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Huge cranes are lifting heaps of dirt in a remote mine in a desert of Australia. There are excavators, scrapers, drills, loaders, lifts and earthmovers working in unison to dig iron, lithium, gold and diamonds. Each machine with thousands of moving parts working 24×7 in a harsh environment requires close monitoring and maintenance to keep it running. This requires dedicated maintenance personnel and a stock of spare parts in all locations. Any breakdown that results in significant downtime means a huge loss. An indoor organic hydroponic farm is humming with lights, fans, nutrient mixers and water pumps in a five-star hotel in the city of Orlando, FL. Strict climate control with optimal conditions is required to keep plants growing well. Any failure of components, even for a short duration of time can affect the quality of the green produce. An electric car is self-driving on the roads of California. The IoT sensors in the engine are continuously feeding data to a central server which is monitoring the engine performance, optimizing the run and predicting any potential failures. The advent of for fast connections, IoT sensors to monitor and fast analytics infrastructure to churn data has enabled the upcoming area of decision science. The heart of decision science is predictive analytics. 5G connectivity What is Predictive analytics? Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze real-time data to make predictions about the future. Advance Analytics allows organizations to become proactive, forward-looking, and help in making future decisions based upon the data instead of a hunch. In this blog, we talk about our implementation of predicting failures of components in an engine based on the continuous collection of sensor data. Predictive Analytics Process Training Data We used data from system/device logs collected from the automobile engine. The sensors built into the engine send various parameter values like device temperature, pressure, strength. Each one of these parameters has multiple states with their threshold values. When the engine runs, the parameter values may cross these threshold limits raising alerts. Approach The way to predict these alerts is to first predict the future values of these parameters based on the values already received. We broke down our approach into two steps- Predict parameter values and generate an alert, if the predicted values go beyond the pre-defined limit. Build a model to predict error events with error messages using predicted values from step 1, so that the user can know beforehand that an error message will come at a certain time with certain probability. Data Preprocessing Since the data generated from device logs was unstructured and untidy, various pre-processing techniques were used to add  structure and meaning: data type conversion removing outliers identifying patterns to examine errors at runtime finding specific commands which yield information about the state and parameter values.missing value treatment Model Building & Prediction We predict the values of parameters for the next one hour and their states based on the already collected data and use of LSTM (Long-short term memory) model. For example, if a parameter has two states, we built models for both the states and continuously predicted the values for the next hour. With the help of predicted parameter values and mapping predicted values with device manuals, we had the information about when a value may go beyond its threshold. Using this information we designed the system to send alerts 5 minutes before the expected alert time. We built a classification model for events as based on the predicted parameter values obtained from step one. To build the classification model we split the data into training and test sets in the ratio of 70:30. To overcome the class imbalance problem we applied an upsampling method on training data and again split that 70% data into training and test sets. The purpose of this splitting is to validate our model on both balanced and imbalanced data. Error or No Error We tested different machine learning algorithms on both balanced and imbalanced data. Classification models used were . Among all these algorithms performance of the Random forest model on both balanced and imbalanced test data was much better than the other two as shown below. Logistic Regression, Gradient Boosting and Random forest As we can see from above that the class error percentage given by the Random forest model is minimum as compared to other algorithms. Model Deployment and Serving We developed a test dashboard called Predictive Maintenance Tool with the following functionality: The user can upload the device logs manually or trigger the tool to know about when an error is going to happen based on the runtime events. Display the graph of predicted values for different parameter values. Place to manually enter the error which happened unexpectedly and not in the device manual but resolved by service/maintenance engineer, so that they can add it for future reference. Challenges Data imbalance caused the problem of less information to classify events as Error and No Error. Untidy data.Multiple errors occurring at the same time.Mapping of predicted parameter values with manuals. Due to fluctuation in devices at run time there is a gap in the generated value of logs. Conclusion The predictive analytics machine learning model worked well to provide alerts before the engine values went beyond thresholds avoiding expensive repair costs to the automobile owners. As machine learning models get embedded into edge devices, a combination of such locally generated predictive alerts with server-side machine learning to provide feedback loop will provide efficiency and disaster recovery in all verticals like automobiles, manufacturing, agriculture, and healthcare. Also published on: https://47billion.com/blog/predictive-analytics-maintenance-of-events-using-machine-learning/

Predictive Analytics for Maintenance Events 

The Dangers of Facebook Marketplace

