Plant-e: heats, shoots and leaves — electricity from living plants

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— the story —

Plants could soon provide our electricity. In a small way they already are doing that in research labs and greenhouses at project Plant-e — a university and commercially sponsored research group at Wageningen University in the Netherlands.

The Plant Microbial Fuel Cell from Plant-e can generate electricity from the natural interaction between plant roots and soil bacteria. It works by taking advantage of the up to 70 percent of organic material produced by a plant’s photo-synthesis process that cannot be used by the plant — and is excreted through the roots.

As natural occurring bacteria around the roots break down this organic residue, electrons are released as a waste product. By placing an electrode close to the bacteria to absorb these electrons, the research team — led by Marjolein Helder PhD — is able to generate electricity.

Helder said: “Solar panels are making more energy per square meter — but we expect to reduce the costs of our system technology in the future. And our system can be used for a variety of applications.”

Plant Microbial Fuel Cells can be used on many scales. An experimental 15 square meter model can produce enough energy to power a computer notebook. Plant-e is working on a system for large scale electricity production in existing green areas like wetlands and rice paddy fields.

Helder said: “Our technology is making electricity — but also could be used as roof insulation or as a water collector. On a bigger scale it’s possible to produce rice and electricity at the same time, and in that way combine food and energy production.”

A first prototype of a green electricity roof has been installed on one building at Wageningen University and researchers are keeping a close eye on what is growing there. The first field pilots will be started in 2014. The technology was patented in 2007.

After 5 years of lab research: Plant-e is now taking the first steps toward commercializing the technology. In the future, bio-electricity from plants could produce as much as 3.2 watts per square meter of plant growth.

w. descriptions from: EuroNews


Plant-e | main
Plant-e | brochure
Plant-e | YouTube channel


video | electricity from plants


— watch • videos from Plant-e —

Plant-e | video: animation
Plant-e | video: the power of plants
Plant-e | video: the next step in development


on the web | essentials

Wageningen Univ. | main
Wageningen Univ. | research institutes: plant research
Wageningen Univ. | research institutes: centre for development innovation
Wageningen Univ. | story: Dutch Innovation Award for Plant-e


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This Drug Combo Extends Lifespan and Healthspan in Mice by Killing ‘Zombie’ Cells

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Aging may seem like the most natural—and inevitable—thing in life. Yet according to a new study in Nature Medicine, rejuvenating an aging body may be as easy as kitchen renovations. Simply swap drill and hammer for a cocktail of two drugs already on the market; rather than pulling out decrepit cabinets, kill off aged “zombie” cells.

These so-called senescent cells are a curious oddity: they’re frail, beat-up, and unable to perform their usual roles. Yet they simply refuse to die. What’s more, zombie cells actively leak inflammatory chemicals into their surroundings, damaging nearby tissue and—in a sense—“spreading” the negative effects of aging.

Yet because they’re extremely rare, amounting to only eight percent of the body’s cells at most, scientists have long wondered just how much they contribute to the aging process.

Now, a team from the Mayo Clinic in Rochester convincingly showed that zombie cells punch far above their weight when it comes to driving age. By transplanting a group of aged cells just one ten-thousandth of a mouse’s total cells into middle-aged mice, the team accelerated the recipients’ aging process, turning them weak and frail in just a few weeks.

In contrast, wiping out senescent cells within ancient mice—the equivalent of a 90-year old human—increased their lifespan by 36 percent, without being haunted by diseases that usually mark late life. All it took was two simple drugs.

“This study is really impressive,” said Dr. Felipe Sierra at the National Institute on Aging, who was not involved in the research.

Senolytic Boom

The new results join an increasing mountain of evidence that senolytics—drugs that kill off aging cells—may be a sort of “silver bullet” against aging.

With age, our cells gradually accumulate damage to their DNA. Although the body has natural defenses against minor perturbations, eventually the damages accumulate to a point of no return. The aged, broken cell is given a terrible choice: suicide, become cancerous, or transform into a strange, half-dormant “zombie-like” state called senescence.

Although scientists initially thought senescence was a guardrail against cancer, the picture changed recently. Zombie cells lurk in aged kidneys, hearts, and brains, actively pumping out a whole load of junk into their surroundings. The secretions are a toxic mix of inflammatory compounds that, among other things, kill off young cells and disable stem cells from producing healthy replacements.

