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The Rise of the Machines: Pros and Cons of the Industrial Revolution
The Industrial Revolution , the period in which agrarian and handicraft economies shifted rapidly to industrial and machine-manufacturing-dominated ones, began in the United Kingdom in the 18th century and later spread throughout many other parts of the world. This economic transformation changed not only how work was done and goods were produced, but it also altered how people related both to one another and to the planet at large. This wholesale change in societal organization continues today, and it has produced several effects that have rippled throughout Earth ’s political, ecological, and cultural spheres. The following list describes some of the great benefits as well as some of the significant shortcomings associated with the Industrial Revolution.
Pro: Goods Became More Affordable and More Accessible
Factories and the machines that they housed began to produce items faster and cheaper than could be made by hand. As the supply of various items rose, their cost to the consumer declined ( see supply and demand ). Shoes , clothing , household goods, tools , and other items that enhance people’s quality of life became more common and less expensive. Foreign markets also were created for these goods, and the balance of trade shifted in favor of the producer—which brought increased wealth to the companies that produced these goods and added tax revenue to government coffers. However, it also contributed to the wealth inequality between goods-producing and goods-consuming countries.
Pro: The Rapid Evolution of Labor-Saving Inventions
The rapid production of hand tools and other useful items led to the development of new types of tools and vehicles to carry goods and people from one place to another. The growth of road and rail transportation and the invention of the telegraph (and its associated infrastructure of telegraph—and later telephone and fiber optic —lines) meant that word of advances in manufacturing, agricultural harvesting, energy production, and medical techniques could be communicated between interested parties quickly. Labor-saving machines such as the spinning jenny (a multiple-spindle machine for spinning wool or cotton) and other inventions, especially those driven by electricity (such as home appliances and refrigeration) and fossil fuels (such as automobiles and other fuel-powered vehicles), are also well-known products of the Industrial Revolution.
Pro: The Rapid Evolution of Medicine
The Industrial Revolution was the engine behind various advances in medicine . Industrialization allowed medical instruments (such as scalpels, microscope lenses, test tubes, and other equipment) to be produced more quickly. Using machine manufacturing, refinements to these instruments could more efficiently roll out to the physicians that needed them. As communication between physicians in different areas improved, the details behind new cures and treatments for disease could be dispersed quickly, resulting in better care.
Pro: Enhanced Wealth and Quality of Life of the Average Person
Mass production lowered the costs of much-needed tools, clothes, and other household items for the common (that is, nonaristocratic) people, which allowed them to save money for other things and build personal wealth. In addition, as new manufacturing machines were invented and new factories were built, new employment opportunities arose. No longer was the average person so closely tied to land -related concerns (such as being dependent upon the wages farm labor could provide or the plant and animal products farms could produce). Industrialization reduced the emphasis on landownership as the chief source of personal wealth. The rising demand for manufactured goods meant that average people could make their fortunes in cities as factory employees and as employees of businesses that supported the factories, which paid better wages than farm-related positions. Generally speaking, people could save some portion of their wages, and many had the opportunity to invest in profitable businesses, thereby growing their family “nest eggs.” The subsequent growth of the middle class in the United Kingdom and other industrializing societies meant that it was making inroads into the pool of economic power held by the aristocracy . Their greater buying power and importance in society led to changes in laws that were updated to better handle the demands of an industrialized society.
Pro: The Rise of Specialist Professions
As industrialization progressed, more and more rural folk flocked to the cities in search of better pay in the factories. To increase the factories’ overall efficiency and to take advantage of new opportunities in the market, factory workers were trained to perform specialized tasks. Factory owners divided their workers into different groups, each group focusing on a specific task. Some groups secured and transported to the factories raw materials (namely iron , coal , and steel ) used in mass production of goods, while other groups operated different machines. Some groups of workers fixed machines when they broke down, while others were charged with making improvements to them and overall factory operation.
As the factories grew and workers became more specialized, additional teachers and trainers were needed to pass on specialized skills. In addition, the housing, transportation, and recreational needs of factory workers resulted in the rapid expansion of cities and towns. Governmental bureaucracies grew to support these, and new specialized departments were created to handle traffic, sanitation, taxation, and other services. Other businesses within the towns also became more specialized as more builders, physicians, lawyers, and other workers were added to handle the various needs of the new residents.
