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#434569 From Parkour to Surgery, Here Are the ...

The robot revolution may not be here quite yet, but our mechanical cousins have made some serious strides. And now some of the leading experts in the field have provided a rundown of what they see as the 10 most exciting recent developments.

Compiled by the editors of the journal Science Robotics, the list includes some of the most impressive original research and innovative commercial products to make a splash in 2018, as well as a couple from 2017 that really changed the game.

1. Boston Dynamics’ Atlas doing parkour

It seems like barely a few months go by without Boston Dynamics rewriting the book on what a robot can and can’t do. Last year they really outdid themselves when they got their Atlas humanoid robot to do parkour, leaping over logs and jumping between wooden crates.

Atlas’s creators have admitted that the videos we see are cherry-picked from multiple attempts, many of which don’t go so well. But they say they’re meant to be inspirational and aspirational rather than an accurate picture of where robotics is today. And combined with the company’s dog-like Spot robot, they are certainly pushing boundaries.

2. Intuitive Surgical’s da Vinci SP platform
Robotic surgery isn’t new, but the technology is improving rapidly. Market leader Intuitive’s da Vinci surgical robot was first cleared by the FDA in 2000, but since then it’s come a long way, with the company now producing three separate systems.

The latest addition is the da Vinci SP (single port) system, which is able to insert three instruments into the body through a single 2.5cm cannula (tube) bringing a whole new meaning to minimally invasive surgery. The system was granted FDA clearance for urological procedures last year, and the company has now started shipping the new system to customers.

3. Soft robot that navigates through growth

Roboticists have long borrowed principles from the animal kingdom, but a new robot design that mimics the way plant tendrils and fungi mycelium move by growing at the tip has really broken the mold on robot navigation.

The editors point out that this is the perfect example of bio-inspired design; the researchers didn’t simply copy nature, they took a general principle and expanded on it. The tube-like robot unfolds from the front as pneumatic pressure is applied, but unlike a plant, it can grow at the speed of an animal walking and can navigate using visual feedback from a camera.

4. 3D printed liquid crystal elastomers for soft robotics
Soft robotics is one of the fastest-growing sub-disciplines in the field, but powering these devices without rigid motors or pumps is an ongoing challenge. A variety of shape-shifting materials have been proposed as potential artificial muscles, including liquid crystal elastomeric actuators.

Harvard engineers have now demonstrated that these materials can be 3D printed using a special ink that allows the designer to easily program in all kinds of unusual shape-shifting abilities. What’s more, their technique produces actuators capable of lifting significantly more weight than previous approaches.

5. Muscle-mimetic, self-healing, and hydraulically amplified actuators
In another effort to find a way to power soft robots, last year researchers at the University of Colorado Boulder designed a series of super low-cost artificial muscles that can lift 200 times their own weight and even heal themselves.

The devices rely on pouches filled with a liquid that makes them contract with the force and speed of mammalian skeletal muscles when a voltage is applied. The most promising for robotics applications is the so-called Peano-HASEL, which features multiple rectangular pouches connected in series that contract linearly, just like real muscle.

6. Self-assembled nanoscale robot from DNA

While you may think of robots as hulking metallic machines, a substantial number of scientists are working on making nanoscale robots out of DNA. And last year German researchers built the first remote-controlled DNA robotic arm.

They created a length of tightly-bound DNA molecules to act as the arm and attached it to a DNA base plate via a flexible joint. Because DNA carries a charge, they were able to get the arm to swivel around like the hand of a clock by applying a voltage and switch direction by reversing that voltage. The hope is that this arm could eventually be used to build materials piece by piece at the nanoscale.

7. DelFly nimble bioinspired robotic flapper

Robotics doesn’t only borrow from biology—sometimes it gives back to it, too. And a new flapping-winged robot designed by Dutch engineers that mimics the humble fruit fly has done just that, by revealing how the animals that inspired it carry out predator-dodging maneuvers.

The lab has been building flapping robots for years, but this time they ditched the airplane-like tail used to control previous incarnations. Instead, they used insect-inspired adjustments to the motions of its twin pairs of flapping wings to hover, pitch, and roll with the agility of a fruit fly. That has provided a useful platform for investigating insect flight dynamics, as well as more practical applications.

