An unprecedented 80 percent of Americans, according to a recent Gallup poll, think the country is deeply divided over its most important values ahead of the November elections. The general public’s polarization now encompasses issues like immigration, health care, identity politics, transgender rights, or whether we should support Ukraine. Fly across the Atlantic and you’ll see the same thing happening in the European Union and the UK.
To try to reverse this trend, Google’s DeepMind built an AI system designed to aid people in resolving conflicts. It’s called the Habermas Machine after Jürgen Habermas, a German philosopher who argued that an agreement in a public sphere can always be reached when rational people engage in discussions as equals, with mutual respect and perfect communication.
But is DeepMind’s Nobel Prize-winning ingenuity really enough to solve our political conflicts the same way they solved chess or StarCraft or predicting protein structures? Is it even the right tool?
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Back in 2019, Google made waves by claiming it had achieved what has been called "quantum supremacy"—the ability of a quantum computer to perform operations that would take a wildly impractical amount of time to simulate on standard computing hardware. That claim proved to be controversial, in that the operations were little more than a benchmark that involved getting the quantum computer to behave like a quantum computer; separately, improved ideas about how to perform the simulation on a supercomputer cut the time required down significantly.
But Google is back with a new exploration of the benchmark, described in a paper published in Nature on Wednesday. It uses the benchmark to identify what it calls a phase transition in the performance of its quantum processor and uses it to identify conditions where the processor can operate with low noise. Taking advantage of that, they again show that, even giving classical hardware every potential advantage, it would take a supercomputer a dozen years to simulate things.
The benchmark in question involves the performance of what are called quantum random circuits, which involves performing a set of operations on qubits and letting the state of the system evolve over time, so that the output depends heavily on the stochastic nature of measurement outcomes in quantum mechanics. Each qubit will have a probability of producing one of two results, but unless that probability is one, there's no way of knowing which of the results you'll actually get. As a result, the output of the operations will be a string of truly random bits.
Enlarge / The Windy Fire blazes through the Long Meadow Grove of giant sequoia trees near The Trail of 100 Giants overnight in Sequoia National Forest on September 21, 2021, near California Hot Springs, California. (credit: David McNew/Getty Images)
Space is more accessible than ever thanks to the proliferation of small satellites and more affordable launch prices, which opened the door to bespoke applications like global pollution monitoring, crop observations, and new ways of collecting weather and climate data.
Now you can add wildfire detection to the list. Satellites have observed wildfires from space for decades, but a new initiative partially funded by Google's philanthropic arm aims to deploy more than 50 small satellites in low-Earth orbit to pinpoint flare-ups as small as a classroom anywhere in the world.
The FireSat constellation, managed by a nonprofit called Earth Fire Alliance (EFA), will be the first satellite fleet dedicated to detecting and tracking wildfires. Google announced a fresh investment of $13 million in the FireSat constellation Monday, building on the tech giant's previous contributions to support the development of custom infrared sensors for the FireSat satellites.
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