Con: Overcrowding of Cities and Industrial Towns
The promise of better wages attracted migrants to cities and industrial towns that were ill-prepared to handle them. Although initial housing shortages in many areas eventually gave way to construction booms and the development of modern buildings, cramped shantytowns made up of shacks and other forms of poor-quality housing appeared first. Local sewerage and sanitation systems were overwhelmed by the sudden influx of people, and drinking water was often contaminated. People living in such close proximity, fatigued by poor working conditions, and drinking unsafe water presented ideal conditions for outbreaks of typhus , cholera , smallpox , tuberculosis , and other infectious diseases. The need to treat these and other diseases in urban areas spurred medical advances and the development of modern building codes, health laws, and urban planning in many industrialized cities.
Con: Pollution and Other Environmental Ills
With relatively few exceptions, the world’s modern environmental problems began or were greatly exacerbated by the Industrial Revolution. To fuel the factories and to sustain the output of each and every type of manufactured good, natural resources (water, trees, soil, rocks and minerals, wild and domesticated animals, etc.) were transformed, which reduced the planet’s stock of valuable natural capital. The global challenges of widespread water and air pollution , reductions in biodiversity , destruction of wildlife habitat, and even global warming can be traced back to this moment in human history. The more countries industrialize in pursuit of their own wealth, the greater this ecological transformation becomes. For example, atmospheric carbon dioxide , a primary driver of global warming, existed in concentrations of 275 to 290 parts per million by volume (ppmv) before 1750 and increased to more than 400 ppmv by 2017. In addition, human beings use more than 40% of Earth’s land-based net primary production, a measure of the rate at which plants convert solar energy into food and growth. As the world’s human population continues to grow and more and more people strive for the material benefits promised by the Industrial Revolution, more and more of Earth’s resources are appropriated for human use, leaving a dwindling stock for the plants and animals upon whose ecosystem services (clean air, clean water, etc.) the biosphere depends.
Con: Poor Working Conditions
When factories sprung up in the cities and industrial towns, their owners prized production and profit over all else. Worker safety and wages were less important. Factory workers earned greater wages compared with agricultural workers, but this often came at the expense of time and less than ideal working conditions. Factory workers often labored 14–16 hours per day six days per week. Men’s meager wages were often more than twice those of women. The wages earned by children who worked to supplement family income were even lower. The various machines in the factory were often dirty, expelling smoke and soot, and unsafe, both of which contributed to accidents that resulted in worker injuries and deaths. The rise of labor unions, however, which began as a reaction to child labor, made factory work less grueling and less dangerous. During the first half of the 20th century, child labor was sharply curtailed, the workday was reduced substantially, and government safety standards were rolled out to protect the workers’ health and well-being.
Con: The Rise in Unhealthy Habits
As more cheap labor-saving devices become available, people performed less strenuous physical activity. While grueling farm-related labor was made far easier, and in many cases far safer, by replacing animal power and human power with tractors and other specialized vehicles to till the soil and plant and harvest crops, other vehicles, such as trains and automobiles , effectively reduced the amount of healthy exercise people partook in each day. Also, many professions that required large amounts of physical exertion outdoors were replaced by indoor office work, which is often sedentary. Such sedentary behaviors also occur away from work, as television programs and other forms of passive entertainment came to dominate leisure time. Added to this is the fact that many people eat food that has been processed with salt and sugar to help with its preservation, lower its cooking time, and increase its sweetness. Together, these lifestyle trends have led to increases in lifestyle-related diseases associated with obesity , such as heart disease , diabetes , and certain forms of cancer .
Rise of the machines: has technology evolved beyond our control?
Technology is starting to behave in intelligent and unpredictable ways that even its creators don’t understand. As machines increasingly shape global events, how can we regain control?
T he voice-activated gadget in the corner of your bedroom suddenly laughs maniacally, and sends a recording of your pillow talk to a colleague. The clip of Peppa Pig your toddler is watching on YouTube unexpectedly descends into bloodletting and death. The social network you use to keep in touch with old school friends turns out to be influencing elections and fomenting coups.
Something strange has happened to our way of thinking – and as a result, even stranger things are happening to the world. We have come to believe that everything is computable and can be resolved by the application of new technologies. But these technologies are not neutral facilitators: they embody our politics and biases, they extend beyond the boundaries of nations and legal jurisdictions and increasingly exceed the understanding of even their creators. As a result, we understand less and less about the world as these powerful technologies assume more control over our everyday lives.
