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#436488 Tech’s Biggest Leaps From the Last 10 ...

Posted on January 3, 2020 by Android

As we enter our third decade in the 21st century, it seems appropriate to reflect on the ways technology developed and note the breakthroughs that were achieved in the last 10 years.

The 2010s saw IBM’s Watson win a game of Jeopardy, ushering in mainstream awareness of machine learning, along with DeepMind’s AlphaGO becoming the world’s Go champion. It was the decade that industrial tools like drones, 3D printers, genetic sequencing, and virtual reality (VR) all became consumer products. And it was a decade in which some alarming trends related to surveillance, targeted misinformation, and deepfakes came online.

For better or worse, the past decade was a breathtaking era in human history in which the idea of exponential growth in information technologies powered by computation became a mainstream concept.

As I did last year for 2018 only, I’ve asked a collection of experts across the Singularity University faculty to help frame the biggest breakthroughs and moments that gave shape to the past 10 years. I asked them what, in their opinion, was the most important breakthrough in their respective fields over the past decade.

My own answer to this question, focused in the space of augmented and virtual reality, would be the stunning announcement in March of 2014 that Facebook acquired Oculus VR for $2 billion. Although VR technology had been around for a while, it was at this precise moment that VR arrived as a consumer technology platform. Facebook, largely fueled by the singular interest of CEO Mark Zuckerberg, has funded the development of this industry, keeping alive the hope that consumer VR can become a sustainable business. In the meantime, VR has continued to grow in sophistication and usefulness, though it has yet to truly take off as a mainstream concept. That will hopefully be a development for the 2020s.

Below is a decade in review across the technology areas that are giving shape to our modern world, as described by the SU community of experts.

Digital Biology
Dr. Tiffany Vora | Faculty Director and Vice Chair, Digital Biology and Medicine, Singularity University

In my mind, this decade of astounding breakthroughs in the life sciences and medicine rests on the achievement of the $1,000 human genome in 2016. More-than-exponentially falling costs of DNA sequencing have driven advances in medicine, agriculture, ecology, genome editing, synthetic biology, the battle against climate change, and our fundamental understanding of life and its breathtaking connections. The “digital” revolution in DNA constituted an important model for harnessing other types of biological information, from personalized bio data to massive datasets spanning populations and species.

Crucially, by aggressively driving down the cost of such analyses, researchers and entrepreneurs democratized access to the source code of life—with attendant financial, cultural, and ethical consequences. Exciting, but take heed: Veritas Genetics spearheaded a $600 genome in 2019, only to have to shutter USA operations due to a money trail tangled with the trade war with China. Stay tuned through the early 2020s to see the pricing of DNA sequencing fall even further … and to experience the many ways that cheaper, faster harvesting of biological data will enrich your daily life.

Cryptocurrency
Alex Gladstein | Chief Strategy Officer, Human Rights Foundation

The past decade has seen Bitcoin go from just an idea on an obscure online message board to a global financial network carrying more than 100 billion dollars in value. And we’re just getting started. One recent defining moment in the cryptocurrency space has been a stunning trend underway in Venezuela, where today, the daily dollar-denominated value of Bitcoin traded now far exceeds the daily dollar-denominated value traded on the Caracas Stock Exchange. It’s just one country, but it’s a significant country, and a paradigm shift.

Governments and corporations are following Bitcoin’s success too, and are looking to launch their own digital currencies. China will launch its “DC/EP” project in the coming months, and Facebook is trying to kickstart its Libra project. There are technical and regulatory uncertainties for both, but one thing is for certain: the era of digital currency has arrived.

Business Strategy and Entrepreneurship
Pascal Finnette | Chair, Entrepreneurship and Open Innovation, Singularity University

For me, without a doubt, the most interesting and quite possibly ground-shifting development in the fields of entrepreneurship and corporate innovation in the last ten years is the rapid maturing of customer-driven product development frameworks such as Lean Startup, and its subsequent adoption by corporates for their own innovation purposes.

