Brain hardwired to respond to others’ itching 03-12

Some behaviors — yawning and scratching, for example — are socially contagious, meaning if one person does it, others are likely to follow suit. Now, researchers at Washington University School of Medicine in St. Louis have found that socially contagious itching is hardwired in the brain.

Studying mice, the scientists have identified what occurs in the brain when a mouse feels itchy after seeing another mouse scratch. The discovery may help scientists understand the neural circuits that control socially contagious behaviors.

“Itching is highly contagious,” said principal investigator Zhou-Feng Chen, PhD, director of the Washington University Center for the Study of Itch. “Sometimes even mentioning itching will make someone scratch. Many people thought it was all in the mind, but our experiments show it is a hardwired behavior and is not a form of empathy.”

For this study, Chen’s team put a mouse in an enclosure with a computer screen. The researchers then played a video that showed another mouse scratching.

“Within a few seconds, the mouse in the enclosure would start scratching, too,” Chen said. “This was very surprising because mice are known for their poor vision. They use smell and touch to explore areas, so we didn’t know whether a mouse would notice a video. Not only did it see the video, it could tell that the mouse in the video was scratching.”

Next, the researchers identified a structure called the suprachiasmatic nucleus (SCN), a brain region that controls when animals fall asleep or wake up. The SCN was highly active after the mouse watched the video of the scratching mouse.

When the mouse saw other mice scratching — in the video and when placed near scratching littermates — the brain’s SCN would release a chemical substance called GRP (gastrin-releasing peptide). In 2007, Chen’s team identified GRP as a key transmitter of itch signals between the skin and the spinal cord.

“The mouse doesn’t see another mouse scratching and then think it might need to scratch, too,” Chen said. “Instead, its brain begins sending out itch signals using GRP as a messenger.”

Chen’s team also used various methods to block GRP or the receptor it binds to on neurons. Mice whose GRP or GRP receptor were blocked in the brains’ SCN region did not scratch when they saw others scratch. But they maintained the ability to scratch normally when exposed to itch-inducing substances.

Chen believes the contagious itch behavior the mice engaged in is something the animals can’t control.

“It’s an innate behavior and an instinct,” he said. “We’ve been able to show that a single chemical and a single receptor are all that’s necessary to mediate this particular behavior. The next time you scratch or yawn in response to someone else doing it, remember it’s really not a choice nor a psychological response; it’s hardwired into your brain.

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New NASA Radar Technique Finds Lost Lunar Spacecraft 03-11

DSS-14 is NASA's 70-meter (230-foot) antenna located at the Goldstone Deep Space Communications Complex in California. It is known as the “Mars Antenna” as it was first to receive signals from the first spacecraft to closely observe Mars, Mariner 4, on March 18, 1966.

Credits: NASA/JPL-Caltech

Finding derelict spacecraft and space debris in Earth’s orbit can be a technological challenge. Detecting these objects in orbit around Earth’s moon is even more difficult. Optical telescopes are unable to search for small objects hidden in the bright glare of the moon.

However, a new technological application of interplanetary radar pioneered by scientists at NASA’s Jet Propulsion Laboratory in Pasadena, California, has successfully located spacecraft orbiting the moon -- one active, and one dormant. This new technique could assist planners of future moon missions.


“We have been able to detect NASA’s Lunar Reconnaissance Orbiter [LRO] and the Indian Space Research Organization’s Chandrayaan-1 spacecraft in lunar orbit with ground-based radar,” said Marina Brozović, a radar scientist at JPL and principal investigator for the test project. “Finding LRO was relatively easy, as we were working with the mission’s navigators and had precise orbit data where it was located. Finding India’s Chandrayaan-1 required a bit more detective work because the last contact with the spacecraft was in August of 2009.”


Add to the mix that the Chandrayaan-1 spacecraft is very small, a cube about five feet (1.5 meters) on each side -- about half the size of a smart car. Although the interplanetary radar has been used to observe small asteroids several million miles from Earth, researchers were not certain that an object of this smaller size as far away as the moon could be detected, even with the world’s most powerful radars. Chandrayaan-1 proved the perfect target for demonstrating the capability of this technique.

This computer generated image depicts the Chandrayaan-1’s location at time it was detected by the Goldstone Solar System radar on July 2, 2016. In the graphic the 120-mile (200-kilometer) wide purple circle represents the width of the Goldstone radar beam at lunar distance. The radar beam was pointed 103 miles (165 kilometers) off the lunar surface. The white box in the upper-right corner of the animation depicts the strength of echo. As the spacecraft entered and exited the radar beam (purple circle), the echo from the spacecraft alternated between being very strong and very weak, as the radar beam scattered from the flat metal surfaces. Once the spacecraft flew outside the beam, the echo was gone.

Credits: NASA/JPL-Caltech

While they all use microwaves, not all radar transmitters are created equal. The average police radar gun has an operational range of about one mile, while air traffic control radar goes to about 60 miles. To find a spacecraft 237,000 miles (380,000 kilometers) away, JPL’s team used NASA's 70-meter (230-foot) antenna at NASA's Goldstone Deep Space Communications Complex in California to send out a powerful beam of microwaves directed toward the moon. Then the radar echoes bounced back from lunar orbit were received by the 100-meter (330-foot) Green Bank Telescope in West Virginia.


Finding a derelict spacecraft at lunar distance that has not been tracked for years is tricky because the moon is riddled with mascons (regions with higher-than-average gravitational pull) that can dramatically affect a spacecraft’s orbit over time, and even cause it to have crashed into the moon. JPL’s orbital calculations indicated that Chandrayaan-1 is still circling some 124 miles (200 kilometers) above the lunar surface, but it was generally considered “lost.”


However, with Chandrayaan-1, the radar team utilized the fact that this spacecraft is in polar orbit around the moon, so it would always cross above the lunar poles on each orbit. So, on July 2, 2016, the team pointed Goldstone and Green Bank at a location about 100 miles (160 kilometers) above the moon’s north pole and waited to see if the lost spacecraft crossed the radar beam. Chandrayaan-1 was predicted to complete one orbit around the moon every two hours and 8 minutes.  Something that had a radar signature of a small spacecraft did cross the beam twice during four hours of observations, and the timings between detections matched the time it would take Chandrayaan-1 to complete one orbit and return to the same position above the moon’s pole.

Radar imagery acquired of the Chandrayaan-1 spacecraft as it flew over the moon’s south pole on July 3, 2016. The imagery was acquired using NASA's 70-meter (230-foot) antenna at the Goldstone Deep Space Communications Complex in California. This is one of four detections of Chandrayaan-1 from that day.