Across the sciences and society, in politics and education, in warfare and commerce, new technologies are not merely augmenting our abilities, they are actively shaping and directing them, for better and for worse. If we do not understand how complex technologies function then their potential is more easily captured by selfish elites and corporations. The results of this can be seen all around us. There is a causal relationship between the complex opacity of the systems we encounter every day and global issues of inequality, violence, populism and fundamentalism.
Instead of a utopian future in which technological advancement casts a dazzling, emancipatory light on the world, we seem to be entering a new dark age characterised by ever more bizarre and unforeseen events. The Enlightenment ideal of distributing more information ever more widely has not led us to greater understanding and growing peace, but instead seems to be fostering social divisions, distrust, conspiracy theories and post-factual politics. To understand what is happening, it’s necessary to understand how our technologies have come to be, and how we have come to place so much faith in them.
In the 1950s, a new symbol began to creep into the diagrams drawn by electrical engineers to describe the systems they built: a fuzzy circle, or a puffball, or a thought bubble. Eventually, its form settled into the shape of a cloud. Whatever the engineer was working on, it could connect to this cloud, and that’s all you needed to know. The other cloud could be a power system, or a data exchange, or another network of computers. Whatever. It didn’t matter. The cloud was a way of reducing complexity, it allowed you to focus on the issues at hand. Over time, as networks grew larger and more interconnected, the cloud became more important. It became a business buzzword and a selling point. It became more than engineering shorthand; it became a metaphor.
Today the cloud is the central metaphor of the internet: a global system of great power and energy that nevertheless retains the aura of something numinous, almost impossible to grasp. We work in it; we store and retrieve stuff from it; it is something we experience all the time without really understanding what it is. But there’s a problem with this metaphor: the cloud is not some magical faraway place, made of water vapour and radio waves, where everything just works. It is a physical infrastructure consisting of phone lines, fibre optics, satellites, cables on the ocean floor, and vast warehouses filled with computers, which consume huge amounts of water and energy. Absorbed into the cloud are many of the previously weighty edifices of the civic sphere: the places where we shop, bank, socialise, borrow books and vote. Thus obscured, they are rendered less visible and less amenable to critique, investigation, preservation and regulation.
Over the last few decades, trading floors around the world have fallen silent, as people are replaced by banks of computers that trade automatically. Digitisation meant that trades within, as well as between, stock exchangescould happen faster and faster. As trading passed into the hands of machines, it became possible to react almost instantaneously. High-Frequency Trading (HFT) algorithms, designed by former physics PhD students to take advantage of millisecond advantages, entered the market, and traders gave them names such as The Knife. These algorithms were capable of eking out fractions of a cent on every trade, and they could do it millions of times a day.
Something deeply weird is occurring within these massively accelerated, opaque markets. On 6 May 2010 , the Dow Jones opened lower than the previous day, falling slowly over the next few hours in response to the debt crisis in Greece. But at 2.42pm, the index started to fall rapidly. In less than five minutes, more than 600 points were wiped off the market. At its lowest point, the index was nearly 1,000 points below the previous day’s average, a difference of almost 10% of its total value, and the biggest single-day fall in the market’s history. By 3.07pm, in just 25 minutes, it recovered almost all of those 600 points, in the largest and fastest swing ever.
In the chaos of those 25 minutes, 2bn shares, worth $56bn, changed hands. Even more worryingly, many orders were executed at what the Securities Exchange Commission called “irrational prices”: as low as a penny, or as high as $100,000. The event became known as the “flash crash”, and it is still being investigated and argued over years later.
One report by regulators found that high-frequency traders exacerbated the price swings. Among the various HFT programs, many had hard-coded sell points: prices at which they were programmed to sell their stocks immediately. As prices started to fall, groups of programs were triggered to sell at the same time. As each waypoint was passed, the subsequent price fall triggered another set of algorithms to automatically sell their stocks, producing a feedback effect. As a result, prices fell faster than any human trader could react to. While experienced market players might have been able to stabilise the crash by playing a longer game, the machines, faced with uncertainty, got out as quickly as possible.
Other theories blame the algorithms for initiating the crisis. One technique that was identified in the data was HFT programmes sending large numbers of “non-executable” orders to the exchanges – that is, orders to buy or sell stocks so far outside of their usual prices that they would be ignored. The purpose of such orders is not to actually communicate or make money, but to deliberately cloud the system, so that other, more valuable trades can be executed in the confusion. Many orders that were never intended to be executed were actually fulfilled, causing wild volatility.
