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#438769 Will Robots Make Good Friends? ...
In the 2012 film Robot and Frank, the protagonist, a retired cat burglar named Frank, is suffering the early symptoms of dementia. Concerned and guilty, his son buys him a “home robot” that can talk, do household chores like cooking and cleaning, and remind Frank to take his medicine. It’s a robot the likes of which we’re getting closer to building in the real world.
The film follows Frank, who is initially appalled by the idea of living with a robot, as he gradually begins to see the robot as both functionally useful and socially companionable. The film ends with a clear bond between man and machine, such that Frank is protective of the robot when the pair of them run into trouble.
This is, of course, a fictional story, but it challenges us to explore different kinds of human-to-robot bonds. My recent research on human-robot relationships examines this topic in detail, looking beyond sex robots and robot love affairs to examine that most profound and meaningful of relationships: friendship.
My colleague and I identified some potential risks, like the abandonment of human friends for robotic ones, but we also found several scenarios where robotic companionship can constructively augment people’s lives, leading to friendships that are directly comparable to human-to-human relationships.
Philosophy of Friendship
The robotics philosopher John Danaher sets a very high bar for what friendship means. His starting point is the “true” friendship first described by the Greek philosopher Aristotle, which saw an ideal friendship as premised on mutual good will, admiration, and shared values. In these terms, friendship is about a partnership of equals.
Building a robot that can satisfy Aristotle’s criteria is a substantial technical challenge and is some considerable way off, as Danaher himself admits. Robots that may seem to be getting close, such as Hanson Robotics’ Sophia, base their behavior on a library of pre-prepared responses: a humanoid chatbot, rather than a conversational equal. Anyone who’s had a testing back-and-forth with Alexa or Siri will know AI still has some way to go in this regard.
Aristotle also talked about other forms of “imperfect” friendship, such as “utilitarian” and “pleasure” friendships, which are considered inferior to true friendship because they don’t require symmetrical bonding and are often to one party’s unequal benefit. This form of friendship sets a relatively very low bar which some robots, like “sexbots” and robotic pets, clearly already meet.
Artificial Amigos
For some, relating to robots is just a natural extension of relating to other things in our world, like people, pets, and possessions. Psychologists have even observed how people respond naturally and socially towards media artefacts like computers and televisions. Humanoid robots, you’d have thought, are more personable than your home PC.
However, the field of “robot ethics” is far from unanimous on whether we can—or should— develop any form of friendship with robots. For an influential group of UK researchers who charted a set of “ethical principles of robotics,” human-robot “companionship” is an oxymoron, and to market robots as having social capabilities is dishonest and should be treated with caution, if not alarm. For these researchers, wasting emotional energy on entities that can only simulate emotions will always be less rewarding than forming human-to-human bonds.
But people are already developing bonds with basic robots, like vacuum-cleaning and lawn-trimming machines that can be bought for less than the price of a dishwasher. A surprisingly large number of people give these robots pet names—something they don’t do with their dishwashers. Some even take their cleaning robots on holiday.
Other evidence of emotional bonds with robots include the Shinto blessing ceremony for Sony Aibo robot dogs that were dismantled for spare parts, and the squad of US troops who fired a 21-gun salute, and awarded medals, to a bomb-disposal robot named “Boomer” after it was destroyed in action.
These stories, and the psychological evidence we have so far, make clear that we can extend emotional connections to things that are very different to us, even when we know they are manufactured and pre-programmed. But do those connections constitute a friendship comparable to that shared between humans?
True Friendship?
A colleague and I recently reviewed the extensive literature on human-to-human relationships to try to understand how, and if, the concepts we found could apply to bonds we might form with robots. We found evidence that many coveted human-to-human friendships do not in fact live up to Aristotle’s ideal.
We noted a wide range of human-to-human relationships, from relatives and lovers to parents, carers, service providers, and the intense (but unfortunately one-way) relationships we maintain with our celebrity heroes. Few of these relationships could be described as completely equal and, crucially, they are all destined to evolve over time.
All this means that expecting robots to form Aristotelian bonds with us is to set a standard even human relationships fail to live up to. We also observed forms of social connectedness that are rewarding and satisfying and yet are far from the ideal friendship outlined by the Greek philosopher.
We know that social interaction is rewarding in its own right, and something that, as social mammals, humans have a strong need for. It seems probable that relationships with robots could help to address the deep-seated urge we all feel for social connection—like providing physical comfort, emotional support, and enjoyable social exchanges—currently provided by other humans.
Our paper also discussed some potential risks. These arise particularly in settings where interaction with a robot could come to replace interaction with people, or where people are denied a choice as to whether they interact with a person or a robot—in a care setting, for instance.
These are important concerns, but they’re possibilities and not inevitabilities. In the literature we reviewed we actually found evidence of the opposite effect: robots acting to scaffold social interactions with others, acting as ice-breakers in groups, and helping people to improve their social skills or to boost their self-esteem.
It appears likely that, as time progresses, many of us will simply follow Frank’s path towards acceptance: scoffing at first, before settling into the idea that robots can make surprisingly good companions. Our research suggests that’s already happening—though perhaps not in a way of which Aristotle would have approved.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Andy Kelly on Unsplash Continue reading →
Posted in Human Robots
#437929 These Were Our Favorite Tech Stories ...
This time last year we were commemorating the end of a decade and looking ahead to the next one. Enter the year that felt like a decade all by itself: 2020. News written in January, the before-times, feels hopelessly out of touch with all that came after. Stories published in the early days of the pandemic are, for the most part, similarly naive.
The year’s news cycle was swift and brutal, ping-ponging from pandemic to extreme social and political tension, whipsawing economies, and natural disasters. Hope. Despair. Loneliness. Grief. Grit. More hope. Another lockdown. It’s been a hell of a year.
Though 2020 was dominated by big, hairy societal change, science and technology took significant steps forward. Researchers singularly focused on the pandemic and collaborated on solutions to a degree never before seen. New technologies converged to deliver vaccines in record time. The dark side of tech, from biased algorithms to the threat of omnipresent surveillance and corporate control of artificial intelligence, continued to rear its head.
Meanwhile, AI showed uncanny command of language, joined Reddit threads, and made inroads into some of science’s grandest challenges. Mars rockets flew for the first time, and a private company delivered astronauts to the International Space Station. Deprived of night life, concerts, and festivals, millions traveled to virtual worlds instead. Anonymous jet packs flew over LA. Mysterious monoliths appeared and disappeared worldwide.
It was all, you know, very 2020. For this year’s (in-no-way-all-encompassing) list of fascinating stories in tech and science, we tried to select those that weren’t totally dated by the news, but rose above it in some way. So, without further ado: This year’s picks.
How Science Beat the Virus
Ed Yong | The Atlantic
“Much like famous initiatives such as the Manhattan Project and the Apollo program, epidemics focus the energies of large groups of scientists. …But ‘nothing in history was even close to the level of pivoting that’s happening right now,’ Madhukar Pai of McGill University told me. … No other disease has been scrutinized so intensely, by so much combined intellect, in so brief a time.”