Credits: NASA/JPL-Caltech

The team used data from the return signal to estimate its velocity and the distance to the target.  This information was then used to update the orbital predictions for Chandrayaan-1.


“It turns out that we needed to shift the location of Chandrayaan-1 by about 180 degrees, or half a cycle from the old orbital estimates from 2009,” said Ryan Park, the manager of JPL’s Solar System Dynamics group, who delivered the new orbit back to the radar team.  “But otherwise, Chandrayaan-1’s orbit still had the shape and alignment that we expected.”

Radar echoes from the spacecraft were obtained seven more times over three months and are in perfect agreement with the new orbital predictions. Some of the follow-up observations were done with the Arecibo Observatory in Puerto Rico, which has the most powerful astronomical radar system on Earth. Arecibo is operated by the National Science Foundation with funding from NASA’s Planetary Defense Coordination Office for the radar capability.


Hunting down LRO and rediscovering Chandrayaan-1 have provided the start for a unique new capability. Working together, the large radar antennas at Goldstone, Arecibo and Green Bank demonstrated that they can detect and track even small spacecraft in lunar orbit. Ground-based radars could possibly play a part in future robotic and human missions to the moon, both for a collisional hazard assessment tool and as a safety mechanism for spacecraft that encounter navigation or communication issues.

JPL manages and operates NASA's Deep Space Network, including the Goldstone Solar System Radar, and hosts the Center for Near-Earth Object Studies for NASA's Near-Earth Object Observations Program, an element of the Planetary Defense Coordination Office within the agency's Science Mission Directorate.

Exciting new possibilities in the Quantum Realm 03-10

The strangeness of the quantum realm opens up exciting new technological possibilities.

A BATHING cap that can watch individual neurons, allowing others to monitor the wearer’s mind. A sensor that can spot hidden nuclear submarines. A computer that can discover new drugs, revolutionise securities trading and design new materials. A global network of communication links whose security is underwritten by unbreakable physical laws. Such—and more—is the promise of quantum technology.

All this potential arises from improvements in scientists’ ability to trap, poke and prod single atoms and wispy particles of light called photons. Today’s computer chips get cheaper and faster as their features get smaller, but quantum mechanics says that at tiny enough scales, particles sail through solids, short-circuiting the chip’s innards. Quantum technologies come at the problem from the other direction. Rather than scale devices down, quantum technologies employ the unusual behaviours of single atoms and particles and scale them up. Like computerisation before it, this unlocks a world of possibilities, with applications in nearly every existing industry—and the potential to spark entirely new ones.

Strange but true

Quantum mechanics—a theory of the behaviour at the atomic level put together in the early 20th century—has a well-earned reputation for weirdness. That is because the world as humanity sees it is not, in fact, how the world works. Quantum mechanics replaced wholesale the centuries-old notion of a clockwork, deterministic universe with a reality that deals in probabilities rather than certainties—one where the very act of measurement affects what is measured. Along with that upheaval came a few truly mind-bending implications, such as the fact that particles are fundamentally neither here nor there but, until pinned down, both here and there at the same time: they are in a “superposition” of here-there-ness. The theory also suggested that particles can be spookily linked: do something to one and the change is felt instantaneously by the other, even across vast reaches of space. This “entanglement” confounded even the theory’s originators.

It is exactly these effects that show such promise now: the techniques that were refined in a bid to learn more about the quantum world are now being harnessed to put it to good use. Gizmos that exploit superposition and entanglement can vastly outperform existing ones—and accomplish things once thought to be impossible.

Improving atomic clocks by incorporating entanglement, for example, makes them more accurate than those used today in satellite positioning. That could improve navigational precision by orders of magnitude, which would make self-driving cars safer and more reliable. And because the strength of the local gravitational field affects the flow of time (according to general relativity, another immensely successful but counter-intuitive theory), such clocks would also be able to measure tiny variations in gravity. That could be used to spot underground pipes without having to dig up the road, or track submarines far below the waves.

Other aspects of quantum theory permit messaging without worries about eavesdroppers. Signals encoded using either superposed or entangled particles cannot be intercepted, duplicated and passed on. That has obvious appeal to companies and governments the world over. China has already launched a satellite that can receive and reroute such signals; a global, unhackable network could eventually follow.

The advantageous interplay between odd quantum effects reaches its zenith in quantum computers. Rather than the 0s and 1s of standard computing, a quantum computer’s bits are in super positions of both, and each “qubit” is entangled with every other. Using algorithms that recast problems in quantum-amenable forms, such computers will be able to chomp their way through calculations that would take today’s best supercomputers millennia. Even as high-security quantum networks are being developed, a countervailing worry is that quantum computers will eventually render obsolete today’s cryptographic techniques, which are based on hard mathematical problems.

Long before that happens, however, smaller quantum computers will make other contributions in industries from energy and logistics to drug design and finance. Even simple quantum computers should be able to tackle classes of problems that choke conventional machines, such as optimising trading strategies or plucking promising drug candidates from scientific literature. Google said last week that such machines are only five years from commercial exploitability. This week IBM, which already runs a publicly accessible, rudimentary quantum computer, announced expansion plans. As our Technology Quarterly in this issue explains, big tech firms and startups alike are developing software to exploit these devices’ curious abilities. A new ecosystem of middlemen is emerging to match new hardware to industries that might benefit.

The solace of quantum

This landscape has much in common with the state of the internet in the early 1990s: a largely laboratory-based affair that had occupied scientists for decades, but in which industry was starting to see broader potential. Blue-chip firms are buying into it, or developing their own research efforts. Startups are multiplying. Governments are investing “strategically”, having paid for the underlying research for many years—a reminder that there are some goods, such as blue-sky scientific work, that markets cannot be relied upon to provide.

Fortunately for quantum technologists, the remaining challenges are mostly engineering ones, rather than scientific. And today’s quantum-enhanced gizmos are just the beginning. What is most exciting about quantum technology is its as yet untapped potential. Experts at the frontier of any transformative technology have a spotty record of foreseeing many of the uses it will find; Thomas Edison thought his phonograph’s strength would lie in elocution lessons. For much of the 20th century “quantum” has, in the popular consciousness, simply signified “weird”. In the 21st, it will come to mean “better”.

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10 Principles of Strategy through Execution 03-07

“We are all in the gutter,” wrote Oscar Wilde, “but some of us are looking at the stars.” That is the nature of strategy through execution. You operate deep in the weeds, managing countless day-to-day tasks and transactions. At the same time, you keep a steady gaze on your company’s long-term goals  and on ways you can stand out from your competitors.