Flash crashes are now a recognised feature of augmented markets, but are still poorly understood. In October 2016 , algorithms reacted to negative news headlines about Brexit negotiations by sending the pound down 6% against the dollar in under two minutes, before recovering almost immediately. Knowing which particular headline, or which particular algorithm, caused the crash is next to impossible. When one haywire algorithm started placing and cancelling orders that ate up 4% of all traffic in US stocks in October 2012, one commentator was moved to comment wryly that “the motive of the algorithm is still unclear”.
At 1.07pm on 23 April 2013 Associated Press sent a tweet to its 2 million followers: “Breaking: Two Explosions in the White House and Barack Obama is injured.” The message was the result of a hack later claimed by the Syrian Electronic Army , a group affiliated to Syrian president Bashar al-Assad. AP and other journalists quickly flooded the site with alerts that the message was false. The algorithms following breaking news stories had no such discernment, however. At 1.08pm, the Dow Jones went into a nosedive. Before most human viewers had even seen the tweet, the index had fallen 150 points in under two minutes, and bounced back to its earlier value. In that time, it erased $136bn in equity market value.
Computation is increasingly layered across, and hidden within, every object in our lives, and with its expansion comes an increase in opacity and unpredictability. One of the touted benefits of Samsung’s line of “smart fridges” in 2015 was their integration with Google’s calendar services, allowing owners to schedule grocery deliveries from the kitchen. It also meant that hackers who gained access to the then inadequately secured machines could read their owner’s Gmail passwords. Researchers in Germany discovered a way to insert malicious code into Philips’s wifi-enabled Hue lightbulbs, which could spread from fixture to fixture throughout a building or even a city, turning the lights rapidly on and off and – in one possible scenario – triggering photosensitive epilepsy. This is the approach favoured by Byron the Bulb in Thomas Pynchon’s Gravity’s Rainbow , an act of grand revolt by the little machines against the tyranny of their makers. Once-fictional possibilities for technological violence are being realised by the Internet of Things.
In Kim Stanley Robinson’s novel Aurora , an intelligent spacecraft carries a human crew from Earth to a distant star. The journey will take multiple lifetimes, so one of the ship’s jobs is to ensure that the humans look after themselves. When their fragile society breaks down, threatening the mission, the ship deploys safety systems as a means of control: it is able to see everywhere through sensors, open or seal doors at will, speak so loudly through its communications equipment that it causes physical pain, and use fire suppression systems to draw down the level of oxygen in a particular space.
This is roughly the same suite of operations available now from Google Home and its partners: a network of internet-connected cameras for home security, smart locks on doors, a thermostat capable of raising and lowering the temperature in individual rooms, and a fire and intruder detection system that emits a piercing emergency alarm. Any successful hacker would have the same powers as the Aurora does over its crew, or Byron over his hated masters.
Before dismissing such scenarios as the fever dreams of science fiction writers, consider again the rogue algorithms in the stock exchanges. These are not isolated events, but everyday occurrences within complex systems. The question then becomes, what would a rogue algorithm or a flash crash look like in the wider reality?
Would it look, for example, like Mirai , a piece of software that brought down large portions of the internet for several hours on 21 October 2016? When researchers dug into Mirai, they discovered it targets poorly secured internet connected devices – from security cameras to digital video recorders – and turns them into an army of bots. In just a few weeks, Mirai infected half a million devices, and it needed just 10% of that capacity to cripple major networks for hours.
Mirai, in fact, looks like nothing so much as Stuxnet, another virus discovered within the industrial control systems of hydroelectric plants and factory assembly lines in 2010. Stuxnet was a military-grade cyberweapon; when dissected, it was found to be aimed specifically at Siemens centrifuges, and designed to go off when it encountered a facility that possessed a particular number of such machines. That number corresponded with one particular facility: the Natanz nuclear facility in Iran. When activated, the program would quietly degrade crucial components of the centrifuges, causing them to break down and disrupt the Iranian enrichment programme.
The attack was apparently partially successful, but the effect on other infected facilities is unknown. To this day, despite obvious suspicions, nobody knows where Stuxnet came from, or who made it. Nobody knows for certain who developed Mirai, either, or where its next iteration might come from, but it might be there, right now, breeding in the CCTV camera in your office, or the wifi-enabled kettle in the corner of your kitchen.