8. Soft exosuit wearable robot

Exoskeletons could prevent workplace injuries, help people walk again, and even boost soldiers’ endurance. Strapping on bulky equipment isn’t ideal, though, so researchers at Harvard are working on a soft exoskeleton that combines specially-designed textiles, sensors, and lightweight actuators.

And last year the team made an important breakthrough by combining their novel exoskeleton with a machine-learning algorithm that automatically tunes the device to the user’s particular walking style. Using physiological data, it is able to adjust when and where the device needs to deliver a boost to the user’s natural movements to improve walking efficiency.

9. Universal Robots (UR) e-Series Cobots
Robots in factories are nothing new. The enormous mechanical arms you see in car factories normally have to be kept in cages to prevent them from accidentally crushing people. In recent years there’s been growing interest in “co-bots,” collaborative robots designed to work side-by-side with their human colleagues and even learn from them.

Earlier this year saw the demise of ReThink robotics, the pioneer of the approach. But the simple single arm devices made by Danish firm Universal Robotics are becoming ubiquitous in workshops and warehouses around the world, accounting for about half of global co-bot sales. Last year they released their latest e-Series, with enhanced safety features and force/torque sensing.

10. Sony’s aibo
After a nearly 20-year hiatus, Sony’s robotic dog aibo is back, and it’s had some serious upgrades. As well as a revamp to its appearance, the new robotic pet takes advantage of advances in AI, with improved environmental and command awareness and the ability to develop a unique character based on interactions with its owner.

The editors note that this new context awareness mark the device out as a significant evolution in social robots, which many hope could aid in childhood learning or provide companionship for the elderly.

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#434559 Can AI Tell the Difference Between a ...

Scarcely a day goes by without another headline about neural networks: some new task that deep learning algorithms can excel at, approaching or even surpassing human competence. As the application of this approach to computer vision has continued to improve, with algorithms capable of specialized recognition tasks like those found in medicine, the software is getting closer to widespread commercial use—for example, in self-driving cars. Our ability to recognize patterns is a huge part of human intelligence: if this can be done faster by machines, the consequences will be profound.

Yet, as ever with algorithms, there are deep concerns about their reliability, especially when we don’t know precisely how they work. State-of-the-art neural networks will confidently—and incorrectly—classify images that look like television static or abstract art as real-world objects like school-buses or armadillos. Specific algorithms could be targeted by “adversarial examples,” where adding an imperceptible amount of noise to an image can cause an algorithm to completely mistake one object for another. Machine learning experts enjoy constructing these images to trick advanced software, but if a self-driving car could be fooled by a few stickers, it might not be so fun for the passengers.

These difficulties are hard to smooth out in large part because we don’t have a great intuition for how these neural networks “see” and “recognize” objects. The main insight analyzing a trained network itself can give us is a series of statistical weights, associating certain groups of points with certain objects: this can be very difficult to interpret.

Now, new research from UCLA, published in the journal PLOS Computational Biology, is testing neural networks to understand the limits of their vision and the differences between computer vision and human vision. Nicholas Baker, Hongjing Lu, and Philip J. Kellman of UCLA, alongside Gennady Erlikhman of the University of Nevada, tested a deep convolutional neural network called VGG-19. This is state-of-the-art technology that is already outperforming humans on standardized tests like the ImageNet Large Scale Visual Recognition Challenge.

They found that, while humans tend to classify objects based on their overall (global) shape, deep neural networks are far more sensitive to the textures of objects, including local color gradients and the distribution of points on the object. This result helps explain why neural networks in image recognition make mistakes that no human ever would—and could allow for better designs in the future.

In the first experiment, a neural network was trained to sort images into 1 of 1,000 different categories. It was then presented with silhouettes of these images: all of the local information was lost, while only the outline of the object remained. Ordinarily, the trained neural net was capable of recognizing these objects, assigning more than 90% probability to the correct classification. Studying silhouettes, this dropped to 10%. While human observers could nearly always produce correct shape labels, the neural networks appeared almost insensitive to the overall shape of the images. On average, the correct object was ranked as the 209th most likely solution by the neural network, even though the overall shapes were an exact match.