Having a close link between strategy and execution is critically important. Your strategy is your promise to deliver value: the things you do for customers, now and in the future, that no other company can do as well. Your execution occurs in the thousands of decisions made each day by people at every level of your company.

Quality, innovation, profitability, and growth all depend on having strategy and execution fit together seamlessly. If they don’t fit — if you can’t deliberately align them in a coherent way — you risk operating at cross-purposes and losing your focus. This problem is all too common. In a recent Strategy& global survey, 700 business executives were asked to rate their company’s top leaders in terms of their skill at strategy creation and at execution. Only 8 percent were credited as being very effective at both.

Strategy&, the strategy consulting business of PwC, has been studying the relationship between strategy and execution for years. We have found that the most iconic enterprises — companies such as Apple, Amazon, Danaher, IKEA, Starbucks, and the Chinese appliance manufacturer Haier, all of which compete successfully time after time — are exceptionally coherent. They put forth a clear winning value proposition, backed up by distinctive capabilities, and apply this mix of strategy and execution to everything they do.

Any company can follow the same path as these successful firms, and an increasing number of companies are doing just that. If you join them, you will need to cultivate the ability to translate the strategic into the everyday. This means linking strategy and execution closely together by creating distinctive, complex capabilities that set your company apart, and applying them to every product and service in your portfolio. These capabilities combine all the elements of execution — technology, human skills, processes, and organizational structures — to deliver your company’s chosen value proposition.

How do you accomplish this on a day-to-day basis? How do you get the strategists and implementers in your company to work together effectively? These 10 principles, derived from our experience at Strategy&, can help you avoid common pitfalls and accelerate your progress. For companies that truly embrace strategy through execution, principles like these become a way of life.

1. Aim High

Don’t compromise your strategy or your execution. Set a lofty ambition for your strategy: not just financial success but sustained value creation, making a better world through your products, services, and presence. Apple’s early goal of making “a computer for the rest of us,” which effectively shaped the personal computer industry, is a classic example.

Next, aim just as high on the execution side, with a dedication to excellence that seems almost obsessive to outsiders. Apple, for instance, has long been known for its intensive interest in every aspect of product design and marketing, iterating endlessly until its notoriously demanding leaders are satisfied. The company’s leaders do not consider execution beneath them; it is part of what makes Apple special.

Together, a strong long-term strategy and a fierce commitment to excellent execution can transform not only a company, but a regional economy. After the 1992 Olympics in Barcelona, a group of local political and business leaders realized, with some disappointment, that the event hadn’t triggered the economic growth they had expected. So they resolved to change the region’s economy in other ways. Led by the mayor, the group created a common base of technologies and practices and set up training programs for local enterprises. By 2014, after two decades of persistent effort, the city had become a hub for research and technology companies. One legacy of the Olympics is a group of about 600 sports-related companies with a collective annual revenue of US$3 billion and 20,000 employees.

In carrying out this first principle, the top executives of your company must lead the way. They must learn to set lofty goals, establish a clear message about why those goals are relevant, and stick to them without compromise. This may take a while, because lofty goals require patience. You need to persevere without lowering your standards, and the confidence to believe you can reach the goals soon enough. Leaders must demonstrate that courage and commitment, or no one else will. At the same time, don’t be surprised if the rewards start to appear sooner than you expect — both financial rewards and the intrinsic pleasure of working with highly capable people on relevant projects. With high aspirations (for example, IKEA’s goal of “creating a better everyday life for the many people” or Amazon’s self-proclaimed role as the “everything store”), you recruit talented people who are deeply committed to being there. That’s one way you’ll know that you’re aiming high enough: The whole organization will start to feel like a better place to work.


2. Build on Your Strengths.

Your company has capabilities that set it apart, things you do better than anyone else. You can use them as a starting point to create greater success. Yet more likely than not, your strongest capabilities have been obscured over the years. If, like most companies, you pursue opportunities that crop up without thinking much about whether you have the prowess needed to capture them, you can gradually lose sight of what you do best, or why customers respond to it.

Take an inventory of your most distinctive capabilities. Look for examples where you have excelled as a company, achieving greatly desired outcomes without heroic efforts. Articulate all the different things that had to happen to make these capabilities work, and figure out what it will take to build on your strengths, so that you can succeed the same way more consistently in the future.

Sometimes a particular episode will bring to light new ways of building on your strengths. That’s what happened at Bombardier Transportation, a division of a Canadian firm and one of the world’s largest manufacturers of railroad equipment. To win a highly competitive bid for supplying 66 passenger train cars to a British rail operator, Bombardier shifted its manufacturing and commercial models to a platform-based approach, which allowed it to use and reuse the same designs for several different types of railway cars. “Platforming,” which was a new operational strategy for the industry, required adjustments to Bombardier’s supplier relationships and product engineering practices. But the benefits were immediate: lower costs, less technology risk, faster time-to-market, and better reliability.

Bombardier won the bid — and, more importantly, learned from the experience, making the episode a model for other bids and contracts. When some Bombardier engineers complained about the platform approach on the grounds that it curtailed their creativity, the leadership had an immediate answer: The platform demonstrated capabilities that competitors couldn’t match and the company’s creativity could be focused on innovation. Additional contracts soon followed.

The more knowledge you have about your own capabilities, the more opportunities you’ll have to build on your strengths. So you should always be analyzing what you do best, gathering data about your practices, and conducting postmortems. In every case, there is something to learn — about your operations, and also about the choices you make and the value you’re able to deliver.


3. Be Ambidextrous

In the physical world, ambidexterity is the ability to use both hands with equal skill and versatility. In business, it’s the ability to manage strategy and execution with equal competence. In some companies, this is known as being “bilingual”: able to speak the language of the boardroom and the shop floor or software center with equal facility. Ambidextrous managers can think about the technical and operational details of a project in depth and then, without missing a beat, can consider its broader ramifications for the industry. If strategy through execution is to become a reality, people across the enterprise need to master ambidexterity.

Lack of ambidexterity can be a key factor in chronic problems. For instance, if IT professionals focus only on execution when they manage ERP upgrades or the adoption of new applications, they may be drawn to vendors for their low rates or expertise on specific platforms instead of their ability to design solutions that support the company’s business strategy. When the installation fails to deliver the capabilities that the company needs, there will be an unplanned revision; the costs will balloon accordingly, and the purchase won’t fulfill its promise.

