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#433950 How the Spatial Web Will Transform Every ...
What is the future of work? Is our future one of ‘technological socialism’ (where technology is taking care of our needs)? Or is our future workplace completely virtualized, whereby we hang out at home in our PJs while walking about our virtual corporate headquarters?
This blog will look at the future of work during the age of Web 3.0… Examining scenarios in which AI, VR, and the spatial web converge to transform every element of our careers, from training to execution to free time.
Three weeks ago, I explored the vast implications of Web 3.0 on news, media, smart advertising, and personalized retail. And to offer a quick recap on what the Spatial Web is and how it works, let’s cover some brief history.
A Quick Recap on Web 3.0
While Web 1.0 consisted of static documents and read-only data (static web pages), Web 2.0 introduced multimedia content, interactive web applications, and participatory social media, all of these mediated by two-dimensional screens.
But over the next two to five years, the convergence of 5G, artificial intelligence, VR/AR, and a trillion-sensor economy will enable us to both map our physical world into virtual space and superimpose a digital data layer onto our physical environments.
Suddenly, all our information will be manipulated, stored, understood, and experienced in spatial ways.
In this third installment of the Web 3.0 series, I’ll be discussing the Spatial Web’s vast implications for:
Professional Training
Delocalized Business and the Virtual Workplace
Smart Permissions and Data Security
Let’s dive in.
Virtual Training, Real-World Results
Virtual and augmented reality have already begun disrupting the professional training market.
Leading the charge, Walmart has already implemented VR across 200 Academy training centers, running over 45 modules and simulating everything from unusual customer requests to a Black Friday shopping rush.
In September 2018, Walmart committed to a 17,000-headset order of the Oculus Go to equip every US Supercenter, neighborhood market, and discount store with VR-based employee training.
In the engineering world, Bell Helicopter is using VR to massively expedite development and testing of its latest aircraft, FCX-001. Partnering with Sector 5 Digital and HTC VIVE, Bell found it could concentrate a typical six-year aircraft design process into the course of six months, turning physical mock-ups into CAD-designed virtual replicas.
But beyond the design process itself, Bell is now one of a slew of companies pioneering VR pilot tests and simulations with real-world accuracy. Seated in a true-to-life virtual cockpit, pilots have now tested countless iterations of the FCX-001 in virtual flight, drawing directly onto the 3D model and enacting aircraft modifications in real-time.
And in an expansion of our virtual senses, several key players are already working on haptic feedback. In the case of VR flight, French company Go Touch VR is now partnering with software developer FlyInside on fingertip-mounted haptic tech for aviation.
Dramatically reducing time and trouble required for VR-testing pilots, they aim to give touch-based confirmation of every switch and dial activated on virtual flights, just as one would experience in a full-sized cockpit mockup. Replicating texture, stiffness, and even the sensation of holding an object, these piloted devices contain a suite of actuators to simulate everything from a light touch to higher-pressured contact, all controlled by gaze and finger movements.
When it comes to other high-risk simulations, virtual and augmented reality have barely scratched the surface.
Firefighters can now combat virtual wildfires with new platforms like FLAIM Trainer or TargetSolutions. And thanks to the expansion of medical AR/VR services like 3D4Medical or Echopixel, surgeons might soon perform operations on annotated organs and magnified incision sites, speeding up reaction times and vastly improving precision.
But perhaps most urgent, Web 3.0 and its VR interface will offer an immediate solution for today’s constant industry turnover and large-scale re-education demands.
VR educational facilities with exact replicas of anything from large industrial equipment to minute circuitry will soon give anyone a second chance at the 21st-century job market.
Want to be an electric, autonomous vehicle mechanic at age 15? Throw on a demonetized VR module and learn by doing, testing your prototype iterations at almost zero cost and with no risk of harming others.
Want to be a plasma physicist and play around with a virtual nuclear fusion reactor? Now you’ll be able to simulate results and test out different tweaks, logging Smart Educational Record credits in the process.
As tomorrow’s career model shifts from a “one-and-done graduate degree” to lifelong education, professional VR-based re-education will allow for a continuous education loop, reducing the barrier to entry for anyone wanting to enter a new industry.
