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

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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#431869 When Will We Finally Achieve True ...

The field of artificial intelligence goes back a long way, but many consider it was officially born when a group of scientists at Dartmouth College got together for a summer, back in 1956. Computers had, over the last few decades, come on in incredible leaps and bounds; they could now perform calculations far faster than humans. Optimism, given the incredible progress that had been made, was rational. Genius computer scientist Alan Turing had already mooted the idea of thinking machines just a few years before. The scientists had a fairly simple idea: intelligence is, after all, just a mathematical process. The human brain was a type of machine. Pick apart that process, and you can make a machine simulate it.
The problem didn’t seem too hard: the Dartmouth scientists wrote, “We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.” This research proposal, by the way, contains one of the earliest uses of the term artificial intelligence. They had a number of ideas—maybe simulating the human brain’s pattern of neurons could work and teaching machines the abstract rules of human language would be important.
The scientists were optimistic, and their efforts were rewarded. Before too long, they had computer programs that seemed to understand human language and could solve algebra problems. People were confidently predicting there would be a human-level intelligent machine built within, oh, let’s say, the next twenty years.
It’s fitting that the industry of predicting when we’d have human-level intelligent AI was born at around the same time as the AI industry itself. In fact, it goes all the way back to Turing’s first paper on “thinking machines,” where he predicted that the Turing Test—machines that could convince humans they were human—would be passed in 50 years, by 2000. Nowadays, of course, people are still predicting it will happen within the next 20 years, perhaps most famously Ray Kurzweil. There are so many different surveys of experts and analyses that you almost wonder if AI researchers aren’t tempted to come up with an auto reply: “I’ve already predicted what your question will be, and no, I can’t really predict that.”
The issue with trying to predict the exact date of human-level AI is that we don’t know how far is left to go. This is unlike Moore’s Law. Moore’s Law, the doubling of processing power roughly every couple of years, makes a very concrete prediction about a very specific phenomenon. We understand roughly how to get there—improved engineering of silicon wafers—and we know we’re not at the fundamental limits of our current approach (at least, not until you’re trying to work on chips at the atomic scale). You cannot say the same about artificial intelligence.
Common Mistakes
Stuart Armstrong’s survey looked for trends in these predictions. Specifically, there were two major cognitive biases he was looking for. The first was the idea that AI experts predict true AI will arrive (and make them immortal) conveniently just before they’d be due to die. This is the “Rapture of the Nerds” criticism people have leveled at Kurzweil—his predictions are motivated by fear of death, desire for immortality, and are fundamentally irrational. The ability to create a superintelligence is taken as an article of faith. There are also criticisms by people working in the AI field who know first-hand the frustrations and limitations of today’s AI.
The second was the idea that people always pick a time span of 15 to 20 years. That’s enough to convince people they’re working on something that could prove revolutionary very soon (people are less impressed by efforts that will lead to tangible results centuries down the line), but not enough for you to be embarrassingly proved wrong. Of the two, Armstrong found more evidence for the second one—people were perfectly happy to predict AI after they died, although most didn’t, but there was a clear bias towards “15–20 years from now” in predictions throughout history.
Measuring Progress
Armstrong points out that, if you want to assess the validity of a specific prediction, there are plenty of parameters you can look at. For example, the idea that human-level intelligence will be developed by simulating the human brain does at least give you a clear pathway that allows you to assess progress. Every time we get a more detailed map of the brain, or successfully simulate another part of it, we can tell that we are progressing towards this eventual goal, which will presumably end in human-level AI. We may not be 20 years away on that path, but at least you can scientifically evaluate the progress.
Compare this to those that say AI, or else consciousness, will “emerge” if a network is sufficiently complex, given enough processing power. This might be how we imagine human intelligence and consciousness emerged during evolution—although evolution had billions of years, not just decades. The issue with this is that we have no empirical evidence: we have never seen consciousness manifest itself out of a complex network. Not only do we not know if this is possible, we cannot know how far away we are from reaching this, as we can’t even measure progress along the way.
There is an immense difficulty in understanding which tasks are hard, which has continued from the birth of AI to the present day. Just look at that original research proposal, where understanding human language, randomness and creativity, and self-improvement are all mentioned in the same breath. We have great natural language processing, but do our computers understand what they’re processing? We have AI that can randomly vary to be “creative,” but is it creative? Exponential self-improvement of the kind the singularity often relies on seems far away.
We also struggle to understand what’s meant by intelligence. For example, AI experts consistently underestimated the ability of AI to play Go. Many thought, in 2015, it would take until 2027. In the end, it took two years, not twelve. But does that mean AI is any closer to being able to write the Great American Novel, say? Does it mean it’s any closer to conceptually understanding the world around it? Does it mean that it’s any closer to human-level intelligence? That’s not necessarily clear.
Not Human, But Smarter Than Humans
But perhaps we’ve been looking at the wrong problem. For example, the Turing test has not yet been passed in the sense that AI cannot convince people it’s human in conversation; but of course the calculating ability, and perhaps soon the ability to perform other tasks like pattern recognition and driving cars, far exceed human levels. As “weak” AI algorithms make more decisions, and Internet of Things evangelists and tech optimists seek to find more ways to feed more data into more algorithms, the impact on society from this “artificial intelligence” can only grow.
It may be that we don’t yet have the mechanism for human-level intelligence, but it’s also true that we don’t know how far we can go with the current generation of algorithms. Those scary surveys that state automation will disrupt society and change it in fundamental ways don’t rely on nearly as many assumptions about some nebulous superintelligence.
Then there are those that point out we should be worried about AI for other reasons. Just because we can’t say for sure if human-level AI will arrive this century, or never, it doesn’t mean we shouldn’t prepare for the possibility that the optimistic predictors could be correct. We need to ensure that human values are programmed into these algorithms, so that they understand the value of human life and can act in “moral, responsible” ways.
Phil Torres, at the Project for Future Human Flourishing, expressed it well in an interview with me. He points out that if we suddenly decided, as a society, that we had to solve the problem of morality—determine what was right and wrong and feed it into a machine—in the next twenty years…would we even be able to do it?
So, we should take predictions with a grain of salt. Remember, it turned out the problems the AI pioneers foresaw were far more complicated than they anticipated. The same could be true today. At the same time, we cannot be unprepared. We should understand the risks and take our precautions. When those scientists met in Dartmouth in 1956, they had no idea of the vast, foggy terrain before them. Sixty years later, we still don’t know how much further there is to go, or how far we can go. But we’re going somewhere.
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#431186 The Coming Creativity Explosion Belongs ...

