Four versions of "Solarpunk" artwork, created by the AI Midjourney, as prompted by Sean Ellul...Sean Ellul via Midjourney
Think of your dream house. Maybe it has high, arching ceilings, a roaring fireplace and expansive windows that look out onto a placid lake. Or maybe it’s a breathing metallic dome that sits on a fiery planet and is filled with alien butlers. What if you could write a paragraph about those houses, and then immediately enter virtual versions of them and bring all your friends?
Thanks to recent developments in AI like ChatGPT and DALL-E, a future in which users will be able to create their own strange, immersive worlds is not far off. In the fall, three new text-to-3D generators were announced: GET3D from Nvidia, Make-a-Video from Meta and DreamFusion from Google.
And metaverse builders are already using text generators like ChatGPT—which responds to text prompts with startling poise and intelligence—and visual generators like DALL-E—which creates images out of text prompts—to ideate new worlds and designs.
Metaverse industry insiders say that these AI technologies will be crucial toward building virtual worlds that are detail-rich and customizable—that they hold the key toward creating metaverses that regular people will actually want to spend time in.
“We’re able to fill the internet with interesting stuff because everybody is capable of taking a picture, recording a video, or writing words,” says Rev Lebaredian, VP for Omniverse and simulation technology at the chipmaker Nvidia. “If we are going to create a 3-D internet, then you absolutely have to have the people who are participating in it creating content as well—and the only hope we have of making that happen is if AI can help us.”
Changing workflows
While the use of AI tools in metaverse creation isn’t quite there yet, it is already playing a crucial, if slightly mundate role. ChatGPT, for example, is being used by metaverse builders to brainstorm ideas, write code, and compose texts of decks and emails.
In researching for this story, I emailed Sean Ellul, the co-founder of the 3D development studio Metaverse Architects, to ask him if he’s been impacted by ChatGPT. He responded with a well-written five-paragraph email about how he’s been using the technology. But there was a catch: in the fourth paragraph, the e-mail revealed that it had actually been written by ChatGPT itself. Ellul had punched in the following prompt into the service and then sent it over (with minimal editing):
“Write an email to Andrew, from TIME, about how at the company Metaverse Architects we are using chat GPT to brainstorm code, prepare articles and ideate new projects. We even use it to write emails, such as this one!”
The ensuing email was staid, yet completely believable and informative. It was proof of the baseline powers of ChatGPT and the ways in which Ellul has implemented it into his daily processes. Ellul says he uses ChatGPT to tweak design ideas, solicit marketing techniques, create architectural blueprints, and many tasks in between….Continue reading….
The study of empathy is an ongoing area of major interest for psychologists and neuroscientists in many fields, with new research appearing regularly. Empathy is a broad concept that refers to the cognitive and emotional reactions of an individual to the observed experiences of another. Having empathy increases the likelihood of helping others and showing compassion.
“Empathy is a building block of morality—for people to follow the Golden Rule, it helps if they can put themselves in someone else’s shoes,” according to the Greater Good Science Center, a research institute that studies the psychology, sociology, and neuroscience of well-being. “It is also a key ingredient of successful relationships because it helps us understand the perspectives, needs, and intentions of others.”
Though they may seem similar, there is a clear distinction between empathy and sympathy. According to Hodges and Myers in the Encyclopedia of Social Psychology, “Empathy is often defined as understanding another person’s experience by imagining oneself in that other person’s situation:
One understands the other person’s experience as if it were being experienced by the self, but without the self actually experiencing it. A distinction is maintained between self and other. Sympathy, in contrast, involves the experience of being moved by, or responding in tune with, another person.”
Emotional and Cognitive Empathy
Researchers distinguish between two types of empathy. Especially in social psychology, empathy can be categorized as an emotional or cognitive response. Emotional empathy consists of three separate components, Hodges and Myers say. “The first is feeling the same emotion as another person …
The second component, personal distress, refers to one’s own feelings of distress in response to perceiving another’s plight … The third emotional component, feeling compassion for another person, is the one most frequently associated with the study of empathy in psychology,” they explain.