But beyond professional training and virtually enriched, real-world work scenarios, Web 3.0 promises entirely virtual workplaces and blockchain-secured authorization systems.
Rise of the Virtual Workplace and Digital Data Integrity
In addition to enabling an annual $52 billion virtual goods marketplace, the Spatial Web is also giving way to “virtual company headquarters” and completely virtualized companies, where employees can work from home or any place on the planet.
Too good to be true? Check out an incredible publicly listed company called eXp Realty.
Launched on the heels of the 2008 financial crisis, eXp Realty beat the odds, going public this past May and surpassing a $1B market cap on day one of trading.
But how? Opting for a demonetized virtual model, eXp’s founder Glenn Sanford decided to ditch brick and mortar from the get-go, instead building out an online virtual campus for employees, contractors, and thousands of agents.
And after years of hosting team meetings, training seminars, and even agent discussions with potential buyers through 2D digital interfaces, eXp’s virtual headquarters went spatial.
What is eXp’s primary corporate value? FUN! And Glenn Sanford’s employees love their jobs.
In a bid to transition from 2D interfaces to immersive, 3D work experiences, virtual platform VirBELA built out the company’s office space in VR, unlocking indefinite scaling potential and an extraordinary new precedent.
Foregoing any physical locations for a centralized VR campus, eXp Realty has essentially thrown out all overhead and entered a lucrative market with barely any upfront costs.
Delocalize with VR, and you can now hire anyone with internet access (right next door or on the other side of the planet), redesign your corporate office every month, throw in an ocean-view office or impromptu conference room for client meetings, and forget about guzzled-up hours in traffic.
Throw in the Spatial Web’s fundamental blockchain-based data layer, and now cryptographically secured virtual IDs will let you validate colleagues’ identities or any of the virtual avatars we will soon inhabit.
This becomes critically important for spatial information logs—keeping incorruptible records of who’s present at a meeting, which data each person has access to, and AI-translated reports of everything discussed and contracts agreed to.
But as I discussed in a previous Spatial Web blog, not only will Web 3.0 and VR advancements allow us to build out virtual worlds, but we’ll soon be able to digitally map our real-world physical offices or entire commercial high rises too.
As data gets added and linked to any given employee’s office, conference room, or security system, we might then access online-merge-offline environments and information through augmented reality.
Imaging showing up at your building’s concierge and your AR glasses automatically check you into the building, authenticating your identity and pulling up any reminders you’ve linked to that specific location.
You stop by a friend’s office, and his smart security system lets you know he’ll arrive in an hour. Need to book a public conference room that’s already been scheduled by another firm’s marketing team? Offer to pay them a fee and, once accepted, a smart transaction will automatically deliver a payment to their company account.
With blockchain-verified digital identities, spatially logged data, and virtually manifest information, business logistics take a fraction of the time, operations grow seamless, and corporate data will be safer than ever.
Final Thoughts
While converging technologies slash the lifespan of Fortune 500 companies, bring on the rise of vast new industries, and transform the job market, Web 3.0 is changing the way we work, where we work, and who we work with.
Life-like virtual modules are already unlocking countless professional training camps, modifiable in real-time and easily updated.
Virtual programming and blockchain-based authentication are enabling smart data logging, identity protection, and on-demand smart asset trading.
And VR/AR-accessible worlds (and corporate campuses) not only demonetize, dematerialize, and delocalize our everyday workplaces, but enrich our physical worlds with AI-driven, context-specific data.
Welcome to the Spatial Web workplace.
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#433907 How the Spatial Web Will Fix What’s ...
Converging exponential technologies will transform media, advertising and the retail world. The world we see, through our digitally-enhanced eyes, will multiply and explode with intelligence, personalization, and brilliance.
This is the age of Web 3.0.
Last week, I discussed the what and how of Web 3.0 (also known as the Spatial Web), walking through its architecture and the converging technologies that enable it.
To recap, while Web 1.0 consisted of static documents and read-only data, Web 2.0 introduced multimedia content, interactive web applications, and participatory social media, all of these mediated by two-dimensional screens—a flat web of sensorily confined information.