Does creativity make human intelligence special?
It may appear so at first glance. Though machines can calculate, analyze, and even perceive, creativity may seem far out of reach. Perhaps this is because we find it mysterious, even in ourselves. How can the output of a machine be anything more than that which is determined by its programmers?
Increasingly, however, artificial intelligence is moving into creativity’s hallowed domain, from art to industry. And though much is already possible, the future is sure to bring ever more creative machines.
What Is Machine Creativity?
Robotic art is just one example of machine creativity, a rapidly growing sub-field that sits somewhere between the study of artificial intelligence and human psychology.
The winning paintings from the 2017 Robot Art Competition are strikingly reminiscent of those showcased each spring at university exhibitions for graduating art students. Like the works produced by skilled artists, the compositions dreamed up by the competition’s robotic painters are aesthetically ambitious. One robot-made painting features a man’s bearded face gazing intently out from the canvas, his eyes locking with the viewer’s. Another abstract painting, “inspired” by data from EEG signals, visually depicts the human emotion of misery with jagged, gloomy stripes of black and purple.
More broadly, a creative machine is software (sometimes encased in a robotic body) that synthesizes inputs to generate new and valuable ideas, solutions to complex scientific problems, or original works of art. In a process similar to that followed by a human artist or scientist, a creative machine begins its work by framing a problem. Next, its software specifies the requirements the solution should have before generating “answers” in the form of original designs, patterns, or some other form of output.
Although the notion of machine creativity sounds a bit like science fiction, the basic concept is one that has been slowly developing for decades.
Nearly 50 years ago while a high school student, inventor and futurist Ray Kurzweil created software that could analyze the patterns in musical compositions and then compose new melodies in a similar style. Aaron, one of the world’s most famous painting robots, has been hard at work since the 1970s.
Industrial designers have used an automated, algorithm-driven process for decades to design computer chips (or machine parts) whose layout (or form) is optimized for a particular function or environment. Emily Howell, a computer program created by David Cope, writes original works in the style of classical composers, some of which have been performed by human orchestras to live audiences.
What’s different about today’s new and emerging generation of robotic artists, scientists, composers, authors, and product designers is their ubiquity and power.