‘It Will Change Everything’: DeepMind’s AI Makes Gigantic Leap in Solving Protein Structures
Ewen Callaway | Nature
“In some cases, AlphaFold’s structure predictions were indistinguishable from those determined using ‘gold standard’ experimental methods such as X-ray crystallography and, in recent years, cryo-electron microscopy (cryo-EM). AlphaFold might not obviate the need for these laborious and expensive methods—yet—say scientists, but the AI will make it possible to study living things in new ways.”
OpenAI’s Latest Breakthrough Is Astonishingly Powerful, But Still Fighting Its Flaws
James Vincent | The Verge
“What makes GPT-3 amazing, they say, is not that it can tell you that the capital of Paraguay is Asunción (it is) or that 466 times 23.5 is 10,987 (it’s not), but that it’s capable of answering both questions and many more beside simply because it was trained on more data for longer than other programs. If there’s one thing we know that the world is creating more and more of, it’s data and computing power, which means GPT-3’s descendants are only going to get more clever.”
Artificial General Intelligence: Are We Close, and Does It Even Make Sense to Try?
Will Douglas Heaven | MIT Technology Review
“A machine that could think like a person has been the guiding vision of AI research since the earliest days—and remains its most divisive idea. …So why is AGI controversial? Why does it matter? And is it a reckless, misleading dream—or the ultimate goal?”
The Dark Side of Big Tech’s Funding for AI Research
Tom Simonite | Wired
“Timnit Gebru’s exit from Google is a powerful reminder of how thoroughly companies dominate the field, with the biggest computers and the most resources. …[Meredith] Whittaker of AI Now says properly probing the societal effects of AI is fundamentally incompatible with corporate labs. ‘That kind of research that looks at the power and politics of AI is and must be inherently adversarial to the firms that are profiting from this technology.’i”
We’re Not Prepared for the End of Moore’s Law
David Rotman | MIT Technology Review
“Quantum computing, carbon nanotube transistors, even spintronics, are enticing possibilities—but none are obvious replacements for the promise that Gordon Moore first saw in a simple integrated circuit. We need the research investments now to find out, though. Because one prediction is pretty much certain to come true: we’re always going to want more computing power.”
Inside the Race to Build the Best Quantum Computer on Earth
Gideon Lichfield | MIT Technology Review
“Regardless of whether you agree with Google’s position [on ‘quantum supremacy’] or IBM’s, the next goal is clear, Oliver says: to build a quantum computer that can do something useful. …The trouble is that it’s nearly impossible to predict what the first useful task will be, or how big a computer will be needed to perform it.”
The Secretive Company That Might End Privacy as We Know It
Kashmir Hill | The New York Times
“Searching someone by face could become as easy as Googling a name. Strangers would be able to listen in on sensitive conversations, take photos of the participants and know personal secrets. Someone walking down the street would be immediately identifiable—and his or her home address would be only a few clicks away. It would herald the end of public anonymity.”
Wrongfully Accused by an Algorithm
Kashmir Hill | The New York Times
“Mr. Williams knew that he had not committed the crime in question. What he could not have known, as he sat in the interrogation room, is that his case may be the first known account of an American being wrongfully arrested based on a flawed match from a facial recognition algorithm, according to experts on technology and the law.”
Predictive Policing Algorithms Are Racist. They Need to Be Dismantled.
Will Douglas Heaven | MIT Technology Review
“A number of studies have shown that these tools perpetuate systemic racism, and yet we still know very little about how they work, who is using them, and for what purpose. All of this needs to change before a proper reckoning can take pace. Luckily, the tide may be turning.”
The Panopticon Is Already Here
Ross Andersen | The Atlantic
“Artificial intelligence has applications in nearly every human domain, from the instant translation of spoken language to early viral-outbreak detection. But Xi [Jinping] also wants to use AI’s awesome analytical powers to push China to the cutting edge of surveillance. He wants to build an all-seeing digital system of social control, patrolled by precog algorithms that identify potential dissenters in real time.”
The Case For Cities That Aren’t Dystopian Surveillance States
Cory Doctorow | The Guardian
“Imagine a human-centered smart city that knows everything it can about things. It knows how many seats are free on every bus, it knows how busy every road is, it knows where there are short-hire bikes available and where there are potholes. …What it doesn’t know is anything about individuals in the city.”
The Modern World Has Finally Become Too Complex for Any of Us to Understand
Tim Maughan | OneZero
“One of the dominant themes of the last few years is that nothing makes sense. …I am here to tell you that the reason so much of the world seems incomprehensible is that it is incomprehensible. From social media to the global economy to supply chains, our lives rest precariously on systems that have become so complex, and we have yielded so much of it to technologies and autonomous actors that no one totally comprehends it all.”
The Conscience of Silicon Valley
Zach Baron | GQ
“What I really hoped to do, I said, was to talk about the future and how to live in it. This year feels like a crossroads; I do not need to explain what I mean by this. …I want to destroy my computer, through which I now work and ‘have drinks’ and stare at blurry simulations of my parents sometimes; I want to kneel down and pray to it like a god. I want someone—I want Jaron Lanier—to tell me where we’re going, and whether it’s going to be okay when we get there. Lanier just nodded. All right, then.”
Yes to Tech Optimism. And Pessimism.
Shira Ovide | The New York Times
“Technology is not something that exists in a bubble; it is a phenomenon that changes how we live or how our world works in ways that help and hurt. That calls for more humility and bridges across the optimism-pessimism divide from people who make technology, those of us who write about it, government officials and the public. We need to think on the bright side. And we need to consider the horribles.”
How Afrofuturism Can Help the World Mend
C. Brandon Ogbunu | Wired
“…[W. E. B. DuBois’] ‘The Comet’ helped lay the foundation for a paradigm known as Afrofuturism. A century later, as a comet carrying disease and social unrest has upended the world, Afrofuturism may be more relevant than ever. Its vision can help guide us out of the rubble, and help us to consider universes of better alternatives.”
Wikipedia Is the Last Best Place on the Internet
Richard Cooke | Wired
“More than an encyclopedia, Wikipedia has become a community, a library, a constitution, an experiment, a political manifesto—the closest thing there is to an online public square. It is one of the few remaining places that retains the faintly utopian glow of the early World Wide Web.”
Can Genetic Engineering Bring Back the American Chestnut?
Gabriel Popkin | The New York Times Magazine
“The geneticists’ research forces conservationists to confront, in a new and sometimes discomfiting way, the prospect that repairing the natural world does not necessarily mean returning to an unblemished Eden. It may instead mean embracing a role that we’ve already assumed: engineers of everything, including nature.”
At the Limits of Thought
David C. Krakauer | Aeon
“A schism is emerging in the scientific enterprise. On the one side is the human mind, the source of every story, theory, and explanation that our species holds dear. On the other stand the machines, whose algorithms possess astonishing predictive power but whose inner workings remain radically opaque to human observers.”
