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#433278 Outdated Evolution: Updating Our ...

Posted on September 9, 2018 by Android

What happens when evolution shapes an animal for tribes of 150 primitive individuals living in a chaotic jungle, and then suddenly that animal finds itself living with millions of others in an engineered metropolis, their pockets all bulging with devices of godlike power?

The result, it seems, is a modern era of tension where archaic forms of governance struggle to keep up with the technological advances of their citizenry, where governmental policies act like constraining bottlenecks rather than spearheads of progress.

Simply put, our governments have failed to adapt to disruptive technologies. And if we are to regain our stability moving forward into a future of even greater disruption, it’s imperative that we understand the issues that got us into this situation and what kind of solutions we can engineer to overcome our governmental weaknesses.

Hierarchy vs. Technological Decentralization
Many of the greatest issues our governments face today come from humanity’s biologically-hardwired desire for centralized hierarchies. This innate proclivity towards building and navigating systems of status and rank were evolutionary gifts handed down to us by our ape ancestors, where each member of a community had a mental map of their social hierarchy. Their nervous systems behaved differently depending on their rank in this hierarchy, influencing their interactions in a way that ensured only the most competent ape would rise to the top to gain access to the best food and mates.

As humanity emerged and discovered the power of language, we continued this practice by ensuring that those at the top of the hierarchies, those with the greatest education and access to information, were the dominant decision-makers for our communities.

However, this kind of structured chain of power is only necessary if we’re operating in conditions of scarcity. But resources, including information, are no longer scarce.

It’s estimated that more than two-thirds of adults in the world now own a smartphone, giving the average citizen the same access to the world’s information as the leaders of our governments. And with global poverty falling from 35.5 percent to 10.9 percent over the last 25 years, our younger generations are growing up seeing automation and abundance as a likely default, where innovations like solar energy, lab-grown meat, and 3D printing are expected to become commonplace.

It’s awareness of this paradigm shift that has empowered the recent rise of decentralization. As information and access to resources become ubiquitous, there is noticeably less need for our inefficient and bureaucratic hierarchies.

For example, if blockchain can prove its feasibility for large-scale systems, it can be used to update and upgrade numerous applications to a decentralized model, including currency and voting. Such innovations would lower the risk of failing banks collapsing the economy like they did in 2008, as well as prevent corrupt politicians from using gerrymandering and long queues at polling stations to deter voter participation.

Of course, technology isn’t a magic wand that should be implemented carelessly. Facebook’s “move fast and break things” approach might have very possibly broken American democracy in 2016, as social media played on some of the worst tendencies humanity can operate on during an election: fear and hostility.

But if decentralized technology, like blockchain’s public ledgers, can continue to spread a sense of security and transparency throughout society, perhaps we can begin to quiet that paranoia and hyper-vigilance our brains evolved to cope with living as apes in dangerous jungles. By decentralizing our power structures, we take away the channels our outdated biological behaviors might use to enact social dominance and manipulation.

The peace of mind this creates helps to reestablish trust in our communities and in our governments. And with trust in the government increased, it’s likely we’ll see our next issue corrected.

From Business and Law to Science and Technology
A study found that 59 percent of US presidents, 68 percent of vice presidents, and 78 percent of secretaries of state were lawyers by education and occupation. That’s more than one out of every two people in the most powerful positions in the American government restricted to a field dedicated to convincing other people (judges) their perspective is true, even if they lack evidence.

And so the scientific method became less important than semantics to our leaders.

Similarly, of the 535 individuals in the American congress, only 24 hold a PhD, only 2 of which are in a STEM field. And so far, it’s not getting better: Trump is the first president since WWII not to name a science advisor.

But if we can use technologies like blockchain to increase transparency, efficiency, and trust in the government, then the upcoming generations who understand decentralization, abundance, and exponential technologies might feel inspired enough to run for government positions. This helps solve that common problem where the smartest and most altruistic people tend to avoid government positions because they don’t want to play the semantic and deceitful game of politics.