Tools and frameworks like the Business Model Canvas, agile (software) development and the aforementioned Lean Startup methodology fundamentally shifted the way we think and go about building products, services, and companies, with many of these tools bursting onto the startup scene in the late 2000s and early 2010s.

As these tools matured they found mass adoption not only in startups around the world, but incumbent companies who eagerly adopted them to increase their own innovation velocity and success.

Energy
Ramez Naam | Co-Chair, Energy and Environment, Singularity University

The 2010s were the decade that saw clean electricity, energy storage, and electric vehicles break through price and performance barriers around the world. Solar, wind, batteries, and EVs started this decade as technologies that had to be subsidized. That was the first phase of their existence. Now they’re entering their third, most disruptive phase, where shifting to clean energy and mobility is cheaper than continuing to use existing coal, gas, or oil infrastructure.

Consider that at the start of 2010, there was no place on earth where building new solar or wind was cheaper than building new coal or gas power generation. By 2015, in some of the sunniest and windiest places on earth, solar and wind had entered their second phase, where they were cost-competitive for new power. And then, in 2018 and 2019, we started to see the edge of the third phase, as building new solar and wind, in some parts of the world, was cheaper than operating existing coal or gas power plants.

Food Technology
Liz Specht, Ph. D | Associate Director of Science & Technology, The Good Food Institute

The arrival of mainstream plant-based meat is easily the food tech advance of the decade. Meat analogs have, of course, been around forever. But only in the last decade have companies like Beyond Meat and Impossible Foods decided to cut animals out of the process and build no-compromise meat directly from plants.

Plant-based meat is already transforming the fast-food industry. For example, the introduction of the Impossible Whopper led Burger King to their most profitable quarter in many years. But the global food industry as a whole is shifting as well. Tyson, JBS, Nestle, Cargill, and many others are all embracing plant-based meat.

Augmented and Virtual Reality
Jody Medich | CEO, Superhuman-x

The breakthrough moment for augmented and virtual reality came in 2013 when Palmer Lucky took apart an Android smartphone and added optic lenses to make the first version of the Oculus Rift. Prior to that moment, we struggled with miniaturizing the components needed to develop low-latency head-worn devices. But thanks to the smartphone race started in 2006 with the iPhone, we finally had a suite of sensors, chips, displays, and computing power small enough to put on the head.

What will the next 10 years bring? Look for AR/VR to explode in a big way. We are right on the cusp of that tipping point when the tech is finally “good enough” for our linear expectations. Given all it can do today, we can’t even picture what’s possible. Just as today we can’t function without our phones, by 2029 we’ll feel lost without some AR/VR product. It will be the way we interact with computing, smart objects, and AI. Tim Cook, Apple CEO, predicts it will replace all of today’s computing devices. I can’t wait.

Philosophy of Technology
Alix Rübsaam | Faculty Fellow, Singularity University, Philosophy of Technology/Ethics of AI

The last decade has seen a significant shift in our general attitude towards the algorithms that we now know dictate much of our surroundings. Looking back at the beginning of the decade, it seems we were blissfully unaware of how the data we freely and willingly surrendered would feed the algorithms that would come to shape every aspect of our daily lives: the news we consume, the products we purchase, the opinions we hold, etc.

If I were to isolate a single publication that contributed greatly to the shift in public discourse on algorithms, it would have to be Cathy O’Neil’s Weapons of Math Destruction from 2016. It remains a comprehensive, readable, and highly informative insight into how algorithms dictate our finances, our jobs, where we go to school, or if we can get health insurance. Its publication represents a pivotal moment when the general public started to question whether we should be OK with outsourcing decision making to these opaque systems.

The ubiquity of ethical guidelines for AI and algorithms published just in the last year (perhaps most comprehensively by the AI Now Institute) fully demonstrates the shift in public opinion of this decade.

Data Science
Ola Kowalewski | Faculty Fellow, Singularity University, Data Innovation

In the last decade we entered the era of internet and smartphone ubiquity. The number of internet users doubled, with nearly 60 percent of the global population connected online and now over 35 percent of the globe owns a smartphone. With billions of people in a state of constant connectedness and therefore in a state of constant surveillance, the companies that have built the tech infrastructure and information pipelines have dominated the global economy. This shift from tech companies being the underdogs to arguably the world’s major powers sets the landscape we enter for the next decade.