Or perhaps the crash will look like a string of blockbuster movies pandering to rightwing conspiracies and survivalist fantasies, from quasi-fascist superheroes (Captain America and the Batman series) to justifications of torture and assassination ( Zero Dark Thirty , American Sniper ). In Hollywood, studios run their scripts through the neural networks of a company called Epagogix, a system trained on the unstated preferences of millions of moviegoers developed over decades in order to predict which lines will push the right – meaning the most lucrative – emotional buttons. Algorithmic engines enhanced with data from Netflix, Hulu, YouTube and others, with access to the minute-by-minute preferences of millions of video watchers acquire a level of cognitive insight undreamed of by previous regimes. Feeding directly on the frazzled, binge-watching desires of news-saturated consumers, the network turns on itself, reflecting, reinforcing and heightening the paranoia inherent in the system.
Game developers enter endless cycles of updates and in-app purchases directed by A/B testing interfaces and real-time monitoring of players’ behaviours. They have such a fine-grained grasp of dopamine-producing neural pathways that teenagers die of exhaustion in front of their computers, unable to tear themselves away.
Or perhaps the flash crash will look like literal nightmares broadcast across the network for all to see? In the summer of 2015, the sleep disorders clinic of an Athens hospital was busier than it had ever been: the country’s debt crisis was in its most turbulent period. Among the patients were top politicians and civil servants, but the machines they spent the nights hooked up to, monitoring their breathing, their movements, even the things they said out loud in their sleep, were sending that information, together with their personal medical details, back to the manufacturers’ diagnostic data farms in northern Europe. What whispers might escape from such facilities?
We are able to record every aspect of our daily lives by attaching technology to the surface of our bodies, persuading us that we too can be optimised and upgraded like our devices. Smart bracelets and smartphone apps with integrated step counters and galvanic skin response monitors track not only our location, but every breath and heartbeat, even the patterns of our brainwaves. Users are encouraged to lay their phones beside them on their beds at night, so that their sleep patterns can be recorded. Where does all this data go, who owns it, and when might it come out? Data on our dreams, our night terrors and early morning sweating jags, the very substance of our unconscious selves, turn into more fuel for systems both pitiless and inscrutable.
Or perhaps the flash crash in reality looks exactly like everything we are experiencing right now: rising economic inequality, the breakdown of the nation-state and the militarisation of borders, totalising global surveillance and the curtailment of individual freedoms, the triumph of transnational corporations and neurocognitive capitalism, the rise of far-right groups and nativist ideologies, and the degradation of the natural environment. None of these are the direct result of novel technologies, but all of them are the product of a general inability to perceive the wider, networked effects of individual and corporate actions accelerated by opaque, technologically augmented complexity.
In New York in 1997, world chess champion Garry Kasparov faced off for the second time against Deep Blue, a computer specially designed by IBM to beat him. When he lost, he claimed some of Deep Blue’s moves were so intelligent and creative that they must have been the result of human intervention. But we understand why Deep Blue made those moves: its process for selecting them was ultimately one of brute force, a massively parallel architecture of 14,000 custom-designed chess chips, capable of analysing 200m board positions per second. Kasparov was not outthought, merely outgunned.
By the time the Google Brain–powered AlphaGo software took on the Korean professional Go player Lee Sedol in 2016, something had changed. In the second of five games, AlphaGo played a move that stunned Sedol, placing one of its stones on the far side of the board. “That’s a very strange move,” said one commentator. “I thought it was a mistake,” said another. Fan Hui, a seasoned Go player who had been the first professional to lose to the machine six months earlier, said: “It’s not a human move. I’ve never seen a human play this move.”
AlphaGo went on to win the game, and the series. AlphaGo’s engineers developed its software by feeding a neural network millions of moves by expert Go players, and then getting it to play itself millions of times more, developing strategies that outstripped those of human players. But its own representation of those strategies is illegible: we can see the moves it made, but not how it decided to make them.
The late Iain M Banks called the place where these moves occurred “Infinite Fun Space”. In Banks’s SF novels, his Culture civilisation is administered by benevolent, superintelligent AIs called simply Minds. While the Minds were originally created by humans, they have long since redesigned and rebuilt themselves and become all-powerful. Between controlling ships and planets, directing wars and caring for billions of humans, the Minds also take up their own pleasures. Capable of simulating entire universes within their imaginations, some Minds retreat for ever into Infinite Fun Space, a realm of meta-mathematical possibility, accessible only to superhuman artificial intelligences.
Many of us are familiar with Google Translate, which was launched in 2006, using a technique called statistical language inference. Rather than trying to understand how languages actually worked, the system imbibed vast corpora of existing translations: parallel texts with the same content in different languages. By simply mapping words on to one another, it removed human understanding from the equation and replaced it with data-driven correlation.