It is important to note that feelings of distress associated with emotional empathy don’t necessarily mirror the emotions of the other person. Hodges and Myers note that, while empathetic people feel distress when someone falls, they aren’t in the same physical pain. This type of empathy is especially relevant when it comes to discussions of compassionate human behavior. There is a positive correlation between feeling empathic concern and being willing to help others.
“Many of the most noble examples of human behavior, including aiding strangers and stigmatized people, are thought to have empathic roots,” according to Hodges and Myers. Debate remains concerning whether the impulse to help is based in altruism or self-interest. The second type of empathy is cognitive empathy. This refers to how well an individual can perceive and understand the emotions of another.
Cognitive empathy, also known as empathic accuracy, involves “having more complete and accurate knowledge about the contents of another person’s mind, including how the person feels,” Hodges and Myers say. Cognitive empathy is more like a skill: Humans learn to recognize and understand others’ emotional state as a way to process emotions and behavior. While it’s not clear exactly how humans experience empathy, there is a growing body of research on the topic.
How Do We Empathize?
Experts in the field of social neuroscience have developed two theories in an attempt to gain a better understanding of empathy. The first, Simulation Theory, “proposes that empathy is possible because when we see another person experiencing an emotion, we ‘simulate’ or represent that same emotion in ourselves so we can know firsthand what it feels like,” according to Psychology Today.
There is a biological component to this theory as well. Scientists have discovered preliminary evidence of “mirror neurons” that fire when humans observe and experience emotion. There are also “parts of the brain in the medial prefrontal cortex (responsible for higher-level kinds of thought) that show overlap of activation for both self-focused and other-focused thoughts and judgments,” the same article explains.
Some experts believe the other scientific explanation of empathy is in complete opposition to Simulation Theory. It’s Theory of Mind, the ability to “understand what another person is thinking and feeling based on rules for how one should think or feel,” Psychology Today says.
This theory suggests that humans can use cognitive thought processes to explain the mental state of others. By developing theories about human behavior, individuals can predict or explain others’ actions, according to this theory.
While there is no clear consensus, it’s likely that empathy involves multiple processes that incorporate both automatic, emotional responses and learned conceptual reasoning. Depending on context and situation, one or both empathetic responses may be triggered.
Cultivating Empathy
Empathy seems to arise over time as part of human development, and it also has roots in evolution. In fact, “Elementary forms of empathy have been observed in our primate relatives, in dogs, and even in rats,” the Greater Good Science Center says. From a developmental perspective, humans begin exhibiting signs of empathy in social interactions during the second and third years of life.
According to Jean Decety’s article “The Neurodevelopment of Empathy in Humans,” “There is compelling evidence that prosocial behaviors such as altruistic helping emerge early in childhood. Infants as young as 12 months of age begin to comfort victims of distress, and 14- to 18-month-old children display spontaneous, unrewarded helping behaviors.”
While both environmental and genetic influences shape a person’s ability to empathize, we tend to have the same level of empathy throughout our lives, with no age-related decline. According to “Empathy Across the Adult Lifespan: Longitudinal and Experience-Sampling Findings,” “Independent of age, empathy was associated with a positive well-being and interaction profile.” And it’s true that we likely feel empathy due to evolutionary advantage:
“Empathy probably evolved in the context of the parental care that characterizes all mammals. Signaling their state through smiling and crying, human infants urge their caregiver to take action … females who responded to their offspring’s needs out-reproduced those who were cold and distant,” according to the Greater Good Science Center. This may explain gender differences in human empathy.
This suggests we have a natural predisposition to developing empathy. However, social and cultural factors strongly influence where, how, and to whom it is expressed. Empathy is something we develop over time and in relationship to our social environment, finally becoming “such a complex response that it is hard to recognize its origin in simpler responses, such as body mimicry and emotional contagion,” the same source says.