Is the Internet Conscious? If It Were, How Would We Know?
Meghan O’Gieblyn | Wired
“Does the internet behave like a creature with an internal life? Does it manifest the fruits of consciousness? There are certainly moments when it seems to. Google can anticipate what you’re going to type before you fully articulate it to yourself. Facebook ads can intuit that a woman is pregnant before she tells her family and friends. It is easy, in such moments, to conclude that you’re in the presence of another mind—though given the human tendency to anthropomorphize, we should be wary of quick conclusions.”
The Internet Is an Amnesia Machine
Simon Pitt | OneZero
“There was a time when I didn’t know what a Baby Yoda was. Then there was a time I couldn’t go online without reading about Baby Yoda. And now, Baby Yoda is a distant, shrugging memory. Soon there will be a generation of people who missed the whole thing and for whom Baby Yoda is as meaningless as it was for me a year ago.”
Digital Pregnancy Tests Are Almost as Powerful as the Original IBM PC
Tom Warren | The Verge
“Each test, which costs less than $5, includes a processor, RAM, a button cell battery, and a tiny LCD screen to display the result. …Foone speculates that this device is ‘probably faster at number crunching and basic I/O than the CPU used in the original IBM PC.’ IBM’s original PC was based on Intel’s 8088 microprocessor, an 8-bit chip that operated at 5Mhz. The difference here is that this is a pregnancy test you pee on and then throw away.”
The Party Goes on in Massive Online Worlds
Cecilia D’Anastasio | Wired
“We’re more stand-outside types than the types to cast a flashy glamour spell and chat up the nearest cat girl. But, hey, it’s Final Fantasy XIV online, and where my body sat in New York, the epicenter of America’s Covid-19 outbreak, there certainly weren’t any parties.”
The Facebook Groups Where People Pretend the Pandemic Isn’t Happening
Kaitlyn Tiffany | The Atlantic
“Losing track of a friend in a packed bar or screaming to be heard over a live band is not something that’s happening much in the real world at the moment, but it happens all the time in the 2,100-person Facebook group ‘a group where we all pretend we’re in the same venue.’ So does losing shoes and Juul pods, and shouting matches over which bands are the saddest, and therefore the greatest.”
Did You Fly a Jetpack Over Los Angeles This Weekend? Because the FBI Is Looking for You
Tom McKay | Gizmodo
“Did you fly a jetpack over Los Angeles at approximately 3,000 feet on Sunday? Some kind of tiny helicopter? Maybe a lawn chair with balloons tied to it? If the answer to any of the above questions is ‘yes,’ you should probably lay low for a while (by which I mean cool it on the single-occupant flying machine). That’s because passing airline pilots spotted you, and now it’s this whole thing with the FBI and the Federal Aviation Administration, both of which are investigating.”
Image Credit: Thomas Kinto / Unsplash Continue reading →
Posted in Human Robots
#436482 50+ Reasons Our Favorite Emerging ...
For most of history, technology was about atoms, the manipulation of physical stuff to extend humankind’s reach. But in the last five or six decades, atoms have partnered with bits, the elemental “particles” of the digital world as we know it today. As computing has advanced at the accelerating pace described by Moore’s Law, technological progress has become increasingly digitized.
SpaceX lands and reuses rockets and self-driving cars do away with drivers thanks to automation, sensors, and software. Businesses find and hire talent from anywhere in the world, and for better and worse, a notable fraction of the world learns and socializes online. From the sequencing of DNA to artificial intelligence and from 3D printing to robotics, more and more new technologies are moving at a digital pace and quickly emerging to reshape the world around us.
In 2019, stories charting the advances of some of these digital technologies consistently made headlines. Below is, what is at best, an incomplete list of some of the big stories that caught our eye this year. With so much happening, it’s likely we’ve missed some notable headlines and advances—as well as some of your personal favorites. In either instance, share your thoughts and candidates for the biggest stories and breakthroughs on Facebook and Twitter.
With that said, let’s dive straight into the year.
Artificial Intelligence
No technology garnered as much attention as AI in 2019. With good reason. Intelligent computer systems are transitioning from research labs to everyday life. Healthcare, weather forecasting, business process automation, traffic congestion—you name it, and machine learning algorithms are likely beginning to work on it. Yet, AI has also been hyped up and overmarketed, and the latest round of AI technology, deep learning, is likely only one piece of the AI puzzle.
This year, Open AI’s game-playing algorithms beat some of the world’s best Dota 2 players, DeepMind notched impressive wins in Starcraft, and Carnegie Mellon University’s Libratus “crushed” pros at six-player Texas Hold‘em.
Speaking of games, AI’s mastery of the incredibly complex game of Go prompted a former world champion to quit, stating that AI ‘”cannot be defeated.”
But it isn’t just fun and games. Practical, powerful applications that make the best of AI’s pattern recognition abilities are on the way. Insilico Medicine, for example, used machine learning to help discover and design a new drug in just 46 days, and DeepMind is focused on using AI to crack protein folding.
Of course, AI can be a double-edged sword. When it comes to deepfakes and fake news, for example, AI makes both easier to create and detect, and early in the year, OpenAI created and announced a powerful AI text generator but delayed releasing it for fear of malicious use.
Recognizing AI’s power for good and ill, the OECD, EU, World Economic Forum, and China all took a stab at defining an ethical framework for the development and deployment of AI.
Computing Systems
Processors and chips kickstarted the digital boom and are still the bedrock of continued growth. While progress in traditional silicon-based chips continues, it’s slowing and getting more expensive. Some say we’re reaching the end of Moore’s Law. While that may be the case for traditional chips, specialized chips and entirely new kinds of computing are waiting in the wings.
In fall 2019, Google confirmed its quantum computer had achieved “quantum supremacy,” a term that means a quantum computer can perform a calculation a normal computer cannot. IBM pushed back on the claim, and it should be noted the calculation was highly specialized. But while it’s still early days, there does appear to be some real progress (and more to come).
Should quantum computing become truly practical, “the implications are staggering.” It could impact machine learning, medicine, chemistry, and materials science, just to name a few areas.
Specialized chips continue to take aim at machine learning—a giant new chip with over a trillion transistors, for example, may make machine learning algorithms significantly more efficient.
Cellular computers also saw advances in 2019 thanks to CRISPR. And the year witnessed the emergence of the first reprogrammable DNA computer and new chips inspired by the brain.
The development of hardware computing platforms is intrinsically linked to software. 2019 saw a continued move from big technology companies towards open sourcing (at least parts of) their software, potentially democratizing the use of advanced systems.
Networks
Increasing interconnectedness has, in many ways, defined the 21st century so far. Your phone is no longer just a phone. It’s access to the world’s population and accumulated knowledge—and it fits in your pocket. Pretty neat. This is all thanks to networks, which had some notable advances in 2019.