“Senescent cells put the brakes on the production of new cells,” explained Dr. James Kirkland, who led the new study.

Back in 2016, another team from the Mayo Clinic genetically tweaked some mice so that their bodies actively destroyed 50–70 percent of senescent cells. The resulting mice had healthier kidneys, stronger hearts, and lived 20 percent longer than their peers.

Then, a year later, another team replicated these findings using a chemical “torpedo” that hunts down senescent cells and efficiently kills them off, all without damaging healthy cells. Not only did the treated aged mice regrow their scraggly fur into rich, luscious pelts, they also had better kidney function and perked up in energy levels, choosing to run and jump rather than sleep huddled in a corner.

“As a concept [for slowing or reversing aging], senolytics is completely valid,” said Sierra, adding that pharmaceutical companies are investigating over a couple dozen potential senolytics in a race to bring the first to market.

The problem, however, is that not everyone is convinced that senescent cells cause aging. They’re extremely low in number even in the elderly. How can such a small fraction of cells wreak so much havoc? And if we really did get rid of them within an already-frail body, wouldn’t that cause even more damage?

An Aging Infection

Kirkland addressed these questions with a series of experiments.

If, his team reasoned, senescent cells actually cause aging, then transplanting them would “infect” healthy, young animals with an aging disease. They manufactured a few million senescent cells by zapping healthy ones with a hefty dose of irradiation, then transplanted between two and five hundred thousand cells into young healthy mice, the equivalent of a 20-to-30-year-old human.

Remarkably, as early as two weeks after the surgery, the recipient mice began displaying signs of aging: they had trouble grasping things and couldn’t walk as fast as mice transplanted with an equal amount of normal cells. The dose mattered: these negative effects only showed up in the group given 500,000 cells.

This means that if senescent cells make up about 0.01 to 0.03 percent of all cells in the body, then that’s enough to trigger the body to physically age, at least in mice.

Even stranger was this: although the transplanted cells only survived for 40 days, the aging effects lasted up to six months. A closer look found that the senescent cells had spread aging throughout nearby tissue like spreading a disease—the recipient mice’s own cells began displaying signs of senescence. What’s more, distant tissues, which did not receive any transplanted cells, also started showing signs of aging.

“Senescence spreading may explain how a small number of transplanted SEN [senescent] cells caused such profound, long-lasting, and deleterious systemic effects,” the authors explained.

The effects were even more prominent when the recipients were 17-month-old mice. Roughly the equivalent of 60-to-70-year-old humans, these mice deteriorated rapidly and were five-times more likely to die in the next year after transplant than middle-aged mice.

An Aging Elixir

The team next gave mice transplanted with senescent cells a cocktail of two drugs (“D+Q”): dasatinib, used to treat leukemia, and quercetin, a plant chemical often found in red wine, kale, and a variety of unregulated supplements.

These are the first senolytics reported, and the team found them by analyzing the different pathways senescent cells use to ward off suicide. Even more importantly: they work in human tissues to kill off senescent cells and reduce their inflammatory secretions.

In young mice transplanted with senescent cells, just three days of D+Q treatment efficiently killed off the aged cells. The recipients were also spared from premature aging, in that they remained relatively physically fit even after the senescent cell assault.

The drug combo also worked its magic in older mice. When given intermittently for four months to geriatric mice, the drugs boosted their appetite and increased overall activity. It’s like a 90-year-old human swallowing a few drugs and suddenly jumping off the couch and running around the block.

Even more impressively, they lived 36 percent longer (that’s huge!) without an increased risk of getting cancer or other age-related diseases. That is, they had a longer healthspan—the current goal of anti-aging research.

“We can say with certainty that senescent cells can cause health problems in young mice, including causing physical dysfunction and lowering survival rates, and that the use of senolytics can significantly improve both healthspan and lifespan in much older naturally-aged animals,” said Kirkland.

A Methuselah Wonder?

The D+Q combo is already in clinical trials to ward off senescence in kidney diseases. But Kirkland isn’t ready to test out the combo on himself.

“People should absolutely not be taking this until we have the results from clinical trials,” he stressed.

He has cause for concern. Other promising anti-aging therapies, such as resveratrol and young blood, all came up short in clinical trials. And the side effects of D+Q can’t be easily brushed off. Dasatinib, as a type of chemotherapy drug, has a danger list dozens of entries long starting with low blood cell counts and bleeding problems.