Global Grand Challenges
Darlene Damm | Vice Chair, Faculty, Global Grand Challenges, Singularity University

The biggest breakthrough over the last decade in social impact and technology is that the social impact sector switched from seeing technology as something problematic to avoid, to one of the most effective ways to create social change. We now see people using exponential technologies to solve all sorts of social challenges in areas ranging from disaster response to hunger to shelter.

The world’s leading social organizations, such as UNICEF and the World Food Programme, have launched their own venture funds and accelerators, and the United Nations recently declared that digitization is revolutionizing global development.

Digital Biology
Raymond McCauley | Chair, Digital Biology, Singularity University, Co-Founder & Chief Architect, BioCurious; Principal, Exponential Biosciences

CRISPR is bringing about a revolution in genetic engineering. It’s obvious, and it’s huge. What may not be so obvious is the widespread adoption of genetic testing. And this may have an even longer-lasting effect. It’s used to test new babies, to solve medical mysteries, and to catch serial killers. Thanks to holiday ads from 23andMe and Ancestry.com, it’s everywhere. Testing your DNA is now a common over-the-counter product. People are using it to set their diet, to pick drugs, and even for dating (or at least picking healthy mates).

And we’re just in the early stages. Further down the line, doing large-scale studies on more people, with more data, will lead to the use of polygenic risk scores to help us rank our genetic potential for everything from getting cancer to being a genius. Can you imagine what it would be like for parents to pick new babies, GATTACA-style, to get the smartest kids? You don’t have to; it’s already happening.

Artificial Intelligence
Neil Jacobstein | Chair, Artificial Intelligence and Robotics, Singularity University

The convergence of exponentially improved computing power, the deep learning algorithm, and access to massive data resulted in a series of AI breakthroughs over the past decade. These included: vastly improved accuracy in identifying images, making self driving cars practical, beating several world champions in Go, and identifying gender, smoking status, and age from retinal fundus photographs.

Combined, these breakthroughs convinced researchers and investors that after 50+ years of research and development, AI was ready for prime-time applications. Now, virtually every field of human endeavor is being revolutionized by machine learning. We still have a long way to go to achieve human-level intelligence and beyond, but the pace of worldwide improvement is blistering.

Hod Lipson | Professor of Engineering and Data Science, Columbia University

The biggest moment in AI in the past decade (and in its entire history, in my humble opinion) was midnight, Pacific time, September 30, 2012: the moment when machines finally opened their eyes. It was the moment when deep learning took off, breaking stagnant decades of machine blindness, when AI couldn’t reliably tell apart even a cat from a dog. That seemingly trivial accomplishment—a task any one-year-old child can do—has had a ripple effect on AI applications from driverless cars to health diagnostics. And this is just the beginning of what is sure to be a Cambrian explosion of AI.

Neuroscience
Divya Chander | Chair, Neuroscience, Singularity University

If the 2000s were the decade of brain mapping, then the 2010s were the decade of brain writing. Optogenetics, a technique for precisely mapping and controlling neurons and neural circuits using genetically-directed light, saw incredible growth in the 2010s.

Also in the last 10 years, neuromodulation, or the ability to rewire the brain using both invasive and non-invasive interfaces and energy, has exploded in use and form. For instance, the Braingate consortium showed us how electrode arrays implanted into the motor cortex could be used by paralyzed people to use their thoughts to direct a robotic arm. These technologies, alone or in combination with robotics, exoskeletons, and flexible, implantable, electronics also make possible a future of human augmentation.

Image Credit: Image by Jorge Guillen from Pixabay Continue reading →

Posted in Human Robots

#436484 If Machines Want to Make Art, Will ...

Posted on January 2, 2020 by Android

Assuming that the emergence of consciousness in artificial minds is possible, those minds will feel the urge to create art. But will we be able to understand it? To answer this question, we need to consider two subquestions: when does the machine become an author of an artwork? And how can we form an understanding of the art that it makes?

