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#432027 We Read This 800-Page Report on the ...

Posted on February 14, 2018 by Android

The longevity field is bustling but still fragmented, and the “silver tsunami” is coming.

That is the takeaway of The Science of Longevity, the behemoth first volume of a four-part series offering a bird’s-eye view of the longevity industry in 2017. The report, a joint production of the Biogerontology Research Foundation, Deep Knowledge Life Science, Aging Analytics Agency, and Longevity.International, synthesizes the growing array of academic and industry ventures related to aging, healthspan, and everything in between.

This is huge, not only in scale but also in ambition. The report, totally worth a read here, will be followed by four additional volumes in 2018, covering topics ranging from the business side of longevity ventures to financial systems to potential tensions between life extension and religion.

And that’s just the first step. The team hopes to publish updated versions of the report annually, giving scientists, investors, and regulatory agencies an easy way to keep their finger on the longevity pulse.

“In 2018, ‘aging’ remains an unnamed adversary in an undeclared war. For all intents and purposes it is mere abstraction in the eyes of regulatory authorities worldwide,” the authors write.

That needs to change.

People often arrive at the field of aging from disparate areas with wildly diverse opinions and strengths. The report compiles these individual efforts at cracking aging into a systematic resource—a “periodic table” for longevity that clearly lays out emerging trends and promising interventions.

The ultimate goal? A global framework serving as a road map to guide the burgeoning industry. With such a framework in hand, academics and industry alike are finally poised to petition the kind of large-scale investments and regulatory changes needed to tackle aging with a unified front.

Infographic depicting many of the key research hubs and non-profits within the field of geroscience.
Image Credit: Longevity.International
The Aging Globe
The global population is rapidly aging. And our medical and social systems aren’t ready to handle this oncoming “silver tsunami.”

Take the medical field. Many age-related diseases such as Alzheimer’s lack effective treatment options. Others, including high blood pressure, stroke, lung or heart problems, require continuous medication and monitoring, placing enormous strain on medical resources.

What’s more, because disease risk rises exponentially with age, medical care for the elderly becomes a game of whack-a-mole: curing any individual disease such as cancer only increases healthy lifespan by two to three years before another one hits.

That’s why in recent years there’s been increasing support for turning the focus to the root of the problem: aging. Rather than tackling individual diseases, geroscience aims to add healthy years to our lifespan—extending “healthspan,” so to speak.

Despite this relative consensus, the field still faces a roadblock. The US FDA does not yet recognize aging as a bona fide disease. Without such a designation, scientists are banned from testing potential interventions for aging in clinical trials (that said, many have used alternate measures such as age-related biomarkers or Alzheimer’s symptoms as a proxy).

Luckily, the FDA’s stance is set to change. The promising anti-aging drug metformin, for example, is already in clinical trials, examining its effect on a variety of age-related symptoms and diseases. This report, and others to follow, may help push progress along.

“It is critical for investors, policymakers, scientists, NGOs, and influential entities to prioritize the amelioration of the geriatric world scenario and recognize aging as a critical matter of global economic security,” the authors say.

Biomedical Gerontology
The causes of aging are complex, stubborn, and not all clear.

But the report lays out two main streams of intervention with already promising results.

The first is to understand the root causes of aging and stop them before damage accumulates. It’s like meddling with cogs and other inner workings of a clock to slow it down, the authors say.

The report lays out several treatments to keep an eye on.

Geroprotective drugs is a big one. Often repurposed from drugs already on the market, these traditional small molecule drugs target a wide variety of metabolic pathways that play a role in aging. Think anti-oxidants, anti-inflammatory, and drugs that mimic caloric restriction, a proven way to extend healthspan in animal models.

More exciting are the emerging technologies. One is nanotechnology. Nanoparticles of carbon, “bucky-balls,” for example, have already been shown to fight viral infections and dangerous ion particles, as well as stimulate the immune system and extend lifespan in mice (though others question the validity of the results).

Blood is another promising, if surprising, fountain of youth: recent studies found that molecules in the blood of the young rejuvenate the heart, brain, and muscles of aged rodents, though many of these findings have yet to be replicated.