Nevertheless, senolytics as a field is rapidly maturing from the backwaters of science into mainstream. As is the idea that aging—rather than being an inevitable part of life—can be reversed, even at advanced stages.

“We once felt that once people are elderly, it’s too late to avoid geriatric syndromes like frailty, loss of independence, decreased muscle strength, and forms of mild cognitive impairment,” said Kirkland. “We have to rethink that.”

Image Credit: POOKAOSTOCK / Shutterstock.com

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Is the Rise of AI on Wall Street for Better or Worse?

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May 6, 2010, is a date that should live in infamy. On that day, the US stock market suffered a trillion-dollar collapse. Do you remember it?

In just 15 minutes from 2:45 pm, the Dow Jones plunged by almost 9 percent. Had it stuck, it would have been among the famous index’s biggest declines in a single day. Hundreds of billions of dollars were wiped off the face value of famous companies in the S&P 500. Stocks in some companies were trading for a single cent.

Almost as soon as the sleepless Wall Street traders had time to adjust to the nightmare scenario unfolding in front of them, the market began to rebound. Within another fifteen minutes, almost all of the losses had been recovered.

It was one of those moments where you can only marvel at the absurdity of what it is to be a modern human. The numbers on the Dow Jones index are just numbers; the value they represent is, more than ever before, based on perceptions: stories we tell each other about companies and the economy.

Yet, had the trillion-dollar wipe-out proved permanent, there would have been real consequences, real layoffs, real suffering. Nothing of value was created or destroyed in the what became known as the “flash crash”; the markets weren’t responding to new information. We, collectively, have designed a system that is beyond our understanding, often beyond our control, and the unseen, intangible hands do not always serve us kindly.

The post-mortem for the flash crash was swift. Some people suspected insider trading, while others suggested technical glitches in the stock market might have been responsible. After five years, one man was charged with market manipulation: Navinder Singh Sarao, a 36-year-old from London who had been betting against the market with a “spoofing algorithm” around the time of the crash. He may have profited to the tune of $12 million. Small change against the trillion-dollar fluctuation.

Various outlets, including Bloomberg, have been skeptical that Sarao, who traded from his childhood bedroom in London, could really be held responsible for the flash crash. After all, while he was clearly no ordinary trader, the volumes of money he was moving around were tiny compared to the overall stakes of the flash crash. As it turns out, Sarao was also defrauded of the vast majority of his profits, and could not post his $5 million bail.

It’s an easier story to tell ourselves that one man’s misdeeds triggered the crash, because it allows us to believe that it’s a fixable problem. But even if you believe Sarao was solely responsible, it’s indicative of how vulnerable and chaotic the system is that his actions could trigger such a massive response.

A big part of the reason for this volatility is the sheer density of automated traders and algorithms that control these financial markets.

The most alert, savvy human is still limited by the speed at which his brain can process signals and move his muscles in response. Not so for an AI. In 2010, when Sarao’s algorithm helped trigger the crash, it was hardly alone: 70 percent of all trading activity was completely automated.

This represents a sea change in how trading is done on Wall Street. The classical idea is that the market tells you about investor confidence, and so what it reflects is information. If it’s widely known or anticipated that, say, Amazon is about to declare a record quarter of earnings, their stock will probably be riding high and reflect the value of that expectation.

In this classic system, the way you “win” and profit is by having access to information—or perhaps an expectation—that others don’t have, or by consistently making better judgement calls.

For an algorithmic trader, it’s different. Stories that contain rumors and hearsay are no good to an AI until they’re converted into a numerical value. At the classical end, algorithms might scan through news stories and the facts and figures of earnings statements to suggest new investment opportunities. They’re essentially doing the classical job of a trader, but many times faster. Natural language processing analyzes the sentiment of news articles and converts it into the AI’s “gut feeling.” If one understood the intricacies of the algorithm and the data it was being fed, one could hope to “game” this system with favorable stories in the media too.

At the more rapid edge, flash traders (sometimes called high-frequency traders, or HFTs), are searching for miniscule advantages. They’re not looking into whether Company A is more likely a better long-term investment than Company B, but instead seeking to profit from fluctuations in price that can take place over seconds or milliseconds, putting in thousands of orders for hundreds of stocks automatically in response to inflows of data. These are the people who built a $300 million cable that was ever-so-slightly straighter than the previous effort, to reduce trading times from 17 to 13 milliseconds—and made a killing in doing so.

