Tag Archives: quantum computing

A Brave Post-Coronavirus New World

A Brave Post-Coronavirus New World

GUEST POST from Greg Satell

In 1973, in the wake of the Arab defeat in the Yom Kippur war with Israel, OPEC instituted an oil embargo on America and its allies. The immediate effects of the crisis was a surge in gas prices and a recession in the west. The ripple effects, however, were far more complex and played out over decades.

The rise in oil prices brought much needed hard currency to the Soviet Union, prolonging its existence and setting the stage for its later demise. The American auto industry, with its passion for big, gas guzzling cars, lost ground to the emergent. The new consciousness of conservation led to the establishment of the Department of Energy.

Today the Covid-19 crisis has given a shock to the system and we’re at a similar inflection point. The most immediate effects have been economic recession and the rapid adoption of digital tools, such as video conferencing. Over the next decade or so, however, the short-term impacts will combine with other more longstanding trends to reshape technology and society.

Pervasive Transformation

We tend to think about innovation as if it were a single event, but the truth is that it’s a process of a process of discovery, engineering and transformation, which takes decades to run its course. For example, Alan Turing discovered the principles of a universal computer in 1936, but it wasn’t until the 1950s and 60s that digital computers became commercially available.

Even then, digital technology, didn’t really begin to become truly transformational until the mid-90s. By this time, it was well understood enough to make the leap from highly integrated systems to modular ecosystems, making the technology cheaper, more functional and more reliable. The number of applications exploded and the market grew quickly.

Still, as the Covid-19 crisis has made clear, we’ve really just been scratching the surface. Although digital technology certainly accelerated the pace of work, it did fairly little to fundamentally change the nature of it. People still commuted to work in an office, where they would attend meetings in person, losing hours of productive time each and every day.

Over the next decade, we will see pervasive transformation. As Mark Zuckerberg has pointed out, once people can work remotely, they can work from anywhere, which will change the nature of cities. Instead of “offsite” meetings, we may very well have “onsite” meetings where people from their home cities over travel to headquarters to do more active collaboration.

These trends will combine with nascent technologies like artificial intelligence and blockchain to revolutionize business processes and supply chains. Organizations that cannot adopt key technologies will very likely find themselves unable to compete.

The Rise of Heterogeneous Computing

The digital age did not begin with personal computers in the 70s and 80s, but started back in the 1950s with the shift from electromechanical calculating machines to transistor based mainframes. However, because so few people used computers back then—they were largely relegated to obscure back office tasks and complex scientific calculations—the transformation took place largely out of public view.

A similar process is taking place today with new architectures such as quantum and neuromorphic computing. While these technologies are not yet commercially viable, they are advancing quickly and will eventually become thousands, if not millions, of times more effective than digital systems.

However, what’s most important to understand is that they are fundamentally different from digital computers and from each other. Quantum computers will create incredibly large computing spaces that will handle unimaginable complexity. Neuromorphoic systems, based on the human brain, will be massively powerful, vastly more efficient and more responsive.

Over the next decade we’ll be shifting to a heterogeneous computing environment, where we use different architectures for different tasks. Most likely, we’ll still use digital technology as an interface to access systems, but increasingly performance will be driven by more advanced architectures.

A Shift From Bits to Atoms

The digital revolution created a virtual world. My generation was the first to grow up with video games and our parents worried that we were becoming detached from reality. Then computers entered offices and Dan Bricklin created Visicalc, the first spreadsheet program. Eventually smartphones and social media appeared and we began spending almost as much time in the virtual world as we did in the physical one.

Essentially, what we created was a simulation economy. We could experiment with business models in our computers, find flaws and fix them before they became real. Computer-aided design (CAD) software allowed us to quickly and cheaply design products in bits before we got down to the hard, slow work of shaping atoms. Because it’s much cheaper to fail in the virtual world than the physical one, this made our economy more efficient.

Today we’re doing similar things at the molecular level. For example, digital technology was combined with synthetic biology to quickly sequence the Covid-19 virus. These same technologies then allowed scientists to design vaccines in days and to bring them to market in less than a year.

A parallel revolution is taking in materials science, while at the same time digital technology is beginning to revolutionize traditional industries such as manufacturing and agriculture. The expanded capabilities of heterogeneous computing will accelerate these trends over the next few decades.

What’s important to understand is that we spend vastly more money on atoms than bits. Even at this advanced stage, information technologies only make up about 6% of GDP in advanced economies. Clearly, there is a lot more opportunity in the other 94%, so the potential of the post-digital world is likely to far outstrip anything we’ve seen in our lifetimes.