The biggest network development of the year may well be the arrival of the first 5G networks.
5G’s faster speeds promise advances across many emerging technologies.
Self-driving vehicles, for example, may become both smarter and safer thanks to 5G C-V2X networks. (Don’t worry with trying to remember that. If they catch on, they’ll hopefully get a better name.)
Wi-Fi may have heard the news and said “hold my beer,” as 2019 saw the introduction of Wi-Fi 6. Perhaps the most important upgrade, among others, is that Wi-Fi 6 ensures that the ever-growing number of network connected devices get higher data rates.
Networks also went to space in 2019, as SpaceX began launching its Starlink constellation of broadband satellites. In typical fashion, Elon Musk showed off the network’s ability to bounce data around the world by sending a Tweet.
Augmented Reality and Virtual Reality
Forget Pokemon Go (unless you want to add me as a friend in the game—in which case don’t forget Pokemon Go). 2019 saw AR and VR advance, even as Magic Leap, the most hyped of the lot, struggled to live up to outsized expectations and sell headsets.
Mixed reality AR and VR technologies, along with the explosive growth of sensor-based data about the world around us, is creating a one-to-one “Mirror World” of our physical reality—a digital world you can overlay on our own or dive into immersively thanks to AR and VR.
Facebook launched Replica, for example, which is a photorealistic virtual twin of the real world that, among other things, will help train AIs to better navigate their physical surroundings.
Our other senses (beyond eyes) may also become part of the Mirror World through the use of peripherals like a newly developed synthetic skin that aim to bring a sense of touch to VR.
AR and VR equipment is also becoming cheaper—with more producers entering the space—and more user-friendly. Instead of a wired headset requiring an expensive gaming PC, the new Oculus Quest is a wireless, self-contained step toward the mainstream.
Niche uses also continue to gain traction, from Google Glass’s Enterprise edition to the growth of AR and VR in professional education—including on-the-job-training and roleplaying emotionally difficult work encounters, like firing an employee.
Digital Biology and Biotech
The digitization of biology is happening at an incredible rate. With wild new research coming to light every year and just about every tech giant pouring money into new solutions and startups, we’re likely to see amazing advances in 2020 added to those we saw in 2019.
None were, perhaps, more visible than the success of protein-rich, plant-based substitutes for various meats. This was the year Beyond Meat was the top IPO on the NASDAQ stock exchange and people stood in line for the plant-based Impossible Whopper and KFC’s Beyond Chicken.
In the healthcare space, a report about three people with HIV who became virus free thanks to a bone marrow transplants of stem cells caused a huge stir. The research is still in relatively early stages, and isn’t suitable for most people, but it does provides a glimmer of hope.
CRISPR technology, which almost deserves its own section, progressed by leaps and bounds. One tweak made CRISPR up to 50 times more accurate, while the latest new CRISPR-based system, CRISPR prime, was described as a “word processor” for gene editing.
Many areas of healthcare stand to gain from CRISPR. For instance, cancer treatment, were a first safety test showed ‘promising’ results.
CRISPR’s many potential uses, however, also include some weird/morally questionable areas, which was exemplified by one the year’s stranger CRISPR-related stories about a human-monkey hybrid embryo in China.
Incidentally, China could be poised to take the lead on CRISPR thanks to massive investments and research programs.
As a consequence of quick advances in gene editing, we are approaching a point where we will be able to design our own biology—but first we need to have a serious conversation as a society about the ethics of gene editing and what lines should be drawn.
3D Printing
3D printing has quietly been growing both market size and the objects the printers are capable of producing. While both are impressive, perhaps the biggest story of 2019 is their increased speed.
One example was a boat that was printed in just three days, which also set three new world records for 3D printing.
3D printing is also spreading in the construction industry. In Mexico, the technology is being used to construct 50 new homes with subsidized mortgages of just $20/month.
3D printers also took care of all parts of a 640 square-meter home in Dubai.
Generally speaking, the use of 3D printing to make parts for everything from rocket engines (even entire rockets) to trains to cars illustrates the sturdiness of the technology, anno 2019.
In healthcare, 3D printing is also advancing the cause of bio-printed organs and, in one example, was used to print vascularized parts of a human heart.
Robotics
Living in Japan, I get to see Pepper, Aibo, and other robots on pretty much a daily basis. The novelty of that experience is spreading to other countries, and robots are becoming a more visible addition to both our professional and private lives.
We can’t talk about robots and 2019 without mentioning Boston Dynamics’ Spot robot, which went on sale for the general public.
Meanwhile, Google, Boston Dynamics’ former owner, rebooted their robotics division with a more down-to-earth focus on everyday uses they hope to commercialize.
SoftBank’s Pepper robot is working as a concierge and receptionist in various countries. It is also being used as a home companion. Not satisfied, Pepper rounded off 2019 by heading to the gym—to coach runners.
Indeed, there’s a growing list of sports where robots perform as well—or better—than humans.
2019 also saw robots launch an assault on the kitchen, including the likes of Samsung’s robot chef, and invade the front yard, with iRobot’s Terra robotic lawnmower.
In the borderlands of robotics, full-body robotic exoskeletons got a bit more practical, as the (by all accounts) user-friendly, battery-powered Sarcos Robotics Guardian XO went commercial.
Autonomous Vehicles
Self-driving cars did not—if you will forgive the play on words—stay quite on track during 2019. The fallout from Uber’s 2018 fatal crash marred part of the year, while some big players ratcheted back expectations on a quick shift to the driverless future. Still, self-driving cars, trucks, and other autonomous systems did make progress this year.
Winner of my unofficial award for best name in self-driving goes to Optimus Ride. The company also illustrates that self-driving may not be about creating a one-size-fits-all solution but catering to specific markets.
Self-driving trucks had a good year, with tests across many countries and states. One of the year’s odder stories was a self-driving truck traversing the US with a delivery of butter.
A step above the competition may be the future slogan (or perhaps not) of Boeing’s self-piloted air taxi that saw its maiden test flight in 2019. It joins a growing list of companies looking to create autonomous, flying passenger vehicles.
2019 was also the year where companies seemed to go all in on last-mile autonomous vehicles. Who wins that particular competition could well emerge during 2020.
Blockchain and Digital Currencies
Bitcoin continues to be the cryptocurrency equivalent of a rollercoaster, but the underlying blockchain technology is progressing more steadily. Together, they may turn parts of our financial systems cashless and digital—though how and when remains a slightly open question.
One indication of this was Facebook’s hugely controversial announcement of Libra, its proposed cryptocurrency. The company faced immediate pushback and saw a host of partners jump ship. Still, it brought the tech into mainstream conversations as never before and is putting the pressure on governments and central banks to explore their own digital currencies.
Deloitte’s in-depth survey of the state of blockchain highlighted how the technology has moved from fintech into just about any industry you can think of.