During the next two to five years, the convergence of 5G, AI, a trillion sensors, and VR/AR will enable us to both map our physical world into virtual space and superimpose a digital layer onto our physical environments.
Web 3.0 is about to transform everything—from the way we learn and educate, to the way we trade (smart) assets, to our interactions with real and virtual versions of each other.
And while users grow rightly concerned about data privacy and misuse, the Spatial Web’s use of blockchain in its data and governance layer will secure and validate our online identities, protecting everything from your virtual assets to personal files.
In this second installment of the Web 3.0 series, I’ll be discussing the Spatial Web’s vast implications for a handful of industries:
News & Media Coverage
Smart Advertising
Personalized Retail
Let’s dive in.
Transforming Network News with Web 3.0
News media is big business. In 2016, global news media (including print) generated 168 billion USD in circulation and advertising revenue.
The news we listen to impacts our mindset. Listen to dystopian news on violence, disaster, and evil, and you’ll more likely be searching for a cave to hide in, rather than technology for the launch of your next business.
Today, different news media present starkly different realities of everything from foreign conflict to domestic policy. And outcomes are consequential. What reporters and news corporations decide to show or omit of a given news story plays a tremendous role in shaping the beliefs and resulting values of entire populations and constituencies.
But what if we could have an objective benchmark for today’s news, whereby crowdsourced and sensor-collected evidence allows you to tour the site of journalistic coverage, determining for yourself the most salient aspects of a story?
Enter mesh networks, AI, public ledgers, and virtual reality.
While traditional networks rely on a limited set of wired access points (or wireless hotspots), a wireless mesh network can connect entire cities via hundreds of dispersed nodes that communicate with each other and share a network connection non-hierarchically.
In short, this means that individual mobile users can together establish a local mesh network using nothing but the computing power in their own devices.
Take this a step further, and a local population of strangers could collectively broadcast countless 360-degree feeds across a local mesh network.
Imagine a scenario in which protests break out across the country, each cluster of activists broadcasting an aggregate of 360-degree videos, all fed through photogrammetry AIs that build out a live hologram of the march in real time. Want to see and hear what the NYC-based crowds are advocating for? Throw on some VR goggles and explore the event with full access. Or cue into the southern Texan border to assess for yourself the handling of immigrant entry and border conflicts.
Take a front seat in the Capitol during tomorrow’s Senate hearing, assessing each Senator’s reactions, questions and arguments without a Fox News or CNN filter. Or if you’re short on time, switch on the holographic press conference and host 3D avatars of live-broadcasting politicians in your living room.
We often think of modern media as taking away consumer agency, feeding tailored and often partisan ideology to a complacent audience. But as wireless mesh networks and agnostic sensor data allow for immersive VR-accessible news sites, the average viewer will necessarily become an active participant in her own education of current events.
And with each of us interpreting the news according to our own values, I envision a much less polarized world. A world in which civic engagement, moderately reasoned dialogue, and shared assumptions will allow us to empathize and make compromises.
The future promises an era in which news is verified and balanced; wherein public ledgers, AI, and new web interfaces bring you into the action and respect your intelligence—not manipulate your ignorance.
Web 3.0 Reinventing Advertising
Bringing about the rise of ‘user-owned data’ and self-established permissions, Web 3.0 is poised to completely disrupt digital advertising—a global industry worth over 192 billion USD.
Currently, targeted advertising leverages tomes of personal data and online consumer behavior to subtly engage you with products you might not want, or sell you on falsely advertised services promising inaccurate results.
With a new Web 3.0 data and governance layer, however, distributed ledger technologies will require advertisers to engage in more direct interaction with consumers, validating claims and upping transparency.
And with a data layer that allows users to own and authorize third-party use of their data, blockchain also holds extraordinary promise to slash not only data breaches and identity theft, but covert advertiser bombardment without your authorization.
Accessing crowdsourced reviews and AI-driven fact-checking, users will be able to validate advertising claims more efficiently and accurately than ever before, potentially rating and filtering out advertisers in the process. And in such a streamlined system of verified claims, sellers will face increased pressure to compete more on product and rely less on marketing.