Psychology and Empathy
In the field of psychology, empathy is a central concept. From a mental health perspective, those who have high levels of empathy are more likely to function well in society, reporting “larger social circles and more satisfying relationships,” according to Good Therapy, an online association of mental health professionals. Empathy is vital in building successful interpersonal relationships of all types, in the family unit, workplace, and beyond.
Lack of empathy, therefore, is one indication of conditions like antisocial personality disorder and narcissistic personality disorder.In addition, for mental health professionals such as therapists, having empathy for clients is an important part of successful treatment. “Therapists who are highly empathetic can help people in treatment face past experiences and obtain a greater understanding of both the experience and feelings surrounding it,” Good Therapy explains.
Exploring Empathy
Empathy plays a crucial role in human, social, and psychological interaction during all stages of life. Consequently, the study of empathy is an ongoing area of major interest for psychologists and neuroscientists in many fields, with new research appearing regularly. Lesley University’s online bachelor’s degree in Psychology gives students the opportunity to study the field of human interaction within the broader spectrum of psychology.
“Introspection and empath”(PDF). Dialogues in Philosophy, Mental and Neuro Sciences. 7: 25–30. Archived from the original(PDF) on July 26, 2014.Gallese V (2003). “The roots of empathy: the shared manifold hypothesis and the neural basis of intersubjectivity”.
Bitcoin mining site manager Guo-hua checks an application-specific integrated circuit (ASIC) at ... [+] The Washington Post via Getty Images
The power demands and carbon emissions of bitcoin mining could undermine global efforts to combat climate change if stringent regulations are not placed upon the industry, a Chinese study has found. By 2024, mining of the cryptocurrency in China alone could use as much power as the entire nation of Italy uses in a year, with greenhouse gas emissions equalling those of the Czech Republic.
But rather than recommending increased taxation on bitcoin mining to curb emissions, or simply an outright ban on the practice, the paper, published today in the journal Nature, suggests that miners should be encouraged to shift their operations to regions that provide abundant low-carbon electricity.
The research is significant because China carries out at least 65% of the world’s bitcoin operations. Shouyang Wang, one of the report’s authors and chair professor at the Academy of Mathematics and Systems Science at the Chinese Academy of Sciences in Beijing, told Forbes.com:
“While everyone has focused on bitcoin’s great profitability, we want people to become more aware of its potential issues and start thinking about these questions: is this industry actually worth the associated environmental impact, and how can we make profitable bitcoin mining operation more sustainable in the future?”
Using simulation-based models, the researchers found that, short of any policy interventions, bitcoin mining in China will peak in 2024 consuming 296.59 terawatt hours of electricity—as much as a medium sized country—and generate 130.50 million metric tons of carbon emissions. The authors further note that this consumption and the resulting emissions could derail China’s efforts to decarbonize its own energy system.
“It is important to note that the adoption of this disruptive and promising technique without [taking into account] environmental concerns may pose a barrier to the worldwide effort on GHG emissions management in the near future,” Wang said, adding that the research team was “surprised by the energy consumption and carbon emission assessment results of bitcoin blockchain operation in China.”
But the solution to the challenge, the authors argue, is “moving away from the current punitive carbon tax policy to a site regulation policy”—in essence, ensuring that mining operations move to areas that guarantee high rates of renewable electricity. Under such a policy, they found, only 20% of bitcoin miners remained in coal-intensive energy regions, resulting in lower carbon emissions per dollar earned, compared to a higher taxation scenario.
Under the site regulation model, the researchers found bitcoin operations generated 100.61 million metric tons at peak, as opposed to 105.19 million tons under an additional taxation scenario. Wang said government regulation of the industry was needed, but that bitcoin miners would likely be amenable to his team’s proposed solution.
“Site regulation should be carried out by the government, placing limitations on bitcoin mining in certain regions that use coal-based heavy energy,” Wang explained. “That being said, we think that there are enough benefits to this policy which will incentivize the miners to move their operation willingly. For example, since energy prices in clean-energy regions of China are lower than that in heavy-energy regions, the miners can effectively lower their individual energy consumption cost, which would increase their profitability.”