Rejuvenation Biotechnology
The second approach is repair and maintenance.

Rather than meddling with inner clockwork, here we force back the hands of a clock to set it back. The main example? Stem cell therapy.

This type of approach would especially benefit the brain, which harbors small, scattered numbers of stem cells that deplete with age. For neurodegenerative diseases like Alzheimer’s, in which neurons progressively die off, stem cell therapy could in theory replace those lost cells and mend those broken circuits.

Once a blue-sky idea, the discovery of induced pluripotent stem cells (iPSCs), where scientists can turn skin and other mature cells back into a stem-like state, hugely propelled the field into near reality. But to date, stem cells haven’t been widely adopted in clinics.

It’s “a toolkit of highly innovative, highly invasive technologies with clinical trials still a great many years off,” the authors say.

But there is a silver lining. The boom in 3D tissue printing offers an alternative approach to stem cells in replacing aging organs. Recent investment from the Methuselah Foundation and other institutions suggests interest remains high despite still being a ways from mainstream use.

A Disruptive Future
“We are finally beginning to see an industry emerge from mankind’s attempts to make sense of the biological chaos,” the authors conclude.

Looking through the trends, they identified several technologies rapidly gaining steam.

One is artificial intelligence, which is already used to bolster drug discovery. Machine learning may also help identify new longevity genes or bring personalized medicine to the clinic based on a patient’s records or biomarkers.

Another is senolytics, a class of drugs that kill off “zombie cells.” Over 10 prospective candidates are already in the pipeline, with some expected to enter the market in less than a decade, the authors say.

Finally, there’s the big gun—gene therapy. The treatment, unlike others mentioned, can directly target the root of any pathology. With a snip (or a swap), genetic tools can turn off damaging genes or switch on ones that promote a youthful profile. It is the most preventative technology at our disposal.

There have already been some success stories in animal models. Using gene therapy, rodents given a boost in telomerase activity, which lengthens the protective caps of DNA strands, live healthier for longer.

“Although it is the prospect farthest from widespread implementation, it may ultimately prove the most influential,” the authors say.

Ultimately, can we stop the silver tsunami before it strikes?

Perhaps not, the authors say. But we do have defenses: the technologies outlined in the report, though still immature, could one day stop the oncoming tidal wave in its tracks.

Now we just have to bring them out of the lab and into the real world. To push the transition along, the team launched Longevity.International, an online meeting ground that unites various stakeholders in the industry.

By providing scientists, entrepreneurs, investors, and policy-makers a platform for learning and discussion, the authors say, we may finally generate enough drive to implement our defenses against aging. The war has begun.

Read the report in full here, and watch out for others coming soon here. The second part of the report profiles 650 (!!!) longevity-focused research hubs, non-profits, scientists, conferences, and literature. It’s an enormously helpful resource—totally worth keeping it in your back pocket for future reference.

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#432009 How Swarm Intelligence Is Making Simple ...

Posted on February 9, 2018 by Android

As a group, simple creatures following simple rules can display a surprising amount of complexity, efficiency, and even creativity. Known as swarm intelligence, this trait is found throughout nature, but researchers have recently begun using it to transform various fields such as robotics, data mining, medicine, and blockchains.

Ants, for example, can only perform a limited range of functions, but an ant colony can build bridges, create superhighways of food and information, wage war, and enslave other ant species—all of which are beyond the comprehension of any single ant. Likewise, schools of fish, flocks of birds, beehives, and other species exhibit behavior indicative of planning by a higher intelligence that doesn’t actually exist.

It happens by a process called stigmergy. Simply put, a small change by a group member causes other members to behave differently, leading to a new pattern of behavior.

When an ant finds a food source, it marks the path with pheromones. This attracts other ants to that path, leads them to the food source, and prompts them to mark the same path with more pheromones. Over time, the most efficient route will become the superhighway, as the faster and easier a path is, the more ants will reach the food and the more pheromones will be on the path. Thus, it looks as if a more intelligent being chose the best path, but it emerged from the tiny, simple changes made by individuals.

