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#431906 Low-Cost Soft Robot Muscles Can Lift 200 ...

Jerky mechanical robots are staples of science fiction, but to seamlessly integrate into everyday life they’ll need the precise yet powerful motor control of humans. Now scientists have created a new class of artificial muscles that could soon make that a reality.
The advance is the latest breakthrough in the field of soft robotics. Scientists are increasingly designing robots using soft materials that more closely resemble biological systems, which can be more adaptable and better suited to working in close proximity to humans.
One of the main challenges has been creating soft components that match the power and control of the rigid actuators that drive mechanical robots—things like motors and pistons. Now researchers at the University of Colorado Boulder have built a series of low-cost artificial muscles—as little as 10 cents per device—using soft plastic pouches filled with electrically insulating liquids that contract with the force and speed of mammalian skeletal muscles when a voltage is applied to them.

Three different designs of the so-called hydraulically amplified self-healing electrostatic (HASEL) actuators were detailed in two papers in the journals Science and Science Robotics last week. They could carry out a variety of tasks, from gently picking up delicate objects like eggs or raspberries to lifting objects many times their own weight, such as a gallon of water, at rapid repetition rates.
“We draw our inspiration from the astonishing capabilities of biological muscle,” Christoph Keplinger, an assistant professor at UC Boulder and senior author of both papers, said in a press release. “Just like biological muscle, HASEL actuators can reproduce the adaptability of an octopus arm, the speed of a hummingbird and the strength of an elephant.”
The artificial muscles work by applying a voltage to hydrogel electrodes on either side of pouches filled with liquid insulators, which can be as simple as canola oil. This creates an attraction between the two electrodes, pulling them together and displacing the liquid. This causes a change of shape that can push or pull levers, arms or any other articulated component.
The design is essentially a synthesis of two leading approaches to actuating soft robots. Pneumatic and hydraulic actuators that pump fluids around have been popular due to their high forces, easy fabrication and ability to mimic a variety of natural motions. But they tend to be bulky and relatively slow.
Dielectric elastomer actuators apply an electric field across a solid insulating layer to make it flex. These can mimic the responsiveness of biological muscle. But they are not very versatile and can also fail catastrophically, because the high voltages required can cause a bolt of electricity to blast through the insulator, destroying it. The likelihood of this happening increases in line with the size of their electrodes, which makes it hard to scale them up. By combining the two approaches, researchers get the best of both worlds, with the power, versatility and easy fabrication of a fluid-based system and the responsiveness of electrically-powered actuators.
One of the designs holds particular promise for robotics applications, as it behaves a lot like biological muscle. The so-called Peano-HASEL actuators are made up of multiple rectangular pouches connected in series, which allows them to contract linearly, just like real muscle. They can lift more than 200 times their weight, but being electrically powered, they exceed the flexing speed of human muscle.
As the name suggests, the HASEL actuators are also self-healing. They are still prone to the same kind of electrical damage as dielectric elastomer actuators, but the liquid insulator is able to immediately self-heal by redistributing itself and regaining its insulating properties.
The muscles can even monitor the amount of strain they’re under to provide the same kind of feedback biological systems would. The muscle’s capacitance—its ability to store an electric charge—changes as the device stretches, which makes it possible to power the arm while simultaneously measuring what position it’s in.
The researchers say this could imbue robots with a similar sense of proprioception or body-awareness to that found in plants and animals. “Self-sensing allows for the development of closed-loop feedback controllers to design highly advanced and precise robots for diverse applications,” Shane Mitchell, a PhD student in Keplinger’s lab and an author on both papers, said in an email.
The researchers say the high voltages required are an ongoing challenge, though they’ve already designed devices in the lab that use a fifth of the voltage of those features in the recent papers.
In most of their demonstrations, these soft actuators were being used to power rigid arms and levers, pointing to the fact that future robots are likely to combine both rigid and soft components, much like animals do. The potential applications for the technology range from more realistic prosthetics to much more dextrous robots that can work easily alongside humans.
It will take some work before these devices appear in commercial robots. But the combination of high-performance with simple and inexpensive fabrication methods mean other researchers are likely to jump in, so innovation could be rapid.
Image Credit: Keplinger Research Group/University of Colorado Continue reading

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#431859 Digitized to Democratized: These Are the ...