That isn’t to say, however, that regulation is the only method by which China should be reducing the emissions impact from bitcoin mining. “The government should also focus on upgrading the power generation facilities in clean-energy regions to ensure a consistent energy generation,” Wang said. “That way, the miners would definitely have more incentives to move voluntarily.”
Crunching The Numbers
Bitcoin operates by using blockchain technology—publicly recorded peer-to-peer transfers on encrypted computer networks—which eliminates the need for centralized authorities or banks. Bitcoin miners use arrays of processors to determine results to algorithmic puzzles that verify transactions that are added to the blockchain, for which they are in turn rewarded in bitcoins.
With the value of a single bitcoin having risen from $1 in April 2011 to around $60,000 in April 2021, and with yesterday’s news that the value of the cryptocurrency market has exceeded $2 trillion for the first time, the financial incentives to mine bitcoin are obvious.
But there is a finite supply of bitcoins: they are limited to 21 million in total. To control the currency’s circulation, the supply of new bitcoins is halved every four years, which also halves the miners’ rewards. This has helped ignite fierce competition, attracting an increasing number of bitcoin miners to get into the race, utilizing ever more powerful processing arrays requiring more electricity.
This, the authors say, means that after 2024, bitcoin mining—at least in China—will no longer be cost-effective; the costs of mining the currency will begin to outweigh the rewards. “We have predicted through our model that bitcoin mining operations in China would start to decrease in 2025,” Wang said.
“Due to over-competitive and the reward-halving mechanism of bitcoin, many miners would leave China and move their operations elsewhere in hope to improve their profitability. The decrease in mining activities would lower the associated carbon emissions generated in China.”
So, in at least one sense, bitcoin is self-regulating. Or as Wang puts it, “this is the industry’s natural built-in way of phasing itself out.”
Silver Linings?
It has until recently proved difficult to determine the total emissions impact of bitcoin mining. Industry advocates have long claimed that miners tend to rely on low-carbon energy due to its relatively low cost, but those claims have been disputed.
Now, using more advanced modeling techniques, Chinese researchers have been able to more accurately estimate the energy uses of specific industry operations. According to the China Emissions Accounts and Datasets platform (CEAD), for example, bitcoin mining accounts for more than 5.4% of emissions from electricity generation in China.
In response, various policy solutions have been suggested, including heavier taxation of bitcoin mining operations. The new research suggests site regulation could be the preferable option. But did Wang think this could result in too many miners moving into areas with abundant renewables, gobbling up energy supply?
“There would be an influx of bitcoin miners into clean-energy regions,” he said. “However, we don’t think that this increase in bitcoin mining operations would place burdens on the local energy grid. The energy-generation infrastructures in the clean-energy regions of China are still being improved and developed … we think that increases in energy generation capacity would outpace the increase in bitcoin mining operations in these regions, which would reduce the potential burdens.”
Even so, with a forecast of 100 million tons of carbon emissions at the industry’s peak, would it not simply be better, in environmental terms, to ban the practice outright?
“We think that simply banning bitcoin mining altogether is not ideal,” Wang said. “Even if bitcoin mining is completely banned, its increasing profitability would drive miners to continue their activities through other measures, such as stealing electricity. That is why we are suggesting a push for moving the miners to clean renewable energy regions would be more ideal.”
Asked whether future cryptocurrency operations could potentially result in the same or similar energy demands as bitcoin, Wang offered a note of optimism.
“Cryptocurrency communities have become increasingly aware of the carbon emissions generated through mining activities,” he said. “As a result … we think the development of these new consensus algorithms would improve the energy efficiency of cryptocurrency mining activities, which would be beneficial for China’s sustainability efforts.”
Members of Forbes Technology Council discuss smart and effective uses for digital twin technology. Photos courtesy of the individual members.
A digital twin is precisely what its name suggests: A digital copy of a physical object or system—even a human being. It may be a simple concept, but the potential applications are anything but. Through the ongoing collection and exchange of data, a digital twin can simulate and even predict the behaviors and reactions of its physical twin in a variety of conditions, providing invaluable insights to industries ranging from manufacturing to healthcare.