“The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives.”

I’ve already mentioned the rapidly advancing fields of robotic art and music. In the realm of scientific research, so-called “robotic scientists” such as Eureqa and Adam and Eve develop new scientific hypotheses; their “insights” have contributed to breakthroughs that are cited by hundreds of academic research papers. In the medical industry, creative machines are hard at work creating chemical compounds for new pharmaceuticals. After it read over seven million words of 20th century English poetry, a neural network developed by researcher Jack Hopkins learned to write passable poetry in a number of different styles and meters.
The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives, including faster processors, ubiquitous sensors and wireless networks, and better algorithms.
As they continue to improve, creative machines—like humans—will perform a broad range of creative activities, ranging from everyday problem solving (sometimes known as “Little C” creativity) to producing once-in-a-century masterpieces (“Big C” creativity). A creative machine’s outputs could range from a design for a cast for a marble sculpture to a schematic blueprint for a clever new gadget for opening bottles of wine.
In the coming decades, by automating the process of solving complex problems, creative machines will again transform our world. Creative machines will serve as a versatile source of on-demand talent.
In the battle to recruit a workforce that can solve complex problems, creative machines will put small businesses on equal footing with large corporations. Art and music lovers will enjoy fresh creative works that re-interpret the style of ancient disciplines. People with a health condition will benefit from individualized medical treatments, and low-income people will receive top-notch legal advice, to name but a few potentially beneficial applications.
How Can We Make Creative Machines, Unless We Understand Our Own Creativity?
One of the most intriguing—yet unsettling—aspects of watching robotic arms skillfully oil paint is that we humans still do not understand our own creative process. Over the centuries, several different civilizations have devised a variety of models to explain creativity.
The ancient Greeks believed that poets drew inspiration from a transcendent realm parallel to the material world where ideas could take root and flourish. In the Middle Ages, philosophers and poets attributed our peculiarly human ability to “make something of nothing” to an external source, namely divine inspiration. Modern academic study of human creativity has generated vast reams of scholarship, but despite the value of these insights, the human imagination remains a great mystery, second only to that of consciousness.
Today, the rise of machine creativity demonstrates (once again), that we do not have to fully understand a biological process in order to emulate it with advanced technology.
Past experience has shown that jet planes can fly higher and faster than birds by using the forward thrust of an engine rather than wings. Submarines propel themselves forward underwater without fins or a tail. Deep learning neural networks identify objects in randomly-selected photographs with super-human accuracy. Similarly, using a fairly straightforward software architecture, creative software (sometimes paired with a robotic body) can paint, write, hypothesize, or design with impressive originality, skill, and boldness.
At the heart of machine creativity is simple iteration. No matter what sort of output they produce, creative machines fall into one of three categories depending on their internal architecture.
Briefly, the first group consists of software programs that use traditional rule-based, or symbolic AI, the second group uses evolutionary algorithms, and the third group uses a variation of a form of machine learning called deep learning that has already revolutionized voice and facial recognition software.
1) Symbolic creative machines are the oldest artificial artists and musicians. In this approach—also known as “good old-fashioned AI (GOFAI) or symbolic AI—the human programmer plays a key role by writing a set of step-by-step instructions to guide the computer through a task. Despite the fact that symbolic AI is limited in its ability to adapt to environmental changes, it’s still possible for a robotic artist programmed this way to create an impressively wide variety of different outputs.
2) Evolutionary algorithms (EA) have been in use for several decades and remain powerful tools for design. In this approach, potential solutions “compete” in a software simulator in a Darwinian process reminiscent of biological evolution. The human programmer specifies a “fitness criterion” that will be used to score and rank the solutions generated by the software. The software then generates a “first generation” population of random solutions (which typically are pretty poor in quality), scores this first generation of solutions, and selects the top 50% (those random solutions deemed to be the best “fit”). The software then takes another pass and recombines the “winning” solutions to create the next generation and repeats this process for thousands (and sometimes millions) of generations.
3) Generative deep learning (DL) neural networks represent the newest software architecture of the three, since DL is data-dependent and resource-intensive. First, a human programmer “trains” a DL neural network to recognize a particular feature in a dataset, for example, an image of a dog in a stream of digital images. Next, the standard “feed forward” process is reversed and the DL neural network begins to generate the feature, for example, eventually producing new and sometimes original images of (or poetry about) dogs. Generative DL networks have tremendous and unexplored creative potential and are able to produce a broad range of original outputs, from paintings to music to poetry.
The Coming Explosion of Machine Creativity
In the near future as Moore’s Law continues its work, we will see sophisticated combinations of these three basic architectures. Since the 1950s, artificial intelligence has steadily mastered one human ability after another, and in the process of doing so, has reduced the cost of calculation, analysis, and most recently, perception. When creative software becomes as inexpensive and ubiquitous as analytical software is today, humans will no longer be the only intelligent beings capable of creative work.
This is why I have to bite my tongue when I hear the well-intended (but shortsighted) advice frequently dispensed to young people that they should pursue work that demands creativity to help them “AI-proof” their futures.
Instead, students should gain skills to harness the power of creative machines.
There are two skills in which humans excel that will enable us to remain useful in a world of ever-advancing artificial intelligence. One, the ability to frame and define a complex problem so that it can be handed off to a creative machine to solve. And two, the ability to communicate the value of both the framework and the proposed solution to the other humans involved.
What will happen to people when creative machines begin to capably tread on intellectual ground that was once considered the sole domain of the human mind, and before that, the product of divine inspiration? While machines engaging in Big C creativity—e.g., oil painting and composing new symphonies—tend to garner controversy and make the headlines, I suspect the real world-changing application of machine creativity will be in the realm of everyday problem solving, or Little C. The mainstream emergence of powerful problem-solving tools will help people create abundance where there was once scarcity.
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#431171 SceneScan: Real-Time 3D Depth Sensing ...