We recognize, of course, that not everyone needs to be equally conversant in the company’s strategy. A typical paper goods manufacturer, for example, employs chemists who research hydrogen bonds to discover ways to make paper towels more absorbent. They may not need to spend much time debating strategy in the abstract, but they do need to be aware of how their role fits in. Like the apocryphal bricklayer who sees himself as building a cathedral, the highly skilled technologists on your team must recognize that they are not merely fulfilling a spec but rather developing a technology unlike anyone else’s, for the sake of building highly distinctive capabilities. They might even help figure out what those capabilities should be.

Similarly, your top leaders don’t have to be experts on hydrogen bonds or cloud-based SQL server hosting, but they do have to be conversant enough with technological and operational details to make the right high-level decisions. No longer can a senior executive credibly say, “I don’t use computers. My staff is my computer.” If your leaders aren’t ambidextrous, they risk being eclipsed or outperformed by someone who is.

In The Self-Made Billionaire Effect: How Extreme Producers Create Massive Value (Portfolio, 2014), John Sviokla and Mitch Cohen suggest using the word producers to describe ambidextrous individuals. Self-made billionaires, such as Spanx founder Sara Blakely, POM Wonderful cofounder Lynda Resnick, Uniqlo founder Tadashi Yanai, and Morningstar founder Joe Manseuto have this quality. They can both envision a blockbuster strategy and figure out in detail how to develop and sell it to customers. There are similarly ambidextrous people in every company, but they often go unappreciated. Find them, recognize and reward them, and give them opportunities to influence others. 

Foster ambidexterity in practices and processes as well as in people. For example, in your annual budgeting exercises, ask people to explain the relationship of each line item to the company’s strategy, and specifically to the capability it is enabling. Over time, this approach will channel investments toward projects with a more strategic rationale. 


4. Clarify Everyone’s Strategic Role

When the leaders of the General Authority of Civil Aviation (GACA) of Saudi Arabia decided to improve the way they ran the country’s 25 airports, they started with the hub in Riyadh, one of the largest airports in the country. They had already outsourced much of their activity, redesigning airport practices and enhancing operations. But not much had changed. Convening the directors and some department leaders, the head of the airport explained that some seemingly minor operational issues — long customs lines, slow boarding processes, and inadequate basic amenities — were not just problems in execution. They stood in the way of the country’s goal of becoming a commercial and logistics hub for Africa, Asia, and Europe. Individual airport employees, he added, could make a difference.

The head of the airport then conducted in-depth sessions with employees on breaking down silos and improving operations. In these sessions, he turned repeatedly to a common theme: Each minor operational improvement would affect the attractiveness of the country for commercial travel and logistics. A wake-up call for staff, the sessions marked a turning point for the airport’s operational success. Other airports in the Saudi system are now expected to follow suit.

The people in your day-to-day operations — wherever they are, and on whatever level — are continually called upon to make decisions on behalf of the enterprise. If they are not motivated to deliver the strategy, the strategy won’t reach the customers. It is well established that financial rewards and other tangible incentives will go only so far in motivating people. Workers cannot make a greater personal commitment unless they understand why their jobs make a difference, and why the company’s advancement will help their own advancement.

Successful leaders spend a great deal of time and attention on the connection between strategy and personal commitment. One such leader has run the trade promotion effectiveness (TPE) capability at two global consumer products goods (CPG) companies over the past several years. CPG companies use this capability to build the momentum of key brands. It involves assembling assortments of products to promote, merchandising them to retailers, arranging in-store displays and online promotions, adjusting prices and discounts to test demand, and assessing the results. A great TPE capability consistently attracts customers and compels them to seek out the same products for months after the campaign ends. TPE and related activities often represent the second-largest item (after the cost of goods sold) on the P&L statement. This in itself indicates the capability’s strategic importance for CPG companies.

In both enterprises, this executive took the time to go up and down the organization, making a case for why the specific mechanics of trade promotion matter to the value proposition of the company and, ultimately, to its survival. He made it a point to talk numbers but didn’t limit the conversation to them. “We spend billions at this company on promotions,” he might say. “We have to get back $100 million in added revenue next year, and another $100 million on top of that the year after.” He then urged employees to develop better promotions that would attract more consumers and increase their synergies with retailers. This combination of numbers and mission made it clear how people’s individual efforts could affect the company’s prospects. 


5. Align Structures to Strategy

Set up all your organizational structures, including your hierarchical design, decision rights, incentives, and metrics, so they reinforce your company’s identity: your value proposition and critical capabilities. If the structures of your company don’t support your strategy, consider removing them or changing them wholesale. Otherwise, they will just get in your way.

Consider, for example, the metrics used to track the results delivered by call center employees. In many companies, these individuals must follow a script and check off that they’ve said everything on the list — even at the risk of irritating potential customers. Better instead to get employees to fully internalize the company’s strategy and grade them on their prowess at solving customer problems.

Danaher, a conglomerate of more than 25 companies specializing in environmental science, life sciences, dental technologies, and industrial manufacturing technologies, is intensely focused on creating value through operational excellence. Critical to this approach are metrics built into the Danaher Business System, the company’s intensive continuous improvement program. Only eight key metrics, called “core value drivers” to underline their strategic relevance, are tracked constantly in all Danaher enterprises. The financial metrics (core growth, operating margin expansion, working capital returns, and return on invested capital) are used not just by investors but also by managers to evaluate the value of their own activities.

Danaher also tracks two customer-facing metrics (on-time delivery and quality as perceived by customers), and two metrics related to employees (retention rates and the percentage of managerial positions filled by internal candidates). Lengthy in-person operating reviews, conducted monthly, are very data driven, focusing on solving problems and improving current practices. The metrics are posted on the shop floor, where anyone can see the progress that’s being made — or not being made — toward clear targets. The meetings are constructive: People feel accountable and challenged, but also encouraged to rise to the challenges.

Data analytics is evolving to the point where it can help revitalize metrics and incentives. A spreadsheet is no longer enough to capture and analyze this body of material; you can use large information management systems programmed to deliver carefully crafted performance data. No matter how complex the input, the final incentives and metrics need to be simple enough to drive clear, consistent behavior. More generally, every structure in your organization should make your capabilities stronger, and focus them on delivering your strategic goals.

6. Transcend Functional Barriers

Great capabilities always transcend functional barriers. Consider Starbucks’ understanding of how to create the right ambience, Haier’s ability to rapidly manufacture home appliances to order, and Amazon’s aptitude for launching products and services enabled by new technologies. These companies all bring people from different functions to work together informally and creatively. Most companies have some experience with this. For example, any effective TPE capability brings together marketing, sales, design, finance, and analytics professionals, all working closely together and learning from one another. The stronger the cross-functional interplay and the more it is supported by the company’s culture, the more effective the promotion.