So what does this mean for humans? Well, a lot. In the past few decades, researchers have developed numerous algorithms and metaheuristics, such as ant colony optimization and particle swarm optimization, and they are rapidly being adopted.

Swarm Robotics
A swarm of robots would work on the same principles as an ant colony: each member has a simple set of rules to follow, leading to self-organization and self-sufficiency.

For example, researchers at Georgia Robotics and InTelligent Systems (GRITS) created a small swarm of simple robots that can spell and play piano. The robots cannot communicate, but based solely on the position of surrounding robots, they are able to use their specially-created algorithm to determine the optimal path to complete their task.

This is also immensely useful for drone swarms.

Last February, Ehang, an aviation company out of China, created a swarm of a thousand drones that not only lit the sky with colorful, intricate displays, but demonstrated the ability to improvise and troubleshoot errors entirely autonomously.

Further, just recently, the University of Cambridge and Koc University unveiled their idea for what they call the Energy Neutral Internet of Drones. Amazingly, this drone swarm would take initiative to share information or energy with other drones that did not receive a communication or are running low on energy.

Militaries all of the world are utilizing this as well.

Last year, the US Department of Defense announced it had successfully tested a swarm of miniature drones that could carry out complex missions cheaper and more efficiently. They claimed, “The micro-drones demonstrated advanced swarm behaviors such as collective decision-making, adaptive formation flying, and self-healing.”

Some experts estimate at least 30 nations are actively developing drone swarms—and even submersible drones—for military missions, including intelligence gathering, missile defense, precision missile strikes, and enhanced communication.

NASA also plans on deploying swarms of tiny spacecraft for space exploration, and the medical community is looking into using swarms of nanobots for precision delivery of drugs, microsurgery, targeting toxins, and biological sensors.

What If Humans Are the Ants?
The strength of any blockchain comes from the size and diversity of the community supporting it. Cryptocurrencies like Bitcoin, Ethereum, and Litecoin are driven by the people using, investing in, and, most importantly, mining them so their blockchains can function. Without an active community, or swarm, their blockchains wither away.

When viewed from a great height, a blockchain performs eerily like an ant colony in that it will naturally find the most efficient way to move vast amounts of information.

Miners compete with each other to perform the complex calculations necessary to add another block, for which the winner is rewarded with the blockchain’s native currency and agreed-upon fees. Of course, the miner with the more powerful computers is more likely to win the reward, thereby empowering the winner’s ability to mine and receive even more rewards. Over time, fewer and fewer miners are going to exist, as the winners are able to more efficiently shoulder more of the workload, in much the same way that ants build superhighways.

Further, a company called Unanimous AI has developed algorithms that allow humans to collectively make predictions. So far, the AI algorithms and their human participants have made some astoundingly accurate predictions, such as the first four winning horses of the Kentucky Derby, the Oscar winners, the Stanley Cup winners, and others. The more people involved in the swarm, the greater their predictive power will be.

To be clear, this is not a prediction based on group consensus. Rather, the swarm of humans uses software to input their opinions in real time, thus making micro-changes to the rest of the swarm and the inputs of other members.

Studies show that swarm intelligence consistently outperforms individuals and crowds working without the algorithms. While this is only the tip of the iceberg, some have suggested swarm intelligence can revolutionize how doctors diagnose a patient or how products are marketed to consumers. It might even be an essential step in truly creating AI.

While swarm intelligence is an essential part of many species’ success, it’s only a matter of time before humans harness its effectiveness as well.

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#431995 The 10 Grand Challenges Facing Robotics ...

Posted on February 7, 2018 by Android

Robotics research has been making great strides in recent years, but there are still many hurdles to the machines becoming a ubiquitous presence in our lives. The journal Science Robotics has now identified 10 grand challenges the field will have to grapple with to make that a reality.

Editors conducted an online survey on unsolved challenges in robotics and assembled an expert panel of roboticists to shortlist the 30 most important topics, which were then grouped into 10 grand challenges that could have major impact in the next 5 to 10 years. Here’s what they came up with.

1. New Materials and Fabrication Schemes
Roboticists are beginning to move beyond motors, gears, and sensors by experimenting with things like artificial muscles, soft robotics, and new fabrication methods that combine multiple functions in one material. But most of these advances have been “one-off” demonstrations, which are not easy to combine.