“The Six Ds are a chain reaction of technological progression, a road map of rapid development that always leads to enormous upheaval and opportunity.”
–Peter Diamandis and Steven Kotler, Bold
We live in incredible times. News travels the globe in an instant. Music, movies, games, communication, and knowledge are ever-available on always-connected devices. From biotechnology to artificial intelligence, powerful technologies that were once only available to huge organizations and governments are becoming more accessible and affordable thanks to digitization.
The potential for entrepreneurs to disrupt industries and corporate behemoths to unexpectedly go extinct has never been greater.
One hundred or fifty or even twenty years ago, disruption meant coming up with a product or service people needed but didn’t have yet, then finding a way to produce it with higher quality and lower costs than your competitors. This entailed hiring hundreds or thousands of employees, having a large physical space to put them in, and waiting years or even decades for hard work to pay off and products to come to fruition.

“Technology is disrupting traditional industrial processes, and they’re never going back.”

But thanks to digital technologies developing at exponential rates of change, the landscape of 21st-century business has taken on a dramatically different look and feel.
The structure of organizations is changing. Instead of thousands of employees and large physical plants, modern start-ups are small organizations focused on information technologies. They dematerialize what was once physical and create new products and revenue streams in months, sometimes weeks.
It no longer takes a huge corporation to have a huge impact.
Technology is disrupting traditional industrial processes, and they’re never going back. This disruption is filled with opportunity for forward-thinking entrepreneurs.
The secret to positively impacting the lives of millions of people is understanding and internalizing the growth cycle of digital technologies. This growth cycle takes place in six key steps, which Peter Diamandis calls the Six Ds of Exponentials: digitization, deception, disruption, demonetization, dematerialization, and democratization.
According to Diamandis, cofounder and chairman of Singularity University and founder and executive chairman of XPRIZE, when something is digitized it begins to behave like an information technology.

Newly digitized products develop at an exponential pace instead of a linear one, fooling onlookers at first before going on to disrupt companies and whole industries. Before you know it, something that was once expensive and physical is an app that costs a buck.
Newspapers and CDs are two obvious recent examples. The entertainment and media industries are still dealing with the aftermath of digitization as they attempt to transform and update old practices tailored to a bygone era. But it won’t end with digital media. As more of the economy is digitized—from medicine to manufacturing—industries will hop on an exponential curve and be similarly disrupted.
Diamandis’s 6 Ds are critical to understanding and planning for this disruption.
The 6 Ds of Exponential Organizations are Digitized, Deceptive, Disruptive, Demonetized, Dematerialized, and Democratized.

Diamandis uses the contrasting fates of Kodak and Instagram to illustrate the power of the six Ds and exponential thinking.
Kodak invented the digital camera in 1975, but didn’t invest heavily in the new technology, instead sticking with what had always worked: traditional cameras and film. In 1996, Kodak had a $28 billion market capitalization with 95,000 employees.
But the company didn’t pay enough attention to how digitization of their core business was changing it; people were no longer taking pictures in the same way and for the same reasons as before.
After a downward spiral, Kodak went bankrupt in 2012. That same year, Facebook acquired Instagram, a digital photo sharing app, which at the time was a startup with 13 employees. The acquisition’s price tag? $1 billion. And Instagram had been founded only 18 months earlier.
The most ironic piece of this story is that Kodak invented the digital camera; they took the first step toward overhauling the photography industry and ushering it into the modern age, but they were unwilling to disrupt their existing business by taking a risk in what was then uncharted territory. So others did it instead.
The same can happen with any technology that’s just getting off the ground. It’s easy to stop pursuing it in the early part of the exponential curve, when development appears to be moving slowly. But failing to follow through only gives someone else the chance to do it instead.
The Six Ds are a road map showing what can happen when an exponential technology is born. Not every phase is easy, but the results give even small teams the power to change the world in a faster and more impactful way than traditional business ever could.
Image Credit: Mohammed Tareq / Shutterstock Continue reading

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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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#430868 These 7 Forces Are Changing the World at ...