Unfortunately, many companies unintentionally diminish their capabilities by allowing functions to operate independently. It’s often easier for the functional leaders to focus on specialized excellence, on “doing my job better” rather than on “what we can accomplish together.” Pressed for time, executives delegate execution to IT, HR, or operational specialists, who are attuned to their areas of expertise but not necessarily to the company’s overall direction. Collaborative efforts bring together people who don’t understand each other or, worse, who pursue competing objectives and agendas. When their narrow priorities conflict, the teams end up stuck in cycles of internal competition. The bigger a company gets, the harder it becomes to resolve these problems.

You can break this cycle by putting together cross-functional teams to blueprint, build, and roll out capabilities. Appoint a single executive for each capability team, accountable for fully developing the capability. Ensure this person has credibility at all levels of the organization. Tap high-quality people from each function for this team, and give the leader the authority to set incentives for performance.

There’s always the risk that these cross-functional teams will be seen as skunkworks, separate from the rest of the enterprise. To guard against this risk, you need a strong dotted line from each team member back to the original function. Sooner or later, the capabilities orientation will probably become habitual, affecting the way people (including functional leaders) see their roles: not as gatekeepers of their expertise, but as contributors to a larger whole.


7. Become a Fully Digital Enterprise

The seventh principle should affect every technological investment you make — and with luck, it will prevent you from making some outdated ones. Embrace digital technology’s potential to transform your company: to create fundamentally new experiences and interactions for your customers, your employees, and every other constituent. Until you use technology this way, many of your IT investments will be wasted; you won’t realize their potential in forming powerful new capabilities.

Complete digitization will inevitably broaden your range of strategic options, enabling you to pursue products, services, and innovations that weren’t feasible before. For example, Under Armour began as a technologically enabled sports apparel company, specializing in microfiber-based synthetic fabrics that felt comfortable under all conditions. To keep its value proposition as an innovator, it aggressively expanded into fitness trackers and the development of smart apparel. The company is now developing clothing that will provide data that can both help athletes raise their game and point the way to design improvements.

Adopting digital technology may mean abandoning expensive legacy IT systems, perhaps more rapidly than you had planned. Customers and employees have come to expect the companies they deal with to be digitally sophisticated. They now take instant access, seamless interoperability, smartphone connectivity, and an intuitively obvious user experience for granted. To be sure, it is expensive and risky to shift digital systems wholesale, and therefore you need to be judicious; some companies are applying the Fit for Growth approach to IT, in which they reconsider every expense, investing more only in those that are directly linked to their most important capabilities. (See “Building Trust while Cutting Costs,” by Vinay Couto, Deniz Caglar, and John Plansky.)

Fortunately, cloud-based technologies provide many more options than were available before. To boost agility and reduce costs, you can outsource some tech activities, while keeping others that are distinctive to your business. You also can use embedded sensors and analytics to share data across your value chain and collaborate more productively (an approach known as “Industry 4.0” and the “Industrial Internet of Things”). The biggest constraint is no longer the cost and difficulty of implementation. It’s your ability to combine business strategy, user experience, and technological prowess in your own distinctive way. 


8. Keep It Simple, Sometimes

Many company leaders wish for more simplicity: just a few products, a clear and simple value chain, and not too many projects on the schedule. Unfortunately, it rarely works out that way. In a large, mainstream company, execution is by nature complex. Capabilities are multifaceted. Different customers want different things. Internal groups design new products or processes without consulting one another. Mergers and acquisitions add entirely new ways of doing things. Although you might clean house every so often, incoherence and complexity creep back in, along with the associated costs and bureaucracy.

Many company leaders wish for more simplicity. Unfortunately, it rarely works out that way.

The answer is to constantly seek simplicity, but in a selective way. Don’t take a machete to your product lineup or org chart. Remember that not all complexity is alike. One advantage of aligning your strategy with your capabilities is that it helps you see your operations more clearly. You can distinguish the complexity that truly adds value (for example, a supply chain tailored to your most important customers) from the complexity that gets in your way (for example, a plethora of suppliers when only one or two are needed).

As Vinay Couto, Deniz Caglar, and John Plansky explain in Fit for Growth: A Guide to Strategic Cost Cutting, Restructuring, and Renewal (Wiley, 2017), effective cost management depends on the ability to ruthlessly cut the investments that don’t drive value. Customer-facing activities can be among the worst offenders. Some customers need more tailored offerings or elaborate processes, but many do not.

For example, Lenovo, a leading computer hardware company with twin headquarters in China and the U.S. (Lenovo’s ThinkPad computer business was acquired with its purchase of IBM’s personal computer business), has a strategy based on cross-pollination of innovation between two entirely different markets. The first is “relationship” customers (large enterprises, government agencies, and educational institutions), which purchase in large volume, need customized software, and are often legacy IBM customers. The second is “transactional” customers (individuals and smaller companies), typically buying one or two computers at a time, all seeking more or less the same few models; these customers, however, are sensitive to cost and good user experience.

Lenovo has a single well-developed hardware and software innovation capability aimed at meeting the needs of both types of customers. But its supply chain capability is bifurcated. The relationship supply chain is complex, designed to provide enterprise customers with greater responsiveness and flexibility. Lenovo’s computer manufacturing plant in Whitsett, N.C., which opened in 2013, was designed for fast shipping, large orders, and high levels of customization. Meanwhile, the company maintains a simpler supply chain with manufacturing sites in low-cost locations for its transactional customers.

The principle “keep it simple, sometimes” is itself more complex than it appears at first glance. It combines three concepts in one: First, be as simple as possible. Second, let your company’s strategy be your guide in adding the right amount of complexity. Third, build the capabilities needed to effectively manage the complexity inherent in serving your markets and customers.


9. Shape Your Value Chain

No company is an island. Every business relies on other companies in its network to help shepherd its products and services from one end of the value chain to the other. As you raise your game, you will raise the game of other operations you work with, including suppliers, distributors, retailers, brokers, and even regulators.

Since these partners are working with you on execution, they should also be actively involved in your strategy. That means selling your strategy to them, getting them excited about taking the partnership to a whole new level, and backing up your strategic commitment with financing, analytics, and operational prowess. For example, when the Brazilian cosmetics company Natura Cosméticos began sourcing ingredients from Amazon rain forest villages, its procurement staff discovered that the supply would be sustainable only if they built deeper relationships with their suppliers. Beyond paying suppliers, they needed to invest in the suppliers’ communities. The company has held to that commitment even during down periods.

