 2016–2022”, Statista Research Department, June 13, 2022. The effectiveness of these technologies has advanced dramatically, with the best facial-identification algorithms having improved 50 times since 2014. 23 William Crumpler, “How accurate are facial recognition systems—and why does it matter?” Center for Strategies and International Studies (CSIS), April 14, 2020. These developments are contributing to profound unease in the relationship between technology and consumers of technology. The Pearson Institute and the Associated Press-NORC Center for Public Affairs Research shows that “about two-thirds of Americans are very or extremely concerned about hacking that involves their personal information, financial institutions, government agencies, or certain utilities.” 24 Chuck Brooks, “More alarming cybersecurity stats for 2021!” Forbes , October 24, 2021.

Shift: Trust expands to cover a broader array of stakeholder concerns and become an enterprise-wide responsibility.

These enormous shifts in technology power and capacity will create many more touchpoints with customers and an exponential wave of new data about customers. Even as IT’s role within the organization becomes more that of an enabler, the expanding digital landscape means that IT must broaden its trust capabilities around security, privacy, and cyber . To date, consumers have largely embraced the convenience that technology provides, from ordering a product online to adjusting the temperature in their homes remotely to monitoring their health through personal devices. In exchange for these conveniences, consumers have traditionally been willing to provide some personal information. But a steady undercurrent of privacy and trust concerns around these ever-more-sophisticated conveniences is raising the stakes on the broad topic of trust. Consumers are becoming more aware of their identity rights, making decisions based on values, and demanding the ethical use of data and responsible AI .

The most obvious concern is around cybersecurity , an ongoing issue that is already on the board-level agenda. But tech-driven trust issues are much broader and are driven by three characteristics. One is the sheer quantity of personal data, such as biometrics, that companies and governments collect, creating concerns about privacy and data misuse. The second is that personal security issues are becoming more pervasive in the physical world. Wired homes, connected cars, and the Internet of Medical Things, for example, are all vectors for attack that can affect people’s well-being. Third is the issue that advanced analytics seem too complex to be understood and controlled, leading to deep unease about people’s relationship with technology. This issue is driving the development of “ explainable AI ” and the movement to debias AI.

Adding to the complexity is the frequent need to manage and secure trust across an entire ecosystem of technologies. Take the wired home, for example. The proliferation of devices—think virtual assistants, security, communications, power management, and entertainment systems—means that a large group of providers will need to agree on standards for managing, in effect, an interconnected security net in the home.

These developments require a complex extension of the boundaries of trust. The significant advantages that many incumbents enjoy—existing relationships with customers and proprietary data—are at risk unless businesses rethink how they manage and nurture that trust. Companies need to consider putting identity and trust management at the core of their customer experience and business processes. That can happen effectively only when companies assign a dedicated leader with real power and board-level prioritization with enterprise-wide responsibility across the entire trust and security landscape. Given the tech underpinnings of this trust environment, IT will need to play a key role in monitoring and remediating, such as assessing the impact of new legislation on AI algorithms, tracking incidents, identifying the number and nature of high-risk data-processing activities and automated decisions, and—perhaps most important—monitoring consumer trust levels and the issues that affect them.

• Who is responsible for the enterprise-wide trust and risk landscape?
• How have you integrated your efforts around customer trust with overall cybersecurity processes?
• What privacy, trust, and security processes are in place to manage the entire life cycle of your data?

It is inevitable that the pace of technological change will continue to accelerate. The successful technology leader of the future will not simply need to adopt new technologies but to build capabilities to absorb continuous change and make it a source of competitive advantage.

Steve Van Kuiken is a senior partner in McKinsey’s New Jersey office.

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• Technology Essay

Essay on Technology

The word "technology" and its uses have immensely changed since the 20th century, and with time, it has continued to evolve ever since. We are living in a world driven by technology. The advancement of technology has played an important role in the development of human civilization, along with cultural changes. Technology provides innovative ways of doing work through various smart and innovative means. 