One of the biggest issues facing the spread of many digital currencies—Bitcoin in particular, you could argue—is how much energy it consumes to mine them. 2019 saw the emergence of several new digital currencies with a much smaller energy footprint.
2019 was also a year where we saw a new kind of digital currency, stablecoins, rise to prominence. As the name indicates, stablecoins are a group of digital currencies whose price fluctuations are more stable than the likes of Bitcoin.
In a geopolitical sense, 2019 was a year of China playing catch-up. Having initially banned blockchain, the country turned 180 degrees and announced that it was “quite close” to releasing a digital currency and a wave of blockchain-programs.
Renewable Energy and Energy Storage
While not every government on the planet seems to be a fan of renewable energy, it keeps on outperforming fossil fuel after fossil fuel in places well suited to it—even without support from some of said governments.
One of the reasons for renewable energy’s continued growth is that energy efficiency levels keep on improving.
As a result, an increased number of coal plants are being forced to close due to an inability to compete, and the UK went coal-free for a record two weeks.
We are also seeing more and more financial institutions refusing to fund fossil fuel projects. One such example is the European Investment Bank.
Renewable energy’s advance is tied at the hip to the rise of energy storage, which also had a breakout 2019, in part thanks to investments from the likes of Bill Gates.
The size and capabilities of energy storage also grew in 2019. The best illustration came from Australia were Tesla’s mega-battery proved that energy storage has reached a stage where it can prop up entire energy grids.
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Posted in Human Robots
#436065 From Mainframes to PCs: What Robot ...
This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.
Autonomous robots are coming around slowly. We already got autonomous vacuum cleaners, autonomous lawn mowers, toys that bleep and blink, and (maybe) soon autonomous cars. Yet, generation after generation, we keep waiting for the robots that we all know from movies and TV shows. Instead, businesses seem to get farther and farther away from the robots that are able to do a large variety of tasks using general-purpose, human anatomy-inspired hardware.
Although these are the droids we have been looking for, anything that came close, such as Willow Garage’s PR2 or Rethink Robotics’ Baxter has bitten the dust. With building a robotic company being particularly hard, compounding business risk with technological risk, the trend goes from selling robots to selling actual services like mowing your lawn, provide taxi rides, fulfilling retail orders, or picking strawberries by the pound. Unfortunately for fans of R2-D2 and C-3PO, these kind of business models emphasize specialized, room- or fridge-sized hardware that is optimized for one very specific task, but does not contribute to a general-purpose robotic platform.
We have actually seen something very similar in the personal computer (PC) industry. In the 1950s, even though computers could be as big as an entire room and were only available to a selected few, the public already had a good idea of what computers would look like. A long list of fictional computers started to populate mainstream entertainment during that time. In a 1962 New York Times article titled “Pocket Computer to Replace Shopping List,” visionary scientist John Mauchly stated that “there is no reason to suppose the average boy or girl cannot be master of a personal computer.”
In 1968, Douglas Engelbart gave us the “mother of all demos,” browsing hypertext on a graphical screen and a mouse, and other ideas that have become standard only decades later. Now that we have finally seen all of this, it might be helpful to examine what actually enabled the computing revolution to learn where robotics is really at and what we need to do next.
The parallels between computers and robots
In the 1970s, mainframes were about to be replaced by the emerging class of mini-computers, fridge-sized devices that cost less than US $25,000 ($165,000 in 2019 dollars). These computers did not use punch-cards, but could be programmed in Fortran and BASIC, dramatically expanding the ease with which potential applications could be created. Yet it was still unclear whether mini-computers could ever replace big mainframes in applications that require fast and efficient processing of large amounts of data, let alone enter every living room. This is very similar to the robotics industry right now, where large-scale factory robots (mainframes) that have existed since the 1960s are seeing competition from a growing industry of collaborative robots that can safely work next to humans and can easily be installed and programmed (minicomputers). As in the ’70s, applications for these devices that reach system prices comparable to that of a luxury car are quite limited, and it is hard to see how they could ever become a consumer product.
Yet, as in the computer industry, successful architectures are quickly being cloned, driving prices down, and entirely new approaches on how to construct or program robotic arms are sprouting left and right. Arm makers are joined by manufacturers of autonomous carts, robotic grippers, and sensors. These components can be combined, paving the way for standard general purpose platforms that follow the model of the IBM PC, which built a capable, open architecture relying as much on commodity parts as possible.
General purpose robotic systems have not been successful for similar reasons that general purpose, also known as “personal,” computers took decades to emerge. Mainframes were custom-built for each application, while typewriters got smarter and smarter, not really leaving room for general purpose computers in between. Indeed, given the cost of hardware and the relatively little abilities of today’s autonomous robots, it is almost always smarter to build a special purpose machine than trying to make a collaborative mobile manipulator smart.
A current example is e-commerce grocery fulfillment. The current trend is to reserve underutilized parts of a brick-and-mortar store for a micro-fulfillment center that stores goods in little crates with an automated retrieval system and a (human) picker. A number of startups like Alert Innovation, Fabric, Ocado Technology, TakeOff Technologies, and Tompkins Robotics, to just name a few, have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves. Such a robotic store clerk would come much closer to our vision of a general purpose robot, but would require many copies of itself that crowd the aisles to churn out hundreds of orders per hour as a microwarehouse could. Although eventually more efficient, the margins in retail are already low and make it unlikely that this industry will produce the technological jump that we need to get friendly C-3POs manning the aisles.
Startups have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves, and would come much closer to our vision of a general purpose robot.
Mainframes were also attacked from the bottom. Fascination with the new digital technology has led to a hobbyist movement to create microcomputers that were sold via mail order or at RadioShack. Initially, a large number of small businesses was selling tens, at most hundreds, of devices, usually as a kit and with wooden enclosures. This trend culminated into the “1977 Trinity” in the form of the Apple II, the Commodore PET, and the Tandy TRS-80, complete computers that were sold for prices around $2500 (TRS) to $5000 (Apple) in today’s dollars. The main application of these computers was their programmability (in BASIC), which would enable consumers to “learn to chart your biorhythms, balance your checking account, or even control your home environment,” according to an original Apple advertisement. Similarly, there exists a myriad of gadgets that explore different aspects of robotics such as mobility, manipulation, and entertainment.
As in the fledgling personal computing industry, the advertised functionality was at best a model of the real deal. A now-famous milestone in entertainment robotics was the original Sony’s Aibo, a robotic dog that was advertised to have many properties that a real dog has such as develop its own personality, play with a toy, and interact with its owner. Released in 1999, and re-launched in 2018, the platform has a solid following among hobbyists and academics who like its programmability, but probably only very few users who accept the device as a pet stand-in.
There also exist countless “build-your-own-robotic-arm” kits. One of the more successful examples is the uArm, which sells for around $800, and is advertised to perform pick and place, assembly, 3D printing, laser engraving, and many other things that sound like high value applications. Using compelling videos of the robot actually doing these things in a constrained environment has led to two successful crowd-funding campaigns, and have established the robot as a successful educational tool.