Use leading-edge digital technology to align analytics and processes across your value chain. In the past, companies that linked operations to customer insight in innovative ways did it through vertical integration, by bringing all parts of the operation in-house. For example, Inditex created a robust in-house network that linked its Zara retail stores with its design and production teams. Real-time purchase data allowed designers to find out what was selling — and what wasn’t — more quickly than their competitors could. This approach has helped Zara introduce more items that would sell quickly while keeping costs down. And it has helped Inditex outpace its rivals in both profitability and growth.

At the time Inditex developed its system, vertical integration was a prerequisite for that kind of integration. But now the technology has changed, and in a cloud-based computer environment, you no longer need full vertical integration. You can achieve the same result through integrated business platforms (some managed by third-party logistics companies such as Genpact, and others being developed as joint ventures). By allowing several companies to share real-time data seamlessly, these platforms enable each participating company to set more ambitious strategic goals. 


10. Cultivate Collective Mastery

The more bound your company is by internal rules and procedures for making and approving decisions, the slower it becomes. Hence the frustration leaders have with the pace of bureaucracy, in which people can’t make decisions because they don’t know what the strategic priorities are — or even what other stakeholders will think. In a world where disruption has become prevalent, your company can’t afford the time or expense of operating this way.

The alternative is what we call collective mastery. This is a cultural attribute, often found in companies where strategy through execution is prevalent. It is the state you reach when communication is fluid, open, and constant. Your strategists understand what will work or not work because they talk easily with functional specialists. Your functional specialists know not only what they’re supposed to do, but why it matters. Everyone moves quickly and decisively, because they have the ingrained judgment to know who to consult, and when. People trust one another to make decisions on behalf of the whole.

Many of the attributes of Silicon Valley companies owe a great deal to the high level of collective mastery in the area. The culture of these companies encourages risk taking, because it’s expected that people will make mistakes — not as a goal, of course, but in the process of learning. People expect their colleagues to be informal, quick-thinking, and unassuming. They rely on systems and processes only when they add value, and are willing to jettison them at other times. With this type of culture, people can focus on getting results.

Collective mastery builds over time when people have the support and encouragement they need to work easily and readily across organizational boundaries, with a high level of trust and frequent informal contact. Even when they hold different perspectives, they get to the point where they understand one another’s thinking.

To operate this way, you have to be flexible. That doesn’t mean giving up your strategy; you still should pursue only opportunities with which you have the capabilities to win. Indeed, knowing what you do best allows you to be closer to the customers who matter, and to give more autonomy to employees. Because you are less distracted by nonstrategic issues, you have the attention and resources to pursue worthwhile opportunities as soon as they arise. Collective mastery also makes it easier to conduct an experiment: to launch a project and learn from the response without making a huge commitment. This high level of fluidity and flexibility is essential for navigating in a volatile economic landscape.

In the end, the 10 principles of strategy through execution will do more than help you achieve your business goals. They will also help build a new kind of culture, one in which people are aware of where you’re going and how you’re going to get there. The capabilities you build, and the value you provide, are larger than any individual can make them. But by creating the right kind of atmosphere, you make it possible to not just stand in the weeds and look at the stars, but reach a higher level than you may ever have thought you would.


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Fixing the Indian state is beyond Brahma, Vishnu, Shiva and all the gods put together: Arvind Subramanian 03-03

Among the three or four things that I have been really passionate about, when I first came, was the need to ramp up public investment. The government has embraced that wholeheartedly. Broadly, the views I have been advocating are reflected in this year’s budget as well. The other thing I feel really passionate about is the GST. I did want this to be a simple low-rate GST and whether I’ve been successful, we’ll have to wait and see. The job is to always create the sort of ecosphere of ideas where all these things are debated. Robert Solow has a famous line: “Ninety per cent of the job of a good economist is to flush out bad ideas like cockroaches”. Those are the things the people will never know — the bad ideas that are flushed out.

Iyer: How has it been for you over the last three years in government? Does bureaucracy fascinate you?

This is, by far, the most exciting thing I’ve done ever. I’m on a 24/7 high all the time and my wife, who is here, can testify to that. One of the big challenges to this is how you persuade people to go along with you. Navigating the bureaucracy is very much part of that. For me, watching and being part of this process is a daily learning experience.

Iyer: There have been several reports on administrative reforms. Do you think certain things can be fixed?

Fixing the Indian state is beyond Brahma, Vishnu, Shiva and all the gods put together. But, I do think a government can improve if they make use of outside talent. The marriage of the insiders with the outsiders produces results. If you don’t get outsiders, there is too much ossification and caution, too much hierarchy in the system to actually generate ideas. Equally, if you just rely on outsiders, they don’t know what’s going on and are not familiar with our ways of decision making.

Iyer: You’ve often talked about the twin balance sheet problem. But, banks been unable to address this issue on their own. You advocated a bad bank. Why is it so politically unpalatable?
If you remember, I came in November 2014 and the first document that we wrote is the mid-year media analysis, in December 2014, which is when we coined the term ‘Twin Balance Sheet’ problem. At least, some of us recognised it early on. It’s also honest to say that there was an urgency and how serious we needed to be, even we didn’t signal it at that point. We were also a little bit behind the curve. There is probably in-built incentive in the system to not reveal the true extent of problems.

We also thought that if growth were to pick up, it would kind of be ... a rising tide can cover all the jagged rocks, and so it would do that. A combination of all these things lulled people in to a sense that the problem is not so big. The final bit of honesty is that it is a very hard problem, which should not be underestimated because nowhere in the world is it easy to say I will forgive the debts of the private sector. Because you create moral hazards, you say what kind of system is this, crony capitalism and so on. The twin balance sheet problem is finally about that, of course. There was a lot of crony capitalism, but a lot of honest mistakes were made.

Down the road, we realised that a big part of the solution has to be to write down the debts of many companies, several of which were large companies, and that is not going to be easy. Making those hard decisions to write down debt, it’s difficult for any political system to do and of course in India you have this overhang of the four Cs. Even honest bureaucrats within the public sector are kind of fearful.

Damodaran: You mean the CAG, the CVC...

I would say the Courts, CAG, CVC, CBI. I don’t mean to cast any aspersions on any of them. They’re doing their job. But there is a kind of pall of anxiety and nervousness hanging over decision making. Put that together anywhere in the world and it is difficult to write off. So, it’s a really hard thing and something we need to crack.

Iyer: In the run up to the ongoing elections, the campaign draws so much from religion-based politics. How does such politics based so much on religion impact development?