In its most controversial form, the flash traders view orders from other market participants fractions of a second before the trades go ahead. The data that they’re feeding on isn’t information about the underlying stock, but about the market fluctuations caused by other people’s valuation of the stock. Predatory traders attempt to decode the signals made by one algorithm, while they attempt to conceal their intentions to avoid being scooped. It is this competition between fast and slow algorithms—and, possibly even attempts by the fastest algorithms to “outwit” the slowest—that helped lead to the flash crash. The algorithms, fed on a diet of each other’s behavior, are quite capable of whipping themselves up into a feedback frenzy without human prompting.

Previously, many of these algorithms used statistical models developed by quants, or quantitative analysts. Many used complex mathematics to convert streams of input data into a prediction of the future price of the stock, and therefore buying and selling instructions.

Now, increasingly, being able to run these models quickly is not enough, and hedge funds are turning to artificial intelligence that can improve its own models using Bayesian reasoning: it updates the model parameters depending on how successful the model has been.

Algorithms are not inherently bad; some have even argued that they stabilized the market during the flash crash more than humans might have done. Indeed, the system is likely so complex already that the only solution we can feasibly give echoes Facebook’s response to concern over use of its platform: smarter algorithms.

But the febrile cauldron of Wall Street is best suited to the worst excesses of algorithmic design and use. There is a huge incentive to make algorithms extremely powerful in decision-making, and avoid any semblance of transparency.

To “beat the market”—which now embodies not only everyone’s knowledge about stocks, but also the activity of the best algorithms that are being deployed—you need to have an edge that no one else has.

This inevitably leads to a lack of transparency. Bernie Madoff seemed to have an edge, generating remarkably consistent returns: it turns out his edge was running a multi-billion-dollar Ponzi scheme.

Every day, more and more impenetrable connections are added to this complex network, adding to the glut of inscrutable decisions. It is part of a broader trend, motivated by the undeniable advantages of efficiency and profit, to place more and more decisions and actions in the hands of black-box algorithms whose behavior is often unpredictable, inexplicable, and unquestionable, even as they become ever more deeply ingrained into the system.

As the flash crash warns us, the consequences of doing this without sufficient understanding can rapidly spiral out of control.

Image Credit: ESB Professional / Shutterstock.com

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featured | Inc. presents: the 26 Most Fascinating Entrepreneurs

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publication: Inc.
section:

story title: 26 Most Fascinating Entrepreneurs
special label: no. 8 | Ray Kurzweil • Kurzweil Technologies + other companies
special deck: Because he’s Edison’s rightful heir.
author: by Adam Hanft
date:


— about the list —

Inc. magazine goes behind the scenes with 26 entrepreneurs who best exemplify: extraordinary drive, creativity, and passion for business. Our top 26 list — one for each year of Inc. — spans the gamut of the entrepreneurial world.

From names you know well: such as Richard Branson, Michael Dell, Martha Stewart.
To the names you don’t know well:

  • Tony Lee — a former janitor who bought out his steel manufacturing employer.
  • Craig Newmark — who’s the opposite of a dot-commer with his no-frills Craigslist site.

No matter what the accomplishment, each entrepreneur profiled here offers a fascinating case study in what it takes to thrive in today’s economy. We love them.

description: by Inc.