Empathy, we argue, is the force behind our capacity to understand works of art. Think of what happens when you are confronted with an artwork. We maintain that, to understand the piece, you use your own conscious experience to ask what could possibly motivate you to make such an artwork yourself—and then you use that first-person perspective to try to come to a plausible explanation that allows you to relate to the artwork. Your interpretation of the work will be personal and could differ significantly from the artist’s own reasons, but if we share sufficient experiences and cultural references, it might be a plausible one, even for the artist. This is why we can relate so differently to a work of art after learning that it is a forgery or imitation: the artist’s intent to deceive or imitate is very different from the attempt to express something original. Gathering contextual information before jumping to conclusions about other people’s actions—in art, as in life—can enable us to relate better to their intentions.

But the artist and you share something far more important than cultural references: you share a similar kind of body and, with it, a similar kind of embodied perspective. Our subjective human experience stems, among many other things, from being born and slowly educated within a society of fellow humans, from fighting the inevitability of our own death, from cherishing memories, from the lonely curiosity of our own mind, from the omnipresence of the needs and quirks of our biological body, and from the way it dictates the space- and time-scales we can grasp. All conscious machines will have embodied experiences of their own, but in bodies that will be entirely alien to us.

We are able to empathize with nonhuman characters or intelligent machines in human-made fiction because they have been conceived by other human beings from the only subjective perspective accessible to us: “What would it be like for a human to behave as x?” In order to understand machinic art as such—and assuming that we stand a chance of even recognizing it in the first place—we would need a way to conceive a first-person experience of what it is like to be that machine. That is something we cannot do even for beings that are much closer to us. It might very well happen that we understand some actions or artifacts created by machines of their own volition as art, but in doing so we will inevitably anthropomorphize the machine’s intentions. Art made by a machine can be meaningfully interpreted in a way that is plausible only from the perspective of that machine, and any coherent anthropomorphized interpretation will be implausibly alien from the machine perspective. As such, it will be a misinterpretation of the artwork.

But what if we grant the machine privileged access to our ways of reasoning, to the peculiarities of our perception apparatus, to endless examples of human culture? Wouldn’t that enable the machine to make art that a human could understand? Our answer is yes, but this would also make the artworks human—not authentically machinic. All examples so far of “art made by machines” are actually just straightforward examples of human art made with computers, with the artists being the computer programmers. It might seem like a strange claim: how can the programmers be the authors of the artwork if, most of the time, they can’t control—or even anticipate—the actual materializations of the artwork? It turns out that this is a long-standing artistic practice.

Suppose that your local orchestra is playing Beethoven’s Symphony No 7 (1812). Even though Beethoven will not be directly responsible for any of the sounds produced there, you would still say that you are listening to Beethoven. Your experience might depend considerably on the interpretation of the performers, the acoustics of the room, the behavior of fellow audience members or your state of mind. Those and other aspects are the result of choices made by specific individuals or of accidents happening to them. But the author of the music? Ludwig van Beethoven. Let’s say that, as a somewhat odd choice for the program, John Cage’s Imaginary Landscape No 4 (March No 2) (1951) is also played, with 24 performers controlling 12 radios according to a musical score. In this case, the responsibility for the sounds being heard should be attributed to unsuspecting radio hosts, or even to electromagnetic fields. Yet, the shaping of sounds over time—the composition—should be credited to Cage. Each performance of this piece will vary immensely in its sonic materialization, but it will always be a performance of Imaginary Landscape No 4.

Why should we change these principles when artists use computers if, in these respects at least, computer art does not bring anything new to the table? The (human) artists might not be in direct control of the final materializations, or even be able to predict them but, despite that, they are the authors of the work. Various materializations of the same idea—in this case formalized as an algorithm—are instantiations of the same work manifesting different contextual conditions. In fact, a common use of computation in the arts is the production of variations of a process, and artists make extensive use of systems that are sensitive to initial conditions, external inputs, or pseudo-randomness to deliberately avoid repetition of outputs. Having a computer executing a procedure to build an artwork, even if using pseudo-random processes or machine-learning algorithms, is no different than throwing dice to arrange a piece of music, or to pursuing innumerable variations of the same formula. After all, the idea of machines that make art has an artistic tradition that long predates the current trend of artworks made by artificial intelligence.