Electronic appliances, gadgets, faster modes of communication, and transport have added to the comfort factor in our lives. It has helped in improving the productivity of individuals and different business enterprises. Technology has brought a revolution in many operational fields. It has undoubtedly made a very important contribution to the progress that mankind has made over the years.

The Advancement of Technology:

Technology has reduced the effort and time and increased the efficiency of the production requirements in every field. It has made our lives easy, comfortable, healthy, and enjoyable. It has brought a revolution in transport and communication. The advancement of technology, along with science, has helped us to become self-reliant in all spheres of life. With the innovation of a particular technology, it becomes part of society and integral to human lives after a point in time.

Technology is Our Part of Life:

Technology has changed our day-to-day lives. Technology has brought the world closer and better connected. Those days have passed when only the rich could afford such luxuries. Because of the rise of globalisation and liberalisation, all luxuries are now within the reach of the average person. Today, an average middle-class family can afford a mobile phone, a television, a washing machine, a refrigerator, a computer, the Internet, etc. At the touch of a switch, a man can witness any event that is happening in far-off places.  

Benefits of Technology in All Fields: 

We cannot escape technology; it has improved the quality of life and brought about revolutions in various fields of modern-day society, be it communication, transportation, education, healthcare, and many more. Let us learn about it.

Technology in Communication:

With the advent of technology in communication, which includes telephones, fax machines, cellular phones, the Internet, multimedia, and email, communication has become much faster and easier. It has transformed and influenced relationships in many ways. We no longer need to rely on sending physical letters and waiting for several days for a response. Technology has made communication so simple that you can connect with anyone from anywhere by calling them via mobile phone or messaging them using different messaging apps that are easy to download.

Innovation in communication technology has had an immense influence on social life. Human socialising has become easier by using social networking sites, dating, and even matrimonial services available on mobile applications and websites.

Today, the Internet is used for shopping, paying utility bills, credit card bills, admission fees, e-commerce, and online banking. In the world of marketing, many companies are marketing and selling their products and creating brands over the internet. 

In the field of travel, cities, towns, states, and countries are using the web to post detailed tourist and event information. Travellers across the globe can easily find information on tourism, sightseeing, places to stay, weather, maps, timings for events, transportation schedules, and buy tickets to various tourist spots and destinations.

Technology in the Office or Workplace:

Technology has increased efficiency and flexibility in the workspace. Technology has made it easy to work remotely, which has increased the productivity of the employees. External and internal communication has become faster through emails and apps. Automation has saved time, and there is also a reduction in redundancy in tasks. Robots are now being used to manufacture products that consistently deliver the same product without defect until the robot itself fails. Artificial Intelligence and Machine Learning technology are innovations that are being deployed across industries to reap benefits.

Technology has wiped out the manual way of storing files. Now files are stored in the cloud, which can be accessed at any time and from anywhere. With technology, companies can make quick decisions, act faster towards solutions, and remain adaptable. Technology has optimised the usage of resources and connected businesses worldwide. For example, if the customer is based in America, he can have the services delivered from India. They can communicate with each other in an instant. Every company uses business technology like virtual meeting tools, corporate social networks, tablets, and smart customer relationship management applications that accelerate the fast movement of data and information.

Technology in Education:

Technology is making the education industry improve over time. With technology, students and parents have a variety of learning tools at their fingertips. Teachers can coordinate with classrooms across the world and share their ideas and resources online. Students can get immediate access to an abundance of good information on the Internet. Teachers and students can access plenty of resources available on the web and utilise them for their project work, research, etc. Online learning has changed our perception of education. 

The COVID-19 pandemic brought a paradigm shift using technology where school-going kids continued their studies from home and schools facilitated imparting education by their teachers online from home. Students have learned and used 21st-century skills and tools, like virtual classrooms, AR (Augmented Reality), robots, etc. All these have increased communication and collaboration significantly. 

Technology in Banking:

Technology and banking are now inseparable. Technology has boosted digital transformation in how the banking industry works and has vastly improved banking services for their customers across the globe.

Technology has made banking operations very sophisticated and has reduced errors to almost nil, which were somewhat prevalent with manual human activities. Banks are adopting Artificial Intelligence (AI) to increase their efficiency and profits. With the emergence of Internet banking, self-service tools have replaced the traditional methods of banking. 