Multi-functional materials merging things like sensing, movement, energy harvesting, or energy storage could allow more efficient robot designs. But combining these various properties in a single machine will require new approaches that blend micro-scale and large-scale fabrication techniques. Another promising direction is materials that can change over time to adapt or heal, but this requires much more research.

2. Bioinspired and Bio-Hybrid Robots
Nature has already solved many of the problems roboticists are trying to tackle, so many are turning to biology for inspiration or even incorporating living systems into their robots. But there are still major bottlenecks in reproducing the mechanical performance of muscle and the ability of biological systems to power themselves.

There has been great progress in artificial muscles, but their robustness, efficiency, and energy and power density need to be improved. Embedding living cells into robots can overcome challenges of powering small robots, as well as exploit biological features like self-healing and embedded sensing, though how to integrate these components is still a major challenge. And while a growing “robo-zoo” is helping tease out nature’s secrets, more work needs to be done on how animals transition between capabilities like flying and swimming to build multimodal platforms.

3. Power and Energy
Energy storage is a major bottleneck for mobile robotics. Rising demand from drones, electric vehicles, and renewable energy is driving progress in battery technology, but the fundamental challenges have remained largely unchanged for years.

That means that in parallel to battery development, there need to be efforts to minimize robots’ power utilization and give them access to new sources of energy. Enabling them to harvest energy from their environment and transmitting power to them wirelessly are two promising approaches worthy of investigation.

4. Robot Swarms
Swarms of simple robots that assemble into different configurations to tackle various tasks can be a cheaper, more flexible alternative to large, task-specific robots. Smaller, cheaper, more powerful hardware that lets simple robots sense their environment and communicate is combining with AI that can model the kind of behavior seen in nature’s flocks.

But there needs to be more work on the most efficient forms of control at different scales—small swarms can be controlled centrally, but larger ones need to be more decentralized. They also need to be made robust and adaptable to the changing conditions of the real world and resilient to deliberate or accidental damage. There also needs to be more work on swarms of non-homogeneous robots with complementary capabilities.

5. Navigation and Exploration
A key use case for robots is exploring places where humans cannot go, such as the deep sea, space, or disaster zones. That means they need to become adept at exploring and navigating unmapped, often highly disordered and hostile environments.

The major challenges include creating systems that can adapt, learn, and recover from navigation failures and are able to make and recognize new discoveries. This will require high levels of autonomy that allow the robots to monitor and reconfigure themselves while being able to build a picture of the world from multiple data sources of varying reliability and accuracy.

6. AI for Robotics
Deep learning has revolutionized machines’ ability to recognize patterns, but that needs to be combined with model-based reasoning to create adaptable robots that can learn on the fly.

Key to this will be creating AI that’s aware of its own limitations and can learn how to learn new things. It will also be important to create systems that are able to learn quickly from limited data rather than the millions of examples used in deep learning. Further advances in our understanding of human intelligence will be essential to solving these problems.

7. Brain-Computer Interfaces
BCIs will enable seamless control of advanced robotic prosthetics but could also prove a faster, more natural way to communicate instructions to robots or simply help them understand human mental states.

Most current approaches to measuring brain activity are expensive and cumbersome, though, so work on compact, low-power, and wireless devices will be important. They also tend to involve extended training, calibration, and adaptation due to the imprecise nature of reading brain activity. And it remains to be seen if they will outperform simpler techniques like eye tracking or reading muscle signals.

8. Social Interaction
If robots are to enter human environments, they will need to learn to deal with humans. But this will be difficult, as we have very few concrete models of human behavior and we are prone to underestimate the complexity of what comes naturally to us.

Social robots will need to be able to perceive minute social cues like facial expression or intonation, understand the cultural and social context they are operating in, and model the mental states of people they interact with to tailor their dealings with them, both in the short term and as they develop long-standing relationships with them.

9. Medical Robotics
Medicine is one of the areas where robots could have significant impact in the near future. Devices that augment a surgeon’s capabilities are already in regular use, but the challenge will be to increase the autonomy of these systems in such a high-stakes environment.