Finally, there exist platforms that allow hobbyist programmers to explore mobility to construct robots that patrol your house, deliver items, or provide their users with telepresence abilities. An example of that is the Misty II. Much like with the original Apple II, there remains a disconnect between the price of the hardware and the fidelity of the applications that were available.
For computers, this disconnect began to disappear with the invention of the first electronic spreadsheet software VisiCalc that spun out of Harvard in 1979 and prompted many people to buy an entire microcomputer just to run the program. VisiCalc was soon joined by WordStar, a word processing application, that sold for close to $2000 in today’s dollars. WordStar, too, would entice many people to buy the entire hardware just to use the software. The two programs are early examples of what became known as “killer application.”
With factory automation being mature, and robots with the price tag of a minicomputer being capable of driving around and autonomously carrying out many manipulation tasks, the robotics industry is somewhere where the PC industry was between 1973—the release of the Xerox Alto, the first computer with a graphical user interface, mouse, and special software—and 1979—when microcomputers in the under $5000 category began to take off.
Killer apps for robots
So what would it take for robotics to continue to advance like computers did? The market itself already has done a good job distilling what the possible killer apps are. VCs and customers alike push companies who have set out with lofty goals to reduce their offering to a simple value proposition. As a result, companies that started at opposite ends often converge to mirror images of each other that offer very similar autonomous carts, (bin) picking, palletizing, depalletizing, or sorting solutions. Each of these companies usually serves a single application to a single vertical—for example bin-picking clothes, transporting warehouse goods, or picking strawberries by the pound. They are trying to prove that their specific technology works without spreading themselves too thin.
Very few of these companies have really taken off. One example is Kiva Systems, which turned into the logistic robotics division of Amazon. Kiva and others are structured around sound value propositions that are grounded in well-known user needs. As these solutions are very specialized, however, it is unlikely that they result into any economies of scale of the same magnitude that early computer users who bought both a spreadsheet and a word processor application for their expensive minicomputer could enjoy. What would make these robotic solutions more interesting is when functionality becomes stackable. Instead of just being able to do bin picking, palletizing, and transportation with the same hardware, these three skills could be combined to model entire processes.
A skill that is yet little addressed by startups and is historically owned by the mainframe equivalent of robotics is assembly of simple mechatronic devices. The ability to assemble mechatronic parts is equivalent to other tasks such as changing a light bulb, changing the batteries in a remote control, or tending machines like a lever-based espresso machine. These tasks would involve the autonomous execution of complete workflows possible using a single machine, eventually leading to an explosion of industrial productivity across all sectors. For example, picking up an item from a bin, arranging it on the robot, moving it elsewhere, and placing it into a shelf or a machine is a process that equally applies to a manufacturing environment, a retail store, or someone’s kitchen.
Image: Robotic Materials Inc.
Autonomous, vision and force-based assembly of the
Siemens robot learning challenge.
Even though many of the above applications are becoming possible, it is still very hard to get a platform off the ground without added components that provide “killer app” value of their own. Interesting examples are Rethink Robotics or the Robot Operating System (ROS). Rethink Robotics’ Baxter and Sawyer robots pioneered a great user experience (like the 1973 Xerox Alto, really the first PC), but its applications were difficult to extend beyond simple pick-and-place and palletizing and depalletizing items.
ROS pioneered interprocess communication software that was adapted to robotic needs (multiple computers, different programming languages) and the idea of software modularity in robotics, but—in the absence of a common hardware platform—hasn’t yet delivered a single application, e.g. for navigation, path planning, or grasping, that performs beyond research-grade demonstration level and won’t get discarded once developers turn to production systems. At the same time, an increasing number of robotic devices, such as robot arms or 3D perception systems that offer intelligent functionality, provide other ways to wire them together that do not require an intermediary computer, while keeping close control over the real-time aspects of their hardware.
Image: Robotic Materials Inc.
Robotic Materials GPR-1 combines a MIR-100 autonomous cart with an UR-5 collaborative robotic arm, an onRobot force/torque sensor and Robotic Materials’ SmartHand to perform out-of-the-box mobile assembly, bin picking, palletizing, and depalletizing tasks.
At my company, Robotic Materials Inc., we have made strides to identify a few applications such as bin picking and assembly, making them configurable with a single click by combining machine learning and optimization with an intuitive user interface. Here, users can define object classes and how to grasp them using a web browser, which then appear as first-class objects in a robot-specific graphical programming language. We have also done this for assembly, allowing users to stack perception-based picking and force-based assembly primitives by simply dragging and dropping appropriate commands together.
While such an approach might answer the question of a killer app for robots priced in the “minicomputer” range, it is unclear how killer app-type value can be generated with robots in the less-than-$5000 category. A possible answer is two-fold: First, with low-cost arms, mobility platforms, and entertainment devices continuously improving, a confluence of technology readiness and user innovation, like with the Apple II and VisiCalc, will eventually happen. For example, there is not much innovation needed to turn Misty into a home security system; the uArm into a low-cost bin-picking system; or an Aibo-like device into a therapeutic system for the elderly or children with autism.
Second, robots and their components have to become dramatically cheaper. Indeed, computers have seen an exponential reduction in price accompanied by an exponential increase in computational power, thanks in great part to Moore’s Law. This development has helped robotics too, allowing us to reach breakthroughs in mobility and manipulation due to the ability to process massive amounts of image and depth data in real-time, and we can expect it to continue to do so.
Is there a Moore’s Law for robots?
One might ask, however, how a similar dynamics might be possible for robots as a whole, including all their motors and gears, and what a “Moore’s Law” would look like for the robotics industry. Here, it helps to remember that the perpetuation of Moore’s Law is not the reason, but the result of the PC revolution. Indeed, the first killer apps for bookkeeping, editing, and gaming were so good that they unleashed tremendous consumer demand, beating the benchmark on what was thought to be physically possible over and over again. (I vividly remember 56 kbps to be the absolute maximum data rate for copper phone lines until DSL appeared.)
That these economies of scale are also applicable to mechatronics is impressively demonstrated by the car industry. A good example is the 2020 Prius Prime, a highly computerized plug-in hybrid, that is available for one third of the cost of my company’s GPR-1 mobile manipulator while being orders of magnitude more complex, sporting an electrical motor, a combustion engine, and a myriad of sensors and computers. It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal. Given that these robots are part of the equation, actively lowering cost of production, this might happen as fast as never before in the history of industrialization.
It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal.
There is one more driver that might make robots exponentially more capable: the cloud. Once a general purpose robot has learned or was programmed with a new skill, it could share it with every other robot. At some point, a grocer who buys a robot could assume that it already knows how to recognize and handle 99 percent of the retail items in the store. Likewise, a manufacturer can assume that the robot can handle and assemble every item available from McMaster-Carr and Misumi. Finally, families could expect a robot to know every kitchen item that Ikea and Pottery Barn is selling. Sounds like a labor intense problem, but probably more manageable than collecting footage for Google’s Street View using cars, tricycles, and snowmobiles, among other vehicles.