Nerian Introduces a High-Performance Successor for the Proven SP1 System
Stereo vision, which is the three-dimensional perception of our environment with two sensors likeour eyes, is a well-known technology. As a passive method – there is no need to emit light in thevisible or invisible spectral range – this technology can open up new possibilities for three dimensional perception, even under difficult conditions.
But as often, the devil is in the details: for most applications, the software implementation withstandard PCs, but also with graphics processors, is too slow. Another complicating factor is thatthese hardware platforms are expensive and not energy-efficient. The solution is to instead usespecialized hardware for image processing. A programmable logic device – a so-called FPGA – cangreatly accelerate the image processing.
As a technology leader, Nerian Vision Technologies has been following this path successfully forthe past two years with the SP1 stereo vision system, which has enabled completely newapplications in the fields of robotics, automation technology, medical technology, autonomousdriving and other domains. Now the company introduces two successors:
SceneScan and SceneScan Pro. Real eye-catchers in a double sense: stereo vision in an elegant design!But more important is, of course, the significantly improved inner workings of the two new modelsin comparison to their predecessor. The new hardware allows processing rates of up to 100 framesper second at resolutions of up to 3 megapixels, which leaves the SP1 far behind:
Photo Credit: Nerian Vision Technologies – www.nerian.com

The table illustrates the difference: while SceneScan Pro has the highest possible computing powerand is designed for the most demanding applications, SceneScan has been cost-reduced forapplications with lower requirements. The customer can thus optimize his embedded vision solution both in terms of costs and technology.
The new duo is completed by Nerian’s proven Karmin stereo cameras. Of course, industrialUSB3Vision cameras by other manufacturers are also supported.This combination not only supports the above-mentioned applications even better, but alsofacilitates completely new and innovative ones. If required, customer-specific adaptations are alsopossible.
ContactNerian Vision TechnologiesOwner: Dr. Konstantin SchauweckerGotenstr. 970771 Leinfelden-EchterdingenGermanyPhone: +49 711 / 2195 9414Email: service@nerian.comWebsite: http://nerian.com
Press Release Authored By: Nerian Vision Technologies
Photo Credit: Nerian Vision Technologies – www.nerian.com
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