Moore’s Law posits that the number of transistors on a microprocessor — and therefore their computing power — will double every two years. It’s held true since Gordon Moore came up with it in 1965, but its imminent end has been predicted for years. As long ago as 2000, the MIT Technology Review raised a warning about the limits of how small and fast silicon technology can get.
The thing is, Moore’s Law isn’t really a law. It’s more of a self-fulfilling prophecy. Moore didn’t describe an immutable truth, like gravity or the conservation of momentum. He simply set our expectations, and lo, the chip makers delivered accordingly.
In fact, the industry keeps finding new ways to pack more power onto tinier chips. Unfortunately, they haven’t found ways to cut costs on the same exponential curve. As Fast Company reported in February 2016, the worldwide semiconductor industry is no longer planning to base its R&D plans for silicon chips around the notion of doubling their power every two years, because it simply can’t afford to keep up that pace in purchasing the incredibly complex manufacturing tools and processes necessary. Besides, current manufacturing technology may not be able to shrink silicon transistors much more than it already has. And in any event, transistors have become so tiny that they may no longer reliably follow the usual laws of physics — which raises questions about how much longer we’ll dare to use them in medical devices or nuclear plants.
So does that mean the era of exponential tech-driven change is about to come to a screeching halt?
Not at all.
Even if silicon chips are approaching their physical and economic limits, there are other ways to continue the exponential growth of computing performance, from new materials for chips to new ways to define computing itself. We’re already seeing technological advances that have nothing to do with transistor speed, like more clever software driven by deep learning and the ability to achieve greater computing power by leveraging cloud resources. And that’s only the tiniest hint of what’s coming next.
Here are a few of the emerging technologies that promise to keep computing performance rocketing ahead:
- In-memory computing. Throughout computing history, the slowest part of processing has been getting the data from the hard disks where it’s stored to random access memory (RAM), where it can be used. A lot of processor power is wasted simply waiting for data to arrive. By contrast, in-memory computing puts massive amounts of data into RAM where it can be processed immediately. Combined with new database, analytics, and systems designs, it can dramatically improve both performance and overall costs.
- Graphene-based microchips. Graphene — one molecule thick and more conductive than any other known material (see The Super Materials Revolution) — can be rolled up into tiny tubes or combined with other materials to move electrons faster, in less space, than even the smallest silicon transistor. This will extend Moore’s Law for microprocessors a few years longer.
- Quantum computing. Even the most sophisticated conventional computer can only assign a one or a zero to each bit. Quantum computing, by contrast, uses quantum bits, or Qubits, which can be a zero, a one, both at once, or some point in between, all at the same time. (See this explainer video from the Verge for a surprisingly understandable overview.) Theoretically, a quantum computer will be able to solve highly complex problems, like analyzing genetic data or testing aircraft systems, millions of times faster than currently possible. Google researchers announced in 2015 that they had developed a new way for qubits to detect and protect against errors, but that’s as close as we’ve come so far.
- Molecular electronics. Researchers at Sweden’s Lund University have used nanotechnology to build a “biocomputer” that can perform parallel calculations by moving multiple protein filaments simultaneously along nanoscopic artificial pathways. This biocomputer is faster than conventional electrical computers that operate sequentially, approximately 99 percent more energy-efficient, and cheaper than both conventional and quantum computers to produce and use. It’s also more likely to be commercialized soon than quantum computing is.
- DNA data storage. Convert data to base 4 and you can encode it on synthetic DNA. Why would we want to do that? Simple: a little bit of DNA stores a whole lot of information. In fact, a group of Swiss researchers speculate that about a teaspoon of DNA could hold all the data humans have generated to date, from the first cave drawings to yesterday’s Facebook status updates. It currently takes a lot of time and money, but gene editing may be the future of big data: Futurism recently reported that Microsoft is investigating the use of synthetic DNA for secure long-term data storage and has been able to encode and recover 100 percent of its initial test data.
- Neuromorphic computing. The goal of neuromorphic technology is to create a computer that’s like the human brain—able to process and learn from data as quickly as the data is generated. So far, we’ve developed chips that train and execute neural networks for deep learning, and that’s a step in the right direction. General Vision’s neuromorphic chip, for example, consists of 1,024 neurons — each one a 256-byte memory based on SRAM combined with 3,000 logic gates — all interconnected and working in parallel.
- Passive Wi-fi. A team of computer scientists and electrical engineers at the University of Washington has developed a way to generate Wi-fi transmissions that use 10,000 times less power than the current battery-draining standard. While this isn’t technically an increase in computing power, it is an exponential increase in connectivity, which will enable other types of advances. Dubbed one of the 10 breakthrough technologies of 2016 by MIT Technology Review, Passive Wi-fi will not only save battery life, but enable a minimal-power Internet of Things, allow previously power-hungry devices to connect via Wi-fi for the first time, and potentially create entirely new modes of communication.
So while we may be approaching the limits of what silicon chips can do, technology itself is still accelerating. It’s unlikely to stop being the driving force in modern life. If anything, its influence will only increase as new computing technologies push robotics, artificial intelligence, virtual reality, nanotechnology, and other world-shaking advances past today’s accepted limits.
In short, exponential growth in computing may not be able to go on forever, but its end is still much farther in the future than we might think.
IoT technologies are beginning to drive new competitive advantage by helping consumers manage their lives (Amazon Echo), save money (Ôasys water usage monitoring), and secure their homes (August Smart Lock). The IoT also has the potential to save lives. In healthcare, this means streaming data from patient monitoring devices to keep caregivers informed of critical indicators or preventing equipment failures in the ER. In manufacturing, the IoT helps drive down the cost of production through real-time alerts on the shop floor that indicate machine issues and automatically correct problems. That means lower costs for consumers.