Autonomous robot assistants will need to be able to recognize human anatomy in a variety of contexts and be able to use situational awareness and spoken commands to understand what’s required of them. In surgery, autonomous robots could perform the routine steps of a procedure, giving way to the surgeon for more complicated patient-specific bits.

Micro-robots that operate inside the human body also hold promise, but there are still many roadblocks to their adoption, including effective delivery systems, tracking and control methods, and crucially, finding therapies where they improve on current approaches.

10. Robot Ethics and Security
As the preceding challenges are overcome and robots are increasingly integrated into our lives, this progress will create new ethical conundrums. Most importantly, we may become over-reliant on robots.

That could lead to humans losing certain skills and capabilities, making us unable to take the reins in the case of failures. We may end up delegating tasks that should, for ethical reasons, have some human supervision, and allow people to pass the buck to autonomous systems in the case of failure. It could also reduce self-determination, as human behaviors change to accommodate the routines and restrictions required for robots and AI to work effectively.

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#431907 The Future of Cancer Treatment Is ...

Posted on January 11, 2018 by Android

In an interview at Singularity University’s Exponential Medicine in San Diego, Richard Wender, chief cancer control officer at the American Cancer Society, discussed how technology has changed cancer care and treatment in recent years.
Just a few years ago, microscopes were the primary tool used in cancer diagnoses, but we’ve come a long way since.
“We still look at a microscope, we still look at what organ the cancer started in,” Wender said. “But increasingly we’re looking at the molecular signature. It’s not just the genomics, and it’s not just the genes. It’s also the cellular environment around that cancer. We’re now targeting our therapies to the mutations that are found in that particular cancer.”
Cancer treatments in the past have been largely reactionary, but they don’t need to be. Most cancer is genetic, which means that treatment can be preventative. This is one reason why newer cancer treatment techniques are searching for actionable targets in the specific gene before the cancer develops.

When asked how artificial intelligence and machine learning technologies are reshaping clinical trials, Wender acknowledged that how clinical trials have been run in the past won’t work moving forward.
“Our traditional ways of learning about cancer were by finding a particular cancer type and conducting a long clinical trial that took a number of years enrolling patients from around the country. That is not how we’re going to learn to treat individual patients in the future.”
Instead, Wender emphasized the need for gathering as much data as possible, and from as many individual patients as possible. This data should encompass clinical, pathological, and molecular data and should be gathered from a patient all the way through their final outcome. “Literally every person becomes a clinical trial of one,” Wender said.
For the best cancer treatment and diagnostics, Wender says the answer is to make the process collaborative by pulling in resources from organizations and companies that are both established and emerging.
It’s no surprise to hear that the best solutions come from pairing together uncommon partners to innovate.
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#431603 What We Can Learn From the Second Life ...