Collaboration is the New Competitive Advantage

Whenever I think back to when we got that first computer back in the 1980s, I marvel at how different the world was then. We didn’t have email or mobile phones, so unless someone was at home or in the office, they were largely unreachable. Without GPS, we had to either remember where things were or ask for directions.

These technologies have clearly changed our lives dramatically, but they were also fairly simple. Email, mobile and GPS were largely standalone technologies. There were, of course, technical challenges, but these were relatively narrow. The “killer apps” of the post-digital era will require a much higher degree of collaboration over a much more diverse set of skills.

To understand how different this new era of innovation will be, consider how IBM developed the PC. Essentially, they sent some talented engineers to Boca Raton for a year and, in that time, developed a marketable product. For quantum computing, however, it is building a vast network, including national labs, research universities, startups and industrial partners.

The same will be true of the post-Covid world. It’s no accident that Zoom has become the killer app of the pandemic. The truth is that the challenges we will face over the next decade will be far too complex for any one organization to tackle it alone. That’s why collaboration is becoming the new competitive advantage. Power will reside not at the top of hierarchies, but at the center of networks and ecosystems.

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

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Competing in a New Era of Innovation

Competing in a New Era of Innovation

GUEST POST from Greg Satell

In 1998, the dotcom craze was going at full steam and it seemed like the entire world was turning upside down. So people took notice when economist Paul Krugman wrote that “by 2005 or so, it will become clear that the internet’s impact on the economy has been no greater than the fax machine’s.”

He was obviously quite a bit off base, but these types of mistakes are incredibly common. As the futurist Roy Amara famously put it, “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” The truth is that it usually takes about 30 years for a technology to go from an initial discovery to a measurable impact.

Today, as we near the end of the digital age and enter a new era of innovation, Amara’s point is incredibly important to keep in mind. New technologies, such as quantum computing, blockchain and gene editing will be overhyped, but really will change the world, eventually. So we need to do more than adapt, we need to prepare for a future we can’t see yet.

Identify A “Hair-On-Fire” Use Case

Today we remember the steam engine for powering factories and railroads. In the process, it made the first industrial revolution possible. Yet that’s not how it started out. Its initial purpose was to pump water out of coal mines. At the time, it would have been tough to get people to imagine a factory that didn’t exist yet, but pretty easy for owners to see that their mine was flooded.

The truth is that innovation is never really about ideas, it’s about solving problems. So when a technology is still nascent, doesn’t gain traction in a large, established market, which by definition is already fairly well served, but in a hair-on-fire use case — a problem that somebody needs solved so badly that they almost literally have their hair on fire.

Early versions of the steam engine, such as Thomas Newcomen’s version, didn’t work well and were ill-suited to running factories or driving locomotives. Still, flooded mines were a major problem, so many were more tolerant of glitches and flaws. Later, after James Watt perfected the steam engine, it became more akin to technology that remember now.

We can see the same principle at work today. Blockchain has not had much impact as an alternative currency, but has gained traction optimizing supply chains. Virtual reality has not really caught on in the entertainment industry, but is making headway in corporate training. That’s probably not where those technologies will end up, but it’s how they make money now.

So in the early stages of a technology, don’t try to imagine how a perfected version fit in, find a problem that somebody needs solved so badly right now that they are willing to put up with some inconvenience.

The truth is that the “next big thing” never turns out like people think it will. Putting a man on the moon, for example, didn’t lead to flying cars like in the Jetsons, but instead to satellites that bring events to us from across the world, help us navigate to the corner store and call our loved ones from a business trip.

Build A Learning Curve

Things that change the world always start out arrive out of context, for the simple reason that the world hasn’t changed yet. So when a new technology first appears, we don’t really know how to use it. It takes time to learn how to leverage its advantages to create an impact.

Consider electricity, which as the economist Paul David explained in a classic paper, was first used in factories to cut down on construction costs (steam engines were heavy and needed extra bracing). What wasn’t immediately obvious was that electricity allowed factories to be designed to optimize workflow, rather than having to be arranged around the power source.

We can see the same forces at work today. Consider Amazon’s recent move to offer quantum computing to its customers through the cloud, even though the technology is so primitive that it has no practical application. Nevertheless, it is potentially so powerful—and so different from digital computing—that firms are willing to pay for the privilege of experimenting with it.

The truth is that it’s better to prepare than it is to adapt. When you are adapting you are, by definition, already behind. That’s why it’s important to build a learning curve early, before a technology has begun to impact your business.

Beware Of Switching Costs

When we look back today, it seems incredible that it took decades for factories to switch from steam to electricity. Besides the extra construction costs to build extra bracing, steam engines were dirty and inflexible. Every machine in the factory needed to be tied to one engine, so if one broke down or needed maintenance, the whole factory had to be shut down.