26 Most Fascinating Entrepreneurs

  1. link | Martha Stewart Martha Stewart Omnimedia
    because she took one for the team.
  2. link | Richard Branson — Virgin Group
    because he’s game for anything. In fact, everything.
  3. link | Michael Dell — Dell Computer
    for being brilliantly straightforward.
  4. link | Jim Sinegal — Costco
    because who knew a big box chain could have a generous soul?
  5. link | Diane von Furstenberg — Diane von Furstenberg Studio
    for staging an elegant come-back.
  6. Julie Azuma — Different Roads to Learning
    for offering hope + help to the parents of autistic children.
  7. Fritz Maytag, Anchor Brewing
    for setting limits.
  8. Ray Kurzweil — Kurzweil Technologies + other companies
    because he is Edison’s rightful heir.
  9. Craig Newmark  — Craigslist
    for putting the free in free markets.
  10. Jack Mitchell — Mitchells/Richards
    because his family business makes an art of customer service.
  11. Frank Robinson — Robinson Helicopter
    for whipping an entire industry into shape.
  12. Mark Melton — Melton Franchise Systems
    for giving immigrants their shot at the American dream.
  13. Michelle Cardinal & Tim O’Leary — Cmedia + Respond2
    for re-writing the rules for husband-and-wife teams.
  14. Mike Lazaridis — Research in Motion
    because someone had to stand up for all those frustrated engineers.
  15. Trip Hawkins — Electronics Arts + Digital Chocolate
    for still scrapping.
  16. Warren Brown — Cake Love + Love Cafe
    because only in America will someone quit a secure job as a lawyer to start a bakery.
  17. Muriel Siebert — Muriel Siebert & Co.
    for being a notable first with a worthy second act.
  18. Chuck Porter — Crispin, Porter + Bogusky
    for verging on reckless.
  19. Katrina Markoff — Vosges Haut
    for setting a completely unreasonable goal for her business.
  20. Barry Steinberg & Craig Sumerel — Direct Tire + Auto Service
    for showing the power of the peer group.
  21. Victoria Parham — Virtual Support Services
    for serving as a mentor to military spouses.
  22. Tom LaTour — Kimpton Hotels + Restaurants
    for staying at fleabag hotels so that we don’t have to.
  23. Mitchell Gold + Bob Williams — Mitchell Gold
    for creating a true comfort zone.
  24. Izzy & Coco Tihanyi — Surf Diva
    for kicking sand in the face of conventional wisdom.
  25. Tony Lee — Ring Masters
    for saving 16 jobs including his own.
  26. Rueben Martinez — Libreria Martinez Books + Art Galleries
    for simultaneously building a business + nurturing Latino culture.


— Number 8: Ray Kurzweil —


no. 8 | Ray Kurzweil Kurzweil Technologies + other companies

At age 17: Ray Kurzweil appeared on television game show I’ve Got A Secret with host Steve Allen. His secret? The piece of music he played had been composed entirely by a computer he invented. That early acclaim only hinted at the remarkable body of invention that Kurzweil would establish over the next 4 decades. Kurzweil said: “I’m excited by the link between dry formulas on a black-board and people’s lives.”

Starting in 1974, Kurzweil invented in rapid succession:

  • a device that recognized printed text
  • the flat-bed scanner
  • a way for machines to connect text to a recorded voice

Combining all 3 technologies, he developed the Kurzweil Reading Machine to assist blind and visually impaired people. His first customer was music legend Stevie Wonder, who called the reading machine “a breakthrough that changed my life.”

Kurzweil sold that business to Xerox co. in 1980. Then he and Stevie Wonder collaborated on a music synthesizer that can replicate the rich tones of a grand piano and other orchestra instruments. He sold that business in 1990. Kurzweil is working on a technology to help hedge funds make stock trades based on instant readings of the market.

They may seem wildly eclectic but Kurzweil’s businesses rely on one basic theme — pattern recognition. Kurzweil said: “I gather as much data as I can to develop patterns at every different level.” His ability to channel that notion into great businesses is a  remarkable pattern.


on the web | background

Wikipedia | Thomas Edison
Wikipedia | entrerpeneurship

* Edison is Thomas Alva Edison

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Why Most of Us Fail to Grasp Coming Exponential Gains in AI

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By now, most of us are familiar with Moore’s Law, the famous maxim that the development of computing power follows an exponential curve, doubling in price-performance (that is, speed per unit cost) every 18 months or so. When it comes to applying Moore’s Law to their own business strategies, however, even visionary thinkers frequently suffer from a giant “AI blind spot.”

I give a lot of talks to successful, strategically-minded business people who can see around corners in their own industries, yet they struggle to grasp what exponential improvement really means. And a lot is riding on this exponential curve, but one technology that is particularly benefiting from it is artificial intelligence.

Capturing Exponential Curves on Paper

One reason people do not grasp how rapidly artificial intelligence is developing is so simple it’s almost laughable: Exponential curves don’t fare well when we humans try to capture them on paper. For very practical reasons, it’s virtually impossible to fully depict the steep trajectory of an exponential curve in a small space such as a chart or a slide. Visually depicting the early stages of an exponential curve is easy. However, as the steeper part of the curve kicks in and numbers rapidly get larger, things get more challenging.

To solve this problem of inadequate visual space, we use a handy math trick known as a logarithm. Using what’s known as a “logarithmic scale,” we learned to squish exponential curves into submission.