Using AR and VR technology at Work (src: shutterstock) As part of my doctoral research on the metaverse, I am creating some sci-fi vignettes which can be used as “imaginaries” of the future of the metaverse. An “imaginary” is a term that social scientists like to use to talk about a vision of the future, usually (thought not always) rooted in science fiction, which starts out with a small group but then gets adopted by a larger collective, companies and governments, etc. and eventually becomes a “sociotechnical imaginary”. See Metaverse . Each Metaverse Imaginary vignette will have a specific theme (see below) and incorporate multiple threads (see below). I will also add some commentary eventually about the state of the imaginary vis a vis startups and larger companies that are toiling to turn these imaginaries into reality. I’m putting these up for feedback and to spur discussion about the various future scenarios of the Metaverse. Imaginary #1 here Let’s jump right into the second vignette, with a short discussion of analyzing this and other imaginaries afterwards. Note that this is a near future imaginary and is more about the Future of Work, but not as extreme as some futures where new jobs and careers will be created in the Metaverse. As an example, in Ready Player One, Wade’s mother had two full time jobs in the OASIS (its fictional version of the Metaverse), one as a telemarketer and one in an online brothel in the Metaverse. Rather this vignette is about how the Metaverse might change the work of people who have existing jobs that might utilize the Metaverse. Other vignettes will get further into the Metaverse economy. Figure 1: Src: Ars Technica Vignette #2: A Day in the Life of an Engineer: AR, AI Assistants and more Susan got up early for her engineering team meeting. Since the Engineering team was spread out between Buenos Aires, San Francisco, Bangalore and Beijing, and corporate headquarters was in London, they had settled on GMT. Why was it that the corporate, which was probably the smallest office got to set a convenient time for the meeting? She brought up her personal assistant Aivie, or AI Avatar, which she had named Laurie. “Laure, can you get me a Chai Latte” she said as she put her smart glasses on. Of course, laure was hoooked into the smartspeaker in the apartment, but she preferred to see her assistant. The glasses were still in AR mode and Laure appeared next to her, in a not quite human form — she looked like a cross between a teddy bear and a cartoon, it was a furry avatar. But she had set it so that only she could see Laure, so she wouldn’t inconveniently appear while in the middle of a work meeting. “Yes, I have sent the order. Would you like to go pick it up?” asked the Aivie. When Susan paused wondering if she had time to go down, Laure responded: “It will be tight to get there in time. I can have it delivered.” “Very good..” muttered Susan as she was getting setup to go to work. She had dressed for work, but this was just a generic cream sweat suit that she used while working at home. The cream color made it easy to mask — like a green screen in the old days of film and TV production. Since it was just downstairs she knew Laure would find a courier that was nearby — all they had to do was pick up the drink and bring it up to her apartment. Luckily, Laure could authorizer her to come up when the front desk called so it wouldn’t bother Susan. The drink arrived just before Susan’s avatar arrived in the the 3D conference room. She had brought up her usual work avatar, which looked just like her but was dressed in more professional clothes. The landscape of the meeting was something that looked like a cross between a playground monkey bars and something out of an old Mad Max movie she’d seen when she was a kid — she thought it was called Thunderdome or something. She was floating up on one of the monkey bars. Her boss, Herbert, the VP of Engineering, was standing in the center of the dome-like contraption with a big screen above him. Technically, the screens were only necessary when viewing the real world, but it was a metaphor left over from the old days when people met in conference rooms and used actual screens to project things. The engineers in her sub-team, Hiram and Lisa popped up near her, and the other sub-teams all appeared on various parts of the 3d space, including the off-shore team she worked closely with in Bangalore, which included Ranjan and Rajiv and Preeti. At least that’s where they were supposed to be in Bangalore — physical meaning didn’t have the same. She sent them private messages of greeting and set up a private chat group so they could talk to each other while the presentation started. Joining Herbert was the main product manager, William for their software, called Automation 3.0. He brought up a 3d Chart that floated in the air of all the dependencies and milestones to get their next release, 3.2, out the door. It was a major upgrade and they’d already been working on it for the better parts of six months, and were behind schedule. Herbert and William reviewed the major objectives and when their turn came, their particular module zoomed up and as the lead engineer, she floated up to each of the specific points and updated the rest of the group on where they were and then turned it over to Ranjan for the offshore challenge they were facing. It was all very routine. Once the status part of the meeting was done, another person, the VP of Customer Success, Sharon, who was also located on the West coast of the US, appeared. “Our software, as you know, drives the robots that manufacture cars for the new Tesla plant in China. We’ve been working closely with them but there’s a problem. It started as a small glitch in the robots and they traced it to our control system.” She brought up a 3D model using a subpatch of realism that simulated the physics of Earth quite well. It was a giant robot arm and a partially constructed Tesla car. “Susan, we need your team to figure out what’s going on. This has become a second tier support issue and has halted their assembly line so we need to give it top priority.” Susan asked if the Beijing team had looked into it. Sharon responded that they were already on-site, she indicated to Chou, the support engineer who was on site, whose avatar lit up floating up on the other side of the giant dome. They had only gotten so far, he explained, his voice booming across the entire dome — it was a strange trick that was used to amplify a voice so that everyone could hear, whereas in a realistic sim you could only hear those immediately near you and sound faded. They had tracked