But perhaps most exciting is the convergence of artificial intelligence and augmented reality.
As Spatial Web networks begin to associate digital information with physical objects and locations, products will begin to “sell themselves.” Each with built-in smart properties, products will become hyper-personalized, communicating information directly to users through Web 3.0 interfaces.
Imagine stepping into a department store in pursuit of a new web-connected fridge. As soon as you enter, your AR goggles register your location and immediately grant you access to a populated register of store products.
As you move closer to a kitchen set that catches your eye, a virtual salesperson—whether by holographic video or avatar—pops into your field of view next to the fridge you’ve been examining and begins introducing you to its various functions and features. You quickly decide you’d rather disable the avatar and get textual input instead, and preferences are reset to list appliance properties visually.
After a virtual tour of several other fridges, you decide on the one you want and seamlessly execute a smart contract, carried out by your smart wallet and the fridge. The transaction takes place in seconds, and the fridge’s blockchain-recorded ownership record has been updated.
Better yet, you head over to a friend’s home for dinner after moving into the neighborhood. While catching up in the kitchen, your eyes fixate on the cabinets, which quickly populate your AR glasses with a price-point and selection of colors.
But what if you’d rather not get auto-populated product info in the first place? No problem!
Now empowered with self-sovereign identities, users might be able to turn off advertising preferences entirely, turning on smart recommendations only when they want to buy a given product or need new supplies.
And with user-centric data, consumers might even sell such information to advertisers directly. Now, instead of Facebook or Google profiting off your data, you might earn a passive income by giving advertisers permission to personalize and market their services. Buy more, and your personal data marketplace grows in value. Buy less, and a lower-valued advertising profile causes an ebb in advertiser input.
With user-controlled data, advertisers now work on your terms, putting increased pressure on product iteration and personalizing products for each user.
This brings us to the transformative future of retail.
Personalized Retail–Power of the Spatial Web
In a future of smart and hyper-personalized products, I might walk through a virtual game space or a digitally reconstructed Target, browsing specific categories of clothing I’ve predetermined prior to entry.
As I pick out my selection, my AI assistant hones its algorithm reflecting new fashion preferences, and personal shoppers—also visiting the store in VR—help me pair different pieces as I go.
Once my personal shopper has finished constructing various outfits, I then sit back and watch a fashion show of countless Peter avatars with style and color variations of my selection, each customizable.
After I’ve made my selection, I might choose to purchase physical versions of three outfits and virtual versions of two others for my digital avatar. Payments are made automatically as I leave the store, including a smart wallet transaction made with the personal shopper at a per-outfit rate (for only the pieces I buy).
Already, several big players have broken into the VR market. Just this year, Walmart has announced its foray into the VR space, shipping 17,000 Oculus Go VR headsets to Walmart locations across the US.
And just this past January, Walmart filed two VR shopping-related patents. In a new bid to disrupt a rapidly changing retail market, Walmart now describes a system in which users couple their VR headset with haptic gloves for an immersive in-store experience, whether at 3am in your living room or during a lunch break at the office.
But Walmart is not alone. Big e-commerce players from Amazon to Alibaba are leaping onto the scene with new software buildout to ride the impending headset revolution.
Beyond virtual reality, players like IKEA have even begun using mobile-based augmented reality to map digitally replicated furniture in your physical living room, true to dimension. And this is just the beginning….
As AR headset hardware undergoes breakneck advancements in the next two to five years, we might soon be able to project watches onto our wrists, swapping out colors, styles, brand, and price points.
Or let’s say I need a new coffee table in my office. Pulling up multiple models in AR, I can position each option using advanced hand-tracking technology and customize height and width according to my needs. Once the smart payment is triggered, the manufacturer prints my newly-customized piece, droning it to my doorstep. As soon as I need to assemble the pieces, overlaid digital prompts walk me through each step, and any user confusions are communicated to a company database.
Perhaps one of the ripest industries for Spatial Web disruption, retail presents one of the greatest opportunities for profit across virtual apparel, digital malls, AI fashion startups and beyond.