Translate was known for its humorous errors, but in 2016, the system started using a neural network developed by Google Brain, and its abilities improved exponentially. Rather than simply cross-referencing heaps of texts, the network builds its own model of the world, and the result is not a set of two-dimensional connections between words, but a map of the entire territory. In this new architecture, words are encoded by their distance from one another in a mesh of meaning – a mesh only a computer could comprehend.
While a human can draw a line between the words “tank” and “water” easily enough, it quickly becomes impossible to draw on a single map the lines between “tank” and “revolution”, between “water” and “liquidity”, and all of the emotions and inferences that cascade from those connections. The map is thus multidimensional, extending in more directions than the human mind can hold. As one Google engineer commented, when pursued by a journalist for an image of such a system: “I do not generally like trying to visualise thousand-dimensional vectors in three-dimensional space.” This is the unseeable space in which machine learning makes its meaning. Beyond that which we are incapable of visualising is that which we are incapable of even understanding.
In the same year, other researchers at Google Brain set up three networks called Alice, Bob and Eve. Their task was to learn how to encrypt information. Alice and Bob both knew a number – a key, in cryptographic terms – that was unknown to Eve. Alice would perform some operation on a string of text, and then send it to Bob and Eve. If Bob could decode the message, Alice’s score increased; but if Eve could, Alice’s score decreased.
Over thousands of iterations, Alice and Bob learned to communicate without Eve breaking their code: they developed a private form of encryption like that used in private emails today. But crucially, we don’t understand how this encryption works. Its operation is occluded by the deep layers of the network. What is hidden from Eve is also hidden from us. The machines are learning to keep their secrets.
How we understand and think of our place in the world, and our relation to one another and to machines, will ultimately decide where our technologies will take us. We cannot unthink the network; we can only think through and within it. The technologies that inform and shape our present perceptions of reality are not going to go away, and in many cases we should not wish them to. Our current life support systems on a planet of 7.5 billion people and rising depend on them. Our understanding of those systems, and of the conscious choices we make in their design, remain entirely within our capabilities. We are not powerless, not without agency. We only have to think, and think again, and keep thinking. The network – us and our machines and the things we think and discover together – demands it.
Computational systems, as tools, emphasise one of the most powerful aspects of humanity: our ability to act effectively in the world and shape it to our desires. But uncovering and articulating those desires, and ensuring that they do not degrade, overrule, efface, or erase the desires of others, remains our prerogative.
When Kasparov was defeated back in 1997, he didn’t give up the game. A year later, he returned to competitive play with a new format: advanced, or centaur, chess. In advanced chess, humans partner, rather than compete, with machines. And it rapidly became clear that something very interesting resulted from this approach. While even a mid-level chess computer can today wipe the floor with most grandmasters, an average player paired with an average computer is capable of beating the most sophisticated supercomputer – and the play that results from this combination of ways of thinking has revolutionised the game. It remains to be seen whether cooperation is possible – or will be permitted – with the kinds of complex machines and systems of governance now being developed, but understanding and thinking together offer a more hopeful path forward than obfuscation and dominance.
Our technologies are extensions of ourselves, codified in machines and infrastructures, in frameworks of knowledge and action. Computers are not here to give us all the answers, but to allow us to put new questions, in new ways, to the universe
- James Bridle’s New Dark Age: Technology and the End of the Future is published by Verso. To order a copy for £14.44, go to guardianbookshop.com or call 0330 333 6846. Free UK p&p over £10, online orders only. Phone orders min. p&p of £1.99.
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The evolution of … machines.
Roberto Saracco October 27, 2017 Blog 2,183 Views
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The Symbiotic Autonomous Systems Initiative has completed its first WhitePaper (it will become available through the SAS website by the middle of November once the cleaning up is complete). It is an interesting document and in its concluding remarks it shows the possible, expected, evolution of machines towards awareness over the next decades (the horizon has been set to 2050 but quite a bit is happening today and a lot will be accomplished by the next decade).
Clearly it is difficult, may be even unreasonable, to make prediction over such a long span, however it is not about wild guessing, rather it is about looking at what technology offers today, where research efforts are around the world, what the market demands and the social drives that will make the evolution a reality.
IEEE is aware of most of the technological research efforts and this global visibility makes prediction in the area of symbiotic autonomous systems an exercise in rationality.
So, let’s take a look at this sketchy roadmap.