it down to her module. Susan agreed and asked Rajiv, who was the best debugger to come with her as soon as the meeting ended. The meeting ended and both Rajiv and Susan found themselves inside the realistic patch, a sub-sim that was a digital twin of the Chinese Tesla Plant. Since Chou and one other engineer was on-site, they could provide a video view and once the assembly line was shut down, they could holo-project her there to help with support. While they waited for the next break in the production line, Chou simulated the procedure using the simulated robotic arm and the half-done car. Tesla had been one of the first car companies to use the Metaverse, building a 3d environment that was a digital replica of one of their plants, using an old Nvidia relic called the Omniverse. When the realistic subpatches came up and the Earthverse 2 went live, the omniverse tech had been incorporated, built by teams of developers from around the world. At any one time, thousands of engineers were upgrading the Earthverse2 and the metaverse in general, depending on which sim and verse you were visiting, things could change right before your eyes. In a realistic sub-patch though they weren’t allowed to do live updates and everything had to be scheduled, since the point was that the laws of physics should be simulated exactly as necessary. “Patch will be updated in 1 hour,” flashed a virtual billboard above their heads. Standing with Susan and Rajiv was Jared, an AI troubleshooter that knew the interfaces between all the systems and could help in diagnosing the problem, though Jared wasn’t good enough to come up with real solutions. Chou had already been working with Jared, who looked like one of the old Dr. Who Doctors, a joke no doubt by the designers of Jared. Susan smiled, she knew one of the developers there in London and he was a Dr. Who fanatic. A few minutes later, the plant had a production break and Susan and Rajiv both shifted to projection mode. They now found themselves inside the actual physical manufacturing plant. Chou was no longer in avatar form. Rajiv looked the same, but Susan had chosen a different set of clothes for this visit — it was her standard “site visit” outfit and avatar. The others knew it was her. Susan now felt like she was actually in the plant though she had only limited mobility based on the portable holo-projector that Chou had brought with him. Chou however could move around easily and she could shift to his point of view (or at least the point of view from his glasses). Jared was also there but didn’t need to be projected. Chou pointed out the problem. “Jared,” said Susan, “please show me the code objects for modules 7–11 and the interfaces”. Rather than seeing a window with code, Jared popped up several holographic boxes, each of which was a module, connected to each other by numerous lines, each of which represented an interaction or exchange of data. Susan zoomed in on the lines between module 7 and 8, using her hand controls with her left hand. She was still sipping the Starbucks in her right hand. “Color code and show the values…” said Susan and the lines started changing colors and she could see text moving back and forth — too much and too fast to read. “Slow it down,” she said, and zoomed in further and again using her left hand selected several of the lines to zoom in on. “I think I see the problem..” she said, and took a sip from her Chai Latte in her right hand. “Let’s go back into the sim and try out a fix and see what that yields…” Things to Think About I’m putting these up for feedback and to spur discussion, so feel free to leave comments. Some questions to think about include: How far in the future is this vignette? (1 year, 5 years, 10 years or more)? What are some of the technologies that we already have, and what are some of the technologies that still need to be developed? Is the development a simple matter of better engineering, or does it require any new breakthroughs?Most of us are used to working remotely via zoom, how does the experience change if you are in a virtual space with other avatars? What kinds of issues come up in a work environment? How does this vignette reflect on issues of identity in the “real” world and among avatars — would you have different sets of clothes for your avatar to wear in different work-related environments?How about this idea of being in AR/VR ad projecting your avatar onto a physical location? Would others there be able to see you? Would you be able to see others? Consider that the metaverse could be a set of very different use cases, each with their own physics engines, but linked together via the avatar as the critical component. What considerations need to happen to make this a reality? How is the idea of a realistic sim different from the metaverse environments that most of us envision when we think of working in the metaverse? How good would the laws of physics have to be? How does this relate to NVidia’s omniverse, or other simulation software? What is the role of AI and virtual humans in the metaverse ? In this vignette, we have Jared, an AI assistant that is not quite AGI (artificial general intelligence) but is an expert on coding, and in fact might be able to pass the Turing Test. Is it possible that AI assistants wi ll know more about our technical systems and be able to debug them faster or in conjunction with humans? In the context of work, what does a “centaur” — a term for a human AI team — provide that neither the human or the AI might be able to provide alone? In this vignette we also have an AI assistant, called Laure, that is a personal assistant. If we use the Metaverse for work, what elements can we bring from our own lives, our assistants and data that knows about us? Would it be the same as installing our own software on a work laptop? Or are we talking about something completely different? How does this relatively short imaginary, relate to past imaginaries (TV shows, movies, or books) and how does this measure up with various visions of the Metaverse being put forward by companies like Meta, Microsoft, NVidia’s Omniverse, Epic, Unity, Decentraland, Upland, The SandBox or others? Also Published Here

An Imaginary Day in the Life of an Engineer in the Metaverse - A Sci Fi Vignette