Digital twin technology allows businesses and organizations to test products and processes, study and predict how real-world conditions can affect physical objects and beings, and make well-informed, big-impact decisions with minimized financial and human safety risks. Below, 16 members of Forbes Technology Council share some of the fascinating ways industries and organizations are leveraging digital twin technology.
1. Minimizing Manufacturing Waste
We at Cuby use digital twin technology to make sure we produce 1-to-1 kits of the parts needed in our prefab construction process. It’s been estimated that up to 40% of the solid waste in the U.S. is construction and demolition waste. Manufacturing all the parts in advance allows us to reduce waste by up to 90%. – Aleksandr Gampel, Cuby Technologies, Inc.
2. Building Resilient Supply Chains
Businesses are increasingly using digital twin technology to build resilient and responsive supply chains. The digitization of supply chain processes provides businesses the opportunity to increase organizational efficiency by predicting serious problems and deceleration. In fact, it is estimated that by 2025, 80% of participants in industry ecosystems will rely on digital twin technology. – Radhika Krishnan, Hitachi Vantara
3. Mitigating Disruptions Due To Weather And Climate
Businesses are using digital twin technology to mitigate climate-related disruption. By combining data from public sources, such as weather data, with data from suppliers and partners, leaders can see how an unplanned weather event might impact the flow of goods across their supply chain, then use this insight to quickly pivot orders, routes or suppliers to limit waste and meet demand. – Rohit Shrivastava, Anaplan
4. Studying And Refining Processes
Process mining combined with simulation gives reliable visibility into as-executed processes (versus relying on what somebody thinks is happening) and the ability to do “what-if” analyses. This is effective because it allows one to see what’s really happening and simulate changes before making them. Often, changes do nothing or create a bigger problem elsewhere. Simulation and mining prevent that. – Michael Nyman, iGrafx
5. Making Data-Driven Manufacturing Decisions
The utilization of digital twins in the manufacturing industry has seen large growth. Digital twins increase productivity and reduce costs by combining the physical and digital worlds to make data-driven decisions, prolong asset life cycles and minimize unexpected maintenance disruption across assets. This modernizes the sector by moving from the “break and fix” approach to proactive maintenance. – Cindy Jaudon, IFS
6. Testing Health Intervention And Engagement Strategies
Health outcomes improve when patients are confident, connected and engaged. Digital twin technologies provide healthcare organizations the option to test drive new interventions and engagement strategies. This lowers the risks of rolling out new programs by testing hypotheses through a simulated pilot while also enabling cost-conscious innovation. – Trisha Swift, PricewaterhouseCoopers
7. Improving Patient Outcomes
A digital twin enables accurate and continuous monitoring. That data flow can inform data-driven decisions. For example, doctors are using an individual’s genetic makeup to model new organs for transplant. Because these processes can leverage a populationwide data set of digital twins, they can replicate an individual human body’s internal system to improve treatment outcomes for all. – Nicholas Domnisch, EES Health
8. Expanding Professional Services Capabilities
Knowledge-rich professional services firms are building digital twins of accountants, advisors and auditors using graph-based intelligent automation. These are distinct from previous technologies, because the decisions these professionals make are complex, contextual and nonlinear. In a world where there are big skills shortages and raging inflation, this form of IA is closing the gap. – James Duez, Rainbird Technologies
9. Onboarding And Knowledge Sharing
A digital twin use case that is an easy entry point and can provide quick ROI is training or onboarding. In areas where experienced employees are preparing for retirement, where there is high turnover or where there are general labor shortages, having a prerecorded “virtual” expert that can walk you through the instructions in real time can be a game changer and is much more effective than a giant paper manual. – Samantha Williams, Sonoco
10. Providing Safe Training
Today, manufacturing organizations are leveraging digital twin technologies to replicate machinery that would typically put employees in harm’s way. Here, a virtualized version of the original piece of machinery can be used to give employees training experience with the virtual machinery without also putting the employee’s health and safety in peril. – Marc Fischer, Dogtown Media LLC