In our next series iteration, I’ll be looking at the tremendous opportunities created by Web 3.0 for the Future of Work and Entertainment.
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#433758 DeepMind’s New Research Plan to Make ...
Making sure artificial intelligence does what we want and behaves in predictable ways will be crucial as the technology becomes increasingly ubiquitous. It’s an area frequently neglected in the race to develop products, but DeepMind has now outlined its research agenda to tackle the problem.
AI safety, as the field is known, has been gaining prominence in recent years. That’s probably at least partly down to the overzealous warnings of a coming AI apocalypse from well-meaning, but underqualified pundits like Elon Musk and Stephen Hawking. But it’s also recognition of the fact that AI technology is quickly pervading all aspects of our lives, making decisions on everything from what movies we watch to whether we get a mortgage.
That’s why DeepMind hired a bevy of researchers who specialize in foreseeing the unforeseen consequences of the way we built AI back in 2016. And now the team has spelled out the three key domains they think require research if we’re going to build autonomous machines that do what we want.
In a new blog designed to provide updates on the team’s work, they introduce the ideas of specification, robustness, and assurance, which they say will act as the cornerstones of their future research. Specification involves making sure AI systems do what their operator intends; robustness means a system can cope with changes to its environment and attempts to throw it off course; and assurance involves our ability to understand what systems are doing and how to control them.
A classic thought experiment designed to illustrate how we could lose control of an AI system can help illustrate the problem of specification. Philosopher Nick Bostrom’s posited a hypothetical machine charged with making as many paperclips as possible. Because the creators fail to add what they might assume are obvious additional goals like not harming people, the AI wipes out humanity so we can’t switch it off before turning all matter in the universe into paperclips.
Obviously the example is extreme, but it shows how a poorly-specified goal can lead to unexpected and disastrous outcomes. Properly codifying the desires of the designer is no easy feat, though; often there are not neat ways to encompass both the explicit and implicit goals in ways that are understandable to the machine and don’t leave room for ambiguities, meaning we often rely on incomplete approximations.
The researchers note recent research by OpenAI in which an AI was trained to play a boat-racing game called CoastRunners. The game rewards players for hitting targets laid out along the race route. The AI worked out that it could get a higher score by repeatedly knocking over regenerating targets rather than actually completing the course. The blog post includes a link to a spreadsheet detailing scores of such examples.
Another key concern for AI designers is making their creation robust to the unpredictability of the real world. Despite their superhuman abilities on certain tasks, most cutting-edge AI systems are remarkably brittle. They tend to be trained on highly-curated datasets and so can fail when faced with unfamiliar input. This can happen by accident or by design—researchers have come up with numerous ways to trick image recognition algorithms into misclassifying things, including thinking a 3D printed tortoise was actually a gun.
Building systems that can deal with every possible encounter may not be feasible, so a big part of making AIs more robust may be getting them to avoid risks and ensuring they can recover from errors, or that they have failsafes to ensure errors don’t lead to catastrophic failure.
And finally, we need to have ways to make sure we can tell whether an AI is performing the way we expect it to. A key part of assurance is being able to effectively monitor systems and interpret what they’re doing—if we’re basing medical treatments or sentencing decisions on the output of an AI, we’d like to see the reasoning. That’s a major outstanding problem for popular deep learning approaches, which are largely indecipherable black boxes.
The other half of assurance is the ability to intervene if a machine isn’t behaving the way we’d like. But designing a reliable off switch is tough, because most learning systems have a strong incentive to prevent anyone from interfering with their goals.
The authors don’t pretend to have all the answers, but they hope the framework they’ve come up with can help guide others working on AI safety. While it may be some time before AI is truly in a position to do us harm, hopefully early efforts like these will mean it’s built on a solid foundation that ensures it is aligned with our goals.
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#433288 The New AI Tech Turning Heads in Video ...
A new technique using artificial intelligence to manipulate video content gives new meaning to the expression “talking head.”
An international team of researchers showcased the latest advancement in synthesizing facial expressions—including mouth, eyes, eyebrows, and even head position—in video at this month’s 2018 SIGGRAPH, a conference on innovations in computer graphics, animation, virtual reality, and other forms of digital wizardry.