By changing this narrative, our governments can begin to fill with techno-progressive individuals who actually understand the technologies that are rapidly reshaping our reality. And this influence of expertise is going to be crucial as our governments are forced to restructure and create new policies to accommodate the incoming disruption.

Clearing Regulations to Begin Safe Experimentation
As exponential technologies become more ubiquitous, we’re likely going to see young kids and garage tinkerers creating powerful AIs and altering genetics thanks to tools like CRISPR and free virtual reality tutorials.

This easy accessibility to such powerful technology means unexpected and rapid progress can occur almost overnight, quickly overwhelming our government’s regulatory systems.

Uber and Airbnb are two of the best examples of our government’s inability to keep up with such technology, both companies achieving market dominance before regulators were even able to consider how to handle them. And when a government has decided against them, they often still continue to operate because people simply choose to keep using the apps.

Luckily, this kind of disruption hasn’t yet posed a major existential threat. But this will change when we see companies begin developing cyborg body parts, brain-computer interfaces, nanobot health injectors, and at-home genetic engineering kits.

For this reason, it’s crucial that we have experts who understand how to update our regulations to be as flexible as is necessary to ensure we don’t create black market conditions like we’ve done with drugs. It’s better to have safe and monitored experimentation, rather than forcing individuals into seedy communities using unsafe products.

Survival of the Most Adaptable
If we hope to be an animal that survives our changing environment, we have to adapt. We cannot cling to the behaviors and systems formed thousands of years ago. We must instead acknowledge that we now exist in an ecosystem of disruptive technology, and we must evolve and update our governments if they’re going to be capable of navigating these transformative impacts.

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Posted in Human Robots

#432880 Google’s Duplex Raises the Question: ...

Posted on May 18, 2018 by Android

By now, you’ve probably seen Google’s new Duplex software, which promises to call people on your behalf to book appointments for haircuts and the like. As yet, it only exists in demo form, but already it seems like Google has made a big stride towards capturing a market that plenty of companies have had their eye on for quite some time. This software is impressive, but it raises questions.

Many of you will be familiar with the stilted, robotic conversations you can have with early chatbots that are, essentially, glorified menus. Instead of pressing 1 to confirm or 2 to re-enter, some of these bots would allow for simple commands like “Yes” or “No,” replacing the buttons with limited ability to recognize a few words. Using them was often a far more frustrating experience than attempting to use a menu—there are few things more irritating than a robot saying, “Sorry, your response was not recognized.”

Google Duplex scheduling a hair salon appointment:

Google Duplex calling a restaurant:

Even getting the response recognized is hard enough. After all, there are countless different nuances and accents to baffle voice recognition software, and endless turns of phrase that amount to saying the same thing that can confound natural language processing (NLP), especially if you like your phrasing quirky.

You may think that standard customer-service type conversations all travel the same route, using similar words and phrasing. But when there are over 80,000 ways to order coffee, and making a mistake is frowned upon, even simple tasks require high accuracy over a huge dataset.

Advances in audio processing, neural networks, and NLP, as well as raw computing power, have meant that basic recognition of what someone is trying to say is less of an issue. Soundhound’s virtual assistant prides itself on being able to process complicated requests (perhaps needlessly complicated).

The deeper issue, as with all attempts to develop conversational machines, is one of understanding context. There are so many ways a conversation can go that attempting to construct a conversation two or three layers deep quickly runs into problems. Multiply the thousands of things people might say by the thousands they might say next, and the combinatorics of the challenge runs away from most chatbots, leaving them as either glorified menus, gimmicks, or rather bizarre to talk to.

Yet Google, who surely remembers from Glass the risk of premature debuts for technology, especially the kind that ask you to rethink how you interact with or trust in software, must have faith in Duplex to show it on the world stage. We know that startups like Semantic Machines and x.ai have received serious funding to perform very similar functions, using natural-language conversations to perform computing tasks, schedule meetings, book hotels, or purchase items.

It’s no great leap to imagine Google will soon do the same, bringing us closer to a world of onboard computing, where Lens labels the world around us and their assistant arranges it for us (all the while gathering more and more data it can convert into personalized ads). The early demos showed some clever tricks for keeping the conversation within a fairly narrow realm where the AI should be comfortable and competent, and the blog post that accompanied the release shows just how much effort has gone into the technology.

Yet given the privacy and ethics funk the tech industry finds itself in, and people’s general unease about AI, the main reaction to Duplex’s impressive demo was concern. The voice sounded too natural, bringing to mind Lyrebird and their warnings of deepfakes. You might trust “Do the Right Thing” Google with this technology, but it could usher in an era when automated robo-callers are far more convincing.

A more human-like voice may sound like a perfectly innocuous improvement, but the fact that the assistant interjects naturalistic “umm” and “mm-hm” responses to more perfectly mimic a human rubbed a lot of people the wrong way. This wasn’t just a voice assistant trying to sound less grinding and robotic; it was actively trying to deceive people into thinking they were talking to a human.

Google is running the risk of trying to get to conversational AI by going straight through the uncanny valley.

“Google’s experiments do appear to have been designed to deceive,” said Dr. Thomas King of the Oxford Internet Institute’s Digital Ethics Lab, according to Techcrunch. “Their main hypothesis was ‘can you distinguish this from a real person?’ In this case it’s unclear why their hypothesis was about deception and not the user experience… there should be some kind of mechanism there to let people know what it is they are speaking to.”

From Google’s perspective, being able to say “90 percent of callers can’t tell the difference between this and a human personal assistant” is an excellent marketing ploy, even though statistics about how many interactions are successful might be more relevant.

In fact, Duplex runs contrary to pretty much every major recommendation about ethics for the use of robotics or artificial intelligence, not to mention certain eavesdropping laws. Transparency is key to holding machines (and the people who design them) accountable, especially when it comes to decision-making.

Then there are the more subtle social issues. One prominent effect social media has had is to allow people to silo themselves; in echo chambers of like-minded individuals, it’s hard to see how other opinions exist. Technology exacerbates this by removing the evolutionary cues that go along with face-to-face interaction. Confronted with a pair of human eyes, people are more generous. Confronted with a Twitter avatar or a Facebook interface, people hurl abuse and criticism they’d never dream of using in a public setting.

Now that we can use technology to interact with ever fewer people, will it change us? Is it fair to offload the burden of dealing with a robot onto the poor human at the other end of the line, who might have to deal with dozens of such calls a day? Google has said that if the AI is in trouble, it will put you through to a human, which might help save receptionists from the hell of trying to explain a concept to dozens of dumbfounded AI assistants all day. But there’s always the risk that failures will be blamed on the person and not the machine.

As AI advances, could we end up treating the dwindling number of people in these “customer-facing” roles as the buggiest part of a fully automatic service? Will people start accusing each other of being robots on the phone, as well as on Twitter?

Google has provided plenty of reassurances about how the system will be used. They have said they will ensure that the system is identified, and it’s hardly difficult to resolve this problem; a slight change in the script from their demo would do it. For now, consumers will likely appreciate moves that make it clear whether the “intelligent agents” that make major decisions for us, that we interact with daily, and that hide behind social media avatars or phone numbers are real or artificial.

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Posted in Human Robots

#432691 Is the Secret to Significantly Longer ...

Posted on May 3, 2018 by Android

Once upon a time, a powerful Sumerian king named Gilgamesh went on a quest, as such characters often do in these stories of myth and legend. Gilgamesh had witnessed the death of his best friend, Enkidu, and, fearing a similar fate, went in search of immortality. The great king failed to find the secret of eternal life but took solace that his deeds would live well beyond his mortal years.

Fast-forward four thousand years, give or take a century, and Gilgamesh (as famous as any B-list celebrity today, despite the passage of time) would probably be heartened to learn that many others have taken up his search for longevity. Today, though, instead of battling epic monsters and the machinations of fickle gods, those seeking to enhance and extend life are cutting-edge scientists and visionary entrepreneurs who are helping unlock the secrets of human biology.

Chief among them is Aubrey de Grey, a biomedical gerontologist who founded the SENS Research Foundation, a Silicon Valley-based research organization that seeks to advance the application of regenerative medicine to age-related diseases. SENS stands for Strategies for Engineered Negligible Senescence, a term coined by de Grey to describe a broad array (seven, to be precise) of medical interventions that attempt to repair or prevent different types of molecular and cellular damage that eventually lead to age-related diseases like cancer and Alzheimer’s.

Many of the strategies focus on senescent cells, which accumulate in tissues and organs as people age. Not quite dead, senescent cells stop dividing but are still metabolically active, spewing out all sorts of proteins and other molecules that can cause inflammation and other problems. In a young body, that’s usually not a problem (and probably part of general biological maintenance), as a healthy immune system can go to work to put out most fires.

However, as we age, senescent cells continue to accumulate, and at some point the immune system retires from fire watch. Welcome to old age.

Of Mice and Men
Researchers like de Grey believe that treating the cellular underpinnings of aging could not only prevent disease but significantly extend human lifespans. How long? Well, if you’re talking to de Grey, Biblical proportions—on the order of centuries.

De Grey says that science has made great strides toward that end in the last 15 years, such as the ability to copy mitochondrial DNA to the nucleus. Mitochondria serve as the power plant of the cell but are highly susceptible to mutations that lead to cellular degeneration. Copying the mitochondrial DNA into the nucleus would help protect it from damage.

Another achievement occurred about six years ago when scientists first figured out how to kill senescent cells. That discovery led to a spate of new experiments in mice indicating that removing these ticking-time-bomb cells prevented disease and even extended their lifespans. Now the anti-aging therapy is about to be tested in humans.

“As for the next few years, I think the stream of advances is likely to become a flood—once the first steps are made, things get progressively easier and faster,” de Grey tells Singularity Hub. “I think there’s a good chance that we will achieve really dramatic rejuvenation of mice within only six to eight years: maybe taking middle-aged mice and doubling their remaining lifespan, which is an order of magnitude more than can be done today.”

Not Horsing Around
Richard G.A. Faragher, a professor of biogerontology at the University of Brighton in the United Kingdom, recently made discoveries in the lab regarding the rejuvenation of senescent cells with chemical compounds found in foods like chocolate and red wine. He hopes to apply his findings to an animal model in the future—in this case,horses.

“We have been very fortunate in receiving some funding from an animal welfare charity to look at potential treatments for older horses,” he explains to Singularity Hub in an email. “I think this is a great idea. Many aspects of the physiology we are studying are common between horses and humans.”

What Faragher and his colleagues demonstrated in a paper published in BMC Cell Biology last year was that resveralogues, chemicals based on resveratrol, were able to reactivate a protein called a splicing factor that is involved in gene regulation. Within hours, the chemicals caused the cells to rejuvenate and start dividing like younger cells.

“If treatments work in our old pony systems, then I am sure they could be translated into clinical trials in humans,” Faragher says. “How long is purely a matter of money. Given suitable funding, I would hope to see a trial within five years.”

Show Them the Money
Faragher argues that the recent breakthroughs aren’t because a result of emerging technologies like artificial intelligence or the gene-editing tool CRISPR, but a paradigm shift in how scientists understand the underpinnings of cellular aging. Solving the “aging problem” isn’t a question of technology but of money, he says.

“Frankly, when AI and CRISPR have removed cystic fibrosis, Duchenne muscular dystrophy or Gaucher syndrome, I’ll be much more willing to hear tales of amazing progress. Go fix a single, highly penetrant genetic disease in the population using this flashy stuff and then we’ll talk,” he says. “My faith resides in the most potent technological development of all: money.”

De Grey is less flippant about the role that technology will play in the quest to defeat aging. AI, CRISPR, protein engineering, advances in stem cell therapies, and immune system engineering—all will have a part.

“There is not really anything distinctive about the ways in which these technologies will contribute,” he says. “What’s distinctive is that we will need all of these technologies, because there are so many different types of damage to repair and they each require different tricks.”

It’s in the Blood
A startup in the San Francisco Bay Area believes machines can play a big role in discovering the right combination of factors that lead to longer and healthier lives—and then develop drugs that exploit those findings.

BioAge Labs raised nearly $11 million last year for its machine learning platform that crunches big data sets to find blood factors, such as proteins or metabolites, that are tied to a person’s underlying biological age. The startup claims that these factors can predict how long a person will live.

“Our interest in this comes out of research into parabiosis, where joining the circulatory systems of old and young mice—so that they share the same blood—has been demonstrated to make old mice healthier and more robust,” Dr. Eric Morgen, chief medical officer at BioAge, tells Singularity Hub.

Based on that idea, he explains, it should be possible to alter those good or bad factors to produce a rejuvenating effect.

“Our main focus at BioAge is to identify these types of factors in our human cohort data, characterize the important molecular pathways they are involved in, and then drug those pathways,” he says. “This is a really hard problem, and we use machine learning to mine these complex datasets to determine which individual factors and molecular pathways best reflect biological age.”

Saving for the Future
Of course, there’s no telling when any of these anti-aging therapies will come to market. That’s why Forever Labs, a biotechnology startup out of Ann Arbor, Michigan, wants your stem cells now. The company offers a service to cryogenically freeze stem cells taken from bone marrow.

The theory behind the procedure, according to Forever Labs CEO Steven Clausnitzer, is based on research showing that stem cells may be a key component for repairing cellular damage. That’s because stem cells can develop into many different cell types and can divide endlessly to replenish other cells. Clausnitzer notes that there are upwards of a thousand clinical studies looking at using stem cells to treat age-related conditions such as cardiovascular disease.

However, stem cells come with their own expiration date, which usually coincides with the age that most people start experiencing serious health problems. Stem cells harvested from bone marrow at a younger age can potentially provide a therapeutic resource in the future.

“We believe strongly that by having access to your own best possible selves, you’re going to be well positioned to lead healthier, longer lives,” he tells Singularity Hub.

“There’s a compelling argument to be made that if you started to maintain the bone marrow population, the amount of nuclear cells in your bone marrow, and to re-up them so that they aren’t declining with age, it stands to reason that you could absolutely mitigate things like cardiovascular disease and stroke and Alzheimer’s,” he adds.

Clausnitzer notes that the stored stem cells can be used today in developing therapies to treat chronic conditions such as osteoarthritis. However, the more exciting prospect—and the reason he put his own 38-year-old stem cells on ice—is that he believes future stem cell therapies can help stave off the ravages of age-related disease.

“I can start reintroducing them not to treat age-related disease but to treat the decline in the stem-cell niche itself, so that I don’t ever get an age-related disease,” he says. “I don’t think that it equates to immortality, but it certainly is a step in that direction.”

Indecisive on Immortality
The societal implications of a longer-living human species are a guessing game at this point. We do know that by mid-century, the global population of those aged 65 and older will reach 1.6 billion, while those older than 80 will hit nearly 450 million, according to the National Academies of Science. If many of those people could enjoy healthy lives in their twilight years, an enormous medical cost could be avoided.

Faragher is certainly working toward a future where human health is ubiquitous. Human immortality is another question entirely.

“The longer lifespans become, the more heavily we may need to control birth rates and thus we may have fewer new minds. This could have a heavy ‘opportunity cost’ in terms of progress,” he says.

And does anyone truly want to live forever?

“There have been happy moments in my life but I have also suffered some traumatic disappointments. No [drug] will wash those experiences out of me,” Faragher says. “I no longer view my future with unqualified enthusiasm, and I do not think I am the only middle-aged man to feel that way. I don’t think it is an accident that so many ‘immortalists’ are young.

“They should be careful what they wish for.”

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Posted in Human Robots

#432671 Stuff 3.0: The Era of Programmable ...

Posted on April 30, 2018 by Android

It’s the end of a long day in your apartment in the early 2040s. You decide your work is done for the day, stand up from your desk, and yawn. “Time for a film!” you say. The house responds to your cues. The desk splits into hundreds of tiny pieces, which flow behind you and take on shape again as a couch. The computer screen you were working on flows up the wall and expands into a flat projection screen. You relax into the couch and, after a few seconds, a remote control surfaces from one of its arms.

In a few seconds flat, you’ve gone from a neatly-equipped office to a home cinema…all within the same four walls. Who needs more than one room?

This is the dream of those who work on “programmable matter.”

In his recent book about AI, Max Tegmark makes a distinction between three different levels of computational sophistication for organisms. Life 1.0 is single-celled organisms like bacteria; here, hardware is indistinguishable from software. The behavior of the bacteria is encoded into its DNA; it cannot learn new things.

Life 2.0 is where humans live on the spectrum. We are more or less stuck with our hardware, but we can change our software by choosing to learn different things, say, Spanish instead of Italian. Much like managing space on your smartphone, your brain’s hardware will allow you to download only a certain number of packages, but, at least theoretically, you can learn new behaviors without changing your underlying genetic code.

Life 3.0 marks a step-change from this: creatures that can change both their hardware and software in something like a feedback loop. This is what Tegmark views as a true artificial intelligence—one that can learn to change its own base code, leading to an explosion in intelligence. Perhaps, with CRISPR and other gene-editing techniques, we could be using our “software” to doctor our “hardware” before too long.

Programmable matter extends this analogy to the things in our world: what if your sofa could “learn” how to become a writing desk? What if, instead of a Swiss Army knife with dozens of tool attachments, you just had a single tool that “knew” how to become any other tool you could require, on command? In the crowded cities of the future, could houses be replaced by single, OmniRoom apartments? It would save space, and perhaps resources too.

Such are the dreams, anyway.

But when engineering and manufacturing individual gadgets is such a complex process, you can imagine that making stuff that can turn into many different items can be extremely complicated. Professor Skylar Tibbits at MIT referred to it as 4D printing in a TED Talk, and the website for his research group, the Self-Assembly Lab, excitedly claims, “We have also identified the key ingredients for self-assembly as a simple set of responsive building blocks, energy and interactions that can be designed within nearly every material and machining process available. Self-assembly promises to enable breakthroughs across many disciplines, from biology to material science, software, robotics, manufacturing, transportation, infrastructure, construction, the arts, and even space exploration.”

Naturally, their projects are still in the early stages, but the Self-Assembly Lab and others are genuinely exploring just the kind of science fiction applications we mooted.

For example, there’s the cell-phone self-assembly project, which brings to mind eerie, 24/7 factories where mobile phones assemble themselves from 3D printed kits without human or robotic intervention. Okay, so the phones they’re making are hardly going to fly off the shelves as fashion items, but if all you want is something that works, it could cut manufacturing costs substantially and automate even more of the process.

One of the major hurdles to overcome in making programmable matter a reality is choosing the right fundamental building blocks. There’s a very important balance to strike. To create fine details, you need to have things that aren’t too big, so as to keep your rearranged matter from being too lumpy. This might make the building blocks useless for certain applications—for example, if you wanted to make tools for fine manipulation. With big pieces, it might be difficult to simulate a range of textures. On the other hand, if the pieces are too small, different problems can arise.

Imagine a setup where each piece is a small robot. You have to contain the robot’s power source and its brain, or at least some kind of signal-generator and signal-processor, all in the same compact unit. Perhaps you can imagine that one might be able to simulate a range of textures and strengths by changing the strength of the “bond” between individual units—your desk might need to be a little bit more firm than your bed, which might be nicer with a little more give.

Early steps toward creating this kind of matter have been taken by those who are developing modular robots. There are plenty of different groups working on this, including MIT, Lausanne, and the University of Brussels.

In the latter configuration, one individual robot acts as a centralized decision-maker, referred to as the brain unit, but additional robots can autonomously join the brain unit as and when needed to change the shape and structure of the overall system. Although the system is only ten units at present, it’s a proof-of-concept that control can be orchestrated over a modular system of robots; perhaps in the future, smaller versions of the same thing could be the components of Stuff 3.0.

You can imagine that with machine learning algorithms, such swarms of robots might be able to negotiate obstacles and respond to a changing environment more easily than an individual robot (those of you with techno-fear may read “respond to a changing environment” and imagine a robot seamlessly rearranging itself to allow a bullet to pass straight through without harm).

Speaking of robotics, the form of an ideal robot has been a subject of much debate. In fact, one of the major recent robotics competitions—DARPA’s Robotics Challenge—was won by a robot that could adapt, beating Boston Dynamics’ infamous ATLAS humanoid with the simple addition of a wheel that allowed it to drive as well as walk.

Rather than building robots into a humanoid shape (only sometimes useful), allowing them to evolve and discover the ideal form for performing whatever you’ve tasked them to do could prove far more useful. This is particularly true in disaster response, where expensive robots can still be more valuable than humans, but conditions can be very unpredictable and adaptability is key.

Further afield, many futurists imagine “foglets” as the tiny nanobots that will be capable of constructing anything from raw materials, somewhat like the “Santa Claus machine.” But you don’t necessarily need anything quite so indistinguishable from magic to be useful. Programmable matter that can respond and adapt to its surroundings could be used in all kinds of industrial applications. How about a pipe that can strengthen or weaken at will, or divert its direction on command?

We’re some way off from being able to order our beds to turn into bicycles. As with many tech ideas, it may turn out that the traditional low-tech solution is far more practical and cost-effective, even as we can imagine alternatives. But as the march to put a chip in every conceivable object goes on, it seems certain that inanimate objects are about to get a lot more animated.

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Posted in Human Robots

#432572 Robots Can Swim, Fetch, Lift, and Dance. ...

Posted on April 23, 2018 by Android

Robotics has come a long way in the past few years. Robots can now fetch items from specific spots in massive warehouses, swim through the ocean to study marine life, and lift 200 times their own weight. They can even perform synchronized dance routines.

But the really big question is—can robots put together an Ikea chair?

A team of engineers from Nanyang Technological University in Singapore decided to find out, detailing their work in a paper published last week in the journal Science Robotics. The team took industrial robot arms and equipped them with parallel grippers, force-detecting sensors, and 3D cameras, and wrote software enabling the souped-up bots to tackle chair assembly. The robots’ starting point was a set of chair parts randomly scattered within reach.

As impressive as the above-mentioned robotic capabilities are, it’s worth noting that they’re mostly limited to a single skill. Putting together furniture, on the other hand, requires using and precisely coordinating multiple skills, including force control, visual localization, hand-eye coordination, and the patience to read each step of the manual without rushing through it and messing everything up.

Indeed, Ikea furniture, while meant to be simple and user-friendly, has left even the best of us scratching our heads and holding a spare oddly-shaped piece of wood as we stare at the desk or bed frame we just put together—or, for the less even-tempered among us, throwing said piece of wood across the room.

It’s a good thing robots don’t have tempers, because it took a few tries for the bots to get the chair assembly right.

Practice makes perfect, though (or in this case, rewriting code makes perfect), and these bots didn’t give up so easily. They had to hone three different skills: identifying which part was which among the scattered, differently-shaped pieces of wood, coordinating their movements to put those pieces in the right place, and knowing how much force to use in various steps of the process (i.e., more force is needed to connect two pieces than to pick up one piece).

A few tries later, the bots were able to assemble the chair from start to finish in about nine minutes.

On the whole, nicely done. But before we applaud the robots’ success too loudly, it’s important to note that they didn’t autonomously assemble the chair. Rather, each step of the process was planned and coded by engineers, down to the millimeter.

However, the team believes this closely-guided chair assembly was just a first step, and they see a not-so-distant future where combining artificial intelligence with advanced robotic capabilities could produce smart bots that would learn to assemble furniture and do other complex tasks on their own.

Future applications mentioned in the paper include electronics and aircraft manufacturing, logistics, and other high-mix, low-volume sectors.

Image Credit: Francisco Suárez-Ruiz and Quang-Cuong Pham/Nanyang Technological University Continue reading →

Posted in Human Robots