Strategies for robot startups
While we are waiting for these two trends—better and better applications and hardware with decreasing cost—to converge, we as a community have to keep exploring what the canonical robotic applications beyond mobility, bin picking, palletizing, depalletizing, and assembly are. We must also continue to solve the fundamental challenges that stand in the way of making these solutions truly general and robust.
For both questions, it might help to look at the strategies that have been critical in the development of the personal computer, which might equally well apply to robotics:
Start with a solution to a problem your customers have. Unfortunately, their problem is almost never that they need your sensor, widget, or piece of code, but something that already costs them money or negatively affects them in some other way. Example: There are many more people who had a problem calculating their taxes (and wanted to buy VisiCalc) than writing their own solution in BASIC.
Build as little of your own hardware as necessary. Your business model should be stronger than the margin you can make on the hardware. Why taking the risk? Example: Why build your own typewriter if you can write the best typewriting application that makes it worth buying a computer just for that?
If your goal is a platform, make sure it comes with a killer application, which alone justifies the platform cost. Example: Microcomputer companies came and went until the “1977 Trinity” intersected with the killer apps spreadsheet and word processors. Corollary: You can also get lucky.
Use an open architecture, which creates an ecosystem where others compete on creating better components and peripherals, while allowing others to integrate your solution into their vertical and stack it with other devices. Example: Both the Apple II and the IBM PC were completely open architectures, enabling many clones, thereby growing the user and developer base.
It’s worthwhile pursuing this. With most business processes already being digitized, general purpose robots will allow us to fill in gaps in mobility and manipulation, increasing productivity at levels only limited by the amount of resources and energy that are available, possibly creating a utopia in which creativity becomes the ultimate currency. Maybe we’ll even get R2-D2.
Nikolaus Correll is an associate professor of computer science at the University of Colorado at Boulder where he works on mobile manipulation and other robotics applications. He’s co-founder and CTO of Robotic Materials Inc., which is supported by the National Science Foundation and the National Institute of Standards and Technology via their Small Business Innovative Research (SBIR) programs. Continue reading →
Posted in Human Robots
#435765 The Four Converging Technologies Giving ...
How each of us sees the world is about to change dramatically.
For all of human history, the experience of looking at the world was roughly the same for everyone. But boundaries between the digital and physical are beginning to fade.
The world around us is gaining layer upon layer of digitized, virtually overlaid information—making it rich, meaningful, and interactive. As a result, our respective experiences of the same environment are becoming vastly different, personalized to our goals, dreams, and desires.
Welcome to Web 3.0, or the Spatial Web. In version 1.0, static documents and read-only interactions limited the internet to one-way exchanges. Web 2.0 provided quite an upgrade, introducing multimedia content, interactive web pages, and participatory social media. Yet, all this was still mediated by two-dimensional screens.
Today, we are witnessing the rise of Web 3.0, riding the convergence of high-bandwidth 5G connectivity, rapidly evolving AR eyewear, an emerging trillion-sensor economy, and powerful artificial intelligence.
As a result, we will soon be able to superimpose digital information atop any physical surrounding—freeing our eyes from the tyranny of the screen, immersing us in smart environments, and making our world endlessly dynamic.
In the third post of our five-part series on augmented reality, we will explore the convergence of AR, AI, sensors, and blockchain and dive into the implications through a key use case in manufacturing.
A Tale of Convergence
Let’s deconstruct everything beneath the sleek AR display.
It all begins with graphics processing units (GPUs)—electric circuits that perform rapid calculations to render images. (GPUs can be found in mobile phones, game consoles, and computers.)
However, because AR requires such extensive computing power, single GPUs will not suffice. Instead, blockchain can now enable distributed GPU processing power, and blockchains specifically dedicated to AR holographic processing are on the rise.
Next up, cameras and sensors will aggregate real-time data from any environment to seamlessly integrate physical and virtual worlds. Meanwhile, body-tracking sensors are critical for aligning a user’s self-rendering in AR with a virtually enhanced environment. Depth sensors then provide data for 3D spatial maps, while cameras absorb more surface-level, detailed visual input. In some cases, sensors might even collect biometric data, such as heart rate and brain activity, to incorporate health-related feedback in our everyday AR interfaces and personal recommendation engines.
The next step in the pipeline involves none other than AI. Processing enormous volumes of data instantaneously, embedded AI algorithms will power customized AR experiences in everything from artistic virtual overlays to personalized dietary annotations.
In retail, AIs will use your purchasing history, current closet inventory, and possibly even mood indicators to display digitally rendered items most suitable for your wardrobe, tailored to your measurements.
In healthcare, smart AR glasses will provide physicians with immediately accessible and maximally relevant information (parsed from the entirety of a patient’s medical records and current research) to aid in accurate diagnoses and treatments, freeing doctors to engage in the more human-centric tasks of establishing trust, educating patients and demonstrating empathy.
Image Credit: PHD Ventures.
Convergence in Manufacturing
One of the nearest-term use cases of AR is manufacturing, as large producers begin dedicating capital to enterprise AR headsets. And over the next ten years, AR will converge with AI, sensors, and blockchain to multiply manufacturer productivity and employee experience.
(1) Convergence with AI
In initial application, digital guides superimposed on production tables will vastly improve employee accuracy and speed, while minimizing error rates.
Already, the International Air Transport Association (IATA) — whose airlines supply 82 percent of air travel — recently implemented industrial tech company Atheer’s AR headsets in cargo management. And with barely any delay, IATA reported a whopping 30 percent improvement in cargo handling speed and no less than a 90 percent reduction in errors.
With similar success rates, Boeing brought Skylight’s smart AR glasses to the runway, now used in the manufacturing of hundreds of airplanes. Sure enough—the aerospace giant has now seen a 25 percent drop in production time and near-zero error rates.
Beyond cargo management and air travel, however, smart AR headsets will also enable on-the-job training without reducing the productivity of other workers or sacrificing hardware. Jaguar Land Rover, for instance, implemented Bosch’s Re’flekt One AR solution to gear technicians with “x-ray” vision: allowing them to visualize the insides of Range Rover Sport vehicles without removing any dashboards.
And as enterprise capabilities continue to soar, AIs will soon become the go-to experts, offering support to manufacturers in need of assembly assistance. Instant guidance and real-time feedback will dramatically reduce production downtime, boost overall output, and even help customers struggling with DIY assembly at home.
Perhaps one of the most profitable business opportunities, AR guidance through centralized AI systems will also serve to mitigate supply chain inefficiencies at extraordinary scale. Coordinating moving parts, eliminating the need for manned scanners at each checkpoint, and directing traffic within warehouses, joint AI-AR systems will vastly improve workflow while overseeing quality assurance.