As part of my doctoral research on the metaverse, I have been creating some sci-fi vignettes which can be used as “imaginaries” of the future of the metaverse. An “imaginary” is a term that social scientists like to use to talk about a vision of the future, usually (though not always) rooted in science fiction, which starts out with a small group but then gets adopted by a larger collective, companies, and governments, etc. and becomes a “sociotechnical imaginary”. As you may know, the term “metaverse” was coined by Neal Stephenson in his 1992 sci-fi cyberpunk novel, Snow Crash. As part of my research I was reading it again and here is the actual quote, and you could consider Stephenson’s description the original imaginary fo the metaverse( Hiro is the main character of the book as revealed by his last name, Protagnoist): “He’s in a computer generated universe that’s drawing onto his goggles and pumping into his earphones. In the lingo, this imaginary place known as the Metaverse. Hiro spends a lot of time in the Metaverse … in the Metaverse, Hiro Protagonist is a warrior-prince.” It turns out that in Snow Crash, the goggles were a mix of AR (Augmented Reality) and VR. I have created a very detailed analysis of the Snow Crash and how it relates to what’s being built today, which will be published separately. Another imaginary that’s perhaps even better known today is Ready Player One, where the “OASIS” is an instantiation of the metaverse. Since Ready Player One was published in 2010, it has a number of updated aspects, which relate to the Internet and the Web, in addition to VR technology. These vignettes are meant to be short - even shorter than a short story in many cases.  Each Metaverse Imaginary vignette will have a specific theme (see below) and incorporate multiple threads (see below). I will also add some commentary eventually about the state of the imaginary vis-a-vis startups and larger companies that are toiling to turn these imaginaries into reality. I’m putting these up for feedback and to spur discussion about the various future scenarios of the metaverse. Let’s jump right into the first vignette, with a short discussion of analyzing this and other imaginaries afterward. I put these up to get feedback and spur discussion, so see the questions at the end. Vignette #1: Virtual Employees and Land and Businesses in the Metaverse What will the Future of Work Look like in the Metaverse? Andreas woke up in the morning — it was 7 am in Dubai. He had moved here after spending a few years in Silicon Valley, after leaving his native country of Germany. The first thing he did was to check on his virtual properties. In the past, he might have used his phone but he just put on his meta-glasses, which had lenses that he updated every year with his prescription. They were no bigger than the physical glasses he had worn at the start of his career. Now 35, he had become an investor in the metaverse during his Silicon Valley days, and now most of his time was spent managing his portfolio of virtual properties. Up came the prices of his 2 properties in North America and 5 in Germany. They had gone up in value — his portfolio was up at least 1 million versecoins this quarter. He swiped his finger in the air over the virtual prices, first to the left to get the historical prices, and then to the right when a virtual menu of currencies popped up. Andreas mock-tapped his finger and then chose Euros to see the conversion using the latest exchange rate. The converted value of the properties, both in terms of what he had paid, his gain and loss, and the rent and other income he had collected showed up. He swirled his finger around the first property, one in Berlin, and then another sub row popped up. It showed the traffic of the avatars that had visited and the money they had spent on buying virtual goods for their avatars that he offered. It was an eSports lounge where avatars gathered from around the world and played games and competed with each other. His take was shown alongside the total amount spent. Since he hadn’t created the actual games they played, they had come from existing game companies like EA and Zynga and Epic and Microsoft/Activision, who kept most of the profits from the games. However, since he was the host of these particular tournaments, his virtual eSports lounge got a cut. While he didn’t collect any money on the virtual skins within the games, Andreas did make money on virtual drinks that players bought for each other and the decorative items they bought for their clothing on their avatars. These items represented both big brands and small indie artists. The latest collection, from an item known only as , a woman (at least a female-looking avatar) with blue hair that had a decidedly anime look who had approached him about featuring her artwork. He brought up the inventory of NFTs, or non-fungible tokens, commonly called nifty’s that had been sold and noticed. Since OtherworldlyClaire had been the featured artists over the past 24 hours, they were mostly sales of her work, though another artist who had science fiction landscapes from last year had also sold some items. OtherworldlyClaire Andreas had just gotten a message from an artist who was from Kenya who had some interestingly looking afro-futurist pieces that could be put on virtual t-shirts and hung on virtual houses. He would go visit her gallery on his next scouting trip as he decided which ones to feature next month. Andreas called up the virtual manager: “Atsuko” he said out loud. She had a realistic anime look, which made sense for his audience for this particular gallery, a gamer-type audience for this particular gallery. “Hello Mr. Andreas, ”came the voice of the twenty-something-looking virtual woman who appeared to him to hang in space over his living room, though it was augmented via his glasses. His apartment’s living room wall on one side was glass, looking out onto the city. The view was interesting, which was one of the reasons he’d bought the condo on the 35th floor of one of Dubai’s exclusive buildings that mostly had European or North American or Chinese residents. If he lifted his chin up, changing the angle of the glasses it almost looked like Atsuko was floating in the clouds next to one of the other office buildings, the Technology Center which housed the middle eastern headquarters of all the big tech companies, including Lightwise, which made the glasses that he was wearing, and