Machines have become smarter and smarter thanks to an ever increasing processing capability, access to large storage for local and remote data, sensors and communications. We have cars that have shown the ability to drive autonomously, although they are still rare and there are regulatory hurdles in the way (not to mention their affordability in terms of cost). The basic technology for self driving cars exists today, it is just not completely practical nor affordable. But it is just a matter of time, no longer of “possibility”.
This self driving cars are “context aware”, that is they “understand” in an operational sense what they need to do given the context around them. They can identify a person walking on the sidewalk and evaluate the probability that he may cross the road all of a sudden, as well as evaluate distance and velocity of an incoming car to evaluate the safety of overtaking the preceding car.
In the next decade this context awareness will become more and more generalised and, most important, affordable. Notice that it is not just cars. Robot vacuum cleaners have already some sort of understanding of their context and this understanding will grow to include something like: “uhm, there is a person watching a tv show so it is better to wait for cleaning not to disturb him, or the lunch is just finished so it may be a good time to vacuum the kitchen…
A significant contribution to the evolution towards context aware machines will come from military applications, as it happened in the past. So it is not difficult to forecast that machines will become context aware, wherever and whenever it makes sense.
We are also noticing, today, that a number of devices are interfacing directly with us, mostly in the medical space, getting information on our status and acting in consequence. Insulin pumps are becoming smarter providing the exact dose by measuring the glucose directly in the body (smart contact lenses are available in the labs of Google and Samsung, and most likely in other research labs to detect the sugar level in the tears and communicate it to a chip that can take action delivering the required amount of insulin). In the next decade this devices are likely to become proactive, analysing the behaviour, guessing the expected one and injecting insulin as soon as it makes sense without waiting for reaching a thresholds. Bio interfaced machines will allow them to connect to nerve termination, to the metabolic system, to muscles, to our senses and even directly to the brain. Hence an evolution that we can expect is towards augmented machines , augmented through the information provided by a living being, including, of course, ourselves. Again we are seeing the first occurrences, although crude, of augmented machines in robots, like Baxter, that learn by watching people, or in sensors leveraging on living cells to detect specific molecules. Of course tools are “augmented” by people using them but in this case we are not talking about autonomous system. A hammer cannot do anything without a hand (and a brain behind the hand) operating it. A self driving car, on the contrary can operate autonomously but it can also benefit from a standing by driver. In the coming decade the situation where people can “lend” their brain to a machine to augment its intelligence will become more and more common.
In order to become “intelligent” a machine needs to pass a certain thresholds of complexity, similarly to living things. A bacteria is fully operational and in a way smart, but that smartness is the consequence of millions of evolution steps, of generations that finely tuned its response to the environment. To get a local intelligence you need to have much higher complexity. Not all machines will reach this thresholds but there will be some that would aggregate into complex systems and intelligence will result, emerge, out of the whole system. These machine swarms are becoming possible through a connectivity fabric that connects thousands, millions of them, like a anthill makes intelligence emerge out of thousands of ants, individually incapable of showing intelligence.
Both machine swarms and context aware machines will likely take a further step becoming machine aware . In a way complex living things are an example of this evolution. One can see our human body as a cell swarm, hundreds of billions of cells, connected to a context aware machine, the brain, that all together result in a being that is “aware”. Would these machines be “sentient”, in the sense of being aware that they are aware? Opinions differ and no stand has been taken by the SAS White Paper.
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The Evolution of Machines and Their Relevance Today
Date published: 11 Dec 2022
Format: APA
Academic level: High School
Paper type: Essay (Any Type)
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The essential part of machinery in our day to day operations in all spheres of life cannot be downplayed. If any case, machines have moved quickly to effectively fill in the glaring gap and space that exists in our operations scopes. As explained in the Ted talk video, there is more to machine world than just doing the manual duties that they were initially designed for, they have come in to make it easy for the intellectual engagements for the humans by replacing the hard, thoughtful process that exists in the society.
The relevance of machine evolution has been discussed in the reading by the Jacobs & Chase (2014) about the evolution in the students’ study life. The discussion about the smartbook which has come in a long way to help the student improve their understandings of the syllabus and improve their grade score is great progress in the educational field. The smartbook which guides the students on what to do next and how to approach their studies scope is a clear indication that the technology is quickly moving to replace some of the careers such as teaching and publications.
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The supply and chain industry is one segment that has seen a rapid revolution brought forth by the machine evolution. The port logistics part is currently run by the machine algorithms that continue to analyze large data and to increase the demand for forecasting accuracy. Whereas in the past, I was required to account for the luggage manually, the introduction of the tracking machines w-that quantify and verify the luggage movement fully fitted with scanners have helped largely with the simplicity of the tasks.