After its initial implementation of AR “vision picking” in 2015, leading courier company DHL recently announced it would continue to use Google’s newest smart lens in warehouses across the world. Motivated by the initial group’s reported 15 percent jump in productivity, DHL’s decision is part of the logistics giant’s $300 million investment in new technologies.
And as direct-to-consumer e-commerce fundamentally transforms the retail sector, supply chain optimization will only grow increasingly vital. AR could very well prove the definitive step for gaining a competitive edge in delivery speeds.
As explained by Vital Enterprises CEO Ash Eldritch, “All these technologies that are coming together around artificial intelligence are going to augment the capabilities of the worker and that’s very powerful. I call it Augmented Intelligence. The idea is that you can take someone of a certain skill level and by augmenting them with artificial intelligence via augmented reality and the Internet of Things, you can elevate the skill level of that worker.”
Already, large producers like Goodyear, thyssenkrupp, and Johnson Controls are using the Microsoft HoloLens 2—priced at $3,500 per headset—for manufacturing and design purposes.
Perhaps the most heartening outcome of the AI-AR convergence is that, rather than replacing humans in manufacturing, AR is an ideal interface for human collaboration with AI. And as AI merges with human capital, prepare to see exponential improvements in productivity, professional training, and product quality.
(2) Convergence with Sensors
On the hardware front, these AI-AR systems will require a mass proliferation of sensors to detect the external environment and apply computer vision in AI decision-making.
To measure depth, for instance, some scanning depth sensors project a structured pattern of infrared light dots onto a scene, detecting and analyzing reflected light to generate 3D maps of the environment. Stereoscopic imaging, using two lenses, has also been commonly used for depth measurements. But leading technology like Microsoft’s HoloLens 2 and Intel’s RealSense 400-series camera implement a new method called “phased time-of-flight” (ToF).
In ToF sensing, the HoloLens 2 uses numerous lasers, each with 100 milliwatts (mW) of power, in quick bursts. The distance between nearby objects and the headset wearer is then measured by the amount of light in the return beam that has shifted from the original signal. Finally, the phase difference reveals the location of each object within the field of view, which enables accurate hand-tracking and surface reconstruction.
With a far lower computing power requirement, the phased ToF sensor is also more durable than stereoscopic sensing, which relies on the precise alignment of two prisms. The phased ToF sensor’s silicon base also makes it easily mass-produced, rendering the HoloLens 2 a far better candidate for widespread consumer adoption.
To apply inertial measurement—typically used in airplanes and spacecraft—the HoloLens 2 additionally uses a built-in accelerometer, gyroscope, and magnetometer. Further equipped with four “environment understanding cameras” that track head movements, the headset also uses a 2.4MP HD photographic video camera and ambient light sensor that work in concert to enable advanced computer vision.
For natural viewing experiences, sensor-supplied gaze tracking increasingly creates depth in digital displays. Nvidia’s work on Foveated AR Display, for instance, brings the primary foveal area into focus, while peripheral regions fall into a softer background— mimicking natural visual perception and concentrating computing power on the area that needs it most.
Gaze tracking sensors are also slated to grant users control over their (now immersive) screens without any hand gestures. Conducting simple visual cues, even staring at an object for more than three seconds, will activate commands instantaneously.
And our manufacturing example above is not the only one. Stacked convergence of blockchain, sensors, AI and AR will disrupt almost every major industry.
Take healthcare, for example, wherein biometric sensors will soon customize users’ AR experiences. Already, MIT Media Lab’s Deep Reality group has created an underwater VR relaxation experience that responds to real-time brain activity detected by a modified version of the Muse EEG. The experience even adapts to users’ biometric data, from heart rate to electro dermal activity (inputted from an Empatica E4 wristband).
Now rapidly dematerializing, sensors will converge with AR to improve physical-digital surface integration, intuitive hand and eye controls, and an increasingly personalized augmented world. Keep an eye on companies like MicroVision, now making tremendous leaps in sensor technology.
While I’ll be doing a deep dive into sensor applications across each industry in our next blog, it’s critical to first discuss how we might power sensor- and AI-driven augmented worlds.
(3) Convergence with Blockchain
Because AR requires much more compute power than typical 2D experiences, centralized GPUs and cloud computing systems are hard at work to provide the necessary infrastructure. Nonetheless, the workload is taxing and blockchain may prove the best solution.
A major player in this pursuit, Otoy aims to create the largest distributed GPU network in the world, called the Render Network RNDR. Built specifically on the Ethereum blockchain for holographic media, and undergoing Beta testing, this network is set to revolutionize AR deployment accessibility.
Alphabet Chairman Eric Schmidt (an investor in Otoy’s network), has even said, “I predicted that 90% of computing would eventually reside in the web based cloud… Otoy has created a remarkable technology which moves that last 10%—high-end graphics processing—entirely to the cloud. This is a disruptive and important achievement. In my view, it marks the tipping point where the web replaces the PC as the dominant computing platform of the future.”
Leveraging the crowd, RNDR allows anyone with a GPU to contribute their power to the network for a commission of up to $300 a month in RNDR tokens. These can then be redeemed in cash or used to create users’ own AR content.
In a double win, Otoy’s blockchain network and similar iterations not only allow designers to profit when not using their GPUs, but also democratize the experience for newer artists in the field.
And beyond these networks’ power suppliers, distributing GPU processing power will allow more manufacturing companies to access AR design tools and customize learning experiences. By further dispersing content creation across a broad network of individuals, blockchain also has the valuable potential to boost AR hardware investment across a number of industry beneficiaries.
On the consumer side, startups like Scanetchain are also entering the blockchain-AR space for a different reason. Allowing users to scan items with their smartphone, Scanetchain’s app provides access to a trove of information, from manufacturer and price, to origin and shipping details.
Based on NEM (a peer-to-peer cryptocurrency that implements a blockchain consensus algorithm), the app aims to make information far more accessible and, in the process, create a social network of purchasing behavior. Users earn tokens by watching ads, and all transactions are hashed into blocks and securely recorded.
The writing is on the wall—our future of brick-and-mortar retail will largely lean on blockchain to create the necessary digital links.
Final Thoughts
Integrating AI into AR creates an “auto-magical” manufacturing pipeline that will fundamentally transform the industry, cutting down on marginal costs, reducing inefficiencies and waste, and maximizing employee productivity.
Bolstering the AI-AR convergence, sensor technology is already blurring the boundaries between our augmented and physical worlds, soon to be near-undetectable. While intuitive hand and eye motions dictate commands in a hands-free interface, biometric data is poised to customize each AR experience to be far more in touch with our mental and physical health.
And underpinning it all, distributed computing power with blockchain networks like RNDR will democratize AR, boosting global consumer adoption at plummeting price points.
As AR soars in importance—whether in retail, manufacturing, entertainment, or beyond—the stacked convergence discussed above merits significant investment over the next decade. The augmented world is only just getting started.
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This article originally appeared on Diamandis.com
Image Credit: Funky Focus / Pixabay Continue reading →
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