You can now access your money, handle transactions like paying bills, money transfers, and online purchases from merchants, and monitor your bank statements anytime and from anywhere in the world. Technology has made banking more secure and safe. You do not need to carry cash in your pocket or wallet; the payments can be made digitally using e-wallets. Mobile banking, banking apps, and cybersecurity are changing the face of the banking industry.

Manufacturing and Production Industry Automation:

At present, manufacturing industries are using all the latest technologies, ranging from big data analytics to artificial intelligence. Big data, ARVR (Augmented Reality and Virtual Reality), and IoT (Internet of Things) are the biggest manufacturing industry players. Automation has increased the level of productivity in various fields. It has reduced labour costs, increased efficiency, and reduced the cost of production.

For example, 3D printing is used to design and develop prototypes in the automobile industry. Repetitive work is being done easily with the help of robots without any waste of time. This has also reduced the cost of the products. 

Technology in the Healthcare Industry:

Technological advancements in the healthcare industry have not only improved our personal quality of life and longevity; they have also improved the lives of many medical professionals and students who are training to become medical experts. It has allowed much faster access to the medical records of each patient. 

The Internet has drastically transformed patients' and doctors’ relationships. Everyone can stay up to date on the latest medical discoveries, share treatment information, and offer one another support when dealing with medical issues. Modern technology has allowed us to contact doctors from the comfort of our homes. There are many sites and apps through which we can contact doctors and get medical help. 

Breakthrough innovations in surgery, artificial organs, brain implants, and networked sensors are examples of transformative developments in the healthcare industry. Hospitals use different tools and applications to perform their administrative tasks, using digital marketing to promote their services.

Technology in Agriculture:

Today, farmers work very differently than they would have decades ago. Data analytics and robotics have built a productive food system. Digital innovations are being used for plant breeding and harvesting equipment. Software and mobile devices are helping farmers harvest better. With various data and information available to farmers, they can make better-informed decisions, for example, tracking the amount of carbon stored in soil and helping with climate change.

Disadvantages of Technology:

People have become dependent on various gadgets and machines, resulting in a lack of physical activity and tempting people to lead an increasingly sedentary lifestyle. Even though technology has increased the productivity of individuals, organisations, and the nation, it has not increased the efficiency of machines. Machines cannot plan and think beyond the instructions that are fed into their system. Technology alone is not enough for progress and prosperity. Management is required, and management is a human act. Technology is largely dependent on human intervention. 

Computers and smartphones have led to an increase in social isolation. Young children are spending more time surfing the internet, playing games, and ignoring their real lives. Usage of technology is also resulting in job losses and distracting students from learning. Technology has been a reason for the production of weapons of destruction.

Dependency on technology is also increasing privacy concerns and cyber crimes, giving way to hackers.

FAQs on Technology Essay

1. What is technology?

Technology refers to innovative ways of doing work through various smart means. The advancement of technology has played an important role in the development of human civilization. It has helped in improving the productivity of individuals and businesses.

2. How has technology changed the face of banking?

Technology has made banking operations very sophisticated. With the emergence of Internet banking, self-service tools have replaced the traditional methods of banking. You can now access your money, handle transactions, and monitor your bank statements anytime and from anywhere in the world. Technology has made banking more secure and safe.

3. How has technology brought a revolution in the medical field?

Patients and doctors keep each other up to date on the most recent medical discoveries, share treatment information, and offer each other support when dealing with medical issues. It has allowed much faster access to the medical records of each patient. Modern technology has allowed us to contact doctors from the comfort of our homes. There are many websites and mobile apps through which we can contact doctors and get medical help.

4. Are we dependent on technology?

Yes, today, we are becoming increasingly dependent on technology. Computers, smartphones, and modern technology have helped humanity achieve success and progress. However, in hindsight, people need to continuously build a healthy lifestyle, sorting out personal problems that arise due to technological advancements in different aspects of human life.

Technological Change

Technological change underpins many of the developments we've seen in health, agriculture, energy, and global development.