However, when you look at the investment from the perspective of a factory owner, things aren’t so clear cut. While electricity was relatively more attractive when building a new factory, junking an existing facility to make way for a new technology didn’t make as much sense. So most factory owners kept what they had.

These types of switching costs still exist today. Consider neuromorphic chips, which are based on the architecture of the human brain and therefore highly suited to artificial intelligence. They are also potentially millions of times more energy efficient than conventional chips. However, existing AI chips also perform very well, can be manufactured in conventional fabs and run conventional AI algorithms, so neuromorphic chips haven’t caught on yet.

All too often, when a new technology emerges we only look at how its performance compares to what exists today and ignore the importance of switching costs—both real and imagined. That’s a big part of the reason we underestimate how long a technology takes to gain traction and underestimate how much impact it will have in the long run.

Find Your Place In The Ecosystem

We tend to see history through the lens of inventions: Watt and his steam engine. Edison and his light bulb. Ford and his assembly line. Yet building a better mousetrap is never enough to truly change the world. Besides the need to identify a use case, build a learning curve and overcome switching costs, every new technology needs an ecosystem to truly drive the future.

Ford’s automobiles needed roads and gas stations, which led to supermarkets, shopping malls and suburbs. Electricity needed secondary inventions, such as home appliances and radios, which created a market for skilled technicians. It is often in the ecosystem, rather than the initial invention, where most of the value is produced.

Today, we can see similar ecosystems beginning to form around emerging technologies. The journal Nature published an analysis which showed that over $450 million was invested in more than 50 quantum startups between 2012 and 2018, but only a handful are actually making quantum computers. The rest are helping to build out the ecosystem.

So for most of us, the opportunities in the post-digital era won’t be creating new technologies themselves, but in the ecosystems they create. That’s where we’ll see new markets emerge, new jobs created and new fortunes to be made.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Importance of Long-Term Innovation

Importance of Long-Term Innovation

GUEST POST from Greg Satell

Scientists studying data from Mars recently found that the red planet may have oceans worth of water embedded in its crust in addition to the ice caps at its poles. The finding is significant because, if we are ever to build a colony there, we will need access to water to sustain life and, eventually, to terraform the planet.

While it’s become fashionable for people to lament short-term thinking and “quarterly capitalism,” it’s worth noting that there are a lot of people working on—and a not insignificant amount of money invested in—colonizing another world. Many dedicate entire careers to a goal they do not expect to be achieved in their lifetime.

The truth is that there is no shortage of organizations that are willing to invest for the long-term. In fact, nascent technologies which are unlikely to pay off for years are still able to attract significant investment. The challenge is to come up with a vision that is compelling enough to inspire others, while still being practical enough that you can still make it happen.

The Road to a Miracle Vaccine

When the FDA announced that it was granting an emergency use authorization for Covid-19 vaccines, everybody was amazed at how quickly they were developed. That sense of wonder only increased when it was revealed that they were designed in a mere matter of days. Traditionally, vaccines take years, if not decades to develop.

Yet appearances can be deceiving. What looked like a 10-month sprint to a miracle cure was actually the culmination of a three-decade effort that started in the 90s with a vision of a young researcher named Katalin Karikó, who believed that a molecule called mRNA could hold the key to reprogramming our cells to produce specific protein molecules.

The problem was that, although theoretically once inside the cytoplasm mRNA could instruct our cell machinery to produce any protein we wanted, our bodies tend to reject it. However, working with her colleague Drew Weissman, Karikó figured out that they could slip it past our natural defenses by slightly modifying the mRNA molecule.

It was that breakthrough that led two startup companies, Moderna and BioNTech to license the technology and for investors to back it. Still, it would still take more than a decade and a pandemic before the bet paid off.

The Hard Road of Hard Tech

In the mid-90s when the Internet started to take off, companies with no profits soon began attracting valuations that seemed insane. Yet the economist W. Brian Arthur explained that under certain conditions—namely high initial investment, low or negligible marginal costs and network effects—firms could defy economic gravity and produce increasing returns.

Arthur’s insight paved the way for the incredible success of Silicon Valley’s brand of venture-funded capitalism. Before long, runaway successes such as Yahoo, Amazon and Google made those who invested in the idea of increasing returns a mountain of money.

Yet the Silicon Valley model only works for a fairly narrow slice of technologies, mostly software and consumer gadgets. For other, so-called “hard technologies,” such as biotech, clean tech, materials science and manufacturing 4.0, the approach isn’t effective. There’s no way to rapidly prototype a cure for cancer or a multimillion-dollar piece of equipment.

Still, over the last decade a new ecosystem has been emerging that specifically targets these technologies. Some, like the LEEP programs at the National Laboratories, are government funded. Others, such as Steve Blank’s I-Corps program, focus on training scientists to become entrepreneurs. There are also increasingly investors who specialize in hard tech.