Machinic art is a term that we believe should be reserved for art made by an artificial mind’s own volition, not for that based on (or directed towards) an anthropocentric view of art. From a human point of view, machinic artworks will still be procedural, algorithmic, and computational. They will be generative, because they will be autonomous from a human artist. And they might be interactive, with humans or other systems. But they will not be the result of a human deferring decisions to a machine, because the first of those—the decision to make art—needs to be the result of a machine’s volition, intentions, and decisions. Only then will we no longer have human art made with computers, but proper machinic art.

The problem is not whether machines will or will not develop a sense of self that leads to an eagerness to create art. The problem is that if—or when—they do, they will have such a different Umwelt that we will be completely unable to relate to it from our own subjective, embodied perspective. Machinic art will always lie beyond our ability to understand it because the boundaries of our comprehension—in art, as in life—are those of the human experience.

This article was originally published at Aeon and has been republished under Creative Commons.

Image Credit: Rene Böhmer / Unsplash Continue reading →

Posted in Human Robots

#436470 Retail Robots Are on the Rise—at Every ...

Posted on December 20, 2019 by Android

The robots are coming! The robots are coming! On our sidewalks, in our skies, in our every store… Over the next decade, robots will enter the mainstream of retail.

As countless robots work behind the scenes to stock shelves, serve customers, and deliver products to our doorstep, the speed of retail will accelerate.

These changes are already underway. In this blog, we’ll elaborate on how robots are entering the retail ecosystem.

Let’s dive in.

Robot Delivery
On August 3rd, 2016, Domino’s Pizza introduced the Domino’s Robotic Unit, or “DRU” for short. The first home delivery pizza robot, the DRU looks like a cross between R2-D2 and an oversized microwave.

LIDAR and GPS sensors help it navigate, while temperature sensors keep hot food hot and cold food cold. Already, it’s been rolled out in ten countries, including New Zealand, France, and Germany, but its August 2016 debut was critical—as it was the first time we’d seen robotic home delivery.

And it won’t be the last.

A dozen or so different delivery bots are fast entering the market. Starship Technologies, for instance, a startup created by Skype founders Janus Friis and Ahti Heinla, has a general-purpose home delivery robot. Right now, the system is an array of cameras and GPS sensors, but upcoming models will include microphones, speakers, and even the ability—via AI-driven natural language processing—to communicate with customers. Since 2016, Starship has already carried out 50,000 deliveries in over 100 cities across 20 countries.

Along similar lines, Nuro—co-founded by Jiajun Zhu, one of the engineers who helped develop Google’s self-driving car—has a miniature self-driving car of its own. Half the size of a sedan, the Nuro looks like a toaster on wheels, except with a mission. This toaster has been designed to carry cargo—about 12 bags of groceries (version 2.0 will carry 20)—which it’s been doing for select Kroger stores since 2018. Domino’s also partnered with Nuro in 2019.

As these delivery bots take to our streets, others are streaking across the sky.

Back in 2016, Amazon came first, announcing Prime Air—the e-commerce giant’s promise of drone delivery in 30 minutes or less. Almost immediately, companies ranging from 7-Eleven and Walmart to Google and Alibaba jumped on the bandwagon.

While critics remain doubtful, the head of the FAA’s drone integration department recently said that drone deliveries may be “a lot closer than […] the skeptics think. [Companies are] getting ready for full-blown operations. We’re processing their applications. I would like to move as quickly as I can.”

In-Store Robots
While delivery bots start to spare us trips to the store, those who prefer shopping the old-fashioned way—i.e., in person—also have plenty of human-robot interaction in store. In fact, these robotics solutions have been around for a while.