A particularly striking example arose when they tried to get the neural networks to classify glass figurines of objects they could already recognize. While you or I might find it easy to identify a glass model of an otter or a polar bear, the neural network classified them as “oxygen mask” and “can opener” respectively. By presenting glass figurines, where the texture information that neural networks relied on for classifying objects is lost, the neural network was unable to recognize the objects by shape alone. The neural network was similarly hopeless at classifying objects based on drawings of their outline.

If you got one of these right, you’re better than state-of-the-art image recognition software. Image Credit: Nicholas Baker, Hongjing Lu, Gennady Erlikhman, Philip J. Kelman. “Deep convolutional networks do not classify based on global object shape.” Plos Computational Biology. 12/7/18. / CC BY 4.0
When the neural network was explicitly trained to recognize object silhouettes—given no information in the training data aside from the object outlines—the researchers found that slight distortions or “ripples” to the contour of the image were again enough to fool the AI, while humans paid them no mind.

The fact that neural networks seem to be insensitive to the overall shape of an object—relying instead on statistical similarities between local distributions of points—suggests a further experiment. What if you scrambled the images so that the overall shape was lost but local features were preserved? It turns out that the neural networks are far better and faster at recognizing scrambled versions of objects than outlines, even when humans struggle. Students could classify only 37% of the scrambled objects, while the neural network succeeded 83% of the time.

Humans vastly outperform machines at classifying object (a) as a bear, while the machine learning algorithm has few problems classifying the bear in figure (b). Image Credit: Nicholas Baker, Hongjing Lu, Gennady Erlikhman, Philip J. Kelman. “Deep convolutional networks do not classify based on global object shape.” Plos Computational Biology. 12/7/18. / CC BY 4.0
“This study shows these systems get the right answer in the images they were trained on without considering shape,” Kellman said. “For humans, overall shape is primary for object recognition, and identifying images by overall shape doesn’t seem to be in these deep learning systems at all.”

Naively, one might expect that—as the many layers of a neural network are modeled on connections between neurons in the brain and resemble the visual cortex specifically—the way computer vision operates must necessarily be similar to human vision. But this kind of research shows that, while the fundamental architecture might resemble that of the human brain, the resulting “mind” operates very differently.

Researchers can, increasingly, observe how the “neurons” in neural networks light up when exposed to stimuli and compare it to how biological systems respond to the same stimuli. Perhaps someday it might be possible to use these comparisons to understand how neural networks are “thinking” and how those responses differ from humans.

But, as yet, it takes a more experimental psychology to probe how neural networks and artificial intelligence algorithms perceive the world. The tests employed against the neural network are closer to how scientists might try to understand the senses of an animal or the developing brain of a young child rather than a piece of software.

By combining this experimental psychology with new neural network designs or error-correction techniques, it may be possible to make them even more reliable. Yet this research illustrates just how much we still don’t understand about the algorithms we’re creating and using: how they tick, how they make decisions, and how they’re different from us. As they play an ever-greater role in society, understanding the psychology of neural networks will be crucial if we want to use them wisely and effectively—and not end up missing the woods for the trees.

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#434245 AI, robotics, automation: The fourth ...

For Chinese guests at Marriott International hotels, the check-in process will soon get easier. The hotel giant announced last summer that it's developing facial recognition systems that will allow guests to check in at a kiosk in less than a minute via a quick scan of their facial features. Continue reading

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#433954 The Next Great Leap Forward? Combining ...

The Internet of Things is a popular vision of objects with internet connections sending information back and forth to make our lives easier and more comfortable. It’s emerging in our homes, through everything from voice-controlled speakers to smart temperature sensors. To improve our fitness, smart watches and Fitbits are telling online apps how much we’re moving around. And across entire cities, interconnected devices are doing everything from increasing the efficiency of transport to flood detection.

In parallel, robots are steadily moving outside the confines of factory lines. They’re starting to appear as guides in shopping malls and cruise ships, for instance. As prices fall and the artificial intelligence (AI) and mechanical technology continues to improve, we will get more and more used to them making independent decisions in our homes, streets and workplaces.

Here lies a major opportunity. Robots become considerably more capable with internet connections. There is a growing view that the next evolution of the Internet of Things will be to incorporate them into the network, opening up thrilling possibilities along the way.