By: Max Roser , Hannah Ritchie and Edouard Mathieu

Almost every development we cover on Our World in Data is underpinned by technological change.

Medical innovations contributed to the decline in child mortality and the improvement in life expectancy . Thanks to the advances in agricultural technologies, higher crop yields and less undernourishment became possible. The long-term decline of global poverty was primarily driven by increased productivity from technological change. Access to energy , electricity, sanitation , and clean water has transformed the lives of billions. Transport, telephones, and the Internet have allowed us to collaborate on problems at a global level.

Emerging technologies are often expensive and, therefore, initially limited to society's richest. A key part of technological progress is making these life-changing and often life-saving innovations affordable for everyone.

Technology has improved our lives in many ways, but these developments are not always positive. Many of humanity’s largest threats — such as nuclear weapons and potentially artificial intelligence — result from technological advances. To mitigate these risks, good governance can be as important as the technology itself.

On this page, you can find our data, visualizations, and writing on the most fundamental technological changes that have shaped our world.

Research & Writing

Key articles on technological change.

Technology over the long run: zoom out to see how dramatically the world can change within a lifetime

The brief history of artificial intelligence: the world has changed fast — what might be next?

Artificial intelligence is transforming our world — it is on all of us to make sure that it goes well

The price of batteries has declined by 97% in the last three decades

Hannah Ritchie

Artificial Intelligence (AI) and Communication Technologies

AI timelines: What do experts in artificial intelligence expect for the future?

Artificial intelligence has advanced despite having few resources dedicated to its development – now investments have increased substantially

The importance of social networks for innovation and productivity

Esteban Ortiz-Ospina

The rise of social media

Energy and agricultural technologies.

Why did renewables become so cheap so fast?

How many people does synthetic fertilizer feed?

Yields vs. land use: how the Green Revolution enabled us to feed a growing population

The world now produces more seafood from fish farms than wild catch

Theory of technological change.

What is Moore's Law?

Max Roser, Hannah Ritchie and Edouard Mathieu

Learning curves: What does it mean for a technology to follow Wright’s Law?

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More From Forbes

Is society moving in the right direction with technology rapidly taking over the world.

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Over the last centuries, society witnessed technological advancements gradually making everyday lives easier, more convenient, and – well, more interesting. In the 21st century, however, technology made a true quantum leap, with augmented reality, blockchain, artificial intelligence, and 3D printing being just a few examples of the most recent inventions.

Though we are getting used to advancements of any kind, is the development of technology really good for society?

Some Areas in Which Technology Has Changed Our Lives

Advancements in technology have already tapped into every area of life, with its impact particularly notable in these segments:

• Lifestyle. From working remotely to ordering food delivery or booking a hotel room online, most people now rely on the Internet as an irreplaceable part of their lives. Advancements in technology have completely re-shaped the everyday routine of a Modern Human.
• Health. Telehealth, EHR, 3D medical imaging, smart wearable devices that track heart rate and blood oxygen level are just a few examples of technological advancements introduced in health care over the last few decades.  
• Privacy. In the digital world, privacy is more important than ever. While the rates of cybercrimes increase, which by the way is also a recent tech innovation, such advancements as cryptography and antivirus software strike a blow, saving users’ privacy online.
• The attitude of the youth. A huge part of the modern generation literally can not imagine their lives without technology: they use smart devices at home and school, google anything they want to know, wear smartwatches, have video calls, and own robots and self-driving cars.
• Business. Such innovations as cloud computing, Big Data, Data Science, AI/ML completely reshaped the modern business landscape. The Covid-19 pandemic accelerated digital transformations, prompting most businesses to switch to remote mode.
• Human behavior. With the mass adoption of mobile smart devices, people virtualized most of their day-to-day tasks. There is a dedicated mobile app for everything: booking a ride, tracking eating and sleeping habits, or even checking how one would look when he/she gets old.

Best High-Yield Savings Accounts Of 2024

Best 5% interest savings accounts of 2024, how has technology affected people.