Unfortunately, the widespread use of logarithmic scales can also cause myopia.

The way a logarithmic scale works is that each tick on a vertical y-axis corresponds not to a constant increment (as in a typical linear scale), but to a multiple, for example a factor of 100. The classic Moore’s Law chart below (Chart 1) uses a logarithmic scale to depict the exponential improvement in the cost of computing power (measured in calculations/second/dollar) over the past 120 years, from mechanical devices in 1900 to today’s powerful silicon-based GPUs.

Chart 1: Exponential improvements in the cost of calculation on a logarithmic scale. Source: Data provided by Ray Kurzweil and updated by DFJ (via Wikimedia Commons)

Now, logarithmic charts have served as a valuable form of shorthand for people who are cognizant of the visual distortion they introduce. In fact, a logarithmic scale is a handy and compact way to depict any curve that rises in a rapid and dramatic fashion over time.

However, logarithmic charts carry a huge, hidden cost: they fool the human eye.

By mathematically collapsing huge numbers, logarithmic charts make exponential increases appear linear. Because they squash unruly exponential growth curves into linear shapes, logarithmic charts make it easy for people to feel comfortable, even complacent, about the speed and magnitude of future exponential gains in computing power.

Our logical brain understands logarithmic charts. But our subconscious brain sees a linear curve and tunes out.

So, what’s an effective way to undo some of the strategic myopia caused by logarithmic charts? Part of the solution lies in going back to the original linear scale.

On Chart 2 below, I used the data to fit an exponential curve and then plotted it using a linear scale on the vertical axis. Once again, the vertical axis represents the processing speed (in gigaflops) that a single dollar can purchase, and the horizontal axis represents time. However, in Chart 2, each tick on the vertical axis corresponds to a simple linear increase of just one gigaflop (rather than an increase of a factor of 100 as in Chart 1). The term “FLOP” is a standard way to measure computing speed, meaning floating-point operations per second, hence FLOPS, megaFLOPS, gigaFLOPS, teraFLOPS, and so on).

hod-lipson-computing-speed-linear-scale
Chart 2: Moore’s Law depicted on a linear scale

Chart 2 shows the actual, real exponential curve that characterizes Moore’s Law. The way this chart is drawn, it’s easy for our human eyes to appreciate how rapidly computing price performance has increased over the past decade.

Yet, there’s something terribly wrong with Chart 2. To a naïve reader of this chart, it would appear as if over the course of the 20th century, the cost and performance of computers did not improve at all. Clearly, this is wrong.

Chart 2 shows how using a linear scale to demonstrate Moore’s Law over time can also be quite blinding. It can make the past appear flat, as if no progress has taken place until only very recently. In addition, the same linear-scale chart can also lead people to incorrectly conclude that their current vantage point in time represents a period of unique “almost vertical” technological progress.

This point leads me to the next major cause of chart-induced AI blindness: linear-scale charts can fool people into believing they live at the height of change.

The Myopia of Living in the Present

Let’s take another look at Chart 2. When viewed from the year 2018, the previous doublings of price-performance that took place every decade throughout most of the 20th century appear flat, almost inconsequential. A person looking at Chart 2 might say to themselves, “Boy, am I lucky to be living today. I remember the year 2009, when I thought my new iPhone was fast! I had no idea how slow it was. Now I’ve finally reached the exciting vertical part!”

I’ve heard people say that we have just passed the “elbow of the hockey stick.”

But there is no such transition point.

Any exponential curve is self-similar—that is, the shape of the curve in the future looks the same as it did in the past. Below, Chart 3 again shows the exponential curve of Moore’s Law on a linear scale, but this time from the perspective of the year 2028. The curve assumes that the growth we have experienced in the past 100 years will continue for at least 10 more years. This chart shows that in 2028, one dollar will buy about 200 gigaflops of computing power.

However, Chart 3 also represents a potential analytical quagmire.

hod-lipson-computing-speed-linear-scale-with-2028
Chart 3: Moore’s Law on a linear scale

Look closely at where today’s computing power (the year 2018) lies on the curve depicted in Chart 3. From the vantage point of a person living and working in the future year 2028, it would appear that there was virtually no improvement in computing power even over the course of the early 21st century. It looks like computing devices used in the year 2018 were just a tiny bit more powerful than those used, say, in 1950. An observer could also conclude that the current year, 2028, represents the culmination of Moore’s Law, where progress in computing power finally takes off.