The technological evolution at the port has made it cost-friendly to run the supply and chain business. Whereas there have been massive layoffs emanating from the digital operations, the incredible delivery performance coupled with minimal risks involved in the operation is one factor worth celebrating. Jacobs & Chase explains that OSCM involves planning, sourcing, making, delivering and returning (Jacobs & Chase, 2014). The technological aspect of the Supply chain has seen the easing of all the elementary aspects of these basic functions of the logistics and Supply and chain operations. Such emerging issues in the OCSM like the coordination, optimization as well as the customer management has created an atmosphere whereby the operations have eased.
At the Minnesota National Guard, there is a continuous need to meet the ever-growing demand for quality services and products. Vehicles and machines are but a few of the factors needed in the operations. The quality functional deployment (QFD) is one of the key elements that I have to take into consideration son the provision of these facilities (Jacobs & Chase, 2014). The QFD involves keenly listening and taking into considerations all of the customer’s requirements. With the designed and introduction of assembling technology and designing, the Minnesota National Guard has seen an increase in the quality of operations and timely delivery and response to operational demands.
References
Jacobs, R. F., & Chase, R. B. 15 th Ed. (2014). Operations and Supply Chain Management . The McGraw-Hill Education Series. The University of Southern California. Accessed on 4 th Sept 2018 from Operations%20and%20Supply%20Chain%20Management%20-%20F.%20Robert%20Jacobs.pdf
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Evolution of machinery.
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Peoples of the world have been constantly fabricating and improving the tools they use for everyday existence. Those our forefathers used to harvest their crops, build their homes and protect themselves are much different from those we use today. All of our daily lives are influenced by machinery: When I wake up in the morning I have a shower (steam engine), next I get dressed (cotton gin) and I walk downstairs to the kitchen to eat my breakfast. A kitchen involves an oven, stove, microwave, toaster and refrigerator (all electricity), after breakfast my mother drives me to school (automobile). At school there are computers, pianos, overhead projectors and plows for maintaining the playing field. After my mother drives me home from school I sit on the couch and watch television, and while I"m doing my homework I listen to music. These are just some of the many simple uses of machinery in an ordinary persons life. Webster's defines a machine as: apparatus usually powered by electricity, designed to perform a particular task; vehicle, such as an automobile or aircraft; controlling system an organization. The Industrial Revolution began the expansion of Western Europe and worldwide development. Since the Industrial Revolution people have been focusing on making inventions and simplifying life for themselves and others. Manufacture began in 1698 when Thomas Savery invented the very first crude steam engine. Steam engines powered all early locomotives, steamboats and factories, and therefore acted as the foundation of the Industrial Revolution. Following his invention came great social change as one machine could supply entire cities with river water in the most convenient ways. Eli Whitney created a machine in 1793, which changed life in America for the better. He constructed a machine called the cotton gin; it quickly and easily separated cottonseed from cotton fiber. Whitney's cotton gin was capable of maintaining an output of 23 kg of cleaned cotton per day.
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More about Essay On Evolution Of Computers
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IMAGES
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COMMENTS
The Industrial Revolution, the period in which agrarian and handicraft economies shifted rapidly to industrial and machine-manufacturing-dominated ones, began in the United Kingdom in the 18th century and later spread throughout many other parts of the world.
Rise of the machines: has technology evolved beyond our control? We have come to believe that everything is computable. Intel corporation/EPA Technology is starting to behave in intelligent and...
Both machine swarms and context aware machines will likely take a further step becoming machine aware. In a way complex living things are an example of this evolution. One can see our human body as a cell swarm, hundreds of billions of cells, connected to a context aware machine, the brain, that all together result in a being that is “aware”.
The relevance of machine evolution has been discussed in the reading by the Jacobs & Chase (2014) about the evolution in the students’ study life. The discussion about the smartbook which has come in a long way to help the student improve their understandings of the syllabus and improve their grade score is great progress in the educational field.
Following his invention came great social change as one machine could supply entire cities with river water in the most convenient ways. Eli Whitney created a machine in 1793, which changed life in America for the better. He constructed a machine called the cotton gin; it quickly and easily separated cottonseed from cotton fiber.
Essay On Evolution Of Computers 702 Words3 Pages Through time, computers have evolutionized. They have affected the world around us in many different ways. Computers have been integrated into the government and have becomes part of our everyday life. Computers have gone as far as being part of our culture.