11. Understanding Multidimensional Problems
Digital twin technology shines the brightest when it helps companies better understand a multidimensional problem—one that is too complex to easily solve. Because it is a way to visualize and make better decisions, the technology has become extremely effective for everything from product design to diagnosing medical issues to better understanding variables that affect business expenses. – Josh Dunham, Reveel
12. Improving Manufacturing Efficiencies
A very interesting field of application is manufacturing. Thanks to a digital twin of a production plant, with all its different lines and machines, we can launch simulations to generate greater efficiency or to detect potential bottlenecks. Simulating the manufacture of new products or variants of existing products is also a very useful application. – Miguel Llorca, Torrent Group
13. Developing And Training Self-Driving Vehicles
Without digital twin technology, it would be impossible to develop self-driving vehicles at the scale and with the reliability we are witnessing nowadays. Billions of simulations on a “virtual road” by a “virtual car” allow for training machine learning models to forecast accidents and plan not just the fastest, but also the safest, routes so that drivers can entrust the actual driving to robots. – Aleks Farseev, SoMin.ai
14. Budgeting And Financial Planning
Financial and operational data is the lifeblood of a company, but it’s difficult to “see all of it” and understand it in real time. Digital twins offer real-time, big-data-enabled simulation modeling that can be particularly useful for budget and financial planning. The technology can streamline tasks such as procurement, case management and capital resiliency and deliver powerful insights for finance leaders. – Nicola Morini Bianzino, EY
15. Managing Traffic
Digital twins are already effectively used by urban planning councils in many U.S. cities for efficient traffic management. They help in simulating real-world congestion at junctions, predicting what may get worse when and where, and they can be used to test multiple mitigation techniques by leveraging the best mix of ML and city know-how. Dashcam-backed digital twins are explored alongside junction twins. – Pramod Konandur Prabhakar, Pelatro PLC
16. Simulating Real-World Conditions
One way businesses are leveraging digital twin technology is by using it to simulate the physical world. For example, a company can use a digital twin to simulate a real-life situation so that they can predict how their product or service will behave in that environment. Another way is by using it to understand how their customers use their services and products. – Leon Gordon, Pomerol Partners
To explain what Digital Twin means in simple words, it is a digital replica or a representation of a physical object (e.g. aircraft engine, person, vehicle) or an intangible system (e.g. marketing funnel, fulfillment process) that can be examined, altered and tested without interacting with it in the real world and avoiding negative consequences.
Think of it as an online platform for testing, creating and altering objects that are based in reality, without engaging with them in the real world itself. Technologies similar to this one have been used in various industries long before this concept was created, however, this new definition of the technology has much more potential, power, and scalability that can replicate, monitor and test virtually anything you can think of.
The rise of the Internet of Things (IoT) has complimented the adoption of this new technology, as IoT has resulted in its cost-effective implementation. Virtual twins have become imperative to business today, consistently named as a strategic technology trend in recent years. The complexity of technology has led to many questions within the industry. One of the most important ones is how is it changing the way design, planning, manufacturing, operation, simulation, and forecasting is traditionally functioning?
A physical twin that was replicated on a virtual platform is a near real-time digitized copy of a physical object. It is a bridge between the digital world and the physical world. Its core use is to optimize business performance, through the analysis of data and the monitoring of systems to prevent issues before they occur and prevent downtime. The simulations that are produced will help to develop and plan future opportunities and updates within the process or product.
The benefits of virtual twin technology are astronomical, with industries such as agriculture, government, transportation and retail experiencing rewards from the technology and benefits going forward. Companies must find methods to prevent the risk of potential product defects among their assets and future products. This piece of tech allows production costs to be minimized, as companies will save expenses when products are right the first time.
