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In recent years, we’ve come to associate the practice of innovation with speed and agility, but accomplishments that truly move the needle can’t be achieved quickly or through mere iteration. We need to set our sights higher.

Why We Need to Think Bigger

One reason for the emphasis on agility and iteration in recent decades is that technology has been fairly stable. Every new generation of computer chips has offered more power and capability butworks exactly like earlier generations. In much the same way, advancements in lithium-ion batteries meant that our devices could shrink, but little else had to change. Today, though, those comfortable old paradigms are running out of steam. Moore’s law Silk Square Scarf Explosion by VIDA VIDA 74BLgd
, and lithium-ion batteries will approach theoretical limits in five to 10 years. These will be replaced with technologies that aren’t nearly as well understood. Other nascent fields, like genomics, nanotechnology, and robotics, require highly skilled specialists to advance them.

In the coming years we are likely to see a new era of innovation that will look more like the 1950s and 1960s (which were about solving fundamental problems, like space flight and the development of mainframe computers) than it will the 1990s or 2000s(whichwere more about improving on earlier technology to create applications). In the next few decades, I predict, much of innovation’s value will shift away from applications and back to fundamental problems. That will require greater focus on sustaining efforts to solve grand challenges.

Defining a Grand Challenge

A grand challenge can take many forms. IBM is one company that has a long history of pursuing grand challenges, such as the Deep Blue project , which defeated former world champion Garry Kasparov at chess, andthe Blue Gene project , which created a new class of “massively parallel” supercomputers. The most recent was the Grand Challenge for Jeopardy! , which led to the development of its current Watson business.

“A successful grand challenge is one that people, even experts in the field, regard as an epiphany and changes assumptions about what’s possible,” Bernard Meyerson , IBM’s chief innovation officer, told me. “The commercial value comes in applying those new possibilities to business problems.”

Others might define it differently. For example, take Talia Milgrom-Elcott , executive director of 100Kin10 , a nonprofit that is spearheading the effort to train 100,000 STEM teachers in 10 years. She told me, “For us in the social sphere, a grand challenge is a collective effort to get at root causes. We’re not only looking for a solution to a problem, but a permanent impact on everyday reality.”

She says they find these root causes through a series of questions: “Keep asking why, and you start to see connections that lead to root causes that have enormous leverage, and that’s where you need to focus your efforts.”

1963: Glauber formulates quantum theory for photons
View timeline | #PhysRev125

High Energy Physics (HEP) papers published after January 1, 2018 in , , and are published open access, paid for centrally by SCOAP. Library subscriptions will be modified accordingly. This arrangement will initially last for two years, up to the end of 2019.

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Figure 1

Initial and final eigenvalue distribution for N = 5 particles injected into n = 8 modes, with input and output mode occupation lists r and s , respectively. The input r is invariant under the permutation π = ( 1 2 3 ) ( 4 5 6 ) ( 7 8 ) , with eigenvalue matrix D = diag ( λ 1 , , λ n ) , λ 1 = λ 2 = λ 3 = 1 , λ 4 = λ 5 = e i ( 2 π / 3 ) , λ 6 = λ 7 = e i ( 4 π / 3 ) , and λ 8 = 1 . The mode assignment list on output, d ( s ) = ( 2 , 2 , 4 , 5 , 6 ) , then defines the associated final eigenvalue distribution by mapping { λ 1 , , λ n } onto Λ ( s ) = { λ d 1 ( s ) , , λ d N ( s ) } = { 1 , 1 , e i ( 2 π / 3 ) , e i ( 2 π / 3 ) , e i ( 4 π / 3 ) } . The initial eigenvalue distribution Λ ini , defined by r together with the cycle lengths of π , is a subset of { λ 1 , , λ n } : Λ ini = { e i ( 2 π / 3 ) , e i ( 4 π / 3 ) , 1 , 1 , 1 } (see main text for the definition of Λ ini ).

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Figure 2

Numerical evaluation of mean transmission probabilities P B / F / D ( r , s , U ) for the example illustrated in Fig. 1 , with bosonic ( B ) and fermionic ( F ) event probabilities depicted by blue bars in panels (a) and (b), respectively. Yellow bars represent event probabilities for distinguishable ( D ) particles, renormalized on the set of singly occupied modes in (b). Data were generated by averaging over 10 000 randomly chosen eigenbases of the permutation operator P , for all output configurations of bosons, fermions, and distinguishable particles, respectively. Forbidden output events are subdivided in classes (I, II, III), where suppression is due to single-particle dynamics (SPD) in classes (I, II), and due to many-particle dynamics (MPD) in (III). suppressed events of classes (II, III) are predicted by our suppression laws (SL) ( 4 ), ( 5 ), while those SPD-suppressed events in (I) need some special consideration, see [ 37 ]. From altogether 792 bosonic and 56fermionic transmission events, the subsets of those with nonvanishing detection probabilities are collected in set (IV), in increasing order.

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