Narnia avatars, which was the largest provider of cross-platform avatars across the world, in addition to the legacy tech giants that had survived from the pre metaverse days like Meta, Microsoft, Google. He used his right thumb to gesture and moved her avatar to the coffee table so she was centered. “There are currently 15 avatars in the Berlin Gaming Lounge location, they are only playing games sporadically, as far as I can tell from their public identities, which are all anthropomorphic, they are mostly students from various parts of Europe and a few from Brazil, where it is the middle of the night.” Andreas nodded. It was a fairly routine way for his virtual employee to talk to him. He knew that Atsuko was also currently catering to each of the avatars in the lounge. “Show me,” he said out loud, and his view shifted, now into more of a virtual reality as his apartment disappeared and he was surrounded by the gamers lounge. He wasn’t actually there, neither was his avatar, this was “ghost mode”, where he could visit a part of the metaverse, particularly one where he had admin rights, unobtrusively. He saw that the 15 avatars were in two groups and a few individuals. “Where’s Arwen,” he asked, looking at the bar, where there was a virtual Atsuko standing. “She hasn’t shown up,” said Atsuko matter-of-factly. “How many times is that?” he asked, staring at Atsuko’s VR avatar behind the bar, which was more detailed and realistic than the augment that had been floating in his living room. She had different colored eyes, heterochromia, which had become one standard way to distinguish AI avatars from real humans. “This is the third time this month that Arwen has missed a shift.” He sighed. It was always the same issue with human employees- they often canceled at the last minute due to some real-world emergency involving their kids or parents or boyfriends or girlfriends. But, even with the level of AI understanding and the restrictions on identifying them, the humans still could tell when a virtual employee was human and he preferred to have human bartenders during peak times. Still, it was better than when he’d first encountered virtual humans in the metaverse, or meta-humans as they were now colloquially called. Like Xerox, it had been the name of a product from Epic, but now had taken on all kinds of humans, but particularly those that were AI and had realistic-looking avatars. A call came through. He indicated by tapping his right ring finger against a specific point on his palm that he wanted to go to his home in the metaverse. It was Rajiv, his friend from Silicon Valley. He was suddenly in a Virtual Reality version of his apartment, looking out over a similar landscape, but much more sci-fi with all kinds of vehicles floating around in what looked like organized virtual highways in the sky outside of Virtual Dubai. If you looked closely you could see that there were many steampunk-like airships amongst the more futuristic Star Wars-like designs. Technically you didn’t need an airship or a spaceship to float around the metaverse, but there was a restriction to flying avatars above 500 feet, which had been put in place by the MAA, the metaverse aviation association subgroup of this particular metaverse’s governance body. The MAA didn’t let just any aircraft fly, but it had to have some justification in the design why it would work, with a few exceptions from popular sci-fiction. Rajiv’s avatar, which looked just like the geeky Indian guy that had become one of his best friends when he was living in Silicon Valley popped into his virtual living room. “Hey man,” said Rajiv. “What’s up?” said Andreas, impatiently. They knew each other well enough to not require the usual pleasantries or catching up. “I’ve got the lead on a new virtual property, it seems really promising … “ started Rajiv. “Where is it?” asked Andreas. “Virtual Shanghai… they just opened up a new section of the city and only locals are about to buy. “Oh that’s expensive,” said Andreas. “You’re not a local, though…” Rajiv’s home in the metaverse, or at least in earthverse-2, an open sourced metaverse that had realistic locations based on real locations on Earth, was in Silicon Valley, where he lived. Another avatar popped up next to Rajiv. “Oh yes I am, meet Yucheng, my Shanghai alter-ego.” Both avatars smiled at the same time and made the same gestures. It wasn’t unusual to have different avatars and more than one identity in different parts of the verse, but usually, that was restricted to fantasy and video game sims where you might take on alien avatar or a warrior or wizard or even fuzzy. It was frowned on for an identimorph in a realistic plane like the earthverse to have another identity. It wasn’t illegal, just considered a little shady. “Listen, “ Yucheng said, sounding a lot like Rajiv but the voice had been altered. “We have tenants lined up already, we need to get in on the pre-sale now… how much spare Bitcoin or versecoins do you have available?” THEME: The Future of Work, Metaverse Economy Keywords: near future, AR, virtual land, AI employees, humans working in the metaverse, NFTs Threads: avatars, AI, digital objects, venues, investments, identomorphic avatars Analysis and Framework, and Future Vignettes I haven’t fully done a commentary on this vignette, but you can see the various technologies at work, including goggles that go from AR to VR, virtual AI characters who can help the user as well as be employees, as well as aspects of the Metaverse economy. The Frameworks that I’m going to use to analyze these is one that I came up with in the fall of 2022 when every started to talk about the metaverse, but not everyone could agree on what that was. This framework consists of the threads of the metaverse, which are like the functional building blocks of a working metaverse, and the themes of the metaverse, which are like higher level use cases. They also incorporate various threads of the metaverse, or the functional building blocks of the metaverse, which I detail in a separate article, but which are also meant to spur discussion about technology and social issues: Identity and AvatarsAI and Virtual HumansVenues in the MetaverseDigital ObjectsBridging the Gap Between Physical and VirtualHuman Interface These threads are assembled into themes, which initially include: The Future of WorkThe Metaverse