It was the Greek philosopher Heraclitus who first said, “The only thing that is constant is change.”
He was onto something. But even he would likely be left speechless at the scale and pace of change the world has experienced in the past 100 years—not to mention the past 10.
Since 1917, the global population has gone from 1.9 billion people to 7.5 billion. Life expectancy has more than doubled in many developing countries and risen significantly in developed countries. In 1917 only eight percent of homes had phones—in the form of landline telephones—while today more than seven in 10 Americans own a smartphone—aka, a supercomputer that fits in their pockets.
And things aren’t going to slow down anytime soon. In a talk at Singularity University’s Global Summit this week in San Francisco, SU cofounder and chairman Peter Diamandis told the audience, “Tomorrow’s speed of change will make today look like we’re crawling.” He then shared his point of view about some of the most important factors driving this accelerating change.
Peter Diamandis at Singularity University’s Global Summit in San Francisco.
Computation
In 1965, Gordon Moore (cofounder of Intel) predicted computer chips would double in power and halve in cost every 18 to 24 months. What became known as Moore’s Law turned out to be accurate, and today affordable computer chips contain a billion or more transistors spaced just nanometers apart.
That means computers can do exponentially more calculations per second than they could thirty, twenty, or ten years ago—and at a dramatically lower cost. This in turn means we can generate a lot more information, and use computers for all kinds of applications they wouldn’t have been able to handle in the past (like diagnosing rare forms of cancer, for example).
Convergence
Increased computing power is the basis for a myriad of technological advances, which themselves are converging in ways we couldn’t have imagined a couple decades ago. As new technologies advance, the interactions between various subsets of those technologies create new opportunities that accelerate the pace of change much more than any single technology can on its own.
A breakthrough in biotechnology, for example, might spring from a crucial development in artificial intelligence. An advance in solar energy could come about by applying concepts from nanotechnology.
Interface Moments
Technology is becoming more accessible even to the most non-techy among us. The internet was once the domain of scientists and coders, but these days anyone can make their own web page, and browsers make those pages easily searchable. Now, interfaces are opening up areas like robotics or 3D printing.
As Diamandis put it, “You don’t need to know how to code to 3D print an attachment for your phone. We’re going from mind to materialization, from intentionality to implication.”
Artificial intelligence is what Diamandis calls “the ultimate interface moment,” enabling everyone who can speak their mind to connect and leverage exponential technologies.
Connectivity
Today there are about three billion people around the world connected to the internet—that’s up from 1.8 billion in 2010. But projections show that by 2025 there will be eight billion people connected. This is thanks to a race between tech billionaires to wrap the Earth in internet; Elon Musk’s SpaceX has plans to launch a network of 4,425 satellites to get the job done, while Google’s Project Loon is using giant polyethylene balloons for the task.
These projects will enable five billion new minds to come online, and those minds will have access to exponential technologies via interface moments.
Sensors
Diamandis predicts that after we establish a 5G network with speeds of 10–100 Gbps, a proliferation of sensors will follow, to the point that there’ll be around 100,000 sensors per city block. These sensors will be equipped with the most advanced AI, and the combination of these two will yield an incredible amount of knowledge.
“By 2030 we’re heading towards 100 trillion sensors,” Diamandis said. “We’re heading towards a world in which we’re going to be able to know anything we want, anywhere we want, anytime we want.” He added that tens of thousands of drones will hover over every major city.
Intelligence
“If you think there’s an arms race going on for AI, there’s also one for HI—human intelligence,” Diamandis said. He explained that if a genius was born in a remote village 100 years ago, he or she would likely not have been able to gain access to the resources needed to put his or her gifts to widely productive use. But that’s about to change.
Private companies as well as military programs are working on brain-machine interfaces, with the ultimate aim of uploading the human mind. The focus in the future will be on increasing intelligence of individuals as well as companies and even countries.
Wealth Concentration
A final crucial factor driving mass acceleration is the increase in wealth concentration. “We’re living in a time when there’s more wealth in the hands of private individuals, and they’re willing to take bigger risks than ever before,” Diamandis said. Billionaires like Mark Zuckerberg, Jeff Bezos, Elon Musk, and Bill Gates are putting millions of dollars towards philanthropic causes that will benefit not only themselves, but humanity at large.
What It All Means
One of the biggest implications of the rate at which the world is changing, Diamandis said, is that the cost of everything is trending towards zero. We are heading towards abundance, and the evidence lies in the reduction of extreme poverty we’ve already seen and will continue to see at an even more rapid rate.
Listening to Diamandis’ optimism, it’s hard not to find it contagious.