Posted on November 26, 2017 by Android

For every new piece of technology that gets developed, you can usually find people saying it will never be useful. The president of the Michigan Savings Bank in 1903, for example, said, “The horse is here to stay but the automobile is only a novelty—a fad.” It’s equally easy to find people raving about whichever new technology is at the peak of the Gartner Hype Cycle, which tracks the buzz around these newest developments and attempts to temper predictions. When technologies emerge, there are all kinds of uncertainties, from the actual capacity of the technology to its use cases in real life to the price tag.
Eventually the dust settles, and some technologies get widely adopted, to the extent that they can become “invisible”; people take them for granted. Others fall by the wayside as gimmicky fads or impractical ideas. Picking which horses to back is the difference between Silicon Valley millions and Betamax pub-quiz-question obscurity. For a while, it seemed that Google had—for once—backed the wrong horse.
Google Glass emerged from Google X, the ubiquitous tech giant’s much-hyped moonshot factory, where highly secretive researchers work on the sci-fi technologies of the future. Self-driving cars and artificial intelligence are the more mundane end for an organization that apparently once looked into jetpacks and teleportation.
The original smart glasses, Google began selling Google Glass in 2013 for $1,500 as prototypes for their acolytes, around 8,000 early adopters. Users could control the glasses with a touchpad, or, activated by tilting the head back, with voice commands. Audio relay—as with several wearable products—is via bone conduction, which transmits sound by vibrating the skull bones of the user. This was going to usher in the age of augmented reality, the next best thing to having a chip implanted directly into your brain.
On the surface, it seemed to be a reasonable proposition. People had dreamed about augmented reality for a long time—an onboard, JARVIS-style computer giving you extra information and instant access to communications without even having to touch a button. After smartphone ubiquity, it looked like a natural step forward.
Instead, there was a backlash. People may be willing to give their data up to corporations, but they’re less pleased with the idea that someone might be filming them in public. The worst aspect of smartphones is trying to talk to people who are distractedly scrolling through their phones. There’s a famous analogy in Revolutionary Road about an old couple’s loveless marriage: the husband tunes out his wife’s conversation by turning his hearing aid down to zero. To many, Google Glass seemed to provide us with a whole new way to ignore each other in favor of our Twitter feeds.
Then there’s the fact that, regardless of whether it’s because we’re not used to them, or if it’s a more permanent feature, people wearing AR tech often look very silly. Put all this together with a lack of early functionality, the high price (do you really feel comfortable wearing a $1,500 computer?), and a killer pun for the users—Glassholes—and the final recipe wasn’t great for Google.
Google Glass was quietly dropped from sale in 2015 with the ominous slogan posted on Google’s website “Thanks for exploring with us.” Reminding the Glass users that they had always been referred to as “explorers”—beta-testing a product, in many ways—it perhaps signaled less enthusiasm for wearables than the original, Google Glass skydive might have suggested.
In reality, Google went back to the drawing board. Not with the technology per se, although it has improved in the intervening years, but with the uses behind the technology.
Under what circumstances would you actually need a Google Glass? When would it genuinely be preferable to a smartphone that can do many of the same things and more? Beyond simply being a fashion item, which Google Glass decidedly was not, even the most tech-evangelical of us need a convincing reason to splash $1,500 on a wearable computer that’s less socially acceptable and less easy to use than the machine you’re probably reading this on right now.
Enter the Google Glass Enterprise Edition.
Piloted in factories during the years that Google Glass was dormant, and now roaring back to life and commercially available, the Google Glass relaunch got under way in earnest in July of 2017. The difference here was the specific audience: workers in factories who need hands-free computing because they need to use their hands at the same time.
In this niche application, wearable computers can become invaluable. A new employee can be trained with pre-programmed material that explains how to perform actions in real time, while instructions can be relayed straight into a worker’s eyeline without them needing to check a phone or switch to email.
Medical devices have long been a dream application for Google Glass. You can imagine a situation where people receive real-time information during surgery, or are augmented by artificial intelligence that provides additional diagnostic information or questions in response to a patient’s symptoms. The quest to develop a healthcare AI, which can provide recommendations in response to natural language queries, is on. The famously untidy doctor’s handwriting—and the associated death toll—could be avoided if the glasses could take dictation straight into a patient’s medical records. All of this is far more useful than allowing people to check Facebook hands-free while they’re riding the subway.
Google’s “Lens” application indicates another use for Google Glass that hadn’t quite matured when the original was launched: the Lens processes images and provides information about them. You can look at text and have it translated in real time, or look at a building or sign and receive additional information. Image processing, either through neural networks hooked up to a cloud database or some other means, is the frontier that enables driverless cars and similar technology to exist. Hook this up to a voice-activated assistant relaying information to the user, and you have your killer application: real-time annotation of the world around you. It’s this functionality that just wasn’t ready yet when Google launched Glass.
Amazon’s recent announcement that they want to integrate Alexa into a range of smart glasses indicates that the tech giants aren’t ready to give up on wearables yet. Perhaps, in time, people will become used to voice activation and interaction with their machines, at which point smart glasses with bone conduction will genuinely be more convenient than a smartphone.
But in many ways, the real lesson from the initial failure—and promising second life—of Google Glass is a simple question that developers of any smart technology, from the Internet of Things through to wearable computers, must answer. “What can this do that my smartphone can’t?” Find your answer, as the Enterprise Edition did, as Lens might, and you find your product.
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