EconomyThe Future of SocializingMetaverse and EntertainmentCustomizability and InteroperabilitySocial Standards in the Metaverse This first vignette was focused on the themseof “the future of Work in the metaverse” and “The Metaverse Economy”. We’ll be exploring these combined with other themes, perhaps doing a deeper dive into the threads and components of the metaverse as well. Things to Think About I’m putting these up for feedback and to spur discussion, so feel free to leave comments. Some questions to think about include: How far in the future is this vignette? (1 year, 5 years, 10 years)? What are some of the technologies that we already have, and what are some of the technologies that still need to be developed? Is the development a simple matter of better engineering, or How does this vignette reflect on issues of identity in the “real” world and how those identities are reflected in avatars? What is the role of AI and virtual humans in the metaverse? How far away are we from “virtual employees” or “AI Avatars” that can do work for us when we aren’t logged in? How does this relatively short imaginary, relate to past imaginaries (TV shows, movies, or books), and how does this measure up with various visions of the Metaverse being put forward by companies like Meta, Microsoft, NVidia, Epic, Unity, Decentraland, Upland, The SandBox or others? Rizwan Virk is founder of Play Labs @ MIT. His new book, The Simulated Multiverse , is out now. Follow him via his website at www.zenentrepreur.com or on Twitter @rizstanford . Also Published Here

Metaverse Imaginaries, Sci Fi Vignette #1: The Future of Work, Land, Investing in the Metaverse

Despite its origin myths about democratizing wealth production, today’s crypto is largely inaccessible. To realize full potential, crypto must do what car-makers do: offer max utility. 1. To Succeed, Crypto Must Make Sense to Everyone Making crypto “accessible” doesn’t mean churning out glossy brochures for this or that new Cryptomobile. Crypto doesn’t need more 1-pagers, infographics, and explainer videos for how easy it is to start mining “digital assets,” how easy it is to “launch an ICO,” or how easy it is to open a Coinbase account and buy Bitcoin. Accessibility is about realizing that crypto needs normal people to have extremely easy entry points into the crypto economy. Crypto’s most successful Cryptomobile will offer everyone . easy ways for earning crypto, not just spending crypto 2. Thesis (A) Ordinary people need to be able to easily earn easily-convertible crypto; crypto needs to give people this functionality. (B) Crypto’s success depends overwhelmingly on this. (C) In other words, crypto needs ordinary people far more than ordinary people need crypto. 3. Today’s Cryptomobile is Complex & Expensive! Everyone observing today’s crypto space likely agrees with that today’s crypto isn’t for novices: Crypto Law Insider To join the “Cult of Crypto” today, one must climb steep learning curves, gain experience, and demonstrate technical know-how in intellectual duels. Even when you follow all initiation rites, you’ll never summit the peak of crypto. Here’s writing to announce the . Kyle Samani launch of Aurora EOS Demanding the “highest levels of technical capability” means setting crypto admission standards that are unattainable for all but a select few insiders. a priori Complexity only benefits those who have resources to control and understand complex processes. When it comes to global scaling, complexity and inaccessibility are not virtues. In automotive terms, today’s Cryptomobile is an over-built rig that claims to be , zombie-proof, Wall Street-proof, government-proof, trust-proof, and every other kind of -proof. future-proof, quantum-proof Crypto’s image of itself. Go anywhere; do anything. Unstoppable. Like the EarthRoamer. Photo credit: ExpeditionPortal.com In reality, today’s Cryptomobile is structurally complex, pretentious (“smart contract”), expensive, inherently unscalable, and eats a ton of gas. By , Crypto didn’t insulate itself from a world of exaggerated threats. It just made bad guys and about what’s happening under all that tint. building up defenses law enforcement even more curious Today’s situation is tragic because it was . so easily avoidable The only “proofing” crypto ever needed to do, from the Bitcoin Whitepaper onwards, was to “fool-proof” all systems and processes. Instead of pretending to be “smart contract” CryptoLawyers, . crypto folks should have kept their eyes on the ground and focused on avoiding dumb landmines It’s not too late to change; but the window of opportunity is . closing quickly 4. The Cryptomobile Can & Must Be Simpler Everyone should see how a Cryptomobile that’s chugging along, running out of gas, and constantly getting broken in to (and ) is a . pulled over big problem Today, many developers are working hard to sell cleaner and leaner Cryptomobiles to the public. But thus far, crypto doesn’t have a runaway “proletarian” bestseller Crypto Model T, Crypto Camry, or Crypto Civic. Instead, Crypto is still very much fixated on “Lambos.” Lambos and EarthRomers are cool, but the solutions to Crypto’s global scaling and problems are crypto use cases that appeal to the . hyperutility maximum number of people To qualify as a , a use case must satisfy three simple criteria: hyperutility crypto app (1) (downloadable app or hardware widget like a Ledger Nano) Here are the keys (2) Here’s on how the brakes, switches, & gears work ( , and here’s how to withdraw your money); a quick primer here’s how to earn crypto with one click (3) (enjoy earning crypto, and tell all your friends about it)! Have fun driving Anything more complicated than that, and you’re making a product or service for millions, not billions, of people. Crypto needs adoption by the world’s billions. Here’s the fastest roadmap to get there. ( : ) Hint it’s an actual map; as in, a blockchain-based world map, and folks getting crypto for mapping . 