Where are the most exciting and viable opportunities right now for companies looking into IoT strategies to drive their business?
Kavis: We have a lot less control over what is coming into companies from all these devices, which is creating many more openings for hackers to get inside an organization. There will be specialized security platforms and services to address this, and hardware companies are putting security on sensors in the field. The IoT offers great opportunities for security experts wanting to specialize in this area.
The future of learning is here and it’s digital, social, continuous, and highly immersive. For companies, traditional training methods, such as classrooms, are still relevant, but they are no longer the prime delivery method for learning. They are slow to set up, are expensive, and consume too many productive hours. Many companies are beginning to view the classroom as a strategy for customized educational needs, such as corporate strategy or branding.
Uber, Netflix, Amazon, Airbnb, Bloom Energy, and health insurer Oscar are among the companies considered highly disruptive in their markets today. They achieved innovation and market share by looking ahead and taking advantage of new technologies faster than competitors or in novel ways. Digital learning enables companies to stay ahead of the curve. Companies need to understand the new technologies before they are even available, so that they can understand the impact on the business and even invent new business models.
We are now in an era that will rival the Industrial Age in terms of transformation. For example, a financial analyst today needs to know how to work with Big Data, including how to ask the right questions and how to use the related information systems. Jim Carroll, a speaker, consultant, and author on business transformation, uses the automotive industry as one rubric for change. “You’ve got folks who are struggling with all this new high-tech gear inside the car or the dashboard,” says Carroll. “And you look at a typical auto dealer or the person manufacturing a car, and the knowledge they need to do their job today is infinitely more complex than it was even 5 or 10 years ago.”
By 2025, Millennials will make up 75% of the workforce, according to the Brookings Institution. Various studies have shown that Millennials crave learning and collaboration and will do whatever it takes to get the information they need expediently. “I’ve got two sons who are 20 and 22 and they seem to learn in an entirely new and different way,” Carroll says. “To borrow from Pink Floyd, it is short, sharp shocks of knowledge ingested. They won’t sit down and read 50 pages of a textbook.” Sophisticated learning programs are one way to keep this generation engaged. “Millennials will be an increasing challenge for companies to attract and retain because of their high expectations,” says Bastian. “They’re not interested just in money but also in a career path and the opportunity for diverse experiences.”
Online courses have become a standard way to gain knowledge, and that’s shifting to even more interactive learning through mobile, which is available anywhere and anytime. Like many large companies, SAP had created a vast library over time of more than 50,000 training assets, which was cumbersome to navigate and manage. The curriculum was organized across regions, lines of business, and disciplines. As a result, mapping learning to broader business goals was difficult.
Develop a close alignment between learning departments and senior business leaders to understand skill gaps, customer needs, and employee shortfalls.
The long-term goal of digital learning: grow the business
Real-time analysis, together with emerging digital technologies and intelligent digital processes, have upended the workplace as we know it; and businesses are today subject to a deep cultural shift in work organisation, culture and management mind set. The impact is a shift towards workers looking at available information as opposed to ‘explorative surgery’ measures when the damage is already done.
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) to the left of the header data support record-to-record navigation within this pop-up. The available technical data is grouped into categories, such as Time, DB, andMemory and Data. Use the tabs to navigate between categories containing data. Tabs for categories without data for the current statistics record are inactive and grayed out.
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buttons, respectively, on the STATS start screen (Figure 1) and in the tabular display of the statistics records (Figure 2).
button on the STATS start screen (Figure 1), which takes you to the statistics directory shown in Figure 4. In the two areas at the top of the screen, you specify conditions that the statistics must fulfill to be included in the list displayed in the lower part of the screen. Statistics are deleted automatically from the database four weeks after they have been saved. You can adjust this default Deleted On date so that the data is still available when you need it. Similarly, you can change the description, which you specified when the statistics were saved. Double-clicking on a row in the directory displays the corresponding set of statistics records, as shown in Figure 2. All capabilities described previously are available.
button. You can also import statistics into an SAP system that is different from the system where the statistics were exported. This enables the analysis of statistics originating from a system that is no longer accessible, or cross-system comparisons of two statistics.
The digital technologies that are transforming the world economy have converted once-solid industry boundaries into permeable membranes through which new players may enter—or exit. But for established firms, the smartest move right now may be to reinvent their existing markets rather than pursue ventures in unfamiliar business segments.We used S&P Global Market Intelligence’s Compustat database to examine the diversification behavior of 1,932 companies in 10 industries between 2007—the year the Android operating system and the iPhone debuted—and 2015. Only a handful of firms reported entering a new market segment or exiting an existing one. Analyzing the overall returns showed companies entering new business segments (as defined by the North American Industry Classification System) increased their revenue by an average US$437 million.
Digital technologies are now fundamental to creating new business opportunities, observes Pontus Siren, a partner at innovation consulting firm Innosight. Companies are finding profitable ideas close to home by using new technologies to transform processes or capitalizing on data they capture from their existing businesses. For example, Disney’s MagicBand, the chip-enabled bracelet that patrons use to buy passes, food, and souvenirs, “is a great example [of] where they are not fundamentally changing the business, but they are transforming the experience,” Siren says.
“The traditional idea of putting up barriers to entry and creating a unique, sustainable competitive advantage is not a winning approach anymore,” says Rogers. He argues that tying value generation to meeting evolving customer needs means that executives must be open to making investments that serve this value.
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