Every year, I could re-create Chart 3, changing only the timespan depicted. The shape of the curve would be identical, only the ticks on the vertical scale would change. Note how the shape of Charts 2 and 3 looks identical, except for the vertical scale. On each such chart, every past point would be flat when viewed from the future, and every future point would appear to be a sharp departure from the past. Alas, such mis-perception would be the path to flawed business strategy, at least when it comes to artificial intelligence.

What Does This Mean?

Exponential rates of change are difficult for the human mind to comprehend and for the eye to see. Exponential curves are unique in the sense that they are mathematically self-similar at every point. What this means is that an ever-doubling curve has no flat part, no ascending part, and none of the “elbow” and “hockey stick” bends many business people are used to talking about. If you zoom in on any portion in the past or the future, its shape looks identical.

As Moore’s Law continues to make itself felt, it’s tempting to think at this very moment we’re reaching a unique period of great change in the development of artificial intelligence (or any other technology that rides on Moore’s Law). However, as long as processing power continues to follow an exponential price-performance curve, each future generation will likely look back on the past as an era of relatively little progress. In turn, the converse will also remain true: each current generation will look 10 years into its future and fail to appreciate just how much advancement in artificial intelligence is yet to come.

The challenge, then, for anyone planning for a future driven by computing’s exponential growth is fighting their brain’s flawed interpretations. Hard as it may sound, you need to hold all three charts in your mind at once—the visual consistency of the logarithmic chart and the drama but deceptive scale of the linear charts—to truly appreciate the power of exponential growth. Because the past will always look flat, and the future will always look vertical.

Image Credit: Mauro Carli / Shutterstock.com

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This Week’s Awesome Stories From Around the Web (Through July 14)

http://img.youtube.com/vi/UYFWjUj4RPQ/0.jpg

COMPUTING

The US May Have Just Pulled Even With China in the Race to Build Supercomputing’s Next Big Thing
Martin Giles | MIT Technology Review
“While there’s plenty of national pride wrapped up in the race to get to exascale first, the work Yelick and other researchers are doing is a reminder that raw exascale computing power isn’t the true test of success here; what really matters is how well it’s harnessed to solve some of the world’s toughest problems.”

MOONSHOTS

Inside X, the Moonshot Factory Racing to Build the Next Google
Alex Davies | Wired
“By launching Loon and Wing into the world, X will soon discover whether it can effectively hatch new Googles—and put Alphabet at the head of industries that don’t yet exist.”

3D PRINTING

3D Printing Is the Future of Factories (For Real This Time)
Jason Pontin | Wired
“The process is one hundred times faster and 80 percent cheaper than laser-based additive manufacturing machines. GE’s machines might make 12 complexly shaped hydraulic manifolds in a day; during that time, Desktop Metal could manufacture 546.”

FUTURE OF FOOD

Is Lab-Grown Meat Really Meat?
Rose Eveleth | Slate
“But there’s another, more immediate battle heating up between the cattle industry and these new entrants into the meaty ring. So buckle up and put on your wonkiest hat, because the labeling war is about to begin.”

GAMING

The Most Important Video Game on the Planet
Brian Feldman | NY Mag
“Analysts estimate that Fortnite is currently raking in more than $300 million a month, and has made its maker, Epic Games, more than $1.2 billion since its battle royale mode launched in late September. That’s all from a game that’s free to download and play unrestricted.”

DIGITAL ART

Become One With Art at Tokyo’s Psychedelic Digital Museum
Steve Dent | Engadget
“Every floor and wall is covered by light, and the images react to the presence of the attendees. While each installation is technically separate, they do bleed together as, for example, fish swim from one room to another.”

Image Source: MORI Building DIGITAL ART MUSEUM teamLab Borderless

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resource | the Nova podcast series

resource | the Nova podcast series

July 14, 2000

— resource —

format: podcast
broadcast: PBS
series title: the Nova podcast series


list | the Nova podcast series

Based on the award-winning TV show Nova.

show title: the Nova podcast
about: brief science stories, from hurricanes to mummies.

show title: the Nova science now podcast
about: conversations w. scientists + journalists.

show title: the Nova vodcast
about: videos from television, animation, reports

show title: the Nova E=mc² podcast
about: Nova asks 10 top physicists to explain Albert Einstein PhD’s famous equation

 

* PBS is the Public Broadcasting Service

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