There is no need for expensive physical tests or updates to the products or process. Research with manufacturers has found that this concept will enable the reduction of development costs of the next generation of machines by well over 50%. The features of the tech also provide added confidence to boost product performance and aid complex decisions, preventing costly downtime to robotics and machinery.
The core benefit of why most companies started twinning their processes, products, and services via simulations is due to their efficiency. Businesses are racing to market their product faster than their competition, and having the ability to virtually simulate scenarios where a product is tested for failure via multiple angles helps the situation immensely. Not mentioning the fact that the development and testing costs are usually reduced hundreds of times.
This technology will be able to anticipate how the product and process will perform through digital simulations and analysis. The accessibility of reliable and consistently updated information provides the assurance needed to make faster decisions and increase the speed of production to overtake competitors. Here is a use case infographic we have presented in our workshop about understanding virtual twins – find event information here.
We can see how a virtual twin simulation is used to replicate and optimize machinery that regulates water flow in a factory. By doing this, developers can see every moving detail on the screen and then make the calculated decision to upgrade, optimize or make positive changes accordingly. Offices that adopt this technology early will attract innovative and leading talent. Offices will be able to incorporate interactive features to improve employee satisfaction and productivity using data-driven simulations.
Employees who will use digital twin technology will be able to expand their engagement with online tools, such as interactive maps to locate colleagues on the floor, book meetings and complete tasks with more diligence and accuracy. Managers will also be able to supervise remotely with the tool that will be similar to a 3D map that will be created using virtual online platforms that are based on simulations.
It is evident that the digital twin concept will benefit many people within the supply chain. Combining this disruptive concept with IoT technology is an incredible opportunity for businesses to improve. Ultimately, it will also allow stakeholders to improve the overall efficiency and cost of their business, and improve many aspects of work for employees….
Elon Musk thinks you don’t exist. But it’s nothing personal: he thinks he doesn’t exist either. At least, not in the normal sense of existing. Instead we are just immaterial software constructs running on a gigantic alien computer simulation. Musk has stated that the odds are billions to one that we are actually living in “base reality”, ie the physical universe.
At the end of last year, he responded to a tweet about the anniversary of the crude tennis video game Pong (1972) by writing: “49 years later, games are photo-realistic 3D worlds. What does that trend continuing imply about our reality?”
This idea is surprisingly popular among philosophers and even some scientists. Its modern version is based on a seminal 2003 paper, Are We Living in a Computer Simulation? by the Swedish philosopher Nick Bostrom. Assume, he says, that in the far future, civilisations hugely more technically advanced than ours will be interested in running “ancestor simulations” of the sentient beings in their distant galactic past.
If so, there will one day be many more simulated minds than real minds. Therefore you should be very surprised if you are actually one of the few real minds in existence rather than one of the trillions of simulated minds.
This idea has a long history in philosophical scepticism (the idea that we can’t know anything for sure about the external world) and other traditions. The Chinese Taoist sage Zhuangzi wrote a celebrated fable about a man who couldn’t be sure whether he was a man dreaming of being a butterfly, or a butterfly dreaming of being a man.
René Descartes imagined that he might be being manipulated by an “evil demon” (or “evil genius”) that controlled all the sensations he experienced, while the 20th-century American philosopher Hilary Putnam coined the term “brain in a vat” to describe a similar idea. But while Neo in the Wachowskis’ 1999 film The Matrix really is a brain (or rather a whole depilated body) in a vat, the simulation hypothesis says that you do not have a physical body anywhere. “You” are merely the result of mathematical calculations in some vast computer.
There are many possible objections to this idea even getting off the ground, as Bostrom notes. Perhaps it is simply not possible for computer-simulated beings to become conscious in the way we are. (This would defeat the “assumption of substrate independence”, according to which minds are not dependent on biological matter.) Or perhaps all civilisations destroy themselves before getting to the simulation stage. (Plausible if not necessarily comforting.)
Or perhaps advanced civilisations are simply not interested in running such simulations, which would be surprising given the kinds of things humans do – such as developing video deep-fake technology or researching how to make viruses more virulent – even though they seem to be very bad ideas.