“The world is becoming better at an extraordinary rate,” he said, pointing out the rises in literacy, democracy, vaccinations, and life expectancy, and the concurrent decreases in child mortality, birth rate, and poverty.
“We’re alive during a pivotal time in human history,” he concluded. “There is nothing we don’t have access to.”
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#430668 Why Every Leader Needs to Be Obsessed ...

This article is part of a series exploring the skills leaders must learn to make the most of rapid change in an increasingly disruptive world. The first article in the series, “How the Most Successful Leaders Will Thrive in an Exponential World,” broadly outlines four critical leadership skills—futurist, technologist, innovator, and humanitarian—and how they work together.
Today’s post, part five in the series, takes a more detailed look at leaders as technologists. Be sure to check out part two of the series, “How Leaders Dream Boldly to Bring New Futures to Life,” part three of the series, “How All Leaders Can Make the World a Better Place,” and part four of the series, “How Leaders Can Make Innovation Everyone’s Day Job”.
In the 1990s, Tower Records was the place to get new music. Successful and popular, the California chain spread far and wide, and in 1998, they took on $110 million in debt to fund aggressive further expansion. This wasn’t, as it turns out, the best of timing.
The first portable digital music player went on sale the same year. The following year brought Napster, a file sharing service allowing users to freely share music online. By 2000, Napster hosted 20 million users swapping songs. Then in 2001, Apple’s iPod and iTunes arrived, and when the iTunes Music Store opened in 2003, Apple sold over a million songs the first week.
As music was digitized, hard copies began to go out of style, and sales and revenue declined.
Tower first filed for bankruptcy in 2004 and again (for the last time) in 2006. The internet wasn’t the only reason for Tower’s demise. Mismanagement and price competition from electronics retailers like Best Buy also played a part. Still, today, the vast majority of music is purchased or streamed entirely online, and record stores are for the most part a niche market.
The writing was on the wall, but those impacted most had trouble reading it.
Why is it difficult for leaders to see technological change coming and right the ship before it’s too late? Why did Tower go all out on expansion just as the next big thing took the stage?
This is one story of many. Digitization has moved beyond music and entertainment, and now many big retailers operating physical stores are struggling to stay relevant. Meanwhile, the pace of change is accelerating, and new potentially disruptive technologies are on the horizon.
More than ever, leaders need to develop a strong understanding of and perspective on technology. They need to survey new innovations, forecast their pace, gauge the implications, and adopt new tools and strategy to change course as an industry shifts, not after it’s shifted.
Simply, leaders need to adopt the mindset of a technologist. Here’s what that means.
Survey the Landscape
Nurturing curiosity is the first step to understanding technological change. To know how technology might disrupt your industry, you have to know what’s in the pipeline and identify which new inventions are directly or indirectly related to your industry.
Becoming more technologically minded takes discipline and focus as well as unstructured time to explore the non-obvious connections between what is right in front of us and what might be. It requires a commitment to ongoing learning and discovery.
Read outside your industry and comfort zone, not just Fast Company and Wired, but Science and Nature to expand your horizons. Identify experts with the ability to demystify specific technology areas—many have a solid following on Twitter or a frequently cited blog.
But it isn’t all about reading. Consider going where the change is happening too.
Visit one of the technology hubs around the world or a local university research lab in your own back yard. Or bring the innovation to you by building an internal exploration lab stocked with the latest technologies, creating a technology advisory board, hosting an internal innovation challenge, or a local pitch night where aspiring entrepreneurs can share their newest ideas.
You might even ask the crowd by inviting anyone to suggest what innovation is most likely to disrupt your product, service, or sector. And don’t hesitate to engage younger folks—the digital natives all around you—by asking questions about what technology they are using or excited about. Consider going on a field trip with them to see how they use technology in different aspects of their lives. Invite the seasoned executives on your team to explore long-term “reverse mentoring” with someone who can expose them to the latest technology and teach them to use it.
Whatever your strategy, the goal should be to develop a healthy obsession with technology.
By exploring fresh perspectives outside traditional work environments and then giving ourselves permission to see how these new ideas might influence existing products and strategies, we have a chance to be ready for what we’re not ready for—but is likely right around the corner.
Estimate the Pace of Progress
