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technolog<br />

Exponentials watch list<br />

In <strong>the</strong> slightly longer term, quantum optimization<br />

solutions—such as <strong>the</strong> tunneling or annealing<br />

mentioned earlier—don’t provide full generalpurpose<br />

compute, but <strong>the</strong>y do address hard<br />

discrete combinatorial optimization problems,<br />

such as finding <strong>the</strong> shortest, fastest, cheapest, or<br />

most efficient way <strong>of</strong> doing a given task. Familiar<br />

examples include airline scheduling, Monte Carlo<br />

simulation, and web search. 35 More current uses<br />

include image recognition, machine learning<br />

and deep learning pattern-based processing, and<br />

intelligence systems algorithmic transparency (in<br />

which an algorithm can explain itself and how it<br />

came to its conclusions).<br />

Quantum optimization addresses limitations<br />

with both rules-based and traditional case-based<br />

reasoning in which multiple case examples are<br />

used to show levels <strong>of</strong> relationships through<br />

a commonality <strong>of</strong> characteristics. A practical<br />

problem for quantum optimization would be not<br />

only recognizing <strong>the</strong> objects in a photo but also<br />

making inferences based on those objects—for<br />

example, detecting a dog, a ball, and a person in<br />

a photo and <strong>the</strong>n inferring that <strong>the</strong> group is going<br />

to play ball toge<strong>the</strong>r. This type <strong>of</strong> “frame problem”<br />

is combinatorically large in classic rules engine<br />

approaches.<br />

As for quantum optimization’s longer-term future,<br />

<strong>the</strong> ability to harness computing power at a scale<br />

that, until recently, seemed unimaginable has<br />

pr<strong>of</strong>ound disruptive implications for both <strong>the</strong><br />

private and public sectors, as well as for society<br />

as a whole. Today, we use statistical methods to<br />

mine patterns, insights, and correlations from big<br />

data. Yet for small data that flows in high-velocity<br />

streams with low repeat rates, <strong>the</strong>se statistical<br />

methods don’t apply; only <strong>the</strong> human brain can<br />

identify and analyze such weak signals and, more<br />

importantly, understand causation—<strong>the</strong> reason why.<br />

In <strong>the</strong> coming <strong>years</strong>, expect quantum computation<br />

to break <strong>the</strong> human monopoly in this area, and<br />

to become one <strong>of</strong> <strong>the</strong> most powerful models <strong>of</strong><br />

probabilistic reasoning available.<br />

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