5. Learning to Drive Think of it like this: it’s 1905 and we’re at the start of the automotive century. Everyone can see the potential of the automobile. The market is frenetic, exciting, innovative, and fragmented. Then different value propositions start being offered to the world: : The automobile and the “automotive market is not for novices. Hire a mechanic on the inside.” Customize your automotive experience. We’re custom coach builders with the “highest technical capability” and we’ll cater to your every wish. We’ll and replace defective parts, no matter the cost. Offer A (Studebaker) send a mechanic to each disgruntled owner Studebaker limousine. Source: Wikipedia. : The automobile and the automotive We build cars that minimize the need for a mechanic on the inside. Our cars are so simple, you can be your own mechanic. You can customize and “choose any color, so long as it’s black.” Our value proposition is simple: your ROI is “extra productivity” from not having to walk or tame horses; but the ROI is also coming from extra nickels and dimes you can earn in your Model T by taking people or goods for a ride. Offer B (Ford) market should be for novices! Ford Model T. Source: Wikipedia. 6. Crypto Today: Lots of Studebakers, No Ford The two offers above are not mutually exclusive. It’s nice when one sector complements the other, and drivers have abundant choice. Competition breeds innovation, and so forth. Furthermore, we understand pushback from many crypto “collectors” who claim to be “investing” in, and “collecting/HODL-ing,” coins that they think represent a diverse portfolio of Studebakers Fords, among others. and But in our view, today’s crypto has a lot of Studebakers and not a single Ford. If you disagree, if you think crypto has its Ford, tell us what that Ford is in a response below. Please understand: ; we want to be pleasantly surprised; and we always want to learn about new CryptoFord candidates. We want you to be right But if your CryptoFord (whether it’s Ethereum or Trinity or Truth or X, Y, Z) doesn’t do the three-step dance above — (1) (2) quick ; (3) — you’re probably wrong. keys; how-to earn crypto earn crypto The closest Ford candidate is the relative ease of . But even that doesn’t satisfy criteria #2 and #3, above. creating one’s own token in Ethereum 7. Crypto’s a Plush & Complex Studebaker Crypto can continue doing what it’s doing and maintain its “healthy growth,” just like Twitter maintains a “healthy network” by not rushing to fix what thinks “ain’t broken.” it Indeed, the market for custom coach-builders and expert mechanics survived the Great Depression, World War I & II, and, most recently, the Great Recession. These service providers continue to do well today. But, be honest, when’s the last time you’ve seen a Studebaker on the road? Exactly. Alternatively, crypto can realize that, despite its exponential adoption curves, . global adoption can and should be much faster 8. Crypto Needs to Identify its Real Competitors Most crypto analysts will concede that a big obstacle to much faster adoption is the lack of a , let alone anything resembling comprehensive . shared interoperability vision inter-crypto interoperability standards There are many technical reasons why crypto lacks interoperability protocols. . Crypto is a very competitive space. Everyone wants to exploit what they think is their first-mover advantage. But there are also human and institutional barriers to cooperation Today’s inter-crypto gamesmanship is like watching a group of friends facing off in a fierce game of . Grand Theft Auto Before the game even starts, everyone thinks they will win because they have full control over their Cryptomobile. They can select different skins, like this , to make one’s Lamborghini Aventador a lot more “stealthy.” Army Camo Pack Because you can make virtually infinite numbers of mods to your rig, weapons, and scripts, it’s that govern your race platform. easy to start believing that you have written the rules At the end of the day, you may have won some battles against your friends. But the people winning the war are the publishers of Grand Theft Auto. The key lesson from this exercise is that Crypto’s biggest competitors are not fellow crypto teams; . it’s not even FAMGA . Crypto’s inability to see this represents one of crypto’s biggest missed opportunities to date. Crypto’s biggest competitors are legal institutions 7. Crypto Needs to Raise Shared Expectations In addition to competing against one another, crypto developers should realize they are also competing against much more powerful forces than just another crew of cypherpunks. See the planes on the runway right behind those cars? When it comes to speed in getting people or things from point A to point B, the people who build and pilot those planes look at #27 and think, “awwwww, how cute.” The inter-crypto race for primacy is only normal, but crypto adoption-drivers shouldn’t just look above steering wheels and “under the hood” to figure out how to go much faster. Very often, the breakthroughs that give orders of magnitude higher performance are whizzing about on totally different runways. The only way to see them is by suspending assumptions and broadening your field of view. 8. Crypto Should Use Different Benchmarks For example, instead of competing against one another in some absolute sense, by reference to quantitative benchmarks (ex: transactions per second), crypto racers may benefit from looking far beyond usual comparable variables (“crypto transactions per second” v. “Visa transactions per second”). Instead of trying to squeeze as much transactional horsepower out of one second as possible, crypto developers may be better served by rethinking the essence and structure of a transaction altogether. Here’s an for crypto. There are . example of a new quantitative performance benchmark many more benchmarks like this 9. Crypto Must Understand The Needs of Crypto “Consumers” Today’s big crypto projects are like B2B operating systems: they serve as platforms upon which other second-tier, third-tier consumer-oriented applications will be developed. Bitcoin is the “Unix of money.” Ethereum is the “Linux of crypto.” IOTA is the “ of the machine economy.” And so on. FreeBSD This approach gives development teams much needed respite from torrents of individual consumer demands for greater transaction speeds, overall utility, more security, etc. But this approach also has the disadvantage of creating a buffer between the developers of crypto operating systems and the end-users of those operating systems. Crypto should stop getting high on its own supply; instead, crypto needs to understand what non-crypto people need from crypto. Today’s biggest need area are easy-to-use applications