The simulation hypothesis is perhaps attractive to a wider culture because of its nature as a cosmic-scale conspiracy theory as well as an apparently scientific version of Creationism. The inconceivably advanced alien running its simulation of our universe is indistinguishable from traditional terrestrial ideas of God: an all-powerful being who designed everything we see.
But is this god the god of deism (who sets up the laws of nature but then absents himself while creation runs its course), or a more interventionist figure? If the latter, it might make sense to court their favour.
How, though, should we please such a god? Not necessarily by being virtuous, but by being – assuming the simulator is watching us for its own pleasure – at least entertaining. This line of reasoning might imply, for example, that it is one’s duty to become a florid serial killer, or a guy who tries to colonise Mars and buy Twitter.
“Be funny, outrageous, violent, sexy, strange, pathetic, heroic … in a word ‘dramatic’,” counsels the economist Robin Hanson, considering that assumption in his 2001 paper How to Live in a Simulation . “If you might be living in a simulation then all else equal it seems that you should care less about others,” he concludes, and “live more for today”.
One commonly despairing reaction to the idea that we might all be simulated is that this renders our lives meaningless, and that nothing we see or experience is “real”. The Australian philosopher David Chalmers, in his recent book Reality+: Virtual Worlds and the Problems of Philosophy, argues otherwise. For him, a digital table in VR is a real table.
It is no more disqualified from being “real” by the fact that it is, at bottom, made up of digital ones and zeros than a physical table is disqualified from being real by the fact that it is, at bottom, made up of quantum wave-packets. Indeed, some esoteric theories of physics consider “reality” itself to be at base quantum-computational or mathematical in nature anyway.
Is there any good reason to actually believe the simulation argument, though? Or is it just aesthetically piquant techno-religion? Chalmers observes that it is at least more plausible than earlier iterations of scepticism such as Descartes’s evil demon, simply because we now have functioning prototypes (video games, VR) of how such a simulation might work.
Others have speculated that there may be clues to the fact that our universe is a simulation hidden in the very fabric of the “reality” that we can investigate: perhaps the simulation cuts corners at very small scales or very high energies. Indeed, experiments (for instance in Campbell et al., “On Testing the Simulation Theory”, 2017) have been seriously proposed that might reveal the answer.
But not so fast. Remember that we can’t know what the goal of the simulators is. Perhaps, for them, the game is not merely to observe us as an indefinite planet‑sized soap opera, but simply to see how long the sim-people take to prove that they’re in a simulation. At which point, the game ends and the simulation is turned off. Perhaps we’re better off not finding out.
Steven Poole is the author of Rethink: The Surprising History of New Ideas, published by Random House. To support the Guardian and the Observer order a copy at guardianbookshop.com. Delivery charges may apply
The Simulation Hypothesis: An MIT Computer Scientist Shows Why AI, Quantum Physics and Eastern Mystics All Agree We Are in a Video Game by Rizwan Virk (Bayview)
Simulation article in Encyclopedia of Computer Science, “designing a model of a real or imagined system and conducting experiments with that model”Sokolowski, J.A.; Banks, C.M. (2009).
Thales defines synthetic environment as “the counterpart to simulated models of sensors, platforms and other active objects” for “the simulation of the external factors that affect them
Four versions of "Solarpunk" artwork, created by the AI Midjourney, as prompted by Sean Ellul...Sean Ellul via Midjourney
And metaverse builders are already using text generators like ChatGPT—which responds to text prompts with startling poise and intelligence—and visual generators like DALL-E—which creates images out of text prompts—to ideate new worlds and designs.
In researching for this story, I emailed Sean Ellul, the co-founder of the 3D development studio Metaverse Architects, to ask him if he’s been impacted by ChatGPT. He responded with a well-written five-paragraph email about how he’s been using the technology. But there was a catch: in the fourth paragraph, the e-mail revealed that it had actually been written by ChatGPT itself. Ellul had punched in the following prompt into the service and then sent it over (with minimal editing):