All evidence suggests that social harmony is an intrinsic precondition for economic development. We’ve been a very early democracy and a cleavaged democracy and that has obviously had an impact on our economic development. While we justly and rightly claim that we’re very proud of our political development, I think it has not been without some cost.

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What to Expect From Artificial Intelligence 03-01

To understand how advances in artificial intelligence are likely to change the workplace — and the work of managers — you need to know where AI delivers the most value.

Major technology companies such as Apple, Google, and Amazon are prominently featuring artificial intelligence (AI) in their product launches and acquiring AI-based startups. The flurry of interest in AI is triggering a variety of reactions — everything from excitement about how the capabilities will augment human labor to trepidation about how they will eliminate jobs. In our view, the best way to assess the impact of radical technological change is to ask a fundamental question: How does the technology reduce costs? Only then can we really figure out how things might change.

To appreciate how useful this framing can be, let’s review the rise of computer technology through the same lens. Moore’s law, the long-held view that the number of transistors on an integrated circuit doubles approximately every two years, dominated information technology until just a few years ago. What did the semiconductor revolution reduce the cost of? In a word: arithmetic.

This answer may seem surprising since computers have become so widespread. We use them to communicate, play games and music, design buildings, and even produce art. But deep down, computers are souped-up calculators. That they appear to do more is testament to the power of arithmetic. The link between computers and arithmetic was clear in the early days, when computers were primarily used for censuses and various military applications. Before semiconductors, “computers” were humans who were employed to do arithmetic problems. Digital computers made arithmetic inexpensive, which eventually resulted in thousands of new applications for everything from data storage to word processing to photography.

AI presents a similar opportunity: to make something that has been comparatively expensive abundant and cheap. The task that AI makes abundant and inexpensive is prediction — in other words, the ability to take information you have and generate information you didn’t previously have. In this article, we will demonstrate how improvement in AI is linked to advances in prediction. We will explore how AI can help us solve problems that were not previously prediction oriented, how the value of some human skills will rise while others fall, and what the implications are for managers. Our speculations are informed by how technological change has affected the cost of previous tasks, allowing us to anticipate how AI may affect what workers and managers do.

Machine Learning and Prediction

The recent advances in AI come under the rubric of what’s known as “machine learning,” which involves programming computers to learn from example data or past experience. Consider, for example, what it takes to identify objects in a basket of groceries. If we could describe how an apple looks, then we could program a computer to recognize apples based on their color and shape. However, there are other objects that are apple-like in both color and shape. We could continue encoding our knowledge of apples in finer detail, but in the real world, the amount of complexity increases exponentially.

Environments with a high degree of complexity are where machine learning is most useful. In one type of training, the machine is shown a set of pictures with names attached. It is then shown millions of pictures that each contain named objects, only some of which are apples. As a result, the machine notices correlations — for example, apples are often red. Using correlates such as color, shape, texture, and, most important, context, the machine references information from past images of apples to predict whether an unidentified new image it’s viewing contains an apple.

When we talk about prediction, we usually mean anticipating what will happen in the future. For example, machine learning can be used to predict whether a bank customer will default on a loan. But we can also apply it to the present by, for instance, using symptoms to develop a medical diagnosis (in effect, predicting the presence of a disease). Using data this way is not new. The mathematical ideas behind machine learning are decades old. Many of the algorithms are even older. So what has changed?

Recent advances in computational speed, data storage, data retrieval, sensors, and algorithms have combined to dramatically reduce the cost of machine learning-based predictions. And the results can be seen in the speed of image recognition and language translation, which have gone from clunky to nearly perfect. All this progress has resulted in a dramatic decrease in the cost of prediction.

The Value of Prediction

So how will improvements in machine learning impact what happens in the workplace? How will they affect one’s ability to complete a task, which might be anything from driving a car to establishing the price for a new product? Once actions are taken, they generate outcomes. (See “The Anatomy of a Task.”) But actions don’t occur in a vacuum. Rather, they are shaped by underlying conditions. For example, a driver’s decision to turn right or left is influenced by predictions about what other drivers will do and what the best course of action may be in light of those predictions.

The Anatomy of a Task

Actions are shaped by the underlying conditions and the resolution of uncertainty to lead to outcomes. Drivers, for example, need to observe the immediate environment and make adjustments to minimize the risk of accidents and avoid bottlenecks. In doing so, they use judgment in combination with prediction.

Seen in this way, it’s useful to distinguish between the cost versus the value of prediction. As we have noted, advances in AI have reduced the cost of prediction. Just as important is what has happened to the value. Prediction becomes more valuable when data is more widely available and more accessible. The computer revolution has enabled huge increases in both the amount and variety of data. As data availability expands, prediction becomes increasingly possible in a wider variety of tasks.

Autonomous driving offers a good example. The technology required for a car to accelerate, turn, and brake without a driver is decades old. Engineers initially focused on directing the car with what computer scientists call “if then else” algorithms, such as “If an object is in front of the car, then brake.” But progress was slow; there were too many possibilities to codify everything. Then, in the early 2000s, several research groups pursued a useful insight: A vehicle could drive autonomously by predicting what a human driver would do in response to a set of inputs (inputs that, in the vehicle’s case, could come from camera images, information using the laser-based measurement method known as LIDAR, and mapping data). The recognition that autonomous driving was a prediction problem solvable with machine learning meant that autonomous vehicles could start to become a reality in the marketplace years earlier than had been anticipated.

Who Judges?

Judgment is the ability to make considered decisions — to understand the impact different actions will have on outcomes in light of predictions. Tasks where the desired outcome can be easily described and there is limited need for human judgment are generally easier to automate. For other tasks, describing a precise outcome can be more difficult, particularly when the desired outcome resides in the minds of humans and cannot be translated into something a machine can understand.
This is not to say that our understanding of human judgment won’t improve and therefore become subject to automation. New modes of machine learning may find ways to examine the relationships between actions and outcomes, and then use the information to improve predictions. We saw an example of this in 2016, when AlphaGo, Google’s DeepMind artificial intelligence program, succeeded in beating one of the top players in the world in the game of Go. AlphaGo honed its capability by analyzing thousands of human-to-human Go games and playing against itself millions of times. It then incorporated the feedback on actions and outcomes to develop more accurate predictions and new strategies.

Examples of machine learning are beginning to appear more in everyday contexts. For instance, x.ai, a New York City-based artificial intelligence startup, provides a virtual personal assistant for scheduling appointments over email and managing calendars. To train the virtual assistants, development team members had the virtual assistants study the email interactions between people as they schedule meetings with one another so that the technology could learn to anticipate the human responses and see the choices humans make. Although this training didn’t produce a formal catalog of outcomes, the idea is to help virtual assistants mimic human judgment so that over time, the feedback can turn some aspects of judgment into prediction problems.