Look closely and you can see a subtle shift taking place. Traditionally, venture investors have been willing to take market risk but not technical risk. In other words, they wanted to see a working prototype, but were willing to take a flyer on whether demand would emerge. This new breed of investors are taking on technical risk on technologies, such as new sources of energy, for which there is little market risk if they can be made to work.

The Quantum Computing Ecosystem

At the end of 2019, Amazon announced Braket, a new quantum computing service that would utilize technologies from companies such as D-Wave, IonQ, and Rigetti. They were not alone. IBM had already been building its network of quantum partners for years which included high profile customers ranging from Goldman Sachs to ExxonMobil to Boeing.

Here’s the catch. Quantum computers can’t be used by anybody for any practical purpose. In fact, there’s nobody on earth who can even tell you definitively how quantum computing should work or exactly what types of problems it can be used to solve. There are, in fact, a number of different approaches being pursued, but none of them have proved out yet.

Nevertheless, an analysis by Nature found that private funding for quantum computing is surging and not just for hardware, but enabling technologies like software and services. The US government has created a $1 billion quantum technology plan and has set up five quantum computing centers at the national labs.

So if quantum computing is not yet a proven technology why is it generating so much interest? The truth is that the smart players understand that the potential of quantum is so massive, and the technology itself so different from anything we’ve ever seen before, that it’s imperative to start early. Get behind and you may never catch up.

In other words, they’re thinking for the long-term.

A Plan Isn’t Enough, You Need To Have A Vision

It’s become fashionable to bemoan the influence of investors and blame them for short-term and “quarterly capitalism,” but that’s just an excuse for failed leadership. If you look at the world’s most valuable companies—the ones investors most highly prize—you’ll find a very different story.

Apple’s Steve Jobs famously disregarded the opinions of investors, (and just about everybody else as well). Amazon’s Jeff Bezos, who habitually keeps margins low in order to increase market share, has long been a Wall Street darling. Microsoft invested heavily in a research division aimed at creating technologies that won’t pan out for years or even decades.

The truth is that it’s not enough to have a long-term plan, you have to have a vision to go along with it. Nobody wants to “wait” for profits, but everybody can get excited about a vision that inspires them. Who doesn’t get thrilled by the possibility of a colony on Mars, miracle cures, revolutionary new materials or a new era of computing?

Here’s the thing: Just because you’re not thinking long-term doesn’t mean somebody else isn’t and, quite frankly, if they are able to articulate a vision to go along with that plan, you don’t stand a chance. You won’t survive. So take some time to look around, to dream a little bit and, maybe, to be inspired to do something worthy of a legacy.

All who wander are not lost.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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Training Your Quantum Human Computer

Quantum Human Computing

What is quantum computing?

According to Wikipedia, “Quantum computing is the use of quantum phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers.”

Rather than try and explain all of the ins and outs of how quantum computing differs from traditional computing and why it matters, I encourage you to check out this YouTube video:

In case you were curious, according to the Guinness Book of World Records, the current record holder for quantum computing is a Google machine capable of processing 72 Quantum Bits. There is supposedly a machine in China capable of 76 Qubits, but it has yet to be fully recognized as the new record holder.

So, what does quantum computing have to do with humanity and the human brain and our collective future?

Is the human brain a quantum computer?

The easy answer is – we’re not sure – but scientists are conducting experiments to try and determine whether the human brain is capable of computing in a quantum way.

As the pace of change in our world accelerates and data proliferates, we will need to train our brains to use less traditional brute force computing of going through every possibility one after another to do more parallel processing, better pattern recognition, and generating an increase in our ability to see insights straight away.

Connect the Dots

But how can we train our brains?

There are many different ways to better prepare your brain as we move from the Information Age to the Age of Insight. Let me start you off with two good ones and invite you to add more in the comments:

1. Connect the Dots

Many of us grew up doing connect-the-dot puzzles, and they seemed pretty easy. But, that is with visual queues. The image above shows a number of different visual queues. Connect the dots, especially without numbers or visual queues are great proving grounds for improving your visual pattern recognition skills.


One of my favorites is the word game DAILY JUMBLE in my local newspaper. You can also play it online. The key here is to work not on using brute force to reorder the letters into a word, but trying to train your brain to just SEE THE WORD – instantly.

Succeeding at this and other ways of training your brain to be more like a quantum computer involves getting better at removing your conscious analytical brain from the picture and letting other parts of your brain take over. It’s not easy. It takes practice – continual practice – because it is really hard to keep the analytical brain out of the way.

So, are you willing to give it a try?

Stay tuned for the next article in this series “The Age of Insight” …

Image credits: Utrecht University, Pixabay

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