that plug normal people into the crypto economy. All crypto developers benefit from successful end-user applications like this. All crypto development teams have a shared responsibility for incubating end-user applications like this. 10. Crypto Platform Shopping is Real The way CleanApp fell into crypto is similar to the way many people learned about crypto. In 2015, we started doing legal mechanics for several crypto projects, and started educating ourselves on the rapidly growing range of crypto projects, platforms, and possibilities. When we started seeing more and more bells and whistles, and so much chrome, and so many Lambos and Maybachs outside, we began to wonder — Could blockchain be the key for a CleanApp? hyperutility Vice versa, was CleanApp the that crypto was desperately searching for? “missing killer app” Maybach Exelero. Source: Wikipedia Predictably, our answer to both questions was “Yes” — . It yields noticeable material improvement, everyone’s better off, etc. etc. etc. a global waste, hazard, incident reporting process would be baller as a set of on-chain markets that are integrated with real off-chain response processes But that’s just context. We’re not plugging anything. This isn’t a sales pitch. It’s analysis. This part of the story is just better told by sharing our own experiences with crypto platform shopping. And so we started looking. Should we start sucking ? Should we get en- -d? Should we seek ? Or go towards ? Ether Tangle ConsenSys OmiseGO 11. Crypto “Features” Must Come Standard One of the things we noticed early on in our research was that all end-user oriented crypto platforms lacked a basic feature — . the ability to easily earn crypto by doing socially-useful work To us, the need for this “feature” is as obvious as the need for a seat belt in a car. As a form of micro-finance, crypto is an ideal vehicle for incentivizing the production of socially-useful material service ( etc.), tracking the inputs, , and reaping the value of global I/O analytics on these underlying processes. like street sweeping, litter cleanup, common-area maintenance, aligning potential transactional actors Despite the obvious value and utility of an app like that, there wasn’t and isn’t a crypto platform that makes development of globally-scalable apps like this easy. Ethereum comes close, but, among other problems, Ethereum’s transaction volume/bandwidth limitations mean that near-real-time rewards corresponding to real-time inputs would be all but impossible at billion-user scales (billions of daily transactions). IOTA seems like a possible fit, and we’re amidst that learning curve at present. So, upon examining the Studebakers and Packards, and noticing that they’re missing seat belts, we did what you’d expect reasonable people to do: we asked the developers for seat belts. Crypto’s response? If you want seat belts, build your own damn car! 12. Don’t Shift Development Burden to Users In order to realize its full potential, crypto needs to continue permitting hyper-specialization. Folks who are really good at mechanism design need to focus on mechanism design; folks who are really good at enterprise integration need to focus on enterprise integration. Not everyone has to be developing platforms. There’s no need to reinvent the wheel. But the only way to free developers to specialize is to assure they will have meaningful choice between the crypto platform Studebakers and crypto platform Fords. 1920 Studebaker “Big-Six” Touring Car: $2350 1920 Ford Model T: $575 13. Hyperutility = Higher Earnings By 1924, Ford had sold its -th car (for comparison, the U.S. population in 1924 was 114,000,000). 10,000,000 The Model T kept getting cheaper ($575 in 1920 v. $295 in 1924), with objective improvements in quality in every product cycle. What was the key to this impressive and unprecedented growth? Answer: the automobile offered . material hyperutility The Model T’s key selling point wasn’t “economical” service in the sense of just cheap do-it-yourself maintenance. An explicit Ford selling point was that buying a Model T . increased the buyer’s earnings Please note: it wasn’t just “the automobile” that made individuals’ lives more efficient, and made sweeping broader economic efficiency gains possible. It was the barrier-smashing that did that. Ford Model T The Model T sold a healthier, more productive, less-stressful, more efficient, and more bountiful future. 14. Be Honest About Risks & Costs Every hyperutility app is like a knife. It can be used to cut apples, or to slice an opponent’s tires. Crypto’s lesson from the car industry in this respect is a contrastive one: . what not to do From early lobbying efforts to dismantle public transportation networks to exploding Ford Pintos to the most recent Volkswagen emissions scandal, the car industry has carved some gnarly skid marks in ethical norms like transparency and commercial good faith. Crypto needs to acknowledge that yes, the Model T killed people. Yes, the Model T ushered in the petrol age, the consequences of which now pose a threat to humanity’s very existence. Yes, the Model T introduced supply-chain and factory-line production methods that were appropriated by warriors to make ghastly mass-killing machines. But the lesson isn’t to stop making knives. The key takeaway from recalling the car industry’s dark sides is to heighten the need for transparency and to be open with drivers regarding the risks of motoring. Hyperutility apps may find it cheaper to externalize costs of production onto others. But as one learns in Economics 101, these costs aren’t ever fully externalized. Ever. Instead of hiding crypto risks and costs, crypto should be open about its risks and costs. This is the smartest way to minimize systemic risks and costs. 15. If You Build It; They Will Come One of the key premises at the start of the analysis is this: . there won’t be a tomorrow’s crypto worth speaking about until and unless crypto becomes accessible for novices This means giving rookies secure and easy pathways for . earning crypto, as opposed to just inventing progressively easier ways to buy crypto The crypto developers who build secure and hyper-useful apps for will write the rules of the 21st century economy. earning crypto [ ] More analysis freely available @CryptoLawReview

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