In 2010, SoftBank introduced Pepper, a humanoid robot capable of understanding human emotion. Pepper is cute: 4 feet tall, with a white plastic body, two black eyes, a dark slash of a mouth, and a base shaped like a mermaid’s tail. Across her chest is a touch screen to aid in communication. And there’s been a lot of communication. Pepper’s cuteness is intentional, as it matches its mission: help humans enjoy life as much as possible.

Over 12,000 Peppers have been sold. She serves ice cream in Japan, greets diners at a Pizza Hut in Singapore, and dances with customers at a Palo Alto electronics store. More importantly, Pepper’s got company.

Walmart uses shelf-stocking robots for inventory control. Best Buy uses a robo-cashier, allowing select locations to operate 24-7. And Lowe’s Home Improvement employs the LoweBot—a giant iPad on wheels—to help customers find the items they need while tracking inventory along the way.

Warehouse Bots
Yet the biggest benefit robots provide might be in-warehouse logistics.

In 2012, when Amazon dished out $775 million for Kiva Systems, few could predict that just 6 years later, 45,000 Kiva robots would be deployed at all of their fulfillment centers, helping process a whopping 306 items per second during the Christmas season.

And many other retailers are following suit.

Order jeans from the Gap, and soon they’ll be sorted, packed, and shipped with the help of a Kindred robot. Remember the old arcade game where you picked up teddy bears with a giant claw? That’s Kindred, only her claw picks up T-shirts, pants, and the like, placing them in designated drop-off zones that resemble tiny mailboxes (for further sorting or shipping).

The big deal here is democratization. Kindred’s robot is cheap and easy to deploy, allowing smaller companies to compete with giants like Amazon.

Final Thoughts
For retailers interested in staying in business, there doesn’t appear to be much choice in the way of robotics.

By 2024, the US minimum wage is projected to be $15 an hour (the House of Representatives has already passed the bill, but the wage hike is meant to unfold gradually between now and 2025), and many consider that number far too low.

Yet, as human labor costs continue to climb, robots won’t just be coming, they’ll be here, there, and everywhere. It’s going to become increasingly difficult for store owners to justify human workers who call in sick, show up late, and can easily get injured. Robots work 24-7. They never take a day off, never need a bathroom break, health insurance, or parental leave.

Going forward, this spells a growing challenge of technological unemployment (a blog topic I will cover in the coming month). But in retail, robotics usher in tremendous benefits for companies and customers alike.

And while professional re-tooling initiatives and the transition of human capital from retail logistics to a booming experience economy take hold, robotic retail interaction and last-mile delivery will fundamentally transform our relationship with commerce.

This blog comes from The Future is Faster Than You Think—my upcoming book, to be released Jan 28th, 2020. To get an early copy and access up to $800 worth of pre-launch giveaways, sign up here!

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If you’d like to learn more and consider joining our 2020 membership, apply here.

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Posted in Human Robots

#436462 Robotic Exoskeletons, Like This One, Are ...

Posted on December 18, 2019 by Android

When you imagine an exoskeleton, chances are it might look a bit like the Guardian XO from Sarcos Robotics. The XO is literally a robot you wear (or maybe, it wears you). The suit’s powered limbs sense your movements and match their position to yours with little latency to give you effortless superstrength and endurance—lifting 200 pounds will feel like 10.

A vision of robots and humankind working together in harmony. Now, isn’t that nice?

Of course, there isn’t anything terribly novel about an exoskeleton. We’ve seen plenty of concepts and demonstrations in the last decade. These include light exoskeletons tailored to industrial settings—some of which are being tested out by the likes of Honda—and healthcare exoskeletons that support the elderly or folks with disabilities.

Full-body powered robotic exoskeletons are a bit rarer, which makes the Sarcos suit pretty cool to look at. But like all things in robotics, practicality matters as much as vision. It’s worth asking: Will anyone buy and use the thing? Is it more than a concept video?

Sarcos thinks so, and they’re excited about it. “If you were to ask the question, what does 30 years and $300 million look like,” Sarcos CEO, Ben Wolff, told IEEE Spectrum, “you’re going to see it downstairs.”