By breaking down tasks into their constituent components, we can begin to see ways AI will affect the workplace. Although the discussion about AI is usually framed in terms of machines versus humans, we see it more in terms of understanding the level of judgment necessary to pursue actions. In cases where whole decisions can be clearly defined with an algorithm (for example, image recognition and autonomous driving), we expect to see computers replace humans. This will take longer in areas where judgment can’t be easily described, although as the cost of prediction falls, the number of such tasks will decline.

Employing Prediction Machines

Major advances in prediction may facilitate the automation of entire tasks. This will require machines that can both generate reliable predictions and rely on those predictions to determine what to do next. For example, for many business-related language translation tasks, the role of human judgment will become limited as prediction-driven translation improves (though judgment might still be important when translations are part of complex negotiations). However, in other contexts, cheaper and more readily available predictions could lead to increased value for human-led judgment tasks. For instance, Google’s Inbox by Gmail can process incoming email messages and propose several short responses, but it asks the human judge which automated response is the most appropriate. Selecting from a list of choices is faster than typing a reply, enabling the user to respond to more emails in less time.

Medicine is an area where AI will likely play a larger role — but humans will still have an important role, too. Although artificial intelligence can improve diagnosis, which is likely to lead to more effective treatments and better patient care, treatment and care will still rely on human judgment.

Different patients have different needs, which humans are better able to respond to than machines. There are many situations where machines may never be able to weigh the relevant pros and cons of doing things one way as opposed to another way in a manner that is acceptable to humans.

The Managerial Challenge

As artificial intelligence technology improves, predictions by machines will increasingly take the place of predictions by humans. As this scenario unfolds, what roles will humans play that emphasize their strengths in judgment while recognizing their limitations in prediction? Preparing for such a future requires considering three interrelated insights:

1. Prediction is not the same as automation. Prediction is an input in automation, but successful automation requires a variety of other activities. Tasks are made up of data, prediction, judgment, and action. Machine learning involves just one component: prediction. Automation also requires that machines be involved with data collection, judgment, and action. For example, autonomous driving involves vision (data); scenarios — given sensory inputs, what action would a human take? (prediction); assessment of consequences (judgment); and acceleration, braking, and steering (action). Medical care can involve information about the patient’s condition (data); diagnostics (prediction); treatment choices (judgment); bedside manner (judgment and action); and physical intervention (action). Prediction is the aspect of automation in which the technology is currently improving especially rapidly, although sensor technology (data) and robotics (action) are also advancing quickly.

2. The most valuable workforce skills involve judgment. In many work activities, prediction has been the bottleneck to automation. In some activities, such as driving, this bottleneck has meant that human workers have remained involved in prediction tasks. Going forward, such human involvement is all but certain to diminish. Instead, employers will want workers to augment the value of prediction; the future’s most valuable skills will be those that are complementary to prediction — in other words, those related to judgment. Consider this analogy: The demand for golf balls rises if the price of golf clubs falls, because golf clubs and golf balls are what economists call complementary goods. Similarly, judgment skills are complementary to prediction and will be in greater demand if the price of prediction falls due to advances in AI. For now, we can only speculate on which aspects of judgment are apt to be most vital: ethical judgment, emotional intelligence, artistic taste, the ability to define tasks well, or some other forms of judgment. However, it seems likely that organizations will have continuing demand for people who can make responsible decisions (requiring ethical judgment), engage customers and employees (requiring emotional intelligence), and identify new opportunities (requiring creativity).

Judgment-related skills will be increasingly valuable in a variety of settings. For example, if prediction leads to cheaper, faster, and earlier diagnosis of diseases, nursing skills related to physical intervention and emotional comfort may become more important. Similarly, as AI becomes better at predicting shopping behavior, skilled human greeters at stores may help differentiate retailers from their competitors. And as AI becomes better at anticipating crimes, private security guards who combine ethical judgment with policing skills may be in greater demand. The part of a task that requires human judgment may change over time, as AI learns to predict human judgment in a particular context. Thus, the judgment aspect of a task will be a moving target, requiring humans to adapt to new situations where judgment is required.

3. Managing may require a new set of talents and expertise. Today, many managerial tasks are predictive. Hiring and promoting decisions, for example, are predicated on prediction: Which job applicant is most likely to succeed in a particular role? As machines become better at prediction, managers’ prediction skills will become less valuable while their judgment skills (which include the ability to mentor, provide emotional support, and maintain ethical standards) become more valuable.
Increasingly, the role of the manager will involve determining how best to apply artificial intelligence, by asking questions such as: What are the opportunities for prediction? What should be predicted?

How should the AI agent learn in order to improve predictions over time? Managing in this context will require judgment both in identifying and applying the most useful predictions, and in being able to weigh the relative costs of different types of errors. Sometimes there will be well-acknowledged objectives (for example, identifying people from their faces). Other times, the objective will be less clear and therefore require judgment to specify the desired outcome. In such cases, managers’ judgment will become a particularly valuable complement to prediction technology.

Looking Ahead

At the dawn of the 21st century, the most common prediction problems in business were classic statistical questions such as inventory management and demand forecasting. However, over the last 10 years, researchers have learned that image recognition, driving, and translation may also be framed as prediction problems. As the range of tasks that are recast as prediction problems continues to grow, we believe the scope of new applications will be extraordinary. The key challenges for executives will be (1) shifting the training of employees from a focus on prediction-related skills to judgment-related ones; (2) assessing the rate and direction of the adoption of AI technologies in order to properly time the shifting of workforce training (not too early, yet not too late); and (3) developing management processes that build the most effective teams of judgment-focused humans and prediction-focused AI agents.

Reproduced from MIT Sloan Management Review.

Boston Dynamics’ Handle robot dominates parkour on wheels in new footage 03-01

Boston Dynamics’ wheeled Handle robot received much fanfare earlier this month when DFJ partner Steve Jurvetson slipped us an early video from a company Keynote. Today we have more details on Handle in the form of a shiny HD video.

We already knew Handle could manage some pretty sick hurdles and spins, but the new video shows us how the robot can operate in tough environments — on hills, in the snow and over uneven terrain. It’s able to do this with a height of 6.5 feet that surpasses that of most humans. On wheels, it can move at a chipper nine mph and manage four-foot vertical jumps. If you’re wondering, the highest human jump ever recorded is 5.3 feet. (Take that Handle!)

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