Home Improvements
Even simple robots become useful when connected to the internet—getting updates about their environment from sensors, say, or learning about their users’ whereabouts and the status of appliances in the vicinity. This lets them lend their bodies, eyes, and ears to give an otherwise impersonal smart environment a user-friendly persona. This can be particularly helpful for people at home who are older or have disabilities.

We recently unveiled a futuristic apartment at Heriot-Watt University to work on such possibilities. One of a few such test sites around the EU, our whole focus is around people with special needs—and how robots can help them by interacting with connected devices in a smart home.

Suppose a doorbell rings that has smart video features. A robot could find the person in the home by accessing their location via sensors, then tell them who is at the door and why. Or it could help make video calls to family members or a professional carer—including allowing them to make virtual visits by acting as a telepresence platform.

Equally, it could offer protection. It could inform them the oven has been left on, for example—phones or tablets are less reliable for such tasks because they can be misplaced or not heard.

Similarly, the robot could raise the alarm if its user appears to be in difficulty.Of course, voice-assistant devices like Alexa or Google Home can offer some of the same services. But robots are far better at moving, sensing and interacting with their environment. They can also engage their users by pointing at objects or acting more naturally, using gestures or facial expressions. These “social abilities” create bonds which are crucially important for making users more accepting of the support and making it more effective.

To help incentivize the various EU test sites, our apartment also hosts the likes of the European Robotic League Service Robot Competition—a sort of Champions League for robots geared to special needs in the home. This brought academics from around Europe to our laboratory for the first time in January this year. Their robots were tested in tasks like welcoming visitors to the home, turning the oven off, and fetching objects for their users; and a German team from Koblenz University won with a robot called Lisa.

Robots Offshore
There are comparable opportunities in the business world. Oil and gas companies are looking at the Internet of Things, for example; experimenting with wireless sensors to collect information such as temperature, pressure, and corrosion levels to detect and possibly predict faults in their offshore equipment.

In the future, robots could be alerted to problem areas by sensors to go and check the integrity of pipes and wells, and to make sure they are operating as efficiently and safely as possible. Or they could place sensors in parts of offshore equipment that are hard to reach, or help to calibrate them or replace their batteries.

The likes of the ORCA Hub, a £36m project led by the Edinburgh Centre for Robotics, bringing together leading experts and over 30 industry partners, is developing such systems. The aim is to reduce the costs and the risks of humans working in remote hazardous locations.

ORCA tests a drone robot. ORCA
Working underwater is particularly challenging, since radio waves don’t move well under the sea. Underwater autonomous vehicles and sensors usually communicate using acoustic waves, which are many times slower (1,500 meters a second vs. 300m meters a second for radio waves). Acoustic communication devices are also much more expensive than those used above the water.

This academic project is developing a new generation of low-cost acoustic communication devices, and trying to make underwater sensor networks more efficient. It should help sensors and underwater autonomous vehicles to do more together in future—repair and maintenance work similar to what is already possible above the water, plus other benefits such as helping vehicles to communicate with one another over longer distances and tracking their location.

Beyond oil and gas, there is similar potential in sector after sector. There are equivalents in nuclear power, for instance, and in cleaning and maintaining the likes of bridges and buildings. My colleagues and I are also looking at possibilities in areas such as farming, manufacturing, logistics, and waste.

First, however, the research sectors around the Internet of Things and robotics need to properly share their knowledge and expertise. They are often isolated from one another in different academic fields. There needs to be more effort to create a joint community, such as the dedicated workshops for such collaboration that we organized at the European Robotics Forum and the IoT Week in 2017.

To the same end, industry and universities need to look at setting up joint research projects. It is particularly important to address safety and security issues—hackers taking control of a robot and using it to spy or cause damage, for example. Such issues could make customers wary and ruin a market opportunity.

We also need systems that can work together, rather than in isolated applications. That way, new and more useful services can be quickly and effectively introduced with no disruption to existing ones. If we can solve such problems and unite robotics and the Internet of Things, it genuinely has the potential to change the world.

Mauro Dragone, Assistant Professor, Cognitive Robotics, Multiagent systems, Internet of Things, Heriot-Watt University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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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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