It is indisputable that thanks to technology, we get a chance to live a life our predecessors could not even dream about. But do all tech advancements bring sole good to our lives? Or, maybe, the impact of tech innovations is quite ambiguous.

Positive Impact Of Technology

When all areas of human activity get rapidly digitized, it’s easy to become desensitized to the importance of innovations and advancements for the overall progress of society. But technology helps us immensely, for instance:

• Now that the agricultural processes are mechanized and automated, farmers may grow and harvest more crops.
• With advancements in transportation, people can travel long distances with speed and comfort – by air, land, or water.
• Communication, enriched with online channels, video calling, and 5G technologies, has reached new heights, connecting people from all corners of the globe.
• Radio, television, electronic media are indispensable means of news and information for people in any country worldwide.
• Education has recently become more accessible to all people, irrespective of their geographical location, thanks to modern technological advancements, such as online classrooms, electronic curricula, digital learning management systems among others.  

How to Get the Most out of Technology o remain empowered, not distressed, by the modern technological advancements, everyone should use them where and when needed .

For instance, you can make use of productivity and time-tracking apps available for smartphones or PCs when feeling a lack of concentration or self-discipline. Similarly, if you are concerned with your health and lifestyle, you can choose from a wide range of wearable devices and fitness apps.

Most of the digital tools can be downloaded free of charge, which makes it even easier to use them.

Negative Impact Of Technology

On the downside, some technological developments prove to be a curse rather than a blessing. Here are a few examples:

• Excessive use of gadgets, lack of offline communication, and social media abuse were proven to cause negative effects on mental health.
• In online communication, people are often hog-tied to express non-verbal cues, which results in misunderstandings and offenses.
• Relationships of those people who spend more time communicating online, compared to offline, may become tense and more fragile over time.
• Younger people, more prone to digital world addictions , sometimes lose the skills and desire to communicate with their peers face-to-face, in the real world.
• Video-gaming addicts often do not leave their homes for weeks and lead sedentary lifestyles, which eventually ruin their health.
• Teenagers spend too much time scrolling through Instagram and TikTok feeds bringing severe mental health problems to them as they tend to compare themselves to the dummy perfect images of others they see online.    

How to Reduce Negative Effects of Technology  Excessive use of technology can do more harm than good, and we should bear this in mind before we rush into digitizing our lives.

It is important to monitor the use of tech in every aspect of daily routine and, while it is not too late, limit the time spent in front of the smartphone screen. 

Also, it's a good idea to use all-in-one apps to manage a complex of tasks (e.g., having all your email accounts and messengers integrated in one place) rather than switching between a dozen of smaller apps for each activity. 

As an alternative to playing a video game or scrolling through social media, find a paper book that would interest you and spend time outdoors regularly. Instead of watching another Netflix episode in front of a TV set, talk to your family or take up gardening.

Spending more time outdoors, without electronic devices, promotes life-work balance and is generally good for a healthy lifestyle.    

Technological Advancements Of The Modern Day

Though it may be tough to predict which advancements technology would bring next, some innovations are already changing our beliefs about the world around us.

For instance, augmented reality (AR) and virtual reality (VR). Something that people would have considered magic just a few decades ago is now gaining popularity in business, gaming, and team building.

Wearable screens and gesture-based computing, other recent innovations, are predicted to soon substitute the usual PC and phone screens.    

Robots, another buzzword in today’s business world, have already replaced humans in some workplaces — robotic arms work at assembly or packing lines. Flying cars will soon address the issue of limited ground space and long traffic jams.

Well, people of Earth are even projected to use technological innovations to colonize other planets in the foreseeable future. The sky is no longer the limit!

Technology improves all aspects of human lives, making them easier and diverse. Though technological advancements are generally seen as a positive change, some people perceive them in a negative light.

Overindulgence in the use of digital apps and smart devices, overreliance on online tools may sometimes lead to tragic effects. Yet, if technological developments are used wisely, they bring nothing but good to society.

Clearly, technology by itself is neither good nor bad. It is only the way and extent to which we use it that matters.

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Home — Essay Samples — Information Science and Technology — Impact of Technology — The Influence of Technology on the World

The Influence of Technology on The World

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