The next step is forecasting when a technology will mature.
One of the most challenging aspects of the changes underway is that in many technology arenas, we are quickly moving from a linear to an exponential pace. It is hard enough to envision what is needed in an industry buffeted by progress that is changing 10% per year, but what happens when technological progress doubles annually? That is another world altogether.
This kind of change can be deceiving. For example, machine learning and big data are finally reaching critical momentum after more than twenty years of being right around the corner. The advances in applications like speech and image recognition that we’ve seen in recent years dwarf what came before and many believe we’ve just begun to understand the implications.
Even as we begin to embrace disruptive change in one technology arena, far more exciting possibilities unfold when we explore how multiple arenas are converging.
Artificial intelligence and big data are great examples. As Hod Lipson, professor of Mechanical Engineering and Data Science at Columbia University and co-author of Driverless: Intelligent Cars and the Road Ahead, says, “AI is the engine, but big data is the fuel. They need each other.”
This convergence paired with an accelerating pace makes for surprising applications.
To keep his research lab agile and open to new uses of advancing technologies, Lipson routinely asks his PhD students, “How might AI disrupt this industry?” to prompt development of applications across a wide spectrum of sectors from healthcare to agriculture to food delivery.
Explore the Consequences
New technology inevitably gives rise to new ethical, social, and moral questions that we have never faced before. Rather than bury our heads in the sand, as leaders we must explore the full range of potential consequences of whatever is underway or still to come.
We can add AI to kids’ toys, like Mattel’s Hello Barbie or use cutting-edge gene editing technology like CRISPR-Cas9 to select for preferred gene sequences beyond basic health. But just because we can do something doesn’t mean we should.
Take time to listen to skeptics and understand the risks posed by technology.
Elon Musk, Stephen Hawking, Steve Wozniak, Bill Gates, and other well-known names in science and technology have expressed concern in the media and via open letters about the risks posed by AI. Microsoft’s CEO, Satya Nadella, has even argued tech companies shouldn’t build artificial intelligence systems that will replace people rather than making them more productive.
Exploring unintended consequences goes beyond having a Plan B for when something goes wrong. It requires broadening our view of what we’re responsible for. Beyond customers, shareholders, and the bottom line, we should understand how our decisions may impact employees, communities, the environment, our broader industry, and even our competitors.
The minor inconvenience of mitigating these risks now is far better than the alternative. Create forums to listen to and value voices outside of the board room and C-Suite. Seek out naysayers, ethicists, community leaders, wise elders, and even neophytes—those who may not share our preconceived notions of right and wrong or our narrow view of our role in the larger world.
The question isn’t: If we build it, will they come? It’s now: If we can build it, should we?
Adopt New Technologies and Shift Course
The last step is hardest. Once you’ve identified a technology (or technologies) as a potential disruptor and understand the implications, you need to figure out how to evolve your organization to make the most of the opportunity. Simply recognizing disruption isn’t enough.
Take today’s struggling brick-and-mortar retail business. Online shopping isn’t new. Amazon isn’t a plucky startup. Both have been changing how we buy stuff for years. And yet many who still own and operate physical stores—perhaps most prominently, Sears—are now on the brink of bankruptcy.
There’s hope though. Netflix began as a DVD delivery service in the 90s, but quickly realized its core business didn’t have staying power. It would have been laughable to stream movies when Netflix was founded. Still, computers and bandwidth were advancing fast. In 2007, the company added streaming to its subscription. Even then it wasn’t a totally compelling product.
But Netflix clearly saw a streaming future would likely end their DVD business.
In recent years, faster connection speeds, a growing content library, and the company’s entrance into original programming have given Netflix streaming the upper hand over DVDs. Since 2011, DVD subscriptions have steadily declined. Yet the company itself is doing fine. Why? It anticipated the shift to streaming and acted on it.
Never Stop Looking for the Next Big Thing
Technology is and will increasingly be a driver of disruption, destabilizing entrenched businesses and entire industries while also creating new markets and value not yet imagined.
When faced with the rapidly accelerating pace of change, many companies still default to old models and established practices. Leading like a technologist requires vigilant understanding of potential sources of disruption—what might make your company’s offering obsolete? The answers may not always be perfectly clear. What’s most important is relentlessly seeking them.
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