The project is called Deep Video Portraits. It relies on a type of AI called generative adversarial networks (GANs) to modify a “target” actor based on the facial and head movement of a “source” actor. As the name implies, GANs pit two opposing neural networks against one another to create a realistic talking head, right down to the sneer or raised eyebrow.
In this case, the adversaries are actually working together: One neural network generates content, while the other rejects or approves each effort. The back-and-forth interplay between the two eventually produces a realistic result that can easily fool the human eye, including reproducing a static scene behind the head as it bobs back and forth.
The researchers say the technique can be used by the film industry for a variety of purposes, from editing facial expressions of actors for matching dubbed voices to repositioning an actor’s head in post-production. AI can not only produce highly realistic results, but much quicker ones compared to the manual processes used today, according to the researchers. You can read the full paper of their work here.
“Deep Video Portraits shows how such a visual effect could be created with less effort in the future,” said Christian Richardt, from the University of Bath’s motion capture research center CAMERA, in a press release. “With our approach, even the positioning of an actor’s head and their facial expression could be easily edited to change camera angles or subtly change the framing of a scene to tell the story better.”
AI Tech Different Than So-Called “Deepfakes”
The work is far from the first to employ AI to manipulate video and audio. At last year’s SIGGRAPH conference, researchers from the University of Washington showcased their work using algorithms that inserted audio recordings from a person in one instance into a separate video of the same person in a different context.
In this case, they “faked” a video using a speech from former President Barack Obama addressing a mass shooting incident during his presidency. The AI-doctored video injects the audio into an unrelated video of the president while also blending the facial and mouth movements, creating a pretty credible job of lip synching.
A previous paper by many of the same scientists on the Deep Video Portraits project detailed how they were first able to manipulate a video in real time of a talking head (in this case, actor and former California governor Arnold Schwarzenegger). The Face2Face system pulled off this bit of digital trickery using a depth-sensing camera that tracked the facial expressions of an Asian female source actor.
A less sophisticated method of swapping faces using a machine learning software dubbed FakeApp emerged earlier this year. Predictably, the tech—requiring numerous photos of the source actor in order to train the neural network—was used for more juvenile pursuits, such as injecting a person’s face onto a porn star.
The application gave rise to the term “deepfakes,” which is now used somewhat ubiquitously to describe all such instances of AI-manipulated video—much to the chagrin of some of the researchers involved in more legitimate uses.
Fighting AI-Created Video Forgeries
However, the researchers are keenly aware that their work—intended for benign uses such as in the film industry or even to correct gaze and head positions for more natural interactions through video teleconferencing—could be used for nefarious purposes. Fake news is the most obvious concern.
“With ever-improving video editing technology, we must also start being more critical about the video content we consume every day, especially if there is no proof of origin,” said Michael Zollhöfer, a visiting assistant professor at Stanford University and member of the Deep Video Portraits team, in the press release.
Toward that end, the research team is training the same adversarial neural networks to spot video forgeries. They also strongly recommend that developers clearly watermark videos that are edited through AI or otherwise, and denote clearly what part and element of the scene was modified.
To catch less ethical users, the US Department of Defense, through the Defense Advanced Research Projects Agency (DARPA), is supporting a program called Media Forensics. This latest DARPA challenge enlists researchers to develop technologies to automatically assess the integrity of an image or video, as part of an end-to-end media forensics platform.
The DARPA official in charge of the program, Matthew Turek, did tell MIT Technology Review that so far the program has “discovered subtle cues in current GAN-manipulated images and videos that allow us to detect the presence of alterations.” In one reported example, researchers have targeted eyes, which rarely blink in the case of “deepfakes” like those created by FakeApp, because the AI is trained on still pictures. That method would seem to be less effective to spot the sort of forgeries created by Deep Video Portraits, which appears to flawlessly match the entire facial and head movements between the source and target actors.
“We believe that the field of digital forensics should and will receive a lot more attention in the future to develop approaches that can automatically prove the authenticity of a video clip,” Zollhöfer said. “This will lead to ever-better approaches that can spot such modifications even if we humans might not be able to spot them with our own eyes.
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