The XO appears to check a few key boxes. For one, it’s user friendly. According to Sarcos, it only takes a few minutes for the uninitiated to strap in and get up to speed. Feeling comfortable doing work with the suit takes a few hours. This is thanks to a high degree of sensor-based automation that allows the robot to seamlessly match its user’s movements.

The XO can also operate for more than a few minutes. It has two hours of battery life, and with spares on hand, it can go all day. The batteries are hot-swappable, meaning you can replace a drained battery with a new one without shutting the system down.

The suit is aimed at manufacturing, where workers are regularly moving heavy stuff around. Additionally, Wolff told CNET, the suit could see military use. But that doesn’t mean Avatar-style combat. The XO, Wolff said, is primarily about logistics (lifting and moving heavy loads) and isn’t designed to be armored, so it won’t likely see the front lines.

The system will set customers back $100,000 a year to rent, which sounds like a lot, but for industrial or military purposes, the six-figure rental may not deter would-be customers if the suit proves itself a useful bit of equipment. (And it’s reasonable to imagine the price coming down as the technology becomes more commonplace and competitors arrive.)

Sarcos got into exoskeletons a couple decades ago and was originally funded by the military (like many robotics endeavors). Videos hit YouTube as long ago as 2008, but after announcing the company was taking orders for the XO earlier this year, Sarcos says they’ll deliver the first alpha units in January, which is a notable milestone.

Broadly, robotics has advanced a lot in recent years. YouTube sensations like Boston Dynamics have regularly earned millions of views (and inevitably, headlines stoking robot fear). They went from tethered treadmill sessions to untethered backflips off boxes. While today’s robots really are vastly superior to their ancestors, they’ve struggled to prove themselves useful. A counterpoint to flashy YouTube videos, the DARPA Robotics Challenge gave birth to another meme altogether. Robots falling over. Often and awkwardly.

This year marks some of the first commercial fruits of a few decades’ research. Boston Dynamics recently started offering its robot dog, Spot, to select customers in 2019. Whether this proves to be a headline-worthy flash in the pan or something sustainable remains to be seen. But between robots with more autonomy and exoskeletons like the XO, the exoskeleton variety will likely be easier to make more practical for various uses.

Whereas autonomous robots require highly advanced automation to navigate uncertain and ever-changing conditions—automation which, at the moment, remains largely elusive (though the likes of Google are pairing the latest AI with robots to tackle the problem)—an exoskeleton mainly requires physical automation. The really hard bits, like navigating and recognizing and interacting with objects, are outsourced to its human operator.

As it turns out, for today’s robots the best AI is still us. We may yet get chipper automatons like Rosy the Robot, but until then, for complicated applications, we’ll strap into our mechs for their strength and endurance, and they’ll wear us for our brains.

Image Credit: Sarcos Robotics Continue reading →

Posted in Human Robots

#436437 Why AI Will Be the Best Tool for ...

Posted on December 8, 2019 by Android

Dmitry Kaminskiy speaks as though he were trying to unload everything he knows about the science and economics of longevity—from senolytics research that seeks to stop aging cells from spewing inflammatory proteins and other molecules to the trillion-dollar life extension industry that he and his colleagues are trying to foster—in one sitting.

At the heart of the discussion with Singularity Hub is the idea that artificial intelligence will be the engine that drives breakthroughs in how we approach healthcare and healthy aging—a concept with little traction even just five years ago.

“At that time, it was considered too futuristic that artificial intelligence and data science … might be more accurate compared to any hypothesis of human doctors,” said Kaminskiy, co-founder and managing partner at Deep Knowledge Ventures, an investment firm that is betting big on AI and longevity.

How times have changed. Artificial intelligence in healthcare is attracting more investments and deals than just about any sector of the economy, according to data research firm CB Insights. In the most recent third quarter, AI healthcare startups raised nearly $1.6 billion, buoyed by a $550 million mega-round from London-based Babylon Health, which uses AI to collect data from patients, analyze the information, find comparable matches, then make recommendations.

Even without the big bump from Babylon Health, AI healthcare startups raised more than $1 billion last quarter, including two companies focused on longevity therapeutics: Juvenescence and Insilico Medicine.

The latter has risen to prominence for its novel use of reinforcement learning and general adversarial networks (GANs) to accelerate the drug discovery process. Insilico Medicine recently published a seminal paper that demonstrated how such an AI system could generate a drug candidate in just 46 days. Co-founder and CEO Alex Zhavoronkov said he believes there is no greater goal in healthcare today—or, really, any venture—than extending the healthy years of the human lifespan.

“I don’t think that there is anything more important than that,” he told Singularity Hub, explaining that an unhealthy society is detrimental to a healthy economy. “I think that it’s very, very important to extend healthy, productive lifespan just to fix the economy.”

An Aging Crisis
The surge of interest in longevity is coming at a time when life expectancy in the US is actually dropping, despite the fact that we spend more money on healthcare than any other nation.

A new paper in the Journal of the American Medical Association found that after six decades of gains, life expectancy for Americans has decreased since 2014, particularly among young and middle-aged adults. While some of the causes are societal, such as drug overdoses and suicide, others are health-related.

While average life expectancy in the US is 78, Kaminskiy noted that healthy life expectancy is about ten years less.

To Zhavoronkov’s point about the economy (a topic of great interest to Kaminskiy as well), the US spent $1.1 trillion on chronic diseases in 2016, according to a report from the Milken Institute, with diabetes, cardiovascular conditions, and Alzheimer’s among the most costly expenses to the healthcare system. When the indirect costs of lost economic productivity are included, the total price tag of chronic diseases in the US is $3.7 trillion, nearly 20 percent of GDP.

“So this is the major negative feedback on the national economy and creating a lot of negative social [and] financial issues,” Kaminskiy said.

Investing in Longevity
That has convinced Kaminskiy that an economy focused on extending healthy human lifespans—including the financial instruments and institutions required to support a long-lived population—is the best way forward.

He has co-authored a book on the topic with Margaretta Colangelo, another managing partner at Deep Knowledge Ventures, which has launched a specialized investment fund, Longevity.Capital, focused on the longevity industry. Kaminskiy estimates that there are now about 20 such investment funds dedicated to funding life extension companies.

In November at the inaugural AI for Longevity Summit in London, he and his collaborators also introduced the Longevity AI Consortium, an academic-industry initiative at King’s College London. Eventually, the research center will include an AI Longevity Accelerator program to serve as a bridge between startups and UK investors.

Deep Knowledge Ventures has committed about £7 million ($9 million) over the next three years to the accelerator program, as well as establishing similar consortiums in other regions of the world, according to Franco Cortese, a partner at Longevity.Capital and director of the Aging Analytics Agency, which has produced a series of reports on longevity.

A Cure for What Ages You
One of the most recent is an overview of Biomarkers for Longevity. A biomarker, in the case of longevity, is a measurable component of health that can indicate a disease state or a more general decline in health associated with aging. Examples range from something as simple as BMI as an indicator of obesity, which is associated with a number of chronic diseases, to sophisticated measurements of telomeres, the protective ends of chromosomes that shorten as we age.

While some researchers are working on moonshot therapies to reverse or slow aging—with a few even arguing we could expand human life on the order of centuries—Kaminskiy said he believes understanding biomarkers of aging could make more radical interventions unnecessary.

In this vision of healthcare, people would be able to monitor their health 24-7, with sensors attuned to various biomarkers that could indicate the onset of everything from the flu to diabetes. AI would be instrumental in not just ingesting the billions of data points required to develop such a system, but also what therapies, treatments, or micro-doses of a drug or supplement would be required to maintain homeostasis.

“Consider it like Tesla with many, many detectors, analyzing the behavior of the car in real time, and a cloud computing system monitoring those signals in real time with high frequency,” Kaminskiy explained. “So the same shall be applied for humans.”

And only sophisticated algorithms, Kaminskiy argued, can make longevity healthcare work on a mass scale but at the individual level. Precision medicine becomes preventive medicine. Healthcare truly